**Linear Models**: Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.

**Models, Statistical**: Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.

**Nonlinear Dynamics**: The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos.

**Algorithms**: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.

**Computer Simulation**: Computer-based representation of physical systems and phenomena such as chemical processes.

**Data Interpretation, Statistical**: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.

**Bayes Theorem**: A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.

**Breeding**: The production of offspring by selective mating or HYBRIDIZATION, GENETIC in animals or plants.

**Models, Genetic**: Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.

**Biostatistics**: The application of STATISTICS to biological systems and organisms involving the retrieval or collection, analysis, reduction, and interpretation of qualitative and quantitative data.

**Longitudinal Studies**: Studies in which variables relating to an individual or group of individuals are assessed over a period of time.

**Regression Analysis**: Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.

**Models, Biological**: Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.

**Likelihood Functions**: Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.

**Least-Squares Analysis**: A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.

**Models, Neurological**: Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.

**Quantitative Trait Loci**: Genetic loci associated with a QUANTITATIVE TRAIT.

**Reproducibility of Results**: The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.

**Time Factors**: Elements of limited time intervals, contributing to particular results or situations.

**Poisson Distribution**: A distribution function used to describe the occurrence of rare events or to describe the sampling distribution of isolated counts in a continuum of time or space.

**Quantitative Trait, Heritable**: A characteristic showing quantitative inheritance such as SKIN PIGMENTATION in humans. (From A Dictionary of Genetics, 4th ed)

**Magnetic Resonance Imaging**: Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques.

**Normal Distribution**: Continuous frequency distribution of infinite range. Its properties are as follows: 1, continuous, symmetrical distribution with both tails extending to infinity; 2, arithmetic mean, mode, and median identical; and 3, shape completely determined by the mean and standard deviation.

**Cross-Sectional Studies**: Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.

**Models, Theoretical**: Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.

**Biometry**: The use of statistical and mathematical methods to analyze biological observations and phenomena.

**Cohort Studies**: Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.

**Risk Factors**: An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.

**Age Factors**: Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.

**Multivariate Analysis**: A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.

**Principal Component Analysis**: Mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components.

**Image Processing, Computer-Assisted**: A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.

**Questionnaires**: Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.

**Polymorphism, Single Nucleotide**: A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.

**United States**

**Parasitic Diseases, Animal**: Infections or infestations with parasitic organisms. The infestation may be experimental or veterinary.

**Statistics as Topic**: The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.

**Brain Mapping**: Imaging techniques used to colocalize sites of brain functions or physiological activity with brain structures.

**Monte Carlo Method**: In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)

**Analysis of Variance**: A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.

**Genotype**: The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.

**Brain**: The part of CENTRAL NERVOUS SYSTEM that is contained within the skull (CRANIUM). Arising from the NEURAL TUBE, the embryonic brain is comprised of three major parts including PROSENCEPHALON (the forebrain); MESENCEPHALON (the midbrain); and RHOMBENCEPHALON (the hindbrain). The developed brain consists of CEREBRUM; CEREBELLUM; and other structures in the BRAIN STEM.

**Software**: Sequential operating programs and data which instruct the functioning of a digital computer.

**Gene Expression Profiling**: The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.

**Signal-To-Noise Ratio**: The comparison of the quantity of meaningful data to the irrelevant or incorrect data.

**Phenotype**: The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.

**Sex Factors**: Maleness or femaleness as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or effect of a circumstance. It is used with human or animal concepts but should be differentiated from SEX CHARACTERISTICS, anatomical or physiological manifestations of sex, and from SEX DISTRIBUTION, the number of males and females in given circumstances.

**Oligonucleotide Array Sequence Analysis**: Hybridization of a nucleic acid sample to a very large set of OLIGONUCLEOTIDE PROBES, which have been attached individually in columns and rows to a solid support, to determine a BASE SEQUENCE, or to detect variations in a gene sequence, GENE EXPRESSION, or for GENE MAPPING.

**Body Mass Index**: An indicator of body density as determined by the relationship of BODY WEIGHT to BODY HEIGHT. BMI=weight (kg)/height squared (m2). BMI correlates with body fat (ADIPOSE TISSUE). Their relationship varies with age and gender. For adults, BMI falls into these categories: below 18.5 (underweight); 18.5-24.9 (normal); 25.0-29.9 (overweight); 30.0 and above (obese). (National Center for Health Statistics, Centers for Disease Control and Prevention)

**Health Care Costs**: The actual costs of providing services related to the delivery of health care, including the costs of procedures, therapies, and medications. It is differentiated from HEALTH EXPENDITURES, which refers to the amount of money paid for the services, and from fees, which refers to the amount charged, regardless of cost.

**Weather**: The state of the ATMOSPHERE over minutes to months.

**Seasons**: Divisions of the year according to some regularly recurrent phenomena usually astronomical or climatic. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)

**Genetic Variation**: Genotypic differences observed among individuals in a population.

**Hip Dysplasia, Canine**: A hereditary disease of the hip joints in dogs. Signs of the disease may be evident any time after 4 weeks of age.

**Air Pollution**: The presence of contaminants or pollutant substances in the air (AIR POLLUTANTS) that interfere with human health or welfare, or produce other harmful environmental effects. The substances may include GASES; PARTICULATE MATTER; or volatile ORGANIC CHEMICALS.

**Neural Networks (Computer)**: A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.

**Logistic Models**: Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.

**Case-Control Studies**: Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.

**Air Pollutants**: Any substance in the air which could, if present in high enough concentration, harm humans, animals, vegetation or material. Substances include GASES; PARTICULATE MATTER; and volatile ORGANIC CHEMICALS.

**Retrospective Studies**: Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.

**Prospective Studies**: Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.

**Pregnancy**: The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.

**Sample Size**: The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups. (From Wassertheil-Smoller, Biostatistics and Epidemiology, 1990, p95)

**Environmental Exposure**: The exposure to potentially harmful chemical, physical, or biological agents in the environment or to environmental factors that may include ionizing radiation, pathogenic organisms, or toxic chemicals.

**Research Design**: A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly.

**Cities**: A large or important municipality of a country, usually a major metropolitan center.

**Aging**: The gradual irreversible changes in structure and function of an organism that occur as a result of the passage of time.

**Mathematical Computing**: Computer-assisted interpretation and analysis of various mathematical functions related to a particular problem.

**Markov Chains**: A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.

**Environment**: The external elements and conditions which surround, influence, and affect the life and development of an organism or population.

**Cattle**: Domesticated bovine animals of the genus Bos, usually kept on a farm or ranch and used for the production of meat or dairy products or for heavy labor.

**Image Interpretation, Computer-Assisted**: Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.

**Severity of Illness Index**: Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.

**Dairying**

**Confidence Intervals**: A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable.

**Artifacts**: Any visible result of a procedure which is caused by the procedure itself and not by the entity being analyzed. Common examples include histological structures introduced by tissue processing, radiographic images of structures that are not naturally present in living tissue, and products of chemical reactions that occur during analysis.

**Treatment Outcome**: Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.

**Quality of Life**: A generic concept reflecting concern with the modification and enhancement of life attributes, e.g., physical, political, moral and social environment; the overall condition of a human life.

**Follow-Up Studies**: Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease.

**Sensitivity and Specificity**: Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)

**Genetic Association Studies**: The analysis of a sequence such as a region of a chromosome, a haplotype, a gene, or an allele for its involvement in controlling the phenotype of a specific trait, metabolic pathway, or disease.

**Obesity**: A status with BODY WEIGHT that is grossly above the acceptable or desirable weight, usually due to accumulation of excess FATS in the body. The standards may vary with age, sex, genetic or cultural background. In the BODY MASS INDEX, a BMI greater than 30.0 kg/m2 is considered obese, and a BMI greater than 40.0 kg/m2 is considered morbidly obese (MORBID OBESITY).

**Genome-Wide Association Study**: An analysis comparing the allele frequencies of all available (or a whole GENOME representative set of) polymorphic markers in unrelated patients with a specific symptom or disease condition, and those of healthy controls to identify markers associated with a specific disease or condition.

**Epistasis, Genetic**: A form of gene interaction whereby the expression of one gene interferes with or masks the expression of a different gene or genes. Genes whose expression interferes with or masks the effects of other genes are said to be epistatic to the effected genes. Genes whose expression is affected (blocked or masked) are hypostatic to the interfering genes.

**Computational Biology**: A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.

**Infant, Newborn**: An infant during the first month after birth.

**Mathematics**: The deductive study of shape, quantity, and dependence. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)

**Insurance Claim Review**: Review of claims by insurance companies to determine liability and amount of payment for various services. The review may also include determination of eligibility of the claimant or beneficiary or of the provider of the benefit; determination that the benefit is covered or not payable under another policy; or determination that the service was necessary and of reasonable cost and quality.

**Climate**: The longterm manifestations of WEATHER. (McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)

**Signal Processing, Computer-Assisted**: Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.

**Environmental Monitoring**: The monitoring of the level of toxins, chemical pollutants, microbial contaminants, or other harmful substances in the environment (soil, air, and water), workplace, or in the bodies of people and animals present in that environment.

**Socioeconomic Factors**: Social and economic factors that characterize the individual or group within the social structure.

**Reference Values**: The range or frequency distribution of a measurement in a population (of organisms, organs or things) that has not been selected for the presence of disease or abnormality.

**Cluster Analysis**: A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.

**African Americans**: Persons living in the United States having origins in any of the black groups of Africa.

**Body Weight**: The mass or quantity of heaviness of an individual. It is expressed by units of pounds or kilograms.

**European Continental Ancestry Group**: Individuals whose ancestral origins are in the continent of Europe.

**Chromosome Mapping**: Any method used for determining the location of and relative distances between genes on a chromosome.

**Neuropsychological Tests**: Tests designed to assess neurological function associated with certain behaviors. They are used in diagnosing brain dysfunction or damage and central nervous system disorders or injury.

**Georgia**

**Genetic Markers**: A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.

**Spain**: Parliamentary democracy located between France on the northeast and Portugual on the west and bordered by the Atlantic Ocean and the Mediterranean Sea.

**Biological Markers**: Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, environmental exposure and its effects, disease diagnosis, metabolic processes, substance abuse, pregnancy, cell line development, epidemiologic studies, etc.

**Birth Weight**: The mass or quantity of heaviness of an individual at BIRTH. It is expressed by units of pounds or kilograms.

**Overweight**: A status with BODY WEIGHT that is above certain standard of acceptable or desirable weight. In the scale of BODY MASS INDEX, overweight is defined as having a BMI of 25.0-29.9 kg/m2. Overweight may or may not be due to increases in body fat (ADIPOSE TISSUE), hence overweight does not equal "over fat".

**Photic Stimulation**: Investigative technique commonly used during ELECTROENCEPHALOGRAPHY in which a series of bright light flashes or visual patterns are used to elicit brain activity.

**Prevalence**: The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time.

**Bias (Epidemiology)**: Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions.

**Diet**: Regular course of eating and drinking adopted by a person or animal.

**Databases, Factual**: Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.

**Artificial Intelligence**: Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.

**Cognition**: Intellectual or mental process whereby an organism obtains knowledge.

**Visual Fields**: The total area or space visible in a person's peripheral vision with the eye looking straightforward.

**Cognition Disorders**: Disturbances in mental processes related to learning, thinking, reasoning, and judgment.

**Psychomotor Performance**: The coordination of a sensory or ideational (cognitive) process and a motor activity.

**China**: A country spanning from central Asia to the Pacific Ocean.

**Anthropometry**: The technique that deals with the measurement of the size, weight, and proportions of the human or other primate body.

**Linkage Disequilibrium**: Nonrandom association of linked genes. This is the tendency of the alleles of two separate but already linked loci to be found together more frequently than would be expected by chance alone.

**Motor Activity**: The physical activity of a human or an animal as a behavioral phenomenon.

**Comorbidity**: The presence of co-existing or additional diseases with reference to an initial diagnosis or with reference to the index condition that is the subject of study. Comorbidity may affect the ability of affected individuals to function and also their survival; it may be used as a prognostic indicator for length of hospital stay, cost factors, and outcome or survival.

**Reaction Time**: The time from the onset of a stimulus until a response is observed.

**Predictive Value of Tests**: In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.

**Siblings**: Persons or animals having at least one parent in common. (American College Dictionary, 3d ed)

**Vegetables**: A food group comprised of EDIBLE PLANTS or their parts.

**Particulate Matter**: Particles of any solid substance, generally under 30 microns in size, often noted as PM30. There is special concern with PM1 which can get down to PULMONARY ALVEOLI and induce MACROPHAGE ACTIVATION and PHAGOCYTOSIS leading to FOREIGN BODY REACTION and LUNG DISEASES.

**Prenatal Exposure Delayed Effects**: The consequences of exposing the FETUS in utero to certain factors, such as NUTRITION PHYSIOLOGICAL PHENOMENA; PHYSIOLOGICAL STRESS; DRUGS; RADIATION; and other physical or chemical factors. These consequences are observed later in the offspring after BIRTH.

**Tomography, Optical Coherence**: An imaging method using LASERS that is used for mapping subsurface structure. When a reflective site in the sample is at the same optical path length (coherence) as the reference mirror, the detector observes interference fringes.

**Ecosystem**: A functional system which includes the organisms of a natural community together with their environment. (McGraw Hill Dictionary of Scientific and Technical Terms, 4th ed)

**Color**: The visually perceived property of objects created by absorption or reflection of specific wavelengths of light.

**Risk Assessment**: The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)

**Visual Field Tests**: Method of measuring and mapping the scope of vision, from central to peripheral of each eye.

**Sex Characteristics**: Those characteristics that distinguish one SEX from the other. The primary sex characteristics are the OVARIES and TESTES and their related hormones. Secondary sex characteristics are those which are masculine or feminine but not directly related to reproduction.

**Residence Characteristics**: Elements of residence that characterize a population. They are applicable in determining need for and utilization of health services.

**Statistics, Nonparametric**: A class of statistical methods applicable to a large set of probability distributions used to test for correlation, location, independence, etc. In most nonparametric statistical tests, the original scores or observations are replaced by another variable containing less information. An important class of nonparametric tests employs the ordinal properties of the data. Another class of tests uses information about whether an observation is above or below some fixed value such as the median, and a third class is based on the frequency of the occurrence of runs in the data. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed, p1284; Corsini, Concise Encyclopedia of Psychology, 1987, p764-5)

**Pattern Recognition, Automated**: In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)

**Animal Husbandry**: The science of breeding, feeding and care of domestic animals; includes housing and nutrition.

**Humidity**: A measure of the amount of WATER VAPOR in the air.

**Litter Size**: The number of offspring produced at one birth by a viviparous animal.

**Haplotypes**: The genetic constitution of individuals with respect to one member of a pair of allelic genes, or sets of genes that are closely linked and tend to be inherited together such as those of the MAJOR HISTOCOMPATIBILITY COMPLEX.

**Smoking**: Inhaling and exhaling the smoke of burning TOBACCO.

**Oxygen**: An element with atomic symbol O, atomic number 8, and atomic weight [15.99903; 15.99977]. It is the most abundant element on earth and essential for respiration.

**Hospitalization**: The confinement of a patient in a hospital.

**Crosses, Genetic**: Deliberate breeding of two different individuals that results in offspring that carry part of the genetic material of each parent. The parent organisms must be genetically compatible and may be from different varieties or closely related species.

**Population Density**: Number of individuals in a population relative to space.

**Sus scrofa**: A species of SWINE, in the family Suidae, comprising a number of subspecies including the domestic pig Sus scrofa domestica.

**Models, Psychological**: Theoretical representations that simulate psychological processes and/or social processes. These include the use of mathematical equations, computers, and other electronic equipment.

**Depression**: Depressive states usually of moderate intensity in contrast with major depression present in neurotic and psychotic disorders.

**Genetic Linkage**: The co-inheritance of two or more non-allelic GENES due to their being located more or less closely on the same CHROMOSOME.

**Genetic Predisposition to Disease**: A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.

**Cardiovascular Diseases**: Pathological conditions involving the CARDIOVASCULAR SYSTEM including the HEART; the BLOOD VESSELS; or the PERICARDIUM.

**Swine**: Any of various animals that constitute the family Suidae and comprise stout-bodied, short-legged omnivorous mammals with thick skin, usually covered with coarse bristles, a rather long mobile snout, and small tail. Included are the genera Babyrousa, Phacochoerus (wart hogs), and Sus, the latter containing the domestic pig (see SUS SCROFA).

**HIV Infections**: Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS).

**Fishes**: A group of cold-blooded, aquatic vertebrates having gills, fins, a cartilaginous or bony endoskeleton, and elongated bodies covered with scales.

**Parity**: The number of offspring a female has borne. It is contrasted with GRAVIDITY, which refers to the number of pregnancies, regardless of outcome.

**Arsenic**: A shiny gray element with atomic symbol As, atomic number 33, and atomic weight 75. It occurs throughout the universe, mostly in the form of metallic arsenides. Most forms are toxic. According to the Fourth Annual Report on Carcinogens (NTP 85-002, 1985), arsenic and certain arsenic compounds have been listed as known carcinogens. (From Merck Index, 11th ed)

**Demography**: Statistical interpretation and description of a population with reference to distribution, composition, or structure.

**Exercise**: Physical activity which is usually regular and done with the intention of improving or maintaining PHYSICAL FITNESS or HEALTH. Contrast with PHYSICAL EXERTION which is concerned largely with the physiologic and metabolic response to energy expenditure.

**Genetics, Population**: The discipline studying genetic composition of populations and effects of factors such as GENETIC SELECTION, population size, MUTATION, migration, and GENETIC DRIFT on the frequencies of various GENOTYPES and PHENOTYPES using a variety of GENETIC TECHNIQUES.

**Psychometrics**: Assessment of psychological variables by the application of mathematical procedures.

**Outcome Assessment (Health Care)**: Research aimed at assessing the quality and effectiveness of health care as measured by the attainment of a specified end result or outcome. Measures include parameters such as improved health, lowered morbidity or mortality, and improvement of abnormal states (such as elevated blood pressure).

**Health Status**: The level of health of the individual, group, or population as subjectively assessed by the individual or by more objective measures.

**Fourier Analysis**: Analysis based on the mathematical function first formulated by Jean-Baptiste-Joseph Fourier in 1807. The function, known as the Fourier transform, describes the sinusoidal pattern of any fluctuating pattern in the physical world in terms of its amplitude and its phase. It has broad applications in biomedicine, e.g., analysis of the x-ray crystallography data pivotal in identifying the double helical nature of DNA and in analysis of other molecules, including viruses, and the modified back-projection algorithm universally used in computerized tomography imaging, etc. (From Segen, The Dictionary of Modern Medicine, 1992)

**Double-Blind Method**: A method of studying a drug or procedure in which both the subjects and investigators are kept unaware of who is actually getting which specific treatment.

**Incidence**: The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases, new or old, in the population at a given time.

**Milk**: The white liquid secreted by the mammary glands. It contains proteins, sugar, lipids, vitamins, and minerals.

**Blood Pressure**: PRESSURE of the BLOOD on the ARTERIES and other BLOOD VESSELS.

**Weaning**: Permanent deprivation of breast milk and commencement of nourishment with other food. (From Stedman, 25th ed)

**Visual Cortex**: Area of the OCCIPITAL LOBE concerned with the processing of visual information relayed via VISUAL PATHWAYS.

**Geography**: The science dealing with the earth and its life, especially the description of land, sea, and air and the distribution of plant and animal life, including humanity and human industries with reference to the mutual relations of these elements. (From Webster, 3d ed)

**Body Size**: The physical measurements of a body.

**Probability**: The study of chance processes or the relative frequency characterizing a chance process.

**Schizophrenia**: A severe emotional disorder of psychotic depth characteristically marked by a retreat from reality with delusion formation, HALLUCINATIONS, emotional disharmony, and regressive behavior.

**Drug Costs**: The amount that a health care institution or organization pays for its drugs. It is one component of the final price that is charged to the consumer (FEES, PHARMACEUTICAL or PRESCRIPTION FEES).

**Biodiversity**: The variety of all native living organisms and their various forms and interrelationships.

**Forecasting**: The prediction or projection of the nature of future problems or existing conditions based upon the extrapolation or interpretation of existing scientific data or by the application of scientific methodology.

**Weight Gain**: Increase in BODY WEIGHT over existing weight.

**Outpatients**: Persons who receive ambulatory care at an outpatient department or clinic without room and board being provided.

**Sequence Analysis, RNA**: A multistage process that includes cloning, physical mapping, subcloning, sequencing, and information analysis of an RNA SEQUENCE.

**Meat**: The edible portions of any animal used for food including domestic mammals (the major ones being cattle, swine, and sheep) along with poultry, fish, shellfish, and game.

**Fruit**: The fleshy or dry ripened ovary of a plant, enclosing the seed or seeds.

**Mortality**: All deaths reported in a given population.

**Continental Population Groups**: Groups of individuals whose putative ancestry is from native continental populations based on similarities in physical appearance.

**Genomics**: The systematic study of the complete DNA sequences (GENOME) of organisms.

**Alleles**: Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.

**Cost of Illness**: The personal cost of acute or chronic disease. The cost to the patient may be an economic, social, or psychological cost or personal loss to self, family, or immediate community. The cost of illness may be reflected in absenteeism, productivity, response to treatment, peace of mind, or QUALITY OF LIFE. It differs from HEALTH CARE COSTS, meaning the societal cost of providing services related to the delivery of health care, rather than personal impact on individuals.

**Urban Population**: The inhabitants of a city or town, including metropolitan areas and suburban areas.

**Japan**

**Brazil**

**Family**: A social group consisting of parents or parent substitutes and children.

**Acoustic Stimulation**: Use of sound to elicit a response in the nervous system.

**Emergency Service, Hospital**: Hospital department responsible for the administration and provision of immediate medical or surgical care to the emergency patient.

**Housing**: Living facilities for humans.

**Survivors**: Persons who have experienced a prolonged survival after serious disease or who continue to live with a usually life-threatening condition as well as family members, significant others, or individuals surviving traumatic life events.

**France**: A country in western Europe bordered by the Atlantic Ocean, the English Channel, the Mediterranean Sea, and the countries of Belgium, Germany, Italy, Spain, Switzerland, the principalities of Andorra and Monaco, and by the duchy of Luxembourg. Its capital is Paris.

**ROC Curve**: A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli.

**Neurons**: The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM.

**Reproduction**: The total process by which organisms produce offspring. (Stedman, 25th ed)

**Population Surveillance**: Ongoing scrutiny of a population (general population, study population, target population, etc.), generally using methods distinguished by their practicability, uniformity, and frequently their rapidity, rather than by complete accuracy.

**Retinal Ganglion Cells**: Neurons of the innermost layer of the retina, the internal plexiform layer. They are of variable sizes and shapes, and their axons project via the OPTIC NERVE to the brain. A small subset of these cells act as photoreceptors with projections to the SUPRACHIASMATIC NUCLEUS, the center for regulating CIRCADIAN RHYTHM.

**Occupational Exposure**: The exposure to potentially harmful chemical, physical, or biological agents that occurs as a result of one's occupation.

**Stress, Psychological**: Stress wherein emotional factors predominate.

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... Statistical Analysis for Business and Economics: Honors S370. 1633 Kaganovich, M. Spring 1999, Section 1633 Class meets: 11:15am - 12:30pm MW in WY 005/WY 125 first class meeting in WY 005 Professor: Michael Kaganovich Office: 254 Wylie Hall, phone 855-6967; messages at 855-1021; e-mail: econstat@indiana.edu Required Computer Program: Microsoft Excel This program is available through the Spreadsheets submenu in all the IUTS clusters. Statistical applications of this computer package will be emphasized in this class. Most of assignments and exams will be computer-based, and the knowledge of EXCEL=s statistical applications will be required. Course Objectives This class builds on your overall quantitative concepts and skills, as well as on the knowledge of basic probability and statistics you obtained in your Finite Math class. It will provide - the understanding of key statistical concepts used in economics and business; - the knowledge of basic statistical methods of data analysis which are rigorously ...

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Introductory Statistics Exploring the World Through Data 1st Edition. Introductory Statistics Exploring the World Through Data by Gould, Robert N. Case Study: Student-to-Teacher Ratio at Private and Public Colleges 2.1 Visualizing Variation in Numerical Data 2.2 Summarizing Important Features of a Numerical Distribution 2.3 Visualizing Variation in Categorical Variables 2.4 Summarizing Categorical Distributions 2.5 Interpreting Graphs Exploring Statistics: Personal Distance. Case Study: Catching Meter Thieves 4.1 Visualizing Variability with a Scatterplot 4.2 Measuring Strength of Association with Correlation 4.3 Modeling

... is a statistical technique for calculating statistics standardized to a population different from that in which the data was collected study designs with a disparate sampling population and population of target inference target population are common in application there may be prohibitive factors barring researchers from directly sampling from the target population such as cost time or ethical concerns a solution to this problem is to use an alternate design strategy e g stratified sampling weighting when correctly applied can potentially improve the efficiency and reduce the bias of unweighted estimators one very early weighted estimator is the horvitz thompson estimator of the mean when the sampling probability is known from which the sampling population is drawn from the target population then the inverse of this probability is used to weight the observations this approach has been generalized to many aspects of statistics under various frameworks in particular there are weighted likelihoods weighted ...

... d - Taguri - 2014 - Biometrics - Wiley Online Library. Vol 70 Issue 3. JOURNAL TOOLS Get New Content Alerts. JOURNAL MENU Journal Home FIND ISSUES Current Issue. All Issues FIND ARTICLES Early View. BIOMETRIC PRACTICE Model selection criterion for causal parameters in structural mean

In statistics, a 'latent class model LCM ' relates a set of observed usually discrete multivariate variables to a set of latent variable s. It is called a latent class model because the latent variable is discrete. 'Latent Class Analysis LCA ' is a subset of structural equation modeling, used to find groups or subtypes of cases in multivariate categorical data. The LCA will attempt to detect the presence of latent classes the disease entities, creating patterns of association in the symptoms. Because the criterion for solving the LCA is to achieve latent classes within which there is no longer any association of one symptom with another because the class is the disease which causes their association, and the set of diseases a patient has or class a case is a member of causes the symptom association, the symptoms will be "conditionally independent", i.e., conditional on class membership, they are no longer related. Related methods Application External links References. Multivariate mixture estimation MME is ...

statistical prediction. plus.maths.org. Skip to Navigation. about Plus Plus sponsors subscribe to Plus terms of use. Search this site:. Home Articles News Packages Podcasts Puzzles Reviews Ebooks Login. View menu View searchbox. statistical prediction. Predicting the final Olympic medal count is a black art. So without further ado, here is our predicted 2012 London Olympic medal count. Read more... Understanding uncertainty: How long will you live. Well, no-one knows exactly, but using stats you can make a good guess. Do you dare to find out. Read more... Understanding uncertainty: The Premier League. This is the second part of our new column on risk and uncertainty. David Spiegelhalter, Winton Professor for the Public Understanding of Risk at the University of Cambridge, continues examining league tables using the Premier League as an example. Find out just how much or how little these simple rankings can tell you. Read more... Understanding uncertainty: A league table lottery. League tables are ...

... In this paper, a new method is developed for analyzing categorical data with nonresponse when there is uncertainty about ignorability, which incorporates the idea that there are many a priori plausible ignorable and nonignorable nonresponse

probability - How to show that these random variables are pairwise independent. - Mathematics Stack Exchange. more stack exchange communities. Stack Exchange. Mathematics Questions. How to show that these random variables are pairwise independent. Given the arrays $C= $ and $S= $ of lengths $N$ with elements that are discrete iid uniform distributed with equal probability p=1/2 of being $\pm$ 1 Consider the random variables for a given $l, n, m$ : $W=C_lC_mC_n$ $X=S_lS_mC_n$ $Y=C_lS_mS_n$ $Z=S_lC_mS_n$ It can be shown that these random variables $W, X, Y, Z$ are zero mean, uniform distributed with equal probability p=1/2 of being $\pm$ 1. Now how can one go about showing that the random variables $W, X, Y, Z$ are pairwise independent. 8 For Bernoulli variables, non-correlation implies independence. More is true: any three random variables amongst W, X, Y and Z are independent while W,X,Y,Z is not since, for example, WXYZ=+1 with probability 1. That is, for Bernoulli variables non-correlation implies ...

... Kevin Karplus karplus at cheep.cse.ucsc.edu. Tue Feb 24 12:18:10 EST 2004. Previous message: estimating K and Lambda from an extreme value distribution Next message: estimating K and Lambda from an extreme value distribution Messages sorted by:. In article gi4qtl7tg2.fsf at pusch.xnet.com, Gordon D. Pusch wrote: ranjeeva r at yahoo.com Ranjeeva writes: I'm trying to fit a set of scores I get from searching a database of 1000 amino acid sequences with a HMM. I want to calculate a p-value for each matching score. My questions are a How do you estimate the scalling factors K and Lambda to fit my scores 1000 to an extreme value distribution. The obvious question would be: Why would you bother, since an HMM directly yields a generative probability estimate. Simply compare the HMM probability estimate to that of a fiducial model, e.g., the random sequence model. However, if you insist on ab using extreme-value theory for this problem, googling on the exact phrase extreme value distribution plus fitting ...

... Universit t Duisburg-Essen DuEPublico Dose-response modeling using

... chat blog. Mathematics Meta. more stack exchange communities. Stack Exchange. sign up log in tour. Help Center Detailed answers to any questions you might have. Mathematics Questions. Sign up. Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. covariance matrix for normal. An iid random sample of 4 is taken from a normal distribution with mean 2 and variance 3. What is the covariance matrix. What is the matrix of mew. If they are not iid, then how would the covariance matrix differ. My solution: If it is iid, the matrix is simply a diagonal matrix with 3 as its entries The mean matrix is just a row matrix with entries 2 If it is not iid, the matrix has diagonals of 3 and non-diagonal entries I am not sure of... Please let me know if my solution is correct and how to find the matrix if they are not iid. statistics share. asked Mar 20 '12 at 21:45. If they are not independent but each individually still has this same ...

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Confidence interval of lifetime distribution using bootstrap method. Confidence interval of lifetime distribution using bootstrap method. Zhou, Yifan 2008 Confidence interval of lifetime distribution using bootstrap method. Abstract Lifetime estimation is significant in engineering asset management. For the Gamma process, which is a commonly used method for lifetime estimation, the conventional confidence interval construction methods do not perform well. This paper adopts bootstrap methods to build confidence intervals of lifetime distribution when the Gamma process is used. Moreover, bootstrap calibration is conducted to assess the coverage probability of the confidence intervals built by these bootstrap methods. The results show that the BCa method is recommended for generating confidence intervals for Gamma processes in this application. Web of Science® citation databases. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards. Citations counts ...

The most widely used orthogonal polynomials are the classical orthogonal polynomials, consisting of the Hermite polynomials, the Laguerre polynomials, the Jacobi polynomials together with their special cases the Gegenbauer polynomials, the Chebyshev polynomials, and the Legendre polynomials. Examples of orthogonal polynomials Properties Relation to moments. Then the inner product is given by : \langle f, g \rangle = \int {x 1} {x 2} f x g x W x \; dx. The classical orthogonal polynomials Jacobi polynomials, Laguerre polynomials, Hermite polynomials, and their special cases Gegenbauer polynomials, Chebyshev polynomials and Legendre polynomials. They include many orthogonal polynomials as special cases, such as the Meixner–Pollaczek polynomials, the continuous Hahn polynomials, the continuous dual Hahn polynomials, and the classical polynomials, described by the Askey scheme The Askey–Wilson polynomials introduce an extra parameter 'q' into the Wilson polynomials. Discrete orthogonal polynomials are orthogonal ...

I discussed this data example in my first couple of boxplot posts and I think this is a case where the beeswarm plot gives you a more useful picture of how the data points are distributed than the boxplots do. The figure below shows a normal Q-Q plot for the number of traffic deaths per 10,000 drivers generated using the qqPlot package. The upper left plot shows the results obtained for the exponential distribution which, like the Gaussian distribution, does not require the specification of a shape parameter. The exponential distribution represents a special case of the gamma distribution, with a shape parameter equal to 1. Alternatively, the Weibull distribution which also includes the exponential distribution as a special case might describe these data values better than any member of the gamma distribution family, and these plots can also be easily generated using the qqPlot command just specify dist = weibull instead of dist = gamma, along with shape = a for some positive value of a other than 1. ...

... The School of Geography. Prospective Students. Research. People. News. Events. About us. Contact. . People menu Home / People / Academic Staff Academic Staff. Research Staff. Support Staff. Emeritus Staff. PhD Students. Visiting and Honorary Staff. Professor Peter Congdon Research Professor in Quantitative Geography and Health Statistics email: p.congdon@qmul.ac.uk Tel: 020 7882 2778 Location: Geography building, Room 204 Profile. Teaching. Research. Publications. PhD Supervision. Public engagement. Profile. I am a quantitative geographer with particular interests in geographic epidemiology, application of spatial statistical methods to area health and health survey data, and spatial demography. Since 2001 I have been a Research Professor in the School of Geography, and am also affiliated to the QMUL Life Sciences Institute. I have authored a range of articles and books, including ‘Applied Bayesian Hierarchical Methods’ CRC, 2010 and ‘Bayesian Statistical Modelling’ Wiley, 2006. My major projects ...

HTML HTML with abstract plain text plain text with abstract BibTeX RIS EndNote, RefMan, ProCite ReDIF JSON in new window. C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation

and with the same expectation vector November 7th 2009, 01:28 PM matheagle X1,X2,X3 and Y1,Y2,Y3 would have to have the same joint distribution. : November 7th 2009, 03:11 PM Laurent Quote: Originally Posted by kingwinner. November 15th 2009, 05:56 PM kingwinner Quote: Originally Posted by Laurent. November 16th 2009, 03:09 AM Laurent Quote: Originally Posted by kingwinner. November 17th 2009, 09:02 AM kingwinner Quote: Originally Posted by Laurent. : November 17th 2009, 03:09 PM Laurent Quote: Originally Posted by kingwinner. So, at least, when you know the joint distribution of X,Y , you know the distributions of X and Y. So the joint distribution tells you if X and Y are independent. You can think of hot spots or peaks where the measure gives more probability, and it gets colder and colder at infinity nearer to 0. Then for instance you may have some very hot spot near 1,2 , which means that. has high probability to be near that point, i.e. with high probability X is near 1 and at the same time Y is near ...

As such, it is an alternative to the multinomial logit model as one method of multiclass classification. It is assumed that we have a series of observations 'Y' 'i', for 'i' = 1...'n', of the outcomes of multi-way choices from a categorical distribution of size 'm' there are 'm' possible choices. Along with each observation 'Y' 'i' is a set of 'k' observed values 'x' '1,i', ..., 'x' 'k,i' of explanatory variables also known as independent variable s, predictor variables, features, etc. The observed outcomes might be "has disease A, has disease B, has disease C, has none of the diseases" for a set of rare diseases with similar symptoms, and the explanatory variables might be characteristics of the patients thought to be pertinent sex, race, age, blood pressure, body-mass index, presence or absence of various symptoms, etc. sex, race, age, income, etc. The multinomial probit model is a statistical model that can be used to predict the likely outcome of an unobserved multi-way trial given the associated ...

bottles alcohols d model stock images new empty login join bottles by vizionair add to cart royalty free license faq all extended uses included formats ds max default scanline d model specifications product id published jan geometry polygonal polygons vertices textures yes materials yes rigged no animated no uv mapped yes unwrapped uvs unknown artist vizionair turbosquid member since january currently sells products achievements live chat now quality guarantee file format conversions report

www malariajournal com figure resolution standard high figure theory predicts that the slope of pfpr in young children i e b and the pfpr in older children i e the plateau p should be correlated the best fit parameters describing these two quantities are plotted here two extreme values were excluded from this plot there was no correlation with p or without p the extreme values smith et al malaria journal doi download authors original image

Volterra Italy - Hotels Volterra Accommodation Hotel in Volterra B B Volterra Lodging Volterra Residences Volterra Farm Holiday Volterra Special Offers Volterra Hospitality Volterra. Volterra Italy Hotels Volterra Accommodation Hotel in Volterra B B Volterra Lodging Volterra Residences Volterra Farm Holiday Volterra Special Offers Volterra Hospitality Volterra. Volterra Volterra. Volterra. Hotels Volterra - Restaurants - Shopping - Typical Products - Services -. Volterra is prevalently Medieval and yet cherishes abundant evidence of the Etruscan period: the Porta all'Arco the Etruscan gate which date from the 4th century B.C., the Acropolis, the defensive walls which are still visible in parts of the town. The Roman period is attested by the important remains of the Teatro di Vallebona which date back to the Augustan period, the Baths and an enormous rectangular water cistern. The Middle Ages are not only visible in its urban structure but too in its buildings, its hause-towers and churches: the Palazzo dei ...

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... th orthogonal optical injection. all Scitation. AIP Publishing AVS: Science Technology of Materials, Interfaces, and Processing Acoustical Society of America American Association of Physicists in Medicine American Association of Physics Teachers American Crystallographic Association, Inc. EVENT EVENTLOG PORTALID aip /PORTALID SESSIONID ue370cqh81ck.x-aip-live-03 /SESSIONID USERAGENT CCBot/2.0 http://commoncrawl.org/faq/ /USERAGENT IDENTITYID guest /IDENTITYID IDENTITY_LIST guest /IDENTITY_LIST IPADDRESS 54.163.7.185 /IPADDRESS EVENTTYPE PERSONALISATION /EVENTTYPE CREATEDON 1443947970408 /CREATEDON /EVENTLOG EVENTLOGPROPERTY ITEM_ID http://aip.metastore.ingenta.com/content/aip/journal/apl/88/10/10.1063/1.2181649 /ITEM_ID TYPE recommendtolibrary /TYPE /EVENTLOGPROPERTY /EVENT. AIP Publishing. Article. Non

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... Stata: Data Analysis and Statistical Software. Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, statalist.org is already up and running. Re: st: types and codes of the non-

... Search Blogs. Measuring harmonic distortion with the ECM8000. Home Forums Rules Articles The diyAudio Store Gallery Blogs Register Donations FAQ Calendar Search Today's Posts Mark Forums Read Search. Thread Tools Search this Thread. diyAudio Member. Join Date: Sep 2003 Location: Bellevue, WA. Measuring harmonic distortion with the ECM8000 I want measure harmonic distortion using my Behringer ECM8000, but I'm afraid the microphone itself introduces distortion, causing invalid results. Does anyone know what maximum SPL the ECM8000 can measure before distortion starts quickly increasing. Has anyone measured the harmonic distortion introduced by the ECM8000 itself. diyAudio Member. What program do you use for measuring distortion. diyAudio Member. The Behringer will also happily measure SPL levels at 120dB, if really high SPL measurements are needed then take a look at Earthworks range or even those used for car SPL competitions. 11th February 2007, 05:45 PM # 4. diyAudio Member. Quote: Originally posted by ...

Itinerary in San Gimignano and Volterra. Italy. Home News Forum Travel Food Wines Culture Lifestyle Fashion Moving to Italy Learn Italian Home Garden Weather Places. Home News Forum Travel. Wine Cooking Italian Style Food Products Food Recipes Italian Food Articles Nonna s food Culture. Beauty Fashion Accessories Fashion Houses Italian Style About Italian Fashion Men s Fashion Women Fashion Moving to Italy. Itinerary in San Gimignano and Volterra. Two medieval towns not to miss in Tuscany. San Gimignano and Volterra are two Tuscan hill towns, physically close to each other, but very different. Volterra. Volterra Volterra is Etruscan in origin and sits in countryside much different from San Gimignano. Volterra. San Gimignano. Follow the signs to Volterra. To get to San Gimignano from Volterra take No. Rather than retracing your tracks, when you leave San Gimignano follow the signs to Poggibonsi. If you are in the area of Florence or Siena, the best approach will be from Poggibonsi to San Gimignano and then on ...

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... all Scitation. Physics of Fluids 1994-present. AIP Publishing AVS: Science Technology of Materials, Interfaces, and Processing Acoustical Society of America American Association of Physicists in Medicine American Association of Physics Teachers American Crystallographic Association, Inc. I'm an author/editor/contributor to this publication. EVENT EVENTLOG PORTALID aip /PORTALID SESSIONID 11cke9e7c162e.x-aip-live-03 /SESSIONID USERAGENT CCBot/2.0 http://commoncrawl.org/faq/ /USERAGENT IDENTITYID guest /IDENTITYID IDENTITY LIST guest /IDENTITY LIST IPADDRESS 54.144.126.195 /IPADDRESS EVENTTYPE PERSONALISATION /EVENTTYPE CREATEDON 1444073402381 /CREATEDON /EVENTLOG EVENTLOGPROPERTY ITEM ID http://aip.metastore.ingenta.com/content/aip/journal/pof2/24/9/10.1063/1.4752764 /ITEM ID TYPE recommendtolibrary /TYPE /EVENTLOGPROPERTY /EVENT. Physics of Fluids 1994-present Recommend this title to your library. Access Key. Free Content. Open Access Content Subscribed Content. Free Trial Content. AIP Publishing. ...

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... PC. Trailers Reviews PS4 Xbox One PC Wii U Movies TV. Now Reading Splinter Cell Chaos Theory In-Depth. Apple iPhone 6s Plus Review The Office: Jim Spent a Lot of Money to Prank Dwight Princess Leia Slave Bikini Sells for... Blunt Talk: "Meth or No Meth, You Still Gotta Floss" Review Why Destiny's Raid Drop System Needs to Change - IGN's Fireteam Chat A Christmas Horror Story Review Daily Deals: 1TB Xbox One With Two Fallout Games, Fire Emblem: Awakening, Save $50 On A PS4 NASA Told Ridley Scott About Water on Mars Early The Martian Review The Daily Fix X1 1TB Bundle and Rise of the Tomb Raider Season Pass Leaked Online - IGN Daily Fix The Man Who Almost Directed The Martian Dr. Tom Clancy's Splinter Cell Chaos Theory /. 14 Jan 2005 Splinter Cell Chaos Theory In-Depth Share. Splinter Cell Chaos Theory is going to amaze gamers very soon. Set to ship for Xbox, PS2, GameCube, and PC on the last Tuesday in March, Chaos Theory looks to be the Splinter Cell we've all be hoping for. Ubisoft recently unveiled ...

It quantifies the number and duration of recurrences of a dynamical system presented by its phase space trajectory. Recurrence plot s are tools which visualise the recurrence behaviour of the phase space trajectory of dynamical systems. The lines correspond to a typical behaviour of the phase space trajectory: whereas the diagonal lines represent such segments of the phase space trajectory which run parallel for some time, the vertical lines represent segments which remain in the same phase space region for some time. The next measure is the percentage of recurrence points which form diagonal lines in the recurrence plot of minimal length \ell \min :. : \text{DET} = \frac{\sum {\ell=\ell \min} N \ell\, P \ell }{\sum {i,j=1} N R i,j },. where P \ell is the frequency distribution of the lengths \ell of the diagonal lines. where P v is the frequency distribution of the lengths v of the vertical lines, which have at least a length of v \min. : \text{L} = \frac{\sum {\ell=\ell \min} N \ell\, P \ell }{\sum ...

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... All issues. News. Forthcoming. Leaflet PDF. Web of Conferences. Advanced Search. Home. All issues. Volume 1 2011. BIO Web of Conferences, 1 2011 00041. Abstract Homepage Table of contents Previous article Next article. Article Abstract. PDF 607.3 KB. Metrics Abstract views: 321 Full-text article: 131. since Thursday, 22 December 2011. Services Same authors - Google Scholar - EDP Sciences database - PubMed. Recommend this article. Download citation. Alert me if this article is cited. Alert me if this article is corrected. Related Articles A historical review of recurrence plots Eur. Phys. J Special Topics 164, 3-12 2008. Two phase flow bifurcation due to turbulence: transition from slugs to bubbles Eur. Phys. J B 2015 88: 239. Recurrence plots 25 years later —Gaining confidence in dynamical transitions EPL, 101 2013 20007. Prosody and synchronization in cognitive neuroscience EPJ Non

Chaos theory at work: gas price spike and four-legged saboteurs. content from Southeast Farm Press. Skip to Navigation Skip to Content Southeast Farm Press. Home Cotton Peanuts Soybeans Grains Tobacco Vegetables Orchard Crops Livestock Markets Government Equipment Weather. Home > Chaos theory at work: gas price spike and four-legged saboteurs Chaos theory at work: gas price spike and four-legged saboteurs Apr 9, 2007. Southeast Farm Press. 0 What do a raccoon and a possum have to do with a 7-cent increase in the wholesale price of gasoline on the West Coast. • Those events disrupted operations for about two hours at an Exxon Mobil refinery and about 10 seconds yep, that’s right: 10 seconds at a Shell Oil Co. refinery. • Next morning, news of the brief refinery disruptions pushed wholesale gasoline prices up 7 cents a gallon. Talk about chaos theory at work. He adds, “If you think it’s bad now, you can’t believe how high prices would go if a major chunk of refining capacity were taken out for a lengthy ...

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... multiple issues in chaos theory the correlation sum is the estimator of the correlation integral which reflects the mean probability that the states at two different times are close c varepsilon frac n sum stackrel i j i neq j n theta varepsilon vec x i vec x j quad vec x i in bbb r m where n is the number of considered states vec x i varepsilon is a threshold distance cdot a norm e g euclidean norm and theta cdot the heaviside step function if only a time series is available the phase space can be reconstructed by using a time delay embedding see takens theorem vec x i u i u i tau ldots u i tau m where u i is the time series m the embedding dimension and tau the time delay the correlation sum is used to estimate the correlation dimension see also correlation integral correlation dimension recurrence quantification analysis references category chaos theory category dynamical systems category dimension theory

showing i on a horizontal axis and j on a vertical axis, where \vec{x} is a phase space trajectory. Often, the phase space does not have a low enough dimension two or three to be pictured, since higher-dimensional phase spaces can only be visualized by projection into the two or three-dimensional sub-spaces. However, making a recurrence plot enables us to investigate certain aspects of the 'm'-dimensional phase space trajectory through a two-dimensional representation. To make the plot, continuous time and continuous phase space are discretized, taking e.g. \vec{x} i - \vec{x} j \. Caused by characteristic behaviour of the phase space trajectory, a recurrence plot contains typical small-scale structures, as single dots, diagonal lines and vertical/horizontal lines or a mixture of the latter, which combines to extended clusters. In contrast to the heuristic approach of the recurrence quantification analysis, which depends on the choice of the embedding parameters, some dynamical invariant s as correlation ...

... in chaos theory the correlation integral is the mean probability that the states at two different times are close c varepsilon lim n rightarrow infty frac n sum stackrel i j i neq j n theta varepsilon vec x i vec x j quad vec x i in bbb r m where n is the number of considered states vec x i varepsilon is a threshold distance cdot a norm e g euclidean norm and theta cdot the heaviside step function if only a time series is available the phase space can be reconstructed by using a time delay embedding see takens theorem vec x i u i u i tau ldots u i tau m where u i is the time series m the embedding dimension and tau the time delay the correlation integral is used to estimate the correlation dimension an estimator of the correlation integral is the correlation sum c varepsilon frac n sum stackrel i j i neq j n theta varepsilon vec x i vec x j quad vec x i in bbb r m see also recurrence quantification analysis references category chaos theory

Extended and Efficient midrange: what do we have here. Search Blogs. Extended and Efficient midrange: what do we have here. Thread Tools Search this Thread. diyAudio Member. Join Date: Dec 2007 Location: Italy. The total harmonic distortion is not a measure of the degree of distastefulness to the listener and it is recommended that its use should be discontinued. D Masa, 1938 Last edited by Telstar; 6th December 2012 at 10:55 AM. diyAudio Member. Join Date: Dec 2007 Location: Italy. The total harmonic distortion is not a measure of the degree of distastefulness to the listener and it is recommended that its use should be discontinued. 6th December 2012, 12:42 PM # 63. diyAudio Member. Join Date: Dec 2007 Location: Italy. The total harmonic distortion is not a measure of the degree of distastefulness to the listener and it is recommended that its use should be discontinued. 6th December 2012, 01:53 PM # 64. Quote: Originally Posted by Telstar. 6th ...

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Deuterated organic salts useful in non

Another approach is to fit a mixed

... 'Polynomial chaos PC ', also called 'Wiener chaos expansion', is a non-sampling-based method to determine evolution of uncertainty in dynamical system, when there is probabilistic uncertainty in the system parameters. Generalized polynomial chaos Arbitrary polynomial chaos See also References. Generalized polynomial chaos. This is popularly known as the generalized polynomial chaos gPC framework. The gPC framework has been applied to applications including stochastic fluid dynamics, stochastic finite elements, solid mechanics, non

... Visual Recurrence Analysis, version 4.0. Eugene Kononov eugenek@ix.netcom.com. Fri, 1 Oct 1999 20:06:59 -0500 CDT. Messages sorted by:. Next message: Susan Brassfield: "just for Bertvan". Hello there,. This is to announce that version 4.0 of Visual Recurrence Analysis VRA software for Windows 95, 98 and NT has been released. Visual Recurrence Analysis is a software for topological analysis,. qualitative and quantitative assessment, and nonparametric prediction. of non

... Homepage of Hil Meijer. Projects. Teaching. I studied Physics and Mathematics in Utrecht. I continued there with Yuri Kuznetsov and Ferdinand Verhulst as my advisors. in Mathematics in december 2006. I then moved to the University of Twente to work as an assistant professor in the chair Applied Analysis of prof.dr. van Gils. My research on Numerical Bifurcation Theory deals with theoretical descriptions and numerical tools for bifurcations in ODEs and maps. The tools classify the dynamics near a bifurcation and allow initialization of nontrivial bifurcation curves. I am an active developer of the matlab toolbox MATCONT and have taught several related tutorials. My research on Neurostimulation centers around the idea that a stimulus steers neuronal dynamics. In neurodegenerative contexts such as epilepsy and Parkinson's disease, there is undesirable oscillatory dynamics that may be quenched by a suitable stimulus. In addition, stimulation offers a window to study and uncover neurophysiological processes. ...

Experimental Boiling Point: 128-130 deg C / 14 mm 277.5769-280.2546 °C / 760 mmHg Alfa Aesar. Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = -0.66 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 246.27 Adapted Stein & Brown method Melting Pt deg C : 51.27 Mean or Weighted MP VP mm Hg,25 deg C : 9.45E-005 Modified Grain method MP exp database : 257-259 deg C BP exp database : 129 @ 14 mm Hg deg C Subcooled liquid VP: 0.0322 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 1e+006 log Kow used: -0.66 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1e+006 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 7.26E-013 atm-m3/mole Group Method: 3.08E-014 atm-m3/mole Henrys LC : 1.806E-011 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: -0.66 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = -0.71 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 70.34 Adapted Stein & Brown method Melting Pt deg C : -67.47 Mean or Weighted MP VP mm Hg,25 deg C : 1.36E+003 Mean VP of Antoine & Grain methods MP exp database : -127.9 deg C BP exp database : 8.2 deg C VP exp database : 1.42E+03 mm Hg at 25 deg C Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 4.751e+005 log Kow used: -0.71 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 5.3303e+005 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Acid Chloride/Halide Henrys Law Constant 25 deg C : Bond Method : 8.92E-003 atm-m3/mole Group Method: Incomplete Henrys LC : 2.082E-004 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: -0.71 KowWin est Log Kaw used: -0.438 HenryWin est Log Koa KOAWIN v1.10 estimate : -0.272 Log Koa experimental ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.62 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 107.63 Adapted Stein & Brown method Melting Pt deg C : -58.07 Mean or Weighted MP VP mm Hg,25 deg C : 12.5 Mean VP of Antoine & Grain methods BP exp database : 125 deg C VP exp database : 1.38E+01 mm Hg at 25 deg C Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 392.3 log Kow used: 2.62 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 7.8558 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Silamines Henrys Law Constant 25 deg C : Bond Method : 8.69E-005 atm-m3/mole Group Method: Incomplete Henrys LC : 6.767E-003 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.62 KowWin est Log Kaw used: -2.449 HenryWin est Log Koa KOAWIN v1.10 estimate : 5.069 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.05 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 261.93 Adapted Stein & Brown method Melting Pt deg C : 34.13 Mean or Weighted MP VP mm Hg,25 deg C : 0.00125 Modified Grain method Subcooled liquid VP: 0.0015 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 423.1 log Kow used: 3.05 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 771.68 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Benzyl Alcohols Henrys Law Constant 25 deg C : Bond Method : 3.77E-007 atm-m3/mole Group Method: Incomplete Henrys LC : 7.179E-007 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.05 KowWin est Log Kaw used: -4.812 HenryWin est Log Koa KOAWIN v1.10 estimate : 7.862 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.75 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 342.93 Adapted Stein & Brown method Melting Pt deg C : 111.88 Mean or Weighted MP VP mm Hg,25 deg C : 5.67E-007 Modified Grain method Subcooled liquid VP: 4.06E-006 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 208.2 log Kow used: 2.75 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1384.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Benzyl Alcohols Henrys Law Constant 25 deg C : Bond Method : 1.30E-008 atm-m3/mole Group Method: 5.35E-013 atm-m3/mole Henrys LC : 6.962E-010 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.75 KowWin est Log Kaw used: -6.275 HenryWin est Log Koa KOAWIN v1.10 estimate : 9.025 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.87 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 796.97 Adapted Stein & Brown method Melting Pt deg C : 349.84 Mean or Weighted MP VP mm Hg,25 deg C : 7.71E-020 Modified Grain method Subcooled liquid VP: 3.95E-016 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 3.671 log Kow used: 2.87 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 0.34771 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Imides Imidazoles Henrys Law Constant 25 deg C : Bond Method : 9.31E-019 atm-m3/mole Group Method: Incomplete Henrys LC : 1.311E-020 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.87 KowWin est Log Kaw used: -16.420 HenryWin est Log Koa KOAWIN v1.10 estimate : 19.290 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.86 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 504.52 Adapted Stein & Brown method Melting Pt deg C : 214.71 Mean or Weighted MP VP mm Hg,25 deg C : 2.01E-010 Modified Grain method Subcooled liquid VP: 2.11E-008 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 2.996 log Kow used: 3.86 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1.2406 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Imidazoles Henrys Law Constant 25 deg C : Bond Method : 1.71E-012 atm-m3/mole Group Method: Incomplete Henrys LC : 2.726E-011 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.86 KowWin est Log Kaw used: -10.155 HenryWin est Log Koa KOAWIN v1.10 estimate : 14.015 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = -2.83 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 446.80 Adapted Stein & Brown method Melting Pt deg C : 187.75 Mean or Weighted MP VP mm Hg,25 deg C : 1.17E-008 Modified Grain method Subcooled liquid VP: 5.9E-007 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 1e+006 log Kow used: -2.83 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1e+006 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 9.88E-016 atm-m3/mole Group Method: Incomplete Henrys LC : 2.542E-015 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: -2.83 KowWin est Log Kaw used: -13.394 HenryWin est Log Koa KOAWIN v1.10 estimate : 10.564 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.43 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 296.50 Adapted Stein & Brown method Melting Pt deg C : 78.73 Mean or Weighted MP VP mm Hg,25 deg C : 0.000724 Modified Grain method Subcooled liquid VP: 0.00235 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 471.5 log Kow used: 2.43 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 2199.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Neutral Organics Henrys Law Constant 25 deg C : Bond Method : 2.33E-007 atm-m3/mole Group Method: 1.28E-005 atm-m3/mole Henrys LC : 3.600E-007 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.43 KowWin est Log Kaw used: -5.021 HenryWin est Log Koa KOAWIN v1.10 estimate : 7.451 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.15 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 250.84 Adapted Stein & Brown method Melting Pt deg C : 46.41 Mean or Weighted MP VP mm Hg,25 deg C : 0.0162 Modified Grain method Subcooled liquid VP: 0.0255 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 90.77 log Kow used: 3.15 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 487.02 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aldehydes Henrys Law Constant 25 deg C : Bond Method : 5.90E-006 atm-m3/mole Group Method: 1.25E-005 atm-m3/mole Henrys LC : 4.674E-005 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.15 KowWin est Log Kaw used: -3.618 HenryWin est Log Koa KOAWIN v1.10 estimate : 6.768 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.59 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 288.05 Adapted Stein & Brown method Melting Pt deg C : 65.52 Mean or Weighted MP VP mm Hg,25 deg C : 0.00153 Modified Grain method Subcooled liquid VP: 0.00367 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 2049 log Kow used: 2.59 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 456.1 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 1.82E-008 atm-m3/mole Group Method: Incomplete Henrys LC : 2.194E-007 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.59 KowWin est Log Kaw used: -6.128 HenryWin est Log Koa KOAWIN v1.10 estimate : 8.718 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = -3.18 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 480.00 Adapted Stein & Brown method Melting Pt deg C : 90.27 Mean or Weighted MP VP mm Hg,25 deg C : 2.06E-011 Modified Grain method Subcooled liquid VP: 8.76E-011 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 1e+006 log Kow used: -3.18 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1e+006 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Neutral Organics Henrys Law Constant 25 deg C : Bond Method : 8.77E-024 atm-m3/mole Group Method: Incomplete Henrys LC : 7.051E-018 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: -3.18 KowWin est Log Kaw used: -21.445 HenryWin est Log Koa KOAWIN v1.10 estimate : 18.265 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.74 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 563.55 Adapted Stein & Brown method Melting Pt deg C : 242.28 Mean or Weighted MP VP mm Hg,25 deg C : 2.9E-012 Modified Grain method Subcooled liquid VP: 6.52E-010 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 32.12 log Kow used: 2.74 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 3578.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Neutral Organics Henrys Law Constant 25 deg C : Bond Method : 1.23E-014 atm-m3/mole Group Method: Incomplete Henrys LC : 4.056E-014 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.74 KowWin est Log Kaw used: -12.299 HenryWin est Log Koa KOAWIN v1.10 estimate : 15.039 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.18 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 453.54 Adapted Stein & Brown method Melting Pt deg C : 190.90 Mean or Weighted MP VP mm Hg,25 deg C : 1.19E-009 Modified Grain method Subcooled liquid VP: 6.54E-008 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 300.7 log Kow used: 2.18 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 7664.9 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Phenols Henrys Law Constant 25 deg C : Bond Method : 4.92E-019 atm-m3/mole Group Method: Incomplete Henrys LC : 1.331E-012 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.18 KowWin est Log Kaw used: -16.696 HenryWin est Log Koa KOAWIN v1.10 estimate : 18.876 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.70 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 752.48 Adapted Stein & Brown method Melting Pt deg C : 330.54 Mean or Weighted MP VP mm Hg,25 deg C : 6.39E-022 Modified Grain method Subcooled liquid VP: 1.84E-018 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 0.5327 log Kow used: 3.70 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 190.07 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Neutral Organics-acid Ureas substituted -acid Henrys Law Constant 25 deg C : Bond Method : 2.54E-023 atm-m3/mole Group Method: Incomplete Henrys LC : 7.807E-022 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.70 KowWin est Log Kaw used: -20.984 HenryWin est Log Koa KOAWIN v1.10 estimate : 24.684 Log Koa experimental database : None Probability of Rapid ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 1.66 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 464.08 Adapted Stein & Brown method Melting Pt deg C : 195.82 Mean or Weighted MP VP mm Hg,25 deg C : 3.67E-011 Modified Grain method Subcooled liquid VP: 2.3E-009 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 8200 log Kow used: 1.66 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 7061.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Phenols Henrys Law Constant 25 deg C : Bond Method : 6.85E-022 atm-m3/mole Group Method: Incomplete Henrys LC : 1.615E-015 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 1.66 KowWin est Log Kaw used: -19.553 HenryWin est Log Koa KOAWIN v1.10 estimate : 21.213 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 1.57 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 247.70 Adapted Stein & Brown method Melting Pt deg C : 53.60 Mean or Weighted MP VP mm Hg,25 deg C : 0.00689 Modified Grain method Subcooled liquid VP: 0.0127 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 3854 log Kow used: 1.57 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 5487.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Phenols Henrys Law Constant 25 deg C : Bond Method : 6.52E-010 atm-m3/mole Group Method: 8.68E-006 atm-m3/mole Henrys LC : 3.249E-007 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 1.57 KowWin est Log Kaw used: -7.574 HenryWin est Log Koa KOAWIN v1.10 estimate : 9.144 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = -0.19 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 527.96 Adapted Stein & Brown method Melting Pt deg C : 225.66 Mean or Weighted MP VP mm Hg,25 deg C : 3.77E-011 Modified Grain method Subcooled liquid VP: 5.35E-009 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 1005 log Kow used: -0.19 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 33800 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Esters Henrys Law Constant 25 deg C : Bond Method : 1.77E-015 atm-m3/mole Group Method: Incomplete Henrys LC : 1.428E-014 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: -0.19 KowWin est Log Kaw used: -13.140 HenryWin est Log Koa KOAWIN v1.10 estimate : 12.950 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 4.89 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 402.04 Adapted Stein & Brown method Melting Pt deg C : 153.15 Mean or Weighted MP VP mm Hg,25 deg C : 3.66E-007 Modified Grain method Subcooled liquid VP: 7.42E-006 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 6.299 log Kow used: 4.89 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 4.8539 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 2.98E-010 atm-m3/mole Group Method: Incomplete Henrys LC : 2.456E-008 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 4.89 KowWin est Log Kaw used: -7.914 HenryWin est Log Koa KOAWIN v1.10 estimate : 12.804 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.94 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 273.66 Adapted Stein & Brown method Melting Pt deg C : 46.30 Mean or Weighted MP VP mm Hg,25 deg C : 0.00499 Modified Grain method Subcooled liquid VP: 0.00781 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 21.05 log Kow used: 3.94 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 39.477 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aldehydes Henrys Law Constant 25 deg C : Bond Method : 1.88E-005 atm-m3/mole Group Method: 3.88E-006 atm-m3/mole Henrys LC : 5.935E-005 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.94 KowWin est Log Kaw used: -3.114 HenryWin est Log Koa KOAWIN v1.10 estimate : 7.054 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.72 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 530.60 Adapted Stein & Brown method Melting Pt deg C : 226.89 Mean or Weighted MP VP mm Hg,25 deg C : 3.12E-011 Modified Grain method Subcooled liquid VP: 4.58E-009 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 36.76 log Kow used: 2.72 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 299.53 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 1.55E-017 atm-m3/mole Group Method: Incomplete Henrys LC : 3.735E-013 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.72 KowWin est Log Kaw used: -15.198 HenryWin est Log Koa KOAWIN v1.10 estimate : 17.918 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.01 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 419.26 Adapted Stein & Brown method Melting Pt deg C : 170.58 Mean or Weighted MP VP mm Hg,25 deg C : 8.81E-008 Modified Grain method Subcooled liquid VP: 2.82E-006 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 22.37 log Kow used: 2.01 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1538.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 8.32E-012 atm-m3/mole Group Method: Incomplete Henrys LC : 1.474E-009 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.01 KowWin est Log Kaw used: -9.468 HenryWin est Log Koa KOAWIN v1.10 estimate : 11.478 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 6.24 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 515.86 Adapted Stein & Brown method Melting Pt deg C : 220.01 Mean or Weighted MP VP mm Hg,25 deg C : 1.08E-011 Modified Grain method Subcooled liquid VP: 1.3E-009 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 0.003024 log Kow used: 6.24 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 0.022768 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Phenols Henrys Law Constant 25 deg C : Bond Method : 8.96E-014 atm-m3/mole Group Method: Incomplete Henrys LC : 1.548E-009 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 6.24 KowWin est Log Kaw used: -11.436 HenryWin est Log Koa KOAWIN v1.10 estimate : 17.676 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 6.40 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 537.81 Adapted Stein & Brown method Melting Pt deg C : 230.26 Mean or Weighted MP VP mm Hg,25 deg C : 1.86E-011 Modified Grain method Subcooled liquid VP: 2.99E-009 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 0.01287 log Kow used: 6.40 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 0.030021 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aromatic Amines Henrys Law Constant 25 deg C : Bond Method : 7.08E-012 atm-m3/mole Group Method: Incomplete Henrys LC : 6.493E-010 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 6.40 KowWin est Log Kaw used: -9.538 HenryWin est Log Koa KOAWIN v1.10 estimate : 15.938 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.94 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 308.06 Adapted Stein & Brown method Melting Pt deg C : 95.71 Mean or Weighted MP VP mm Hg,25 deg C : 3.11E-005 Modified Grain method Subcooled liquid VP: 0.000151 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 76.1 log Kow used: 3.94 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 972.03 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Phenols Henrys Law Constant 25 deg C : Bond Method : 2.21E-010 atm-m3/mole Group Method: 4.25E-010 atm-m3/mole Henrys LC : 1.045E-007 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.94 KowWin est Log Kaw used: -8.044 HenryWin est Log Koa KOAWIN v1.10 estimate : 11.984 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 6.59 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 483.02 Adapted Stein & Brown method Melting Pt deg C : 192.08 Mean or Weighted MP VP mm Hg,25 deg C : 1.3E-009 Modified Grain method Subcooled liquid VP: 7.35E-008 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 0.08978 log Kow used: 6.59 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 0.026605 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 4.09E-010 atm-m3/mole Group Method: Incomplete Henrys LC : 7.346E-009 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 6.59 KowWin est Log Kaw used: -7.777 HenryWin est Log Koa KOAWIN v1.10 estimate : 14.367 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 0.16 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 470.24 Adapted Stein & Brown method Melting Pt deg C : 198.70 Mean or Weighted MP VP mm Hg,25 deg C : 2.27E-009 Modified Grain method Subcooled liquid VP: 1.54E-007 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 1686 log Kow used: 0.16 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 7067.9 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Imides Imidazoles Henrys Law Constant 25 deg C : Bond Method : 1.85E-012 atm-m3/mole Group Method: Incomplete Henrys LC : 3.440E-013 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 0.16 KowWin est Log Kaw used: -10.121 HenryWin est Log Koa KOAWIN v1.10 estimate : 10.281 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.43 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 250.84 Adapted Stein & Brown method Melting Pt deg C : 40.15 Mean or Weighted MP VP mm Hg,25 deg C : 0.0186 Modified Grain method Subcooled liquid VP: 0.0255 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 370.7 log Kow used: 2.43 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1342.6 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aldehydes Henrys Law Constant 25 deg C : Bond Method : 2.18E-006 atm-m3/mole Group Method: Incomplete Henrys LC : 1.314E-005 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.43 KowWin est Log Kaw used: -4.050 HenryWin est Log Koa KOAWIN v1.10 estimate : 6.480 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1

Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 0.02 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 241.83 Adapted Stein & Brown method Melting Pt deg C : 44.82 Mean or Weighted MP VP mm Hg,25 deg C : 0.00346 Modified Grain method Subcooled liquid VP: 0.00524 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 7.848e+005 log Kow used: 0.02 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1e+006 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Neutral Organics-acid Henrys Law Constant 25 deg C : Bond Method : 4.69E-011 atm-m3/mole Group Method: 1.58E-011 atm-m3/mole Henrys LC : 6.853E-010 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 0.02 KowWin est Log Kaw used: -8.717 HenryWin est Log Koa KOAWIN v1.10 estimate : 8.737 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN ...

We propose a

**model**that allows the description of a general studio mixing process as a**linear**stationary process of " ...**Linear**Mixing**Models**for Active Listening of Music Productions in Realistic Studio Conditions. ... Scientific audio scene analysis rather focuses on "natural" mixtures and most often uses**linear**(convolutive)**models**of point ... Such a**model**can be used to allow the recovery of the isolated tracks while preserving the professional sound quality of the ...http://aes.org/e-lib/browse.cfm?elib=16232

Model Reduction by Moment Matching for Linear and Nonlinear Systems - IEEE......

**linear**and nonlinear systems is addressed using the notion of moment. A re-visitation of the ... The**model**reduction problem for (single-input, single-output) ...http://ieeexplore.ieee.org/document/5437314/?reload=true

British Library EThOS: Ridge regresion techniques for the linear modelMathematical sciences, general

http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238112

British Library EThOS: The canonical theory of the supersymmetric non-linear ...The canonical theory of the supersymmetric non-

**linear**#sigma#-**model**Author: Popat, P. C. ...http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235077

British Library EThOS: Developing low distortion linear and nonlinear circuits...Developing low distortion

**linear**and nonlinear circuits with GaAs FETs using the Parker Skellern**model**. ...http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243599

Linear channel model for multilevel two dimensional optical storage -...Moinian, A. and Stankovic, L. and Honary, B. and Coene, W. (2003)

**Linear**channel**model**for multilevel two dimensional optical ...**linear**channel**model**, multilevel, two dimensional, optical storage, Electrical engineering. Electronics Nuclear engineering. ... This paper looks at a**linear**channel**model**for multilevel two dimensional optical storage ...http://strathprints.strath.ac.uk/38559/

DML-CZ - Czech Digital Mathematics Library: Adaptive maximum-likelihood-like...Víšek: Adaptive estimation in

**linear**regression**model**. Part 2. Asymptotic normality. Kyber- netika 28 (1991), 2, 100-119. MR ... Víšek: Adaptive estimation in**linear**regression**model**. Part 1. Consistency. Kybernetika 28 (1991), 1, 26-36. MR 1159872 ... Adaptive maximum-likelihood-like estimation in**linear****models**. I. Consistency. (English). Kybernetika, vol. 28 (1992), issue 5, ... 5] R. C. Rao:**Linear**Statistical Inference and Its Applications. J. Wiley, New York 1973. MR 0346957 , Zbl 0256.62002http://dml.cz/dmlcz/125864

Qualitative analysis of the dynamics of genetic regulatory networks using...Qualitative analysis of the dynamics of genetic regulatory networks using piecewise-

**linear****models**...**Models**: Development and reduction of**models**of bacterial regulatory networks. Methods : Analysis and simulation of bacterial ...http://ibis.inrialpes.fr/article789.html

DML-CZ - Czech Digital Mathematics Library: Large adaptive estimation in linear...Víšek: Adaptive estimation in

**linear**regression**model**and test of symmetry of residuals. To appear in the Proceedings of the ... 9] H. Koul, F. DeWet: Minimum distance estimation in a**linear**regression**model**. Ann. Statist. 11 (1983), 921 - 932. MR 0707942 ... 12] D. Ruppert, R.J. Carroll: Trimmed least squares estimation in**linear****model**. J. Amer. Statist. Assoc. 75 (1980), 828 - 838. ... Víšek: Adaptive Maximum-likelihood-like Estimation in**Linear****Model**. Research Report No. 1654, Institute of ...http://dml.cz/dmlcz/124973

Farm planning for a typical crop-livestock integrated farm : an application of...... the crop rotation

**model**was incorporated in the composite mixed integer**linear**farm planning**model**. In order to demonstrate the ...**Modelling**South African traffic for large networks - an extension of the gravity**model**for traffic demand**modelling** Swarts, ...**Modelling**of mixing, mass trasfer and phase distribution in a Peirce-Smith converter**model** Chibwe DK; Akdogan G; Aldrich C; ... The mathematical**modelling**and simulation of a conventional single main rotor helicopter and the ...http://scholar.sun.ac.za/handle/10019.1/49965

Applied **Linear**Equations: Mixture Problem - Concept - Algebra 2 Video by Brightstorm... /

**Linear**Equations. / Applied**Linear**Equations: Mixture Problem. Learn math, science, English SAT ACT from high-quaility study videos by expert teachers Preview playing in 3. Applied**Linear**Equations: Mixture Problem - Concept. In order to understand graphing inverse functions, students should review the definition of inverse functions, how to find the inverse algebraically and how to prove inverse functions. In this example we are going to be using**linear**equations to solve a mixture problem. And so for this particular word problem it's all about caffeine. You're still dragging a little bit after that so you go back buy another drink that is 10 percent, 10 ounces larger and 40 percent caffeine. How large was our second drink. Okay, so we have a first cup, have a second cup and we have a sort of ending result cup, your stomach if you will or some sort of collaborate collection of all that caffeine. Okay, our second drink is 40 percent caffeine and we don't know ...http://brightstorm.com/math/algebra-2/linear-equations/applied-linear-equations-mixture-problems/

HON S370 1598 Statistical Analysis for Business and Economics: Honors... Statistical Analysis for Business and Economics: Honors S370. 1633 Kaganovich, M. Spring 1999, Section 1633 Class meets: 11:15am - 12:30pm MW in WY 005/WY 125 first class meeting in WY 005 Professor: Michael Kaganovich Office: 254 Wylie Hall, phone 855-6967; messages at 855-1021; e-mail: econstat@indiana.edu Required Computer Program: Microsoft Excel This program is available through the Spreadsheets submenu in all the IUTS clusters. Statistical applications of this computer package will be emphasized in this class. Most of assignments and exams will be computer-based, and the knowledge of EXCEL=s statistical applications will be required. Course Objectives This class builds on your overall quantitative concepts and skills, as well as on the knowledge of basic probability and statistics you obtained in your Finite Math class. It will provide - the understanding of key statistical concepts used in economics and business; - the knowledge of basic statistical methods of data analysis which are rigorously ...

http://indiana.edu/~deanfac/blspr99/hon/hon_s370_1598.html

[MRG+1] Return attribute n iter for **linear****models**dependent on the enet solver by MechCoder · PullReturn attribute n iter for

**linear****models**dependent on the enet solver by MechCoder · Pull Request #3349 · scikit-learn/scikit-learn · GitHub. Skip to content. Sign up Sign in. This repository. Explore. Features. Enterprise. Pricing. Watch. 891. Star. 7,766. Fork. 4,595. scikit-learn. / scikit-learn. Code Issues. Pull requests. Wiki. Pulse Graphs HTTPS clone URL. Subversion checkout URL. You can clone with. HTTPS or. Subversion. Download ZIP. Loading…. Return attribute n iter for**linear****models**dependent on the enet solver #3349. Merged. ogrisel merged 6 commits into scikit-learn : master from MechCoder : return niter Jul 16, 2014. +1,888. −1,900. . Conversation 86. Commits 6. Files changed 5. Labels None yet. Milestone No milestone. Assignee No one assigned. 9 participants. Owner. MechCoder commented. Jul 7, 2014. This should be quick review and will help to finish or close the other pull requests. @agramfort @ogrisel please have a ...https://github.com/scikit-learn/scikit-learn/pull/3349

Introductory Statistics Exploring the World Through Data 1st Edition | 9780321322159 | eCampus.comIntroductory Statistics Exploring the World Through Data 1st Edition. Introductory Statistics Exploring the World Through Data by Gould, Robert N. Case Study: Student-to-Teacher Ratio at Private and Public Colleges 2.1 Visualizing Variation in Numerical Data 2.2 Summarizing Important Features of a Numerical Distribution 2.3 Visualizing Variation in Categorical Variables 2.4 Summarizing Categorical Distributions 2.5 Interpreting Graphs Exploring Statistics: Personal Distance. Case Study: Catching Meter Thieves 4.1 Visualizing Variability with a Scatterplot 4.2 Measuring Strength of Association with Correlation 4.3 Modeling

**Linear**Trends 4.4 Evaluating the**Linear**Model Exploring Statistics: Guessing the Age of Famous People. 8.1 The Main Ingredients of Hypothesis Testing 8.2 Characterizing p-values 8.3 Hypothesis Testing in Four Steps 8.4 Comparing Proportions from Two Populations 8.5 Understanding Hypothesis Testing Exploring Statistics: Identifying Flavors of Gum through Smell. Case Study: ...http://ecampus.com/introductory-statistics-exploring-world/bk/9780321322159

Inverse probability weighting... is a statistical technique for calculating statistics standardized to a population different from that in which the data was collected study designs with a disparate sampling population and population of target inference target population are common in application there may be prohibitive factors barring researchers from directly sampling from the target population such as cost time or ethical concerns a solution to this problem is to use an alternate design strategy e g stratified sampling weighting when correctly applied can potentially improve the efficiency and reduce the bias of unweighted estimators one very early weighted estimator is the horvitz thompson estimator of the mean when the sampling probability is known from which the sampling population is drawn from the target population then the inverse of this probability is used to weight the observations this approach has been generalized to many aspects of statistics under various frameworks in particular there are weighted likelihoods weighted ...

https://en.wikipedia.org/wiki/Inverse_probability_weighting

Model selection criterion for causal parameters in structural mean **models**based on a quasi-likelihoo... d - Taguri - 2014 - Biometrics - Wiley Online Library. Vol 70 Issue 3. JOURNAL TOOLS Get New Content Alerts. JOURNAL MENU Journal Home FIND ISSUES Current Issue. All Issues FIND ARTICLES Early View. BIOMETRIC PRACTICE Model selection criterion for causal parameters in structural mean

**models**based on a quasi-likelihood. Masataka Taguri 1,*, Yutaka Matsuyama 2 and Yasuo Ohashi 2 Article first published online: 12 MAR 2014 DOI: 10.1111/biom.12165 2014, The International Biometric Society. Issue. Biometrics Volume 70, Issue 3, pages 721 730, September 2014. Additional Information How to Cite Taguri, M., Matsuyama, Y. and Ohashi, Y. 2014, Model selection criterion for causal parameters in structural mean**models**based on a quasi-likelihood. Author Information 1 Department of Biostatistics and Epidemiology, Graduate School of Medicine, Yokohama City University, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan 2 Department of Biostatistics, School of Public Health, Graduate School of ...http://onlinelibrary.wiley.com/doi/10.1111/biom.12165/citedby

Latent class modelIn statistics, a 'latent class model LCM ' relates a set of observed usually discrete multivariate variables to a set of latent variable s. It is called a latent class model because the latent variable is discrete. 'Latent Class Analysis LCA ' is a subset of structural equation modeling, used to find groups or subtypes of cases in multivariate categorical data. The LCA will attempt to detect the presence of latent classes the disease entities, creating patterns of association in the symptoms. Because the criterion for solving the LCA is to achieve latent classes within which there is no longer any association of one symptom with another because the class is the disease which causes their association, and the set of diseases a patient has or class a case is a member of causes the symptom association, the symptoms will be "conditionally independent", i.e., conditional on class membership, they are no longer related. Related methods Application External links References. Multivariate mixture estimation MME is ...

https://en.wikipedia.org/wiki/Latent_class_model

statistical prediction | plus.maths.orgstatistical prediction. plus.maths.org. Skip to Navigation. about Plus Plus sponsors subscribe to Plus terms of use. Search this site:. Home Articles News Packages Podcasts Puzzles Reviews Ebooks Login. View menu View searchbox. statistical prediction. Predicting the final Olympic medal count is a black art. So without further ado, here is our predicted 2012 London Olympic medal count. Read more... Understanding uncertainty: How long will you live. Well, no-one knows exactly, but using stats you can make a good guess. Do you dare to find out. Read more... Understanding uncertainty: The Premier League. This is the second part of our new column on risk and uncertainty. David Spiegelhalter, Winton Professor for the Public Understanding of Risk at the University of Cambridge, continues examining league tables using the Premier League as an example. Find out just how much or how little these simple rankings can tell you. Read more... Understanding uncertainty: A league table lottery. League tables are ...

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A Bayesian model averaging approach to analyzing categorical data with nonignorable nonresponse... In this paper, a new method is developed for analyzing categorical data with nonresponse when there is uncertainty about ignorability, which incorporates the idea that there are many a priori plausible ignorable and nonignorable nonresponse

**models**. HTML HTML with abstract plain text plain text with abstract BibTeX RIS EndNote, RefMan, ProCite ReDIF JSON in new window. Keywords: Missing data ; Nonignorable nonresponse ; Bayesian model averaging ; Census coverage ; Census enumerations ; Credible intervals ;. References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.: as HTML HTML with abstract plain text plain text with abstract BibTeX RIS EndNote, RefMan, ProCite ReDIF JSON in new window Nandram B. " Hierarchical Bayesian Nonresponse**Models**for Binary Data From Small Areas With Uncertainty About Ignorability ," Journal ...https://ideas.repec.org/a/eee/csdana/v57y2013i1p600-614.html

probability - How to show that these random variables are pairwise independent? - Mathematics Stackprobability - How to show that these random variables are pairwise independent. - Mathematics Stack Exchange. more stack exchange communities. Stack Exchange. Mathematics Questions. How to show that these random variables are pairwise independent. Given the arrays $C= $ and $S= $ of lengths $N$ with elements that are discrete iid uniform distributed with equal probability p=1/2 of being $\pm$ 1 Consider the random variables for a given $l, n, m$ : $W=C_lC_mC_n$ $X=S_lS_mC_n$ $Y=C_lS_mS_n$ $Z=S_lC_mS_n$ It can be shown that these random variables $W, X, Y, Z$ are zero mean, uniform distributed with equal probability p=1/2 of being $\pm$ 1. Now how can one go about showing that the random variables $W, X, Y, Z$ are pairwise independent. 8 For Bernoulli variables, non-correlation implies independence. More is true: any three random variables amongst W, X, Y and Z are independent while W,X,Y,Z is not since, for example, WXYZ=+1 with probability 1. That is, for Bernoulli variables non-correlation implies ...

http://math.stackexchange.com/questions/205597/how-to-show-that-these-random-variables-are-pairwise-independent

estimating K and Lambda from an extreme value distribution... Kevin Karplus karplus at cheep.cse.ucsc.edu. Tue Feb 24 12:18:10 EST 2004. Previous message: estimating K and Lambda from an extreme value distribution Next message: estimating K and Lambda from an extreme value distribution Messages sorted by:. In article gi4qtl7tg2.fsf at pusch.xnet.com, Gordon D. Pusch wrote: ranjeeva r at yahoo.com Ranjeeva writes: I'm trying to fit a set of scores I get from searching a database of 1000 amino acid sequences with a HMM. I want to calculate a p-value for each matching score. My questions are a How do you estimate the scalling factors K and Lambda to fit my scores 1000 to an extreme value distribution. The obvious question would be: Why would you bother, since an HMM directly yields a generative probability estimate. Simply compare the HMM probability estimate to that of a fiducial model, e.g., the random sequence model. However, if you insist on ab using extreme-value theory for this problem, googling on the exact phrase extreme value distribution plus fitting ...

http://bio.net/bionet/mm/comp-bio/2004-February/002705.html

Dose-response modeling using **linear**Splines... Universit t Duisburg-Essen DuEPublico Dose-response modeling using

**linear**Splines. In den Korb E-Mail senden Statistik Dose-response modeling using**linear**Splines Dr. Dateibereich 19740 Text anzeigen PDF Download als ZIP-Datei 1,64 MB in einer Datei, zuletzt ge ndert am 14.04.2008 Dateiliste / Details. Datei Dateien ge ndert am Gr e Doktorarbeit Martin Kappler.pdf 14.04.2008 14:38:41 1,64 MB. Dateibereich 19741 Text anzeigen PDF Download als ZIP-Datei 106 KB in 2 Dateien, zuletzt ge ndert am 14.04.2008 Dateiliste / Details. Datei Dateien ge ndert am Gr e Abstrakt deutsch.pdf 14.04.2008 14:39:04 53,1 KB. The aim of this work is to examine**linear**splines for modeling dose-response relations in comparison to these standard methods, as well as to the more complex techniques of fractional polynomials and additive**models**. In this context, the dose-response relation between exposure to phenanthrene and excretion of the urinary metabolites 1-, 2-+9-, 3- and 4-OH-phenanthrene is ...http://duepublico.uni-duisburg-essen.de/servlets/DocumentServlet?id=18095

statistics - covariance matrix for normal - Mathematics Stack Exchange... chat blog. Mathematics Meta. more stack exchange communities. Stack Exchange. sign up log in tour. Help Center Detailed answers to any questions you might have. Mathematics Questions. Sign up. Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. covariance matrix for normal. An iid random sample of 4 is taken from a normal distribution with mean 2 and variance 3. What is the covariance matrix. What is the matrix of mew. If they are not iid, then how would the covariance matrix differ. My solution: If it is iid, the matrix is simply a diagonal matrix with 3 as its entries The mean matrix is just a row matrix with entries 2 If it is not iid, the matrix has diagonals of 3 and non-diagonal entries I am not sure of... Please let me know if my solution is correct and how to find the matrix if they are not iid. statistics share. asked Mar 20 '12 at 21:45. If they are not independent but each individually still has this same ...

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.. Time Series MT .. Time Series MT 2.1time series mt time series mt times series mt provides for comprehensive treatment of time series

**models**including model diagnostics mle estimation and forecasts time series mt tools covers panel series**models**including random effects and fixed effects while allowing for unbalanced panels features new estimate**models**with multiple structural breaks new estimate threshold autoregressive**models**new rolling and recursive ols estimation least squares dummy variable model for multivariate data with bias correction of the parameters hamiltonâ s regime switching regression model seasonal varmax**models**time series cross sectional regression**models**weighted maximum likelihood thread safe arima model estimation and forecasts exact full information maximum likelihood estimation of varmax varma arimax and ecm**models**standard time series diagnostic tests including unit root tests cointegration tests and lag selection tests examples ...http://aptech.com/products/gauss-applications/time-series-mt/

Confidence interval of lifetime distribution using bootstrap method | QUT ePrintsConfidence interval of lifetime distribution using bootstrap method. Confidence interval of lifetime distribution using bootstrap method. Zhou, Yifan 2008 Confidence interval of lifetime distribution using bootstrap method. Abstract Lifetime estimation is significant in engineering asset management. For the Gamma process, which is a commonly used method for lifetime estimation, the conventional confidence interval construction methods do not perform well. This paper adopts bootstrap methods to build confidence intervals of lifetime distribution when the Gamma process is used. Moreover, bootstrap calibration is conducted to assess the coverage probability of the confidence intervals built by these bootstrap methods. The results show that the BCa method is recommended for generating confidence intervals for Gamma processes in this application. Web of Science® citation databases. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards. Citations counts ...

http://eprints.qut.edu.au/17637/

Orthogonal polynomialsThe most widely used orthogonal polynomials are the classical orthogonal polynomials, consisting of the Hermite polynomials, the Laguerre polynomials, the Jacobi polynomials together with their special cases the Gegenbauer polynomials, the Chebyshev polynomials, and the Legendre polynomials. Examples of orthogonal polynomials Properties Relation to moments. Then the inner product is given by : \langle f, g \rangle = \int {x 1} {x 2} f x g x W x \; dx. The classical orthogonal polynomials Jacobi polynomials, Laguerre polynomials, Hermite polynomials, and their special cases Gegenbauer polynomials, Chebyshev polynomials and Legendre polynomials. They include many orthogonal polynomials as special cases, such as the Meixner–Pollaczek polynomials, the continuous Hahn polynomials, the continuous dual Hahn polynomials, and the classical polynomials, described by the Askey scheme The Askey–Wilson polynomials introduce an extra parameter 'q' into the Wilson polynomials. Discrete orthogonal polynomials are orthogonal ...

https://en.wikipedia.org/wiki/Orthogonal_polynomials

ExploringDataBlog: March 2011I discussed this data example in my first couple of boxplot posts and I think this is a case where the beeswarm plot gives you a more useful picture of how the data points are distributed than the boxplots do. The figure below shows a normal Q-Q plot for the number of traffic deaths per 10,000 drivers generated using the qqPlot package. The upper left plot shows the results obtained for the exponential distribution which, like the Gaussian distribution, does not require the specification of a shape parameter. The exponential distribution represents a special case of the gamma distribution, with a shape parameter equal to 1. Alternatively, the Weibull distribution which also includes the exponential distribution as a special case might describe these data values better than any member of the gamma distribution family, and these plots can also be easily generated using the qqPlot command just specify dist = weibull instead of dist = gamma, along with shape = a for some positive value of a other than 1. ...

http://exploringdatablog.blogspot.co.uk/2011_03_01_archive.html

Professor Peter Congdon... The School of Geography. Prospective Students. Research. People. News. Events. About us. Contact. . People menu Home / People / Academic Staff Academic Staff. Research Staff. Support Staff. Emeritus Staff. PhD Students. Visiting and Honorary Staff. Professor Peter Congdon Research Professor in Quantitative Geography and Health Statistics email: p.congdon@qmul.ac.uk Tel: 020 7882 2778 Location: Geography building, Room 204 Profile. Teaching. Research. Publications. PhD Supervision. Public engagement. Profile. I am a quantitative geographer with particular interests in geographic epidemiology, application of spatial statistical methods to area health and health survey data, and spatial demography. Since 2001 I have been a Research Professor in the School of Geography, and am also affiliated to the QMUL Life Sciences Institute. I have authored a range of articles and books, including ‘Applied Bayesian Hierarchical Methods’ CRC, 2010 and ‘Bayesian Statistical Modelling’ Wiley, 2006. My major projects ...

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La stabilité politique, une condition nécessaire mais pas suffisante pour attirer les firmes multiHTML HTML with abstract plain text plain text with abstract BibTeX RIS EndNote, RefMan, ProCite ReDIF JSON in new window. C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation

**Models**; Multiple Variables - - - Cross-Sectional**Models**; Spatial**Models**; Treatment Effect**Models**; Quantile Regressions; Social Interaction**Models**. C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation**Models**; Multiple Variables - - -**Models**with Panel Data; Spatio-temporal**Models**. F21 - International Economics - - International Factor Movements and International Business - - - International Investment; Long-Term Capital Movements. F23 - International Economics - - International Factor Movements and International Business - - - Multinational Firms; International Business. References listed on IDEAS Please report citation or reference errors to, or, if you are the registered author of the cited work, log in to your RePEc Author ...https://ideas.repec.org/a/rej/journl/v11y2008i27p67-81.html

Two random variables equal in distribution?and with the same expectation vector November 7th 2009, 01:28 PM matheagle X1,X2,X3 and Y1,Y2,Y3 would have to have the same joint distribution. : November 7th 2009, 03:11 PM Laurent Quote: Originally Posted by kingwinner. November 15th 2009, 05:56 PM kingwinner Quote: Originally Posted by Laurent. November 16th 2009, 03:09 AM Laurent Quote: Originally Posted by kingwinner. November 17th 2009, 09:02 AM kingwinner Quote: Originally Posted by Laurent. : November 17th 2009, 03:09 PM Laurent Quote: Originally Posted by kingwinner. So, at least, when you know the joint distribution of X,Y , you know the distributions of X and Y. So the joint distribution tells you if X and Y are independent. You can think of hot spots or peaks where the measure gives more probability, and it gets colder and colder at infinity nearer to 0. Then for instance you may have some very hot spot near 1,2 , which means that. has high probability to be near that point, i.e. with high probability X is near 1 and at the same time Y is near ...

http://mathhelpforum.com/advanced-statistics/107711-two-random-variables-equal-distribution-print.html

Multinomial probitAs such, it is an alternative to the multinomial logit model as one method of multiclass classification. It is assumed that we have a series of observations 'Y' 'i', for 'i' = 1...'n', of the outcomes of multi-way choices from a categorical distribution of size 'm' there are 'm' possible choices. Along with each observation 'Y' 'i' is a set of 'k' observed values 'x' '1,i', ..., 'x' 'k,i' of explanatory variables also known as independent variable s, predictor variables, features, etc. The observed outcomes might be "has disease A, has disease B, has disease C, has none of the diseases" for a set of rare diseases with similar symptoms, and the explanatory variables might be characteristics of the patients thought to be pertinent sex, race, age, blood pressure, body-mass index, presence or absence of various symptoms, etc. sex, race, age, income, etc. The multinomial probit model is a statistical model that can be used to predict the likely outcome of an unobserved multi-way trial given the associated ...

https://en.wikipedia.org/wiki/Multinomial_probit

15 bottles alcohols 3d modelbottles alcohols d model stock images new empty login join bottles by vizionair add to cart royalty free license faq all extended uses included formats ds max default scanline d model specifications product id published jan geometry polygonal polygons vertices textures yes materials yes rigged no animated no uv mapped yes unwrapped uvs unknown artist vizionair turbosquid member since january currently sells products achievements live chat now quality guarantee file format conversions report

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www.malariajournal.com - Figurewww malariajournal com figure resolution standard high figure theory predicts that the slope of pfpr in young children i e b and the pfpr in older children i e the plateau p should be correlated the best fit parameters describing these two quantities are plotted here two extreme values were excluded from this plot there was no correlation with p or without p the extreme values smith et al malaria journal doi download authors original image

http://malariajournal.com/content/6/1/131/figure/F3

Volterra Italy - Hotels Volterra Accommodation Hotel in Volterra B&B Volterra Lodging Volterra ResiVolterra Italy - Hotels Volterra Accommodation Hotel in Volterra B B Volterra Lodging Volterra Residences Volterra Farm Holiday Volterra Special Offers Volterra Hospitality Volterra. Volterra Italy Hotels Volterra Accommodation Hotel in Volterra B B Volterra Lodging Volterra Residences Volterra Farm Holiday Volterra Special Offers Volterra Hospitality Volterra. Volterra Volterra. Volterra. Hotels Volterra - Restaurants - Shopping - Typical Products - Services -. Volterra is prevalently Medieval and yet cherishes abundant evidence of the Etruscan period: the Porta all'Arco the Etruscan gate which date from the 4th century B.C., the Acropolis, the defensive walls which are still visible in parts of the town. The Roman period is attested by the important remains of the Teatro di Vallebona which date back to the Augustan period, the Baths and an enormous rectangular water cistern. The Middle Ages are not only visible in its urban structure but too in its buildings, its hause-towers and churches: the Palazzo dei ...

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Non**linear**Analysis of Continuum Theories: Statics and Dynamics | Isaac Newton Institute for MathematNon

**linear**Analysis of Continuum Theories: Statics and Dynamics. Isaac Newton Institute for Mathematical Sciences. Study at Cambridge. About the University. Research at Cambridge. Study at Cambridge. Why Cambridge. International students. Continuing education. Executive and professional education. Courses in education. About the University. How the University and Colleges work. International Cambridge. News. Events. Giving to Cambridge. Research at Cambridge. For current students. Email phone search. Overview A Brief History Governance Fellowships Testimonials Art and Artefacts Isaac Newton Resources Job Vacancies Contact Us How to Find Us Science. Overview Scientific Programmes. Overview Current Programmes The Mathematics of Liquid Crystals. Overview Participants Preprints Seminars Workshops and Other Events. Overview Non**linear**Analysis of Continuum Theories: Statics and Dynamics. Overview Participants Seminars Speakers Timetable. Future Programmes Past Programmes Programmes by Year Outreach ...http://newton.ac.uk/event/mlcw03

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Non**linear**dynamics of a regenerative cutting process - Springer... Non

**linear**dynamics of a regenerative cutting process. Affiliated with Institute of Physics, Chemnitz University of Technology. Affiliated with Institute of Physics, Chemnitz University of Technology. Keywords Cutting process 0-1 test Multiscale entropy. About This Journal. Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn. Related Content. Soc. CrossRef. CrossRef. CrossRef. CrossRef. CrossRef. Gradisek, J., Govekar, E., Grabec, I.: Time series analysis in metal cutting: chatter versus chatter-free cutting. Gradisek, J., Govekar, E., Grabec, I.: Using coarse-grained entropy rate to detect chatter in cutting. Fofana, M.S.: Delay dynamical systems and applications to non**linear**machine-tool chatter. Litak, G., Rusinek, R., Teter, A.: Non**linear**analysis of experimental time series of a straight turning process. Gottwald, G.A., Melbourne, I.: A new test for chaos in deterministic systems. Gottwald, G.A., Melbourne, I.: Testing for chaos in ...http://link.springer.com/article/10.1007/s11071-012-0344-z

Software Option Analyzes Non**linear**Behavior of Active Components | Test & Measurement content from MSoftware Option Analyzes Non

**linear**Behavior of Active Components. Test Measurement content from Microwaves RF. Active components Passive components Analog Semiconductors Digital semiconductors Mixed-signal semiconductors Services Software Systems Materials Test Measurement - analyzers Test Measurement - generators. Home > Technologies > Test Measurement > Software Option Analyzes Non**linear**Behavior of Active Components Software Option Analyzes Non**linear**Behavior of Active Components Jun 16, 2008. 0 A software option adds non**linear**vector network analyzer NVNA capability to Agilent Technologies' PNA-X microwave network analyzer. It features non**linear**component characterization, new non**linear**scattering parameters, and non**linear**pulse envelope domain capabilities. Tools specifically designed to make non**linear**measurements are required to accurately characterize the behavior of active devices, particularly high-power amplifiers and ...http://mwrf.com/test-and-measurement/software-option-analyzes-nonlinear-behavior-active-components

Non**linear**dynamics accompanying polarization switching in vertical-cavity surface-emitting lasers wi... th orthogonal optical injection. all Scitation. AIP Publishing AVS: Science Technology of Materials, Interfaces, and Processing Acoustical Society of America American Association of Physicists in Medicine American Association of Physics Teachers American Crystallographic Association, Inc. EVENT EVENTLOG PORTALID aip /PORTALID SESSIONID ue370cqh81ck.x-aip-live-03 /SESSIONID USERAGENT CCBot/2.0 http://commoncrawl.org/faq/ /USERAGENT IDENTITYID guest /IDENTITYID IDENTITY_LIST guest /IDENTITY_LIST IPADDRESS 54.163.7.185 /IPADDRESS EVENTTYPE PERSONALISATION /EVENTTYPE CREATEDON 1443947970408 /CREATEDON /EVENTLOG EVENTLOGPROPERTY ITEM_ID http://aip.metastore.ingenta.com/content/aip/journal/apl/88/10/10.1063/1.2181649 /ITEM_ID TYPE recommendtolibrary /TYPE /EVENTLOGPROPERTY /EVENT. AIP Publishing. Article. Non

**linear**dynamics accompanying polarization switching in vertical-cavity surface-emitting lasers with orthogonal optical injection. Scitation Author Page. PubMed. Google Scholar. 88 , 101106 2006 ; ...http://scitation.aip.org/content/aip/journal/apl/88/10/10.1063/1.2181649

Non**linear**analysis of renal autoregulation in rats using principal dynamic modes.... Document Detail. Non

**linear**analysis of renal autoregulation in rats using principal dynamic modes. MedLine Citation:. This article presents results of the use of a novel methodology employing principal dynamic modes PDM for modeling the non**linear**dynamics of renal autoregulation in rats. The analyzed experimental data are broadband 0-0.5 Hz blood pressure-flow data generated by pseudorandom forcing and collected in normotensive and hypertensive rats for two levels of pressure forcing as measured by the standard deviation of the pressure fluctuation. The PDMs are computed from first-order and second-order kernel estimates obtained from the data via the Laguerre expansion technique. The results demonstrate that two PDMs suffice for obtaining a satisfactory non**linear**dynamic model of renal autoregulation under these conditions, for both normotensive and hypertensive rats. Furthermore, the two PDMs appear to correspond to the two main autoregulatory mechanisms: the first to the myogenic ...http://biomedsearch.com/nih/Nonlinear-analysis-renal-autoregulation-in/9916757.html

Re: st: types and codes of the non-**linear****models**... Stata: Data Analysis and Statistical Software. Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, statalist.org is already up and running. Re: st: types and codes of the non-

**linear****models**. From. Nick Cox njcoxstata@gmail.com. To. statalist@hsphsun2.harvard.edu. Subject. Re: st: types and codes of the non-**linear****models**. Date. Thu, 21 Feb 2013 14:27:17 +0000. There is unfortunately no point in trying to interpret the result of this fit. The intercept has exploded to 3.9 x 10 116 and no standard error can be calculated for it. Conversely, the coefficient has a suspiciously narrow c.i. of. I'll wager that nothing in epidemiology is that certain, other than the probability that we all die sometime. If you plot this fit against your data, my bet is that this will leap out at you as a lousy fit. That doesn't necessarily mean that the model equation is no use for your data, but it ...http://stata.com/statalist/archive/2013-02/msg00822.html

Measuring harmonic distortion with the ECM8000 - diyAudio... Search Blogs. Measuring harmonic distortion with the ECM8000. Home Forums Rules Articles The diyAudio Store Gallery Blogs Register Donations FAQ Calendar Search Today's Posts Mark Forums Read Search. Thread Tools Search this Thread. diyAudio Member. Join Date: Sep 2003 Location: Bellevue, WA. Measuring harmonic distortion with the ECM8000 I want measure harmonic distortion using my Behringer ECM8000, but I'm afraid the microphone itself introduces distortion, causing invalid results. Does anyone know what maximum SPL the ECM8000 can measure before distortion starts quickly increasing. Has anyone measured the harmonic distortion introduced by the ECM8000 itself. diyAudio Member. What program do you use for measuring distortion. diyAudio Member. The Behringer will also happily measure SPL levels at 120dB, if really high SPL measurements are needed then take a look at Earthworks range or even those used for car SPL competitions. 11th February 2007, 05:45 PM # 4. diyAudio Member. Quote: Originally posted by ...

http://diyaudio.com/forums/multi-way/96133-measuring-harmonic-distortion-ecm8000.html

Itinerary in San Gimignano and Volterra | ItalyItinerary in San Gimignano and Volterra. Italy. Home News Forum Travel Food Wines Culture Lifestyle Fashion Moving to Italy Learn Italian Home Garden Weather Places. Home News Forum Travel. Wine Cooking Italian Style Food Products Food Recipes Italian Food Articles Nonna s food Culture. Beauty Fashion Accessories Fashion Houses Italian Style About Italian Fashion Men s Fashion Women Fashion Moving to Italy. Itinerary in San Gimignano and Volterra. Two medieval towns not to miss in Tuscany. San Gimignano and Volterra are two Tuscan hill towns, physically close to each other, but very different. Volterra. Volterra Volterra is Etruscan in origin and sits in countryside much different from San Gimignano. Volterra. San Gimignano. Follow the signs to Volterra. To get to San Gimignano from Volterra take No. Rather than retracing your tracks, when you leave San Gimignano follow the signs to Poggibonsi. If you are in the area of Florence or Siena, the best approach will be from Poggibonsi to San Gimignano and then on ...

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OSA | Non**linear**properties of left-handed composite metamaterialsOSA. Non

**linear**properties of left-handed composite metamaterials. Login or Create Account. Authors Use these formats for best results: Smith or J Smith Use a comma to separate multiple people: J Smith, RL Jones, Macarthur Any : All :. For best results, use the separate Authors field to search for author names. Author name searching: Use these formats for best results: Smith or J Smith Use a comma to separate multiple people: J Smith, RL Jones, Macarthur Note: Author names will be searched in the keywords field, also, but that may find papers where the person is mentioned, rather than papers they authored. Journal of Optical Technology. Journal of the Optical Society of America A. Journal of the Optical Society of America B. Optics Express. Journal of Optical Networking 2002-2009. Journal of the Optical Society of America 1917-1983. Optics News 1975-1989. Journal of Optical Technology. Journal of the Optical Society of America A. Journal of the Optical Society of America B. Optics Express. Journal of ...https://osapublishing.org/abstract.cfm?uri=CLEO-2004-CMF7

Quadratic non**linear**properties of poled glasses - ePrints Sotonquadratic non

**linear**properties of poled glasses eprints soton advanced search university home eprints soton policies latest additions download statistics browse by year browse by subject browse by school login rss rss atom quadratic non**linear**properties of poled glasses russell p st j and kazansky p g quadratic non**linear**properties of poled glasses in cleo conference on lasers and electro optics amsterdam nl aug sep download full text not available from this repository description abstract recent progress on the poling of glasses in bulk and optical fibre form will be reviewed second order non**linear**ities of order pm v have been realised in fibres and efficient quasi phase matching structures should soon be available item type conference or workshop item paper related urls http www orc soton ac uk vie ml pid subjects q science qc physics t technology tk electrical engineering electronics nuclear engineering divisions university structure pre august optoelectronics research centre ...http://eprints.soton.ac.uk/77141/

Chaotic dynamics and oxygen transport in thin films of aerotactic bacteria... all Scitation. Physics of Fluids 1994-present. AIP Publishing AVS: Science Technology of Materials, Interfaces, and Processing Acoustical Society of America American Association of Physicists in Medicine American Association of Physics Teachers American Crystallographic Association, Inc. I'm an author/editor/contributor to this publication. EVENT EVENTLOG PORTALID aip /PORTALID SESSIONID 11cke9e7c162e.x-aip-live-03 /SESSIONID USERAGENT CCBot/2.0 http://commoncrawl.org/faq/ /USERAGENT IDENTITYID guest /IDENTITYID IDENTITY LIST guest /IDENTITY LIST IPADDRESS 54.144.126.195 /IPADDRESS EVENTTYPE PERSONALISATION /EVENTTYPE CREATEDON 1444073402381 /CREATEDON /EVENTLOG EVENTLOGPROPERTY ITEM ID http://aip.metastore.ingenta.com/content/aip/journal/pof2/24/9/10.1063/1.4752764 /ITEM ID TYPE recommendtolibrary /TYPE /EVENTLOGPROPERTY /EVENT. Physics of Fluids 1994-present Recommend this title to your library. Access Key. Free Content. Open Access Content Subscribed Content. Free Trial Content. AIP Publishing. ...

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Non**linear**effect of pile-up in the quantification of a small animal PET scanner... . Non

**linear**effect of pile-up in the quantification of a small animal PET scanner. e-Archivo Repository. Versión en español. Login. . e-Archivo Home → Investigación → Departamentos → Departamento de Bioingeniería e Ingeniería Aeroespacial → Área de Imagen e Instrumentación BiiG → DBIAB - BIIG - Book Chapters → View Item. JavaScript is disabled for your browser. Some features of this site may not work without it. Non**linear**effect of pile-up in the quantification of a small animal PET scanner. Author s : Vicente, Esther ; España, Samuel ; López Herraiz, Joaquín ; Herranz, Elena ; Desco, Manuel ; Vaquero, Juan José ; Udías, José Manuel. Publisher: IEEE. Issued date: 2008-10. Citation: 2008 IEEE Nuclear Science Symposium Conference Record, Oct. 2008, p. 5391 - 5395. ISBN: 978-1-4244-2714-7. ISSN: 1082-3654. DOI: 10.1109/NSSMIC.2008.4774451. Sponsor: This work was supported in part by the MEC FPA2007-07393, CDTEAM CENIT-Ingenio 2010 Ministerio de Industria, Spain, CPAN Consolider-Ingenio 2010 ...http://e-archivo.uc3m.es/handle/10016/12209

Sage-N to Co-Market Its Proteomics Platform with Non**linear**Dynamics' LC/MS Software | GEN News HiSage-N to Co-Market Its Proteomics Platform with Non

**linear**Dynamics' LC/MS Software. Transitioning from Traditional Assay Formats to HTRF Technology. The Pons Asinorum of Diagnostic Genomics. Jobs Report: Data, Business, and Regulatory Savvy Driving New Hiring. FDA Nominee Driven by "Data, Data, Data". More GEN Exclusives ». Transitioning from Traditional Assay Formats to HTRF Technology. The Pons Asinorum of Diagnostic Genomics. Jobs Report: Data, Business, and Regulatory Savvy Driving New Hiring. FDA Nominee Driven by "Data, Data, Data". More GEN Exclusives ». Market & Tech Analysis. GEN Exclusives More. Transitioning from Traditional Assay Formats to HTRF Technology Sensitivity of Fluorescence Coupled to Low... The Pons Asinorum of Diagnostic Genomics Non-Small Cell Lung Carcinoma: ROSter for... GEN News Highlights More » Feb 25, 2011 Sage-N to Co-Market Its Proteomics Platform with Non**linear**Dynamics' LC/MS Software Page 1 of 1 Sage-N Research signed an agreement to co-market ...http://genengnews.com/gen-news-highlights/sage-n-to-co-market-its-proteomics-platform-with-nonlinear-dynamics-lc-ms-software/81244743/?kwrd=Nonlinear Dynamics

Characterization of stickiness by means of recurrence... all Scitation. Chaos: An Interdisciplinary Journal of Non

**linear**Science. AIP Publishing AVS: Science Technology of Materials, Interfaces, and Processing Acoustical Society of America American Association of Physicists in Medicine American Association of Physics Teachers American Crystallographic Association, Inc. Chaos: An Interdisciplinary Journal of Non**linear**Science Recommend this title to your library. AIP Publishing. Chaos: An Interdisciplinary Journal of Nonlinea…. Article. No metrics data to plot. The full text of this article is not currently available. Characterization of stickiness by means of recurrence. Scitation Author Page. PubMed. Google Scholar. Affiliations: 1 Non**linear**Dynamics Group, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany. Chaos 17 , 043101 2007 ; http://dx.doi.org/10.1063/1.2785159. Moreover, several measures from the recurrence quantification analysis are used to quantify these patterns. We find that the patterns in the RPs of ...http://scitation.aip.org/content/aip/journal/chaos/17/4/10.1063/1.2785159

3aUW11 A model of non**linear**behavior of ice cracks.auw a model of non

**linear**behavior of ice cracks asa th meeting washington dc may jun auw a model of non**linear**behavior of ice cracks lev a ostrovsky noaa erl etl cires broadway r e et boulder co alexander e ekimov andrey v lebedev alexander m sutin inst of appl phys of russian acad sci nizhni novgorod russia a work presented at the previous asa meeting has demonstrated that the cracks may provide anomalously strong vibroacoustic non**linear**ity in ice experimental data were obtained from the field experiments on a fresh water lake where a strong subharmonic signal was in particular registered here a theoretical model is suggested for the description of the effects observed and possible use of them for characterizing the crack parameters the model is based on consideration of non**linear**flexural oscillations of the ice plate with a crack which may be opened from its upper or lower part due to flexural oscillations together with the ...http://auditory.org/asamtgs/asa95wsh/3aUW/3aUW11.html

Splinter Cell Chaos Theory In-Depth - IGN... PC. Trailers Reviews PS4 Xbox One PC Wii U Movies TV. Now Reading Splinter Cell Chaos Theory In-Depth. Apple iPhone 6s Plus Review The Office: Jim Spent a Lot of Money to Prank Dwight Princess Leia Slave Bikini Sells for... Blunt Talk: "Meth or No Meth, You Still Gotta Floss" Review Why Destiny's Raid Drop System Needs to Change - IGN's Fireteam Chat A Christmas Horror Story Review Daily Deals: 1TB Xbox One With Two Fallout Games, Fire Emblem: Awakening, Save $50 On A PS4 NASA Told Ridley Scott About Water on Mars Early The Martian Review The Daily Fix X1 1TB Bundle and Rise of the Tomb Raider Season Pass Leaked Online - IGN Daily Fix The Man Who Almost Directed The Martian Dr. Tom Clancy's Splinter Cell Chaos Theory /. 14 Jan 2005 Splinter Cell Chaos Theory In-Depth Share. Splinter Cell Chaos Theory is going to amaze gamers very soon. Set to ship for Xbox, PS2, GameCube, and PC on the last Tuesday in March, Chaos Theory looks to be the Splinter Cell we've all be hoping for. Ubisoft recently unveiled ...

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Recurrence quantification analysisIt quantifies the number and duration of recurrences of a dynamical system presented by its phase space trajectory. Recurrence plot s are tools which visualise the recurrence behaviour of the phase space trajectory of dynamical systems. The lines correspond to a typical behaviour of the phase space trajectory: whereas the diagonal lines represent such segments of the phase space trajectory which run parallel for some time, the vertical lines represent segments which remain in the same phase space region for some time. The next measure is the percentage of recurrence points which form diagonal lines in the recurrence plot of minimal length \ell \min :. : \text{DET} = \frac{\sum {\ell=\ell \min} N \ell\, P \ell }{\sum {i,j=1} N R i,j },. where P \ell is the frequency distribution of the lengths \ell of the diagonal lines. where P v is the frequency distribution of the lengths v of the vertical lines, which have at least a length of v \min. : \text{L} = \frac{\sum {\ell=\ell \min} N \ell\, P \ell }{\sum ...

https://en.wikipedia.org/wiki/Recurrence_quantification_analysis

Non**linear**Systems in Medicine... . . . Non

**linear**Systems in Medicine. DSpace/Manakin Repository. DASH Home. Harvard Medical School. HMS Scholarly Articles View Item. Login. Non**linear**Systems in Medicine. Show simple item record. dc.contributor.author. Higgins, John Matthew. dc.date.accessioned. 2011-04-03T23:58:42Z. dc.date.issued. 2002. dc.identifier.citation. Higgins, John P. 2002. Non**linear**systems in medicine. The Yale Journal of Biology and Medicine 75 5-6 : 247-260. en US. dc.identifier.issn. 0044-0086. en US. dc.identifier.uri. http://nrs.harvard.edu/urn-3:HUL.InstRepos:4791064. dc.description.abstract. Many achievements in medicine have come from applying**linear**theory to problems. Most current methods of data analysis use**linear****models**, which are based on proportionality between two variables and/or relationships described by**linear**differential equations. However, non**linear**behavior commonly occurs within human systems due to their complex dynamic nature; this cannot be ...http://dash.harvard.edu/handle/1/4791064?show=full

2 br Volterra Villa Vacation Rental Getaway | forGetaway.com2 br Volterra Villa Vacation Rental Getaway. Vacation Home Rentals. Tuscany. Pisa. Volterra. 2 br Volterra Villa Vacation Rental Getaway. Property Listing Expired This property is disabled. Please browse our active Volterra Vacation Rentals. Podere Bellosguardo - Onice. 2 br Volterra Villa. Property Type: Villa Bed/Bath: 2 / 1 Sleeps: 6 Pets: Yes Rental Rates: 450 - 680 per week. Description Availability Rates Amenities Map. Unit Description: First floor apartment with entrance via external staircase, large sitting-dining room with kitchenette and double sofa bed, double bedroom, twin bedroom and bathroom with shower. EQUIPMENT: communal washing machine, SAT TV, oven, freezer. Overall Property Description: The beautiful farmhouse, carefully restored respecting Tuscan traditions, divided in two spacious and comfortable holiday apartments where guests can feel at ease in a genuine family environment. Podere Bellosguardo is located in the peace and quiet of the splendid hills of the Alta Val di Cecina, with ...

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2 br Volterra Villa Vacation Rental Getaway | forGetaway.com2 br Volterra Villa Vacation Rental Getaway. forGetaway.com. Vacation Home Rentals. Italy. Tuscany. Pisa. Volterra. 2 br Volterra Villa Vacation Rental Getaway. Property Listing Expired This property is disabled. Please browse our active Volterra Vacation Rentals. Pelagaccio - Pelagaccio 6. 2 br Volterra Villa. Back to Search Results. Property Type: Villa Bed/Bath: 2 / 1 Sleeps: 6 Pets: Yes Rental Rates: 450 - 1,170 per week. Description Availability Rates Amenities Map. Unit Description: 60 m2 1st floor: living-room with double sofa-bed, dining area and kitchen corner freezer, 2 double bedrooms one with 4-poster bed, bathroom with shower. Overall Property Description: Nice farmhouse quietly situated affording beautiful extensive views over the surrounding landscape. The house has been recently restored with its original architectural features preserved and turned into 10 charming apartments, all of which tastefully furnished in a typical Tuscan style. Distances: the small town of Villamagna with food shops ...

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electromagnetism - Non-**linear**dynamics of classical hydrogen atom - Physics Stack Exchange... Stack Exchange. Physics Questions. Physics Stack Exchange is a question and answer site for active researchers, academics and students of physics. Non-

**linear**dynamics of classical hydrogen atom. I'd like to know if there have been attempts in solving the full problem of the dynamics of a classical hydrogen atom. electromagnetism classical-electrodynamics non-**linear**-dynamics classical-mechanics share. Also, I already answered your question completely--- there is no dimensional parameter to allow a stable orbit--- you can't make a size using electromagnetic constants, the charge of the electron, and the mass of the electron/proton. It's obvious that orbits have a size but you could express such a size by the dimensional constants you prefer, and this won't affect the very existence of the orbits, in other words I think you are wrong about the fact that you must introduce ℏ to make stable orbits pop out: if the equations of motion predict the existence of stable orbits, these ...http://physics.stackexchange.com/questions/41937/non-linear-dynamics-of-classical-hydrogen-atom

Mean stress effects on random fatigue analysis of non**linear**structures - ePrints Sotonmean stress effects on random fatigue analysis of non

**linear**structures eprints soton advanced search university home eprints soton policies latest additions download statistics browse by year browse by subject browse by school login rss rss atom mean stress effects on random fatigue analysis of non**linear**structures sweitzer k a and ferguson n s mean stress effects on random fatigue analysis of non**linear**structures in proceedings of the twelfth international congress on sound of vibration th international congress on sound and vibration icsv th international congress on sound and vibration download full text not available from this repository original publication url http www icsv ist utl pt papers paper php id item type book section additional information paper related urls http www icsv ist utl pt p edings php http www icsv ist utl pt p php id subjects ...http://eprints.soton.ac.uk/28506/

BIO Web of Conferences... All issues. News. Forthcoming. Leaflet PDF. Web of Conferences. Advanced Search. Home. All issues. Volume 1 2011. BIO Web of Conferences, 1 2011 00041. Abstract Homepage Table of contents Previous article Next article. Article Abstract. PDF 607.3 KB. Metrics Abstract views: 321 Full-text article: 131. since Thursday, 22 December 2011. Services Same authors - Google Scholar - EDP Sciences database - PubMed. Recommend this article. Download citation. Alert me if this article is cited. Alert me if this article is corrected. Related Articles A historical review of recurrence plots Eur. Phys. J Special Topics 164, 3-12 2008. Two phase flow bifurcation due to turbulence: transition from slugs to bubbles Eur. Phys. J B 2015 88: 239. Recurrence plots 25 years later —Gaining confidence in dynamical transitions EPL, 101 2013 20007. Prosody and synchronization in cognitive neuroscience EPJ Non

**linear**Biomed Phys 2013 1: 6. Analyzing convergence and synchronicity of business and growth cycles in the euro area ...http://bio-conferences.org/articles/bioconf/abs/2011/01/bioconf_skills_00041/bioconf_skills_00041.html

Chaos theory at work: gas price spike and four-legged 'saboteurs' | [primary-term] content from SoutChaos theory at work: gas price spike and four-legged saboteurs. content from Southeast Farm Press. Skip to Navigation Skip to Content Southeast Farm Press. Home Cotton Peanuts Soybeans Grains Tobacco Vegetables Orchard Crops Livestock Markets Government Equipment Weather. Home > Chaos theory at work: gas price spike and four-legged saboteurs Chaos theory at work: gas price spike and four-legged saboteurs Apr 9, 2007. Southeast Farm Press. 0 What do a raccoon and a possum have to do with a 7-cent increase in the wholesale price of gasoline on the West Coast. • Those events disrupted operations for about two hours at an Exxon Mobil refinery and about 10 seconds yep, that’s right: 10 seconds at a Shell Oil Co. refinery. • Next morning, news of the brief refinery disruptions pushed wholesale gasoline prices up 7 cents a gallon. Talk about chaos theory at work. He adds, “If you think it’s bad now, you can’t believe how high prices would go if a major chunk of refining capacity were taken out for a lengthy ...

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Gradiometer Based on Non**linear**Magneto-Optic Rotation | SBIR.govGradiometer Based on Non

**linear**Magneto-Optic Rotation. SBIR.gov. sba. SBIR. The SBIR Program SBIR Mission and Program Goals SBIR Participation Agencies Three-Phase Program Competitive Opportunity for Small Business SBIR Policy Directive SBIR/STTR Inter-agency Policy Committee: Fueling Small Business Innovation Reports Annual Report Transmittal Letters STTR. The STTR Program STTR Mission and Program Goals STTR Participating Agencies Three-Phase Program Competitive Opportunity for Small Business STTR Policy Directive Tibbetts Awards and Hall of Fame. Recognizing Excellence SBIR Hall of Fame Tibbetts Awards Roland Tibbetts SBIR Tibbetts Award Eligibility Selection Process Recent Awards Award Ceremonies FAST Partnership Program. Open Future Closed Solicitation Listing Health-Related Funding Awards. Award Listing Company Listing Analytics Dashboard State Summary Report Annual Reports. News SBIR Pulse Success Stories SBA Blog SBA Newsletter OSTP Events. Events Webinars Calendar SBIR Road Tour National ...https://sbir.gov/sbirsearch/detail/312282

Correlation sum... multiple issues in chaos theory the correlation sum is the estimator of the correlation integral which reflects the mean probability that the states at two different times are close c varepsilon frac n sum stackrel i j i neq j n theta varepsilon vec x i vec x j quad vec x i in bbb r m where n is the number of considered states vec x i varepsilon is a threshold distance cdot a norm e g euclidean norm and theta cdot the heaviside step function if only a time series is available the phase space can be reconstructed by using a time delay embedding see takens theorem vec x i u i u i tau ldots u i tau m where u i is the time series m the embedding dimension and tau the time delay the correlation sum is used to estimate the correlation dimension see also correlation integral correlation dimension recurrence quantification analysis references category chaos theory category dynamical systems category dimension theory

https://en.wikipedia.org/wiki/Correlation_sum

Recurrence plotshowing i on a horizontal axis and j on a vertical axis, where \vec{x} is a phase space trajectory. Often, the phase space does not have a low enough dimension two or three to be pictured, since higher-dimensional phase spaces can only be visualized by projection into the two or three-dimensional sub-spaces. However, making a recurrence plot enables us to investigate certain aspects of the 'm'-dimensional phase space trajectory through a two-dimensional representation. To make the plot, continuous time and continuous phase space are discretized, taking e.g. \vec{x} i - \vec{x} j \. Caused by characteristic behaviour of the phase space trajectory, a recurrence plot contains typical small-scale structures, as single dots, diagonal lines and vertical/horizontal lines or a mixture of the latter, which combines to extended clusters. In contrast to the heuristic approach of the recurrence quantification analysis, which depends on the choice of the embedding parameters, some dynamical invariant s as correlation ...

https://en.wikipedia.org/wiki/Recurrence_plot

Correlation integral... in chaos theory the correlation integral is the mean probability that the states at two different times are close c varepsilon lim n rightarrow infty frac n sum stackrel i j i neq j n theta varepsilon vec x i vec x j quad vec x i in bbb r m where n is the number of considered states vec x i varepsilon is a threshold distance cdot a norm e g euclidean norm and theta cdot the heaviside step function if only a time series is available the phase space can be reconstructed by using a time delay embedding see takens theorem vec x i u i u i tau ldots u i tau m where u i is the time series m the embedding dimension and tau the time delay the correlation integral is used to estimate the correlation dimension an estimator of the correlation integral is the correlation sum c varepsilon frac n sum stackrel i j i neq j n theta varepsilon vec x i vec x j quad vec x i in bbb r m see also recurrence quantification analysis references category chaos theory

https://en.wikipedia.org/wiki/Correlation_integral

Extended and Efficient midrange: what do we have here? - Page 7 - diyAudioExtended and Efficient midrange: what do we have here. Search Blogs. Extended and Efficient midrange: what do we have here. Thread Tools Search this Thread. diyAudio Member. Join Date: Dec 2007 Location: Italy. The total harmonic distortion is not a measure of the degree of distastefulness to the listener and it is recommended that its use should be discontinued. D Masa, 1938 Last edited by Telstar; 6th December 2012 at 10:55 AM. diyAudio Member. Join Date: Dec 2007 Location: Italy. The total harmonic distortion is not a measure of the degree of distastefulness to the listener and it is recommended that its use should be discontinued. 6th December 2012, 12:42 PM # 63. diyAudio Member. Join Date: Dec 2007 Location: Italy. The total harmonic distortion is not a measure of the degree of distastefulness to the listener and it is recommended that its use should be discontinued. 6th December 2012, 01:53 PM # 64. Quote: Originally Posted by Telstar. 6th ...

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Highly Non**linear**Solitary Waves in Periodic Dimer Granular Chains - CaltechAUTHORS... CaltechAUTHORS A Caltech Library Service Home. Browse. Simple Search. Advanced Search. Simple Deposit. Advanced Deposit. Contact Us. Login. Highly Non

**linear**Solitary Waves in Periodic Dimer Granular Chains. Porter, Mason A. and Daraio, Chiara and Herbold, Eric B. and Szelengowicz, Ivan and Kevrekidis, P. G 2007 Highly Non**linear**Solitary Waves in Periodic Dimer Granular Chains. In Press http://resolver.caltech.edu/CaltechSOLIDS:2007.002. PDF. See Usage Policy. Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechSOLIDS:2007.002 Abstract We report the propagation of highly non**linear**solitary waves in heterogeneous, periodic granular media using experiments, numerical simulations, and theoretical analysis. We examine periodic arrangements of particles in experiments in which stiffer/heavier beads stainless steel are alternated with softer/lighter ones polytetrafluoroethylene beads. We find excellent agreement between experiments and numerics in a model with ...http://authors.library.caltech.edu/28618/

Tom Clancy's Splinter Cell Chaos Theory for W - G4tv... . G4TV. Home. News. Games. Top Games. News. Videos. Features. Previews. Reviews. New Releases. Trailers. Cheats. Images. Indie Games. Reviews. Recent Reviews ZombiU Review. Far Cry 3 Review. Call of Duty: Black Ops Declassified Review. LEGO Lord of the Rings Review. All Game Reviews. Cheats. Xbox 360. PS3. Wii. PC. DS. PSP. Videos. Video Index. Viral Videos. Video Game Trailers. Tech TV Vault. Multiplex PS3 Portal. Wii Portal. VOD. Podcasts. Mobile. Shows. TV Shows Attack of the Show. X-Play. LOST. Heroes. G4 Movies. Bomb Patrol Afghanistan. Ninja Warrior. American Ninja Warrior. Cheaters. Cops. Campus PD. TechTV Vault. Schedule. Channel Finder. Web Shows Feedback. The MMO Report. Fighting Words. Talkabouts. Fresh Ink Online. First 15. Sessler's Soapbox. Events. TGS 2012. Pax Prime 2012. Gamescom 2012. E3 2012. EVO 2012. Top 100 Games. Comic-Con 2012. VGDM Best Story. PAX East 2012. VGDM 2011. IndieCade. TGS 2011. Community. G4 Store. Forums. Video Viewer Mail. AOTS Viewer Army. G4 On Twitter, Facebook ...

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Patent US5323482 - Deuterated organic salts useful in non**linear**optical applications - Google PatentDeuterated organic salts useful in non

**linear**optical applications are disclosed herein. Deuterated organic salts useful in non**linear**optical applications US 5323482 A Abstract Deuterated organic salts useful in non**linear**optical applications are disclosed herein. An optical waveguide comprising a light transmitting core material and a cladding material, wherein said core material i comprises a deuterated chemical compound in a first crystalline form; ii has optically second order non**linear**properties, and said cladding material iii comprises a deuterated chemical compound in second crystalline form; iv has a lower index of refraction than said core material; v partially or entirely encloses said core; vi can be converted to said core material. More particularly, deuterated organic salts are prepared for use in non**linear**optical applications. The instant invention, therefore, is based on the preparation of deuterated organic salts, such as the compound perdeuterated ...http://google.com/patents/US5323482?dq=5463388

Current Issues in the Analysis of Incomplete Longitudinal DataAnother approach is to fit a mixed

**linear**model with random slopes and/or intercepts to the longitudinal data, and use the random coefficients as predictors in the logistic regression model. He Daniel Li and Liqun Wang, University of Manitoba Second-order least squares estimation for non**linear**mixed effects**models**The main approach for the estimation of non**linear**mixed effects**models**focuses on the maximum likelihood method. We propose two estimators for non**linear**mixed effects**models**where the distributions of the regression random errors are nonparametric and those of random effects are parametric but not necessarily normal. Zhenguo Qiu, University of Northern British Columbia Variations in NICU Length of Stay among Survivors in Canadian Neonatal Intensive Care Units, 1996-97 Previous studies have reported variation in Canadian neonatal intensive care units NICU length of stay LOS but little is known about the reason for this variation. Annie Qu, Oregon ...http://fields.utoronto.ca/programs/scientific/NPCDS/05-06/incomplete/posterabstracts.html

Polynomial chaos... 'Polynomial chaos PC ', also called 'Wiener chaos expansion', is a non-sampling-based method to determine evolution of uncertainty in dynamical system, when there is probabilistic uncertainty in the system parameters. Generalized polynomial chaos Arbitrary polynomial chaos See also References. Generalized polynomial chaos. This is popularly known as the generalized polynomial chaos gPC framework. The gPC framework has been applied to applications including stochastic fluid dynamics, stochastic finite elements, solid mechanics, non

**linear**estimation, the evaluation of finite word-length effects in non-**linear**fixed-point digital systems and probabilistic robust control. Arbitrary polynomial chaos. Recently chaos expansion received a generalization towards the arbitrary polynomial chaos expansion aPC, which is a so-called data-driven generalization of the PC. Like all polynomial chaos expansion techniques, aPC approximates the dependence of simulation model output on model parameters by ...https://en.wikipedia.org/wiki/Polynomial_chaos

Evolution - October 1999: Visual Recurrence Analysis, version 4.0... Visual Recurrence Analysis, version 4.0. Eugene Kononov eugenek@ix.netcom.com. Fri, 1 Oct 1999 20:06:59 -0500 CDT. Messages sorted by:. Next message: Susan Brassfield: "just for Bertvan". Hello there,. This is to announce that version 4.0 of Visual Recurrence Analysis VRA software for Windows 95, 98 and NT has been released. Visual Recurrence Analysis is a software for topological analysis,. qualitative and quantitative assessment, and nonparametric prediction. of non

**linear**/chaotic time series. It can detect hidden patterns and. determinism in time series using a graphical device known as the. recurrence plot. VRA first expands a given one-dimensional time. series into a higher-dimensional space, in which the dynamics of. VRA then constructs a. recurrence plot, which is essentially a graphical representation of. the correlation integral in such a way so that the time dependence. VRA is very fast and highly interactive. A major addition to VRA v4.0 is non**linear**. time series prediction using ...http://www2.asa3.org/archive/evolution/199910/0000.html

Hil Meijer on Non**linear**Neuroscience at University of Twente... Homepage of Hil Meijer. Projects. Teaching. I studied Physics and Mathematics in Utrecht. I continued there with Yuri Kuznetsov and Ferdinand Verhulst as my advisors. in Mathematics in december 2006. I then moved to the University of Twente to work as an assistant professor in the chair Applied Analysis of prof.dr. van Gils. My research on Numerical Bifurcation Theory deals with theoretical descriptions and numerical tools for bifurcations in ODEs and maps. The tools classify the dynamics near a bifurcation and allow initialization of nontrivial bifurcation curves. I am an active developer of the matlab toolbox MATCONT and have taught several related tutorials. My research on Neurostimulation centers around the idea that a stimulus steers neuronal dynamics. In neurodegenerative contexts such as epilepsy and Parkinson's disease, there is undesirable oscillatory dynamics that may be quenched by a suitable stimulus. In addition, stimulation offers a window to study and uncover neurophysiological processes. ...

http://wwwhome.math.utwente.nl/~meijerhge/

spermidine | C7H19N3 | ChemSpiderExperimental Boiling Point: 128-130 deg C / 14 mm 277.5769-280.2546 °C / 760 mmHg Alfa Aesar. Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = -0.66 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 246.27 Adapted Stein & Brown method Melting Pt deg C : 51.27 Mean or Weighted MP VP mm Hg,25 deg C : 9.45E-005 Modified Grain method MP exp database : 257-259 deg C BP exp database : 129 @ 14 mm Hg deg C Subcooled liquid VP: 0.0322 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 1e+006 log Kow used: -0.66 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1e+006 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 7.26E-013 atm-m3/mole Group Method: 3.08E-014 atm-m3/mole Henrys LC : 1.806E-011 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: -0.66 ...

http://chemspider.com/Chemical-Structure.1071.html

(11C)Carbonyl dichloride | 11CCl2O | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = -0.71 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 70.34 Adapted Stein & Brown method Melting Pt deg C : -67.47 Mean or Weighted MP VP mm Hg,25 deg C : 1.36E+003 Mean VP of Antoine & Grain methods MP exp database : -127.9 deg C BP exp database : 8.2 deg C VP exp database : 1.42E+03 mm Hg at 25 deg C Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 4.751e+005 log Kow used: -0.71 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 5.3303e+005 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Acid Chloride/Halide Henrys Law Constant 25 deg C : Bond Method : 8.92E-003 atm-m3/mole Group Method: Incomplete Henrys LC : 2.082E-004 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: -0.71 KowWin est Log Kaw used: -0.438 HenryWin est Log Koa KOAWIN v1.10 estimate : -0.272 Log Koa experimental ...

http://chemspider.com/Chemical-Structure.397501.html

Hexamethyldisilazane | C6H19NSi2 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.62 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 107.63 Adapted Stein & Brown method Melting Pt deg C : -58.07 Mean or Weighted MP VP mm Hg,25 deg C : 12.5 Mean VP of Antoine & Grain methods BP exp database : 125 deg C VP exp database : 1.38E+01 mm Hg at 25 deg C Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 392.3 log Kow used: 2.62 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 7.8558 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Silamines Henrys Law Constant 25 deg C : Bond Method : 8.69E-005 atm-m3/mole Group Method: Incomplete Henrys LC : 6.767E-003 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.62 KowWin est Log Kaw used: -2.449 HenryWin est Log Koa KOAWIN v1.10 estimate : 5.069 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 ...

http://chemspider.com/Chemical-Structure.13238.html

1-(4-Chlorophenyl)-2-methyl-1-propanol | C10H13ClO | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.05 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 261.93 Adapted Stein & Brown method Melting Pt deg C : 34.13 Mean or Weighted MP VP mm Hg,25 deg C : 0.00125 Modified Grain method Subcooled liquid VP: 0.0015 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 423.1 log Kow used: 3.05 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 771.68 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Benzyl Alcohols Henrys Law Constant 25 deg C : Bond Method : 3.77E-007 atm-m3/mole Group Method: Incomplete Henrys LC : 7.179E-007 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.05 KowWin est Log Kaw used: -4.812 HenryWin est Log Koa KOAWIN v1.10 estimate : 7.862 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

http://chemspider.com/Chemical-Structure.175302.html

Bis(hydroxymethyl)durene | C12H18O2 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.75 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 342.93 Adapted Stein & Brown method Melting Pt deg C : 111.88 Mean or Weighted MP VP mm Hg,25 deg C : 5.67E-007 Modified Grain method Subcooled liquid VP: 4.06E-006 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 208.2 log Kow used: 2.75 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1384.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Benzyl Alcohols Henrys Law Constant 25 deg C : Bond Method : 1.30E-008 atm-m3/mole Group Method: 5.35E-013 atm-m3/mole Henrys LC : 6.962E-010 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.75 KowWin est Log Kaw used: -6.275 HenryWin est Log Koa KOAWIN v1.10 estimate : 9.025 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 ...

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3-Butyl-7-ethyl-8-({[3-(1-pyrrolidinylsulfonyl)phenyl]amino}methyl)-3,7-dihydro-1H-purine-2,6-dioLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.87 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 796.97 Adapted Stein & Brown method Melting Pt deg C : 349.84 Mean or Weighted MP VP mm Hg,25 deg C : 7.71E-020 Modified Grain method Subcooled liquid VP: 3.95E-016 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 3.671 log Kow used: 2.87 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 0.34771 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Imides Imidazoles Henrys Law Constant 25 deg C : Bond Method : 9.31E-019 atm-m3/mole Group Method: Incomplete Henrys LC : 1.311E-020 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.87 KowWin est Log Kaw used: -16.420 HenryWin est Log Koa KOAWIN v1.10 estimate : 19.290 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

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3-{1-[(3-Chlorophenyl)amino]-4-methyl-1H-imidazol-5-yl}benzonitrile | C17H13ClN4 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.86 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 504.52 Adapted Stein & Brown method Melting Pt deg C : 214.71 Mean or Weighted MP VP mm Hg,25 deg C : 2.01E-010 Modified Grain method Subcooled liquid VP: 2.11E-008 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 2.996 log Kow used: 3.86 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1.2406 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Imidazoles Henrys Law Constant 25 deg C : Bond Method : 1.71E-012 atm-m3/mole Group Method: Incomplete Henrys LC : 2.726E-011 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.86 KowWin est Log Kaw used: -10.155 HenryWin est Log Koa KOAWIN v1.10 estimate : 14.015 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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8-oxoguanine | C5H3N5O2 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = -2.83 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 446.80 Adapted Stein & Brown method Melting Pt deg C : 187.75 Mean or Weighted MP VP mm Hg,25 deg C : 1.17E-008 Modified Grain method Subcooled liquid VP: 5.9E-007 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 1e+006 log Kow used: -2.83 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1e+006 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 9.88E-016 atm-m3/mole Group Method: Incomplete Henrys LC : 2.542E-015 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: -2.83 KowWin est Log Kaw used: -13.394 HenryWin est Log Koa KOAWIN v1.10 estimate : 10.564 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

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Calone | C10H10O3 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.43 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 296.50 Adapted Stein & Brown method Melting Pt deg C : 78.73 Mean or Weighted MP VP mm Hg,25 deg C : 0.000724 Modified Grain method Subcooled liquid VP: 0.00235 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 471.5 log Kow used: 2.43 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 2199.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Neutral Organics Henrys Law Constant 25 deg C : Bond Method : 2.33E-007 atm-m3/mole Group Method: 1.28E-005 atm-m3/mole Henrys LC : 3.600E-007 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.43 KowWin est Log Kaw used: -5.021 HenryWin est Log Koa KOAWIN v1.10 estimate : 7.451 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

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4-Bromo-2-methylbenzaldehyde | C8H7BrO | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.15 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 250.84 Adapted Stein & Brown method Melting Pt deg C : 46.41 Mean or Weighted MP VP mm Hg,25 deg C : 0.0162 Modified Grain method Subcooled liquid VP: 0.0255 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 90.77 log Kow used: 3.15 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 487.02 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aldehydes Henrys Law Constant 25 deg C : Bond Method : 5.90E-006 atm-m3/mole Group Method: 1.25E-005 atm-m3/mole Henrys LC : 4.674E-005 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.15 KowWin est Log Kaw used: -3.618 HenryWin est Log Koa KOAWIN v1.10 estimate : 6.768 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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1-(2,4-Dimethoxyphenyl)-N,N-dimethyl-2-propanamine | C13H21NO2 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.59 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 288.05 Adapted Stein & Brown method Melting Pt deg C : 65.52 Mean or Weighted MP VP mm Hg,25 deg C : 0.00153 Modified Grain method Subcooled liquid VP: 0.00367 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 2049 log Kow used: 2.59 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 456.1 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 1.82E-008 atm-m3/mole Group Method: Incomplete Henrys LC : 2.194E-007 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.59 KowWin est Log Kaw used: -6.128 HenryWin est Log Koa KOAWIN v1.10 estimate : 8.718 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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D-Glucose-6-dihydrogen phosphate | C6H13O9P | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = -3.18 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 480.00 Adapted Stein & Brown method Melting Pt deg C : 90.27 Mean or Weighted MP VP mm Hg,25 deg C : 2.06E-011 Modified Grain method Subcooled liquid VP: 8.76E-011 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 1e+006 log Kow used: -3.18 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1e+006 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Neutral Organics Henrys Law Constant 25 deg C : Bond Method : 8.77E-024 atm-m3/mole Group Method: Incomplete Henrys LC : 7.051E-018 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: -3.18 KowWin est Log Kaw used: -21.445 HenryWin est Log Koa KOAWIN v1.10 estimate : 18.265 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

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N-(2,3-Dimethyl-5,6,7,8-tetrahydrofuro[2,3-b]quinolin-4-yl)-2-(2-oxo-1-pyrrolidinyl)acetamide | CLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.74 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 563.55 Adapted Stein & Brown method Melting Pt deg C : 242.28 Mean or Weighted MP VP mm Hg,25 deg C : 2.9E-012 Modified Grain method Subcooled liquid VP: 6.52E-010 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 32.12 log Kow used: 2.74 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 3578.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Neutral Organics Henrys Law Constant 25 deg C : Bond Method : 1.23E-014 atm-m3/mole Group Method: Incomplete Henrys LC : 4.056E-014 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.74 KowWin est Log Kaw used: -12.299 HenryWin est Log Koa KOAWIN v1.10 estimate : 15.039 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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N-(6-Chloro-7-hydroxy-5-methyl[1,2,4]triazolo[1,5-a]pyrimidin-2-yl)propanamide | C9H10ClN5O2 | ChLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.18 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 453.54 Adapted Stein & Brown method Melting Pt deg C : 190.90 Mean or Weighted MP VP mm Hg,25 deg C : 1.19E-009 Modified Grain method Subcooled liquid VP: 6.54E-008 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 300.7 log Kow used: 2.18 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 7664.9 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Phenols Henrys Law Constant 25 deg C : Bond Method : 4.92E-019 atm-m3/mole Group Method: Incomplete Henrys LC : 1.331E-012 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.18 KowWin est Log Kaw used: -16.696 HenryWin est Log Koa KOAWIN v1.10 estimate : 18.876 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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N-[(2-Isopropyl-3-oxo-3,4-dihydro-1(2H)-quinoxalinyl)carbonyl]phenylalanylisoleucine | C27H34N4O5Log Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.70 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 752.48 Adapted Stein & Brown method Melting Pt deg C : 330.54 Mean or Weighted MP VP mm Hg,25 deg C : 6.39E-022 Modified Grain method Subcooled liquid VP: 1.84E-018 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 0.5327 log Kow used: 3.70 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 190.07 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Neutral Organics-acid Ureas substituted -acid Henrys Law Constant 25 deg C : Bond Method : 2.54E-023 atm-m3/mole Group Method: Incomplete Henrys LC : 7.807E-022 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.70 KowWin est Log Kaw used: -20.984 HenryWin est Log Koa KOAWIN v1.10 estimate : 24.684 Log Koa experimental database : None Probability of Rapid ...

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Afzelechin | C15H14O5 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 1.66 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 464.08 Adapted Stein & Brown method Melting Pt deg C : 195.82 Mean or Weighted MP VP mm Hg,25 deg C : 3.67E-011 Modified Grain method Subcooled liquid VP: 2.3E-009 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 8200 log Kow used: 1.66 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 7061.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Phenols Henrys Law Constant 25 deg C : Bond Method : 6.85E-022 atm-m3/mole Group Method: Incomplete Henrys LC : 1.615E-015 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 1.66 KowWin est Log Kaw used: -19.553 HenryWin est Log Koa KOAWIN v1.10 estimate : 21.213 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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1,3-Benzodioxol-4-ol | C7H6O3 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 1.57 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 247.70 Adapted Stein & Brown method Melting Pt deg C : 53.60 Mean or Weighted MP VP mm Hg,25 deg C : 0.00689 Modified Grain method Subcooled liquid VP: 0.0127 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 3854 log Kow used: 1.57 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 5487.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Phenols Henrys Law Constant 25 deg C : Bond Method : 6.52E-010 atm-m3/mole Group Method: 8.68E-006 atm-m3/mole Henrys LC : 3.249E-007 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 1.57 KowWin est Log Kaw used: -7.574 HenryWin est Log Koa KOAWIN v1.10 estimate : 9.144 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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Tazanolast | C13H15N5O3 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = -0.19 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 527.96 Adapted Stein & Brown method Melting Pt deg C : 225.66 Mean or Weighted MP VP mm Hg,25 deg C : 3.77E-011 Modified Grain method Subcooled liquid VP: 5.35E-009 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 1005 log Kow used: -0.19 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 33800 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Esters Henrys Law Constant 25 deg C : Bond Method : 1.77E-015 atm-m3/mole Group Method: Incomplete Henrys LC : 1.428E-014 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: -0.19 KowWin est Log Kaw used: -13.140 HenryWin est Log Koa KOAWIN v1.10 estimate : 12.950 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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Desformylflustrabromine | C16H21BrN2 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 4.89 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 402.04 Adapted Stein & Brown method Melting Pt deg C : 153.15 Mean or Weighted MP VP mm Hg,25 deg C : 3.66E-007 Modified Grain method Subcooled liquid VP: 7.42E-006 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 6.299 log Kow used: 4.89 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 4.8539 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 2.98E-010 atm-m3/mole Group Method: Incomplete Henrys LC : 2.456E-008 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 4.89 KowWin est Log Kaw used: -7.914 HenryWin est Log Koa KOAWIN v1.10 estimate : 12.804 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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Bourgeonal | C13H18O | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.94 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 273.66 Adapted Stein & Brown method Melting Pt deg C : 46.30 Mean or Weighted MP VP mm Hg,25 deg C : 0.00499 Modified Grain method Subcooled liquid VP: 0.00781 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 21.05 log Kow used: 3.94 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 39.477 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aldehydes Henrys Law Constant 25 deg C : Bond Method : 1.88E-005 atm-m3/mole Group Method: 3.88E-006 atm-m3/mole Henrys LC : 5.935E-005 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.94 KowWin est Log Kaw used: -3.114 HenryWin est Log Koa KOAWIN v1.10 estimate : 7.054 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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N-(1,3-Diphenyl-1H-pyrazol-5-yl)-N2,N2-dimethylalaninamide | C20H22N4O | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.72 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 530.60 Adapted Stein & Brown method Melting Pt deg C : 226.89 Mean or Weighted MP VP mm Hg,25 deg C : 3.12E-011 Modified Grain method Subcooled liquid VP: 4.58E-009 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 36.76 log Kow used: 2.72 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 299.53 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 1.55E-017 atm-m3/mole Group Method: Incomplete Henrys LC : 3.735E-013 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.72 KowWin est Log Kaw used: -15.198 HenryWin est Log Koa KOAWIN v1.10 estimate : 17.918 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

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Dacemazine [INN] | C16H16N2OS | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.01 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 419.26 Adapted Stein & Brown method Melting Pt deg C : 170.58 Mean or Weighted MP VP mm Hg,25 deg C : 8.81E-008 Modified Grain method Subcooled liquid VP: 2.82E-006 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 22.37 log Kow used: 2.01 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1538.4 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 8.32E-012 atm-m3/mole Group Method: Incomplete Henrys LC : 1.474E-009 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.01 KowWin est Log Kaw used: -9.468 HenryWin est Log Koa KOAWIN v1.10 estimate : 11.478 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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Solvent Violet 13 | C21H15NO3 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 6.24 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 515.86 Adapted Stein & Brown method Melting Pt deg C : 220.01 Mean or Weighted MP VP mm Hg,25 deg C : 1.08E-011 Modified Grain method Subcooled liquid VP: 1.3E-009 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 0.003024 log Kow used: 6.24 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 0.022768 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Phenols Henrys Law Constant 25 deg C : Bond Method : 8.96E-014 atm-m3/mole Group Method: Incomplete Henrys LC : 1.548E-009 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 6.24 KowWin est Log Kaw used: -11.436 HenryWin est Log Koa KOAWIN v1.10 estimate : 17.676 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

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2-Amino-1-cyclohexyl-4,5-diphenyl-1H-pyrrole-3-carbonitrile | C23H23N3 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 6.40 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 537.81 Adapted Stein & Brown method Melting Pt deg C : 230.26 Mean or Weighted MP VP mm Hg,25 deg C : 1.86E-011 Modified Grain method Subcooled liquid VP: 2.99E-009 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 0.01287 log Kow used: 6.40 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 0.030021 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aromatic Amines Henrys Law Constant 25 deg C : Bond Method : 7.08E-012 atm-m3/mole Group Method: Incomplete Henrys LC : 6.493E-010 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 6.40 KowWin est Log Kaw used: -9.538 HenryWin est Log Koa KOAWIN v1.10 estimate : 15.938 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

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3,5-Diisopropyl-1,2-benzenediol | C12H18O2 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 3.94 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 308.06 Adapted Stein & Brown method Melting Pt deg C : 95.71 Mean or Weighted MP VP mm Hg,25 deg C : 3.11E-005 Modified Grain method Subcooled liquid VP: 0.000151 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 76.1 log Kow used: 3.94 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 972.03 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Phenols Henrys Law Constant 25 deg C : Bond Method : 2.21E-010 atm-m3/mole Group Method: 4.25E-010 atm-m3/mole Henrys LC : 1.045E-007 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 3.94 KowWin est Log Kaw used: -8.044 HenryWin est Log Koa KOAWIN v1.10 estimate : 11.984 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1 ...

http://chemspider.com/Chemical-Structure.67610.html

1-Benzyl-4-[2-(diphenylmethoxy)ethyl]piperidine | C27H31NO | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 6.59 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 483.02 Adapted Stein & Brown method Melting Pt deg C : 192.08 Mean or Weighted MP VP mm Hg,25 deg C : 1.3E-009 Modified Grain method Subcooled liquid VP: 7.35E-008 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 0.08978 log Kow used: 6.59 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 0.026605 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aliphatic Amines Henrys Law Constant 25 deg C : Bond Method : 4.09E-010 atm-m3/mole Group Method: Incomplete Henrys LC : 7.346E-009 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 6.59 KowWin est Log Kaw used: -7.777 HenryWin est Log Koa KOAWIN v1.10 estimate : 14.367 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

http://chemspider.com/Chemical-Structure.8127716.html

1,3,8-Trimethyl-3,7-dihydro-1H-purine-2,6-dione | C8H10N4O2 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 0.16 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 470.24 Adapted Stein & Brown method Melting Pt deg C : 198.70 Mean or Weighted MP VP mm Hg,25 deg C : 2.27E-009 Modified Grain method Subcooled liquid VP: 1.54E-007 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 1686 log Kow used: 0.16 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 7067.9 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Imides Imidazoles Henrys Law Constant 25 deg C : Bond Method : 1.85E-012 atm-m3/mole Group Method: Incomplete Henrys LC : 3.440E-013 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 0.16 KowWin est Log Kaw used: -10.121 HenryWin est Log Koa KOAWIN v1.10 estimate : 10.281 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : ...

http://chemspider.com/Chemical-Structure.85748.html

(4-Bromophenyl)acetaldehyde | C8H7BrO | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 2.43 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 250.84 Adapted Stein & Brown method Melting Pt deg C : 40.15 Mean or Weighted MP VP mm Hg,25 deg C : 0.0186 Modified Grain method Subcooled liquid VP: 0.0255 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 370.7 log Kow used: 2.43 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1342.6 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Aldehydes Henrys Law Constant 25 deg C : Bond Method : 2.18E-006 atm-m3/mole Group Method: Incomplete Henrys LC : 1.314E-005 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 2.43 KowWin est Log Kaw used: -4.050 HenryWin est Log Koa KOAWIN v1.10 estimate : 6.480 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN v4.10 : Biowin1

**Linear**...http://chemspider.com/Chemical-Structure.9227773.html

3-Hydroxyvaleric acid | C5H10O3 | ChemSpiderLog Octanol-Water Partition Coef SRC : Log Kow KOWWIN v1.67 estimate = 0.02 Boiling Pt, Melting Pt, Vapor Pressure Estimations MPBPWIN v1.42 : Boiling Pt deg C : 241.83 Adapted Stein & Brown method Melting Pt deg C : 44.82 Mean or Weighted MP VP mm Hg,25 deg C : 0.00346 Modified Grain method Subcooled liquid VP: 0.00524 mm Hg 25 deg C, Mod-Grain method Water Solubility Estimate from Log Kow WSKOW v1.41 : Water Solubility at 25 deg C mg/L : 7.848e+005 log Kow used: 0.02 estimated no-melting pt equation used Water Sol Estimate from Fragments: Wat Sol v1.01 est = 1e+006 mg/L ECOSAR Class Program ECOSAR v0.99h : Class es found: Neutral Organics-acid Henrys Law Constant 25 deg C : Bond Method : 4.69E-011 atm-m3/mole Group Method: 1.58E-011 atm-m3/mole Henrys LC : 6.853E-010 atm-m3/mole Log Octanol-Air Partition Coefficient 25 deg C : Log Kow used: 0.02 KowWin est Log Kaw used: -8.717 HenryWin est Log Koa KOAWIN v1.10 estimate : 8.737 Log Koa experimental database : None Probability of Rapid Biodegradation BIOWIN ...

http://chemspider.com/Chemical-Structure.96952.html

**Inverse probability weighting**: Inverse probability weighting is a statistical technique for calculating statistics standardized to a population different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application.

**Nonlinear system**: In physics and other sciences, a nonlinear system, in contrast to a linear system, is a system which does not satisfy the superposition principle – meaning that the output of a nonlinear system is not directly proportional to the input.

**Clonal Selection Algorithm**: In artificial immune systems, Clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen-antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation.

**Interval boundary element method**: Interval boundary element method is classical boundary element method with the interval parameters.

**Hyperparameter**: In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis.

**Plant breeding**

**Biostatistics (journal)**: Biostatistics is a peer-reviewed scientific journal covering biostatistics, that is, statistics for biological and medical research. The journals that had cited Biostatistics the most by 2008Journal Citation Reports 2008, Science Edition were Biometrics, Journal of the American Statistical Association, Biometrika, Statistics in Medicine, and Journal of the Royal Statistical Society, Series B.

**Regression dilution**: Regression dilution, also known as regression attenuation, is the biasing of the regression slope towards zero (or the underestimation of its absolute value), caused by errors in the independent variable.

**Matrix model**: == Mathematics and physics ==

**Decoding methods**: In coding theory, decoding is the process of translating received messages into codewords of a given code. There have been many common methods of mapping messages to codewords.

**The Unscrambler**: The Unscrambler® X is a commercial software product for multivariate data analysis, used for calibration of multivariate data which is often in the application of analytical data such as near infrared spectroscopy and Raman spectroscopy, and development of predictive models for use in real-time spectroscopic analysis of materials. The software was originally developed in 1986 by Harald MartensHarald Martens, Terje Karstang, Tormod Næs (1987) Improved selectivity in spectroscopy by multivariate calibration Journal of Chemometrics 1(4):201-219 and later by CAMO Software.

**Generalizability theory**: Generalizability theory, or G Theory, is a statistical framework for conceptualizing, investigating, and designing reliable observations. It is used to determine the reliability (i.

**Temporal analysis of products**: Temporal Analysis of Products (TAP), (TAP-2), (TAP-3) is an experimental technique for studying

**Hyperintensity**

**STO-nG basis sets**: STO-nG basis sets are minimal basis sets, where n primitive Gaussian orbitals are fitted to a single Slater-type orbital (STO). n originally took the values 2 - 6.

**Von Neumann regular ring**: In mathematics, a von Neumann regular ring is a ring R such that for every a in R there exists an x in R such that . To avoid the possible confusion with the regular rings and regular local rings of commutative algebra (which are unrelated notions), von Neumann regular rings are also called absolutely flat rings, because these rings are characterized by the fact that every left module is flat.

**A-scan ultrasound biometry**: A-scan ultrasound biometry, commonly referred to as an A-scan, is routine type of diagnostic test used in ophthalmology. The A-scan provides data on the length of the eye, which is a major determinant in common sight disorders.

**QRISK**: QRISK2 (the most recent version of QRISK) is a prediction algorithm for cardiovascular disease (CVD) that uses traditional risk factors (age, systolic blood pressure, smoking status and ratio of total serum cholesterol to high-density lipoprotein cholesterol) together with body mass index, ethnicity, measures of deprivation, family history, chronic kidney disease, rheumatoid arthritis, atrial fibrillation, diabetes mellitus, and antihypertensive treatment.

**Age adjustment**: In epidemiology and demography, age adjustment, also called age standardization, is a technique used to allow populations to be compared when the age profiles of the populations are quite different.

**RV coefficient**: In statistics, the RV coefficient

**Image fusion**: In computer vision, Multisensor Image fusion is the process of combining relevant information from two or more images into a single image.Haghighat, M.

**Closed-ended question**: A closed-ended question is a question format that limits respondents with a list of answer choices from which they must choose to answer the question.Dillman D.

**WGAViewer**: WGAViewer is a bioinformatics software tool which is designed to visualize, annotate, and help interpret the results generated from a genome wide association study (GWAS). Alongside the P values of association, WGAViewer allows a researcher to visualize and consider other supporting evidence, such as the genomic context of the SNP, linkage disequilibrium (LD) with ungenotyped SNPs, gene expression database, and the evidence from other GWAS projects, when determining the potential importance of an individual SNP.

**List of Parliamentary constituencies in Kent**: The ceremonial county of Kent,

**Dactylogyrus**: Dactylogyrus is a genus of the Dactylogyridae family. They are commonly known as gill flukes

**Regularized canonical correlation analysis**: Regularized canonical correlation analysis is a way of using ridge regression to solve the singularity problem in the cross-covariance matrices of canonical correlation analysis. By converting \operatorname{cov}(X, X) and \operatorname{cov}(Y, Y) into \operatorname{cov}(X, X) + \lambda I_X and \operatorname{cov}(Y, Y) + \lambda I_Y, it ensures that the above matrices will have reliable inverses.

**Monte Carlo methods for option pricing**: In mathematical finance, a Monte Carlo option model uses Monte Carlo methods Although the term 'Monte Carlo method' was coined by Stanislaw Ulam in the 1940s, some trace such methods to the 18th century French naturalist Buffon, and a question he asked about the results of dropping a needle randomly on a striped floor or table. See Buffon's needle.

**Mac OS X Server 1.0**

**Gene signature**: A gene signature is a group of genes in a cell whose combined expression patternItadani H, Mizuarai S, Kotani H. Can systems biology understand pathway activation?

**Phenotype microarray**: The phenotype microarray approach is a technology for high-throughput phenotyping of cells.

**Cellular microarray**: A cellular microarray is a laboratory tool that allows for the multiplex interrogation of living cells on the surface of a solid support. The support, sometimes called a "chip", is spotted with varying materials, such as antibodies, proteins, or lipids, which can interact with the cells, leading to their capture on specific spots.

**Citizen Weather Observer Program**: The Citizen Weather Observer Program (CWOP) is a network of privately owned electronic weather stations concentrated in the United States but also located in over 150 countries. Network participation allows volunteers with computerized weather stations to send automated surface weather observations to the National Weather Service (NWS) by way of the Meteorological Assimilation Data Ingest System (MADIS).

**Four Seasons Baltimore and Residences**: Four Seasons Hotel Baltimore is currently a 22 story highrise hotel complex building which opened on November 14, 2011. The building's construction began back in 2007 and went through several changes.

**Genetic variation**: right|thumb

**Dog breeding**: Dog breeding is the practice of mating selected dogs with the intent to maintain or produce specific qualities and characteristics. When dogs reproduce without such human intervention, their offsprings' characteristics are determined by natural selection, while "dog breeding" refers specifically to the artificial selection of dogs, in which dogs are intentionally bred by their owners.

**Air pollution**: Air pollution is the introduction of particulates, biological molecules, or other harmful materials into Earth's atmosphere, causing diseases, death to humans, damage to other living organisms such as animals and food crops, or the natural or built environment. Air pollution may come from anthropogenic or natural sources.

**Physical neural network**: A physical neural network is a type of artificial neural network in which an electrically adjustable resistance material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches which simulate neural networks.

**Nested case-control study**: A nested case control (NCC) study is a variation of a case-control study in which only a subset of controls from the cohort are compared to the incident cases. In a case-cohort study, all incident cases in the cohort are compared to a random subset of participants who do not develop the disease of interest.

**P-Anisidine**

**Prenatal nutrition**: Nutrition and weight management before and during :pregnancy has a profound effect on the development of infants. This is a rather critical time for healthy fetal development as infants rely heavily on maternal stores and nutrient for optimal growth and health outcome later in life.

**Vladimir Andreevich Markov**: Vladimir Andreevich Markov (; May 8, 1871 – January 18, 1897) was a Russian mathematician, known for proving the Markov brothers' inequality with his older brother Andrey Markov. He died of tuberculosis at the age of 25.

**Evolution in Variable Environment**

**Beef cattle**: Beef cattle are cattle raised for meat production (as distinguished from dairy cattle, used for milk production). The meat of adult cattle is known as beef.

**Supercow (dairy)**: Supercow (or super cow) is a term used in the dairy industry to denote lines or individual animals that have superior milk production: that is, which produce more milk per day, or in some cases produce more fat per gallon of milk. Biology of the super cow.

**EEGLAB**: EEGLAB is a MATLAB toolbox distributed under the free GNU GPL license for processing data from electroencephalography (EEG), magnetoencephalography (MEG), and other electrophysiological signals. Along with all the basic processing tools, EEGLAB implements independent component analysis (ICA), time/frequency analysis, artifact rejection, and several modes of data visualization.

**Time-trade-off**: Time-Trade-Off (TTO) is a tool used in health economics to help determine the quality of life of a patient or group. The individual will be presented with a set of directions such as:

**Assay sensitivity**: Assay sensitivity is a property of a clinical trial defined as the ability of a trial to distinguish an effective treatment from a less effective or ineffective intervention. Without assay sensitivity, a trial is not internally valid and is not capable of comparing the efficacy of two interventions.

**Classification of obesity**: Obesity is a medical condition in which excess body fat has accumulated to the extent that it has an adverse effect on health.WHO 2000 p.

**Population stratification**: Population stratification is the presence of a systematic difference in allele frequencies between subpopulations in a population possibly due to different ancestry, especially in the context of association studies. Population stratification is also referred to as population structure, in this context.

**PSI Protein Classifier**: PSI Protein Classifier is a program generalizing the results of both successive and independent iterations of the PSI-BLAST program. PSI Protein Classifier determines belonging of the found by PSI-BLAST proteins to the known families.

**Bill Parry (mathematician)**

**Hadley Centre for Climate Prediction and Research**: 140px|right

**Demodulation**: Demodulation is the act of extracting the original information-bearing signal from a modulated carrier wave. A demodulator is an electronic circuit (or computer program in a software-defined radio) that is used to recover the information content from the modulated carrier wave.

**African-American family structure**: The family structure of African-Americans has long been a matter of national public policy interest.Moynihan's War on Poverty report A 1965 report by Daniel Patrick Moynihan, known as The Moynihan Report, examined the link between black poverty and family structure.

**Chromosome regions**

**Repeatable Battery for the Assessment of Neuropsychological Status**: The Repeatable Battery for the Assessment of Neuropsychological Status is a neuropsychological assessment initially introduced in 1998. It consists of ten subtests which give five scores, one for each of the five domains tested (immediate memory, visuospatial/constructional, language, attention, delayed memory).

**Emory University Hospital**: Emory University Hospital is a 587-bed facility in Atlanta, Georgia, specializing in the care of the acutely ill adults. Emory University Hospital is staffed exclusively by Emory University School of Medicine faculty who also are members of The Emory Clinic.

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**Effect of growth hormone treatment on adult height of children with idiopathic short stature. Genentech Collaborative Group.**

BACKGROUND: Short-term administration of growth hormone to children with idiopathic short stature results in increases in growth rate and standard-deviation scores for height. However, the effect of long-term growth hormone therapy on adult height in these children is unknown. METHODS: We studied 121 children with idiopathic short stature, all of whom had an initial height below the third percentile, low growth rates, and maximal stimulated serum concentrations of growth hormone of at least 10 microg per liter. The children were treated with growth hormone (0.3 mg per kilogram of body weight per week) for 2 to 10 years. Eighty of these children have reached adult height, with a bone age of at least 16 years in the boys and at least 14 years in the girls, and pubertal stage 4 or 5. The difference between the predicted adult height before treatment and achieved adult height was compared with the corresponding difference in three untreated normal or short-statured control groups. RESULTS: In the 80 children who have reached adult height, growth hormone treatment increased the mean standard-deviation score for height (number of standard deviations from the mean height for chronologic age) from -2.7 to -1.4. The mean (+/-SD) difference between predicted adult height before treatment and achieved adult height was +5.0+/-5.1 cm for boys and +5.9+/-5.2 cm for girls. The difference between predicted and achieved adult height among treated boys was 9.2 cm greater than the corresponding difference among untreated boys with initial standard-deviation scores of less than -2, and the difference among treated girls was 5.7 cm greater than the difference among untreated girls. CONCLUSION: Long-term administration of growth hormone to children with idiopathic short stature can increase adult height to a level above the predicted adult height and above the adult height of untreated historical control children. (+info)

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**Capture-recapture models including covariate effects.**

Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the authors compare log-linear and logit models with and without covariates, and use simulated data to compare estimates from different models. The crude estimate of population size is biased when the sources are not independent. Analyses adjusting for covariates produce less biased estimates. In the absence of covariates, or where all covariates are categorical, the log-linear model and the logit model are equivalent. The log-linear model cannot include continuous variables. To minimize potential bias in estimating incidence, covariates should be included in the design and analysis of multiple-source disease registries. (+info)

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**The impact of a multidisciplinary approach on caring for ventilator-dependent patients.**

OBJECTIVE: To determine the clinical and financial outcomes of a highly structured multidisciplinary care model for patients in an intensive care unit (ICU) who require prolonged mechanical ventilation. The structured model outcomes (protocol group) are compared with the preprotocol outcomes. DESIGN: Descriptive study with financial analysis. SETTING: A twelve-bed medical-surgical ICU in a non-teaching tertiary referral center in Ogden, Utah. STUDY PARTICIPANTS: During a 54 month period, 469 consecutive intensive care patients requiring mechanical ventilation for longer than 72 hours who did not meet exclusion criteria were studied. INTERVENTIONS: A multidisciplinary team was formed to coordinate the care of ventilator-dependent patients. Care was integrated by daily collaborative bedside rounds, monthly meetings, and implementation of numerous guidelines and protocols. Patients were followed from the time of ICU admission until the day of hospital discharge. MAIN OUTCOME MEASURES: Patients were assigned APACHE II scores on admission to the ICU, and were divided into eight diagnostic categories. ICU length of stay, hospital length of stay, costs, charges, reimbursement, and in-hospital mortality were measured. RESULTS: Mortality in the preprotocol and protocol group, after adjustment for APACHE II scores, remained statistically unchanged (21-23%). After we implemented the new care model, we demonstrated significant decreases in the mean survivor's ICU length of stay (19.8 days to 14.7 days, P= 0.001), hospital length of stay (34.6 days to 25.9 days, P=0.001), charges (US$102500 to US$78500, P=0.001), and costs (US$71900 to US$58000, P=0.001). CONCLUSIONS: Implementation of a structured multidisciplinary care model to care for a heterogeneous population of ventilator-dependent ICU patients was associated with significant reductions in ICU and hospital lengths of stay, charges, and costs. Mortality rates were unaffected. (+info)

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**Nonlinear tension summation of different combinations of motor units in the anesthetized cat peroneus longus muscle.**

The purpose of this study was to examine the linearity of summation of the forces produced by the stimulation of different combinations of type identified motor units (MUs) in the cat peroneus longus muscle (PL) under isometric conditions. The muscle was fixed at its twitch optimal length, and the tension produced by the single MU was recorded during 24- and 72-Hz stimulation. The summation analysis was first carried out for MUs belonging to the same functional group, and then different combinations of fast fatigable (FF) MUs were added to the nonfatigable slow (S) and fatigue resistant (FR) group. The tension resulting from the combined stimulation of increasing numbers of MUs (measured tension) was evaluated and compared with the linearly predicted value, calculated by adding algebraically the tension produced by the individual MUs assembled in the combination (calculated tension). Tension summation displayed deviations from linearity. S and FR MUs mainly showed marked more than linear summation; FF MUs yielded either more or less than linear summation; and, when the FF units were recruited after the S and FR MUs, less than linear summation always occurred. The magnitude of the nonlinear summation appeared stimulus frequency dependent for the fatigable FF and FI group. The relationship between measured tension and calculated tension for each MU combination was examined, and linear regression lines were fitted to each set of data. The high correlation coefficients and the different slope values for the different MU-type combinations suggested that the nonlinear summation was MU-type specific. The mechanisms of nonlinear summations are discussed by considering the consequences of internal shortening and thus the mechanical interactions among MUs and shifts in muscle fiber length to a more or less advantageous portion of single MU length-tension curves. (+info)

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**Short-latency vergence eye movements induced by radial optic flow in humans: dependence on ambient vergence level.**

Radial patterns of optic flow, such as those experienced by moving observers who look in the direction of heading, evoke vergence eye movements at short latency. We have investigated the dependence of these responses on the ambient vergence level. Human subjects faced a large tangent screen onto which two identical random-dot patterns were back-projected. A system of crossed polarizers ensured that each eye saw only one of the patterns, with mirror galvanometers to control the horizontal positions of the images and hence the vergence angle between the two eyes. After converging the subject's eyes at one of several distances ranging from 16.7 cm to infinity, both patterns were replaced with new ones (using a system of shutters and two additional projectors) so as to simulate the radial flow associated with a sudden 4% change in viewing distance with the focus of expansion/contraction imaged in or very near both foveas. Radial-flow steps induced transient vergence at latencies of 80-100 ms, expansions causing increases in convergence and contractions the converse. Based on the change in vergence 90-140 ms after the onset of the steps, responses were proportional to the preexisting vergence angle (and hence would be expected to be inversely proportional to viewing distance under normal conditions). We suggest that this property assists the observer who wants to fixate ahead while passing through a visually cluttered area (e.g., a forest) and so wants to avoid making vergence responses to the optic flow created by the nearby objects in the periphery. (+info)

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**Survival after breast cancer in Ashkenazi Jewish BRCA1 and BRCA2 mutation carriers.**

BACKGROUND: Studies of survival following breast and ovarian cancers in BRCA1 and/or BRCA2 mutation carriers have yielded conflicting results. We undertook an analysis of a community-based study of Ashkenazi Jews to investigate the effect of three founder mutations in BRCA1 and BRCA2 on survival among patients with breast or ovarian cancer. METHODS: We collected blood samples and questionnaire data from 5318 Ashkenazi Jewish volunteers. The blood samples were tested for 185delAG (two nucleotide deletion) and 5382insC (single nucleotide insertion) mutations in BRCA1 and the 6174delT (single nucleotide deletion) mutation in BRCA2. To estimate survival differences in the affected relatives according to their BRCA1 and/or BRCA2 mutation carrier status, we devised and applied a novel extension of the kin-cohort method. RESULTS: Fifty mutation carriers reported that 58 of their first-degree relatives had been diagnosed with breast cancer and 10 with ovarian cancer; 907 noncarriers reported 979 first-degree relatives with breast cancer and 116 with ovarian cancer. Kaplan-Meier estimates of median survival after breast cancer were 16 years (95% confidence interval [CI] = 11-40) in the relatives of carriers and 18 years (95% CI = 15-22) in the relatives of noncarriers, a difference that was not statistically significant (two-sided P = .87). There was also no difference in survival times among the 126 first-degree relatives with ovarian cancer. We found no survival difference between patients with breast or ovarian cancer who were inferred carriers of BRCA1 and/or BRCA2 mutations and noncarriers. CONCLUSIONS: Carriers of BRCA1 and BRCA2 mutations appeared to have neither better nor worse survival prognosis. (+info)

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**Long-term results of GH therapy in GH-deficient children treated before 1 year of age.**

OBJECTIVES: To evaluate the long-term effects of GH therapy in early diagnosed GH-deficient patients treated before 1 year of age. STUDY DESIGN: We studied all 59 patients (33 males) recorded by Association France-Hypophyse and treated with GH (0.50+/-0.15 IU/kg (S.D.) per week) before 1 year of age. Clinical presentation and growth parameters under GH treatment were analyzed. RESULTS: Neonatal manifestations of hypopituitarism were frequent: hypoglycemia (n=50), jaundice (n=25) and micropenis (n=17/33). Although birth length was moderately reduced (-0.9+/-1.4), growth retardation at diagnosis (5.8+/-3.8 months) was severe (-3.5+/-1.9 standard deviation scores (SDS)). Fifty patients (85%) had thyrotropin and/or corticotropin deficiency. After a mean duration of GH therapy of 8.0+/-3.6 years, change in height SDS was +3.11+/-2.06 S.D., exceeding 4 SDS in 19 patients. Only 9 patients (15%) did not reach a height of -2 S.D. for chronological age and 20 patients (34%) exceeded their target height. Pretreatment height SDS was independently associated with total catch-up growth. CONCLUSION: Conventional doses of GH allow normalization of height in patients with early GH deficiency and treatment. (+info)

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**Changes in body composition and leptin levels during growth hormone (GH) treatment in short children with various GH secretory capacities.**

OBJECTIVE: The aim of this study was to follow changes in body composition, estimated by dual-energy X-ray absorptiometry (DXA), in relation to changes in leptin during the first year of GH therapy in order to test the hypothesis that leptin is a metabolic signal involved in the regulation of GH secretion in children. DESIGN AND METHODS: In total, 33 prepubertal children were investigated. Their mean (S.D.) chronological age at the start of GH treatment was 11.5 (1.6) years, and their mean height was -2.33 (0.38) S.D. scores (SDS). GH was administered subcutaneously at a daily dose of 0.1 (n=26) or 0.2 (n=7) IU/kg body weight. Ten children were in the Swedish National Registry for children with GH deficiency, and twenty-three children were involved in trials of GH treatment for idiopathic short stature. Spontaneous 24-h GH secretion was studied in 32 of the children. In the 24-h GH profiles, the maximum level of GH was determined and the secretion rate estimated by deconvolution analysis (GHt). Serum leptin levels were measured at the start of GH treatment and after 10 and 30 days and 3, 6 and 12 months of treatment. Body composition measurements, by DXA, were performed at baseline and 12 months after the onset of GH treatment. RESULTS: After 12 months of GH treatment, mean height increased from -2.33 to -1.73 SDS and total body fat decreased significantly by 3.0 (3.3)%. Serum leptin levels were decreased significantly at all time points studied compared with baseline. There was a significant correlation between the change in total body fat and the change in serum leptin levels during the 12 months of GH treatment, whereas the leptin concentration per unit fat mass did not change. In a multiple stepwise linear regression analysis with 12 month change in leptin levels as the dependent variable, the percentage change in fat over 12 months, the baseline fat mass (%) of body mass and GHt accounted for 24.0%, 11.5% and 12.2% of the variability respectively. CONCLUSIONS: There are significant correlations between changes in leptin and fat and endogenous GH secretion in short children with various GH secretory capacities. Leptin may be the messenger by which the adipose tissue affects hypothalamic regulation of GH secretion. (+info)

#### estimation in linear regression model

- Czech Digital Mathematics Library: Large adaptive estimation in linear regression model. (dml.cz)

#### regression model

- That's just a fancy name for a linear regression model in which the dependent variable is a binary dummy variable, and the coefficients are estimated by OLS. (blogspot.com)
- First, a general observation about measurement errors and any regression model. (blogspot.com)

#### nonlinear

- British Library EThOS: Developing low distortion linear and nonlinear circuits with GaAs FETs using the Parker Skellern model. (bl.uk)
- In the proposed model a BNWF (Beam on Nonlinear Winkler Foundation) approach is used consisting of simple nonlinear springs, dash pots and contact elements. (ac.ir)
- Different parts of a BNWF (Beam on Nonlinear Winkler Foundation) model, together with quantitative and qualitative findings and conclusions for dynamic nonlinear response of offshore piles, are discussed and addressed in detail. (ac.ir)
- The proposed BNWF model (only using the existing features of the available general finite element software) could easily be implemented in a more comprehensive model of nonlinear seismic response analysis of pile supported offshore platforms. (ac.ir)

#### Digital Mathematics Library

- Czech Digital Mathematics Library: Adaptive maximum-likelihood-like estimation in linear models. (dml.cz)

#### Poisson

- Poisson regression models were fit in each city, controlling for temperature and long-term time trends. (mdpi.com)

#### deviation

- In this study, risk is incorporated in the mixed integer programrnmg farm planning model as a deviation from the expected values of an activity of returns. (sun.ac.za)

#### parameters

- The columns explore what happens to the model when various parameters are changed. (carleton.edu)

#### mathematical

- The mathematical model developed includes crop production, dairy production and wool sheep production activities, which permitted the consideration of five crop types within a crop rotation system. (sun.ac.za)
- In order to demonstrate the application of the mathematical farm planning model formulated, a case study is presented. (sun.ac.za)
- Results of the mathematical model indicated that farm profit is dependent on the cropping strategy selected. (sun.ac.za)
- Economics -- Mathematical models. (gwu.edu)
- His research publications are in the areas of Linear Systems, Mathematical Systems Theory, Control Theory and Design, Approximate Algebraic Computations, Mathematical Methods for Control, Systems Theory of Measurement, Systems and Control to Complex Systems, Integrated Systems Design, and History of Systems and Control. (city.ac.uk)

#### quantitative

- Because much of economics is quantitative and model-driven, spreadsheets are a natural tool to use in teaching economics. (carleton.edu)

#### framework

- IEEE defines a process model as 'a framework containing the processes, activities, and tasks involved in the development, operation, and maintenance of a software product, spanning the life of the system from the definition of its requirements to the termination of its use. (ecomputernotes.com)
- Process models provide a framework for defining process status criteria and measures for software development. (ecomputernotes.com)

#### identification

- It provides easy to use and friendly environment for identification of linear, fuzzy and neural models. (europa.eu)

#### outputs

- Table lists the inputs and outputs of each phase of waterfall model. (ecomputernotes.com)
- Residuals are generated as differences of models outputs and process values. (europa.eu)

#### Advantages

- Other advantages of the software process model are listed below. (ecomputernotes.com)

#### neural

- Process modelling using fuzzy logic and neural networks for fault detection' toolbox is the software package intended to identify, verify and simulate models of process variables. (europa.eu)
- Fuzzy and neural process models are created in the off-line part. (europa.eu)
- In the 'Process modelling using fuzzy logic and neural networks for fault detection' on-line part, residuals are yielded based on models provided by the off-line part of the toolbox. (europa.eu)
- Process modelling using fuzzy logic and neural networks for fault detection' and 'Fault diagnosis using information systems and fuzzy reasoning' toolbox cooperate with each other in AMandD Advance Monitoring and Diagnostic System. (europa.eu)
- The preliminary tests of the 'Process modelling using fuzzy logic and neural networks for fault detection' were conducted on: IDR Urea Synthesis Section of Urea Manufacturing Process in Nitrogen Factory 'Pulawy' SA, Steam Generator Laboratory Stand in LAIL Universite Des Sciences et Technologies de Lille, Laboratory Stand for Diagnostic of Industrial Process at Warsaw University of Technology. (europa.eu)

#### assumptions

- By making proper assumptions, this paper develops a mixed integer linear programming model to suggest a decision planning for the farm planning problem faced by an integratedcrop- livestock production farmer. (sun.ac.za)

#### Explore

- Spreadsheets not only speed calculation so more examples may be covered, they allow students to explore models that are too complex for undergraduates to solve analytically. (carleton.edu)

#### theory

- Theory of linear economic models. (gwu.edu)
- The theory of linear programming -- 4. (gwu.edu)

#### assess

- Mixed models were used to assess the rate of progression in PWV and the association between CV factors and PWV over time. (diabetesjournals.org)

#### Results

- The Results also showed that the incorporation of risk in the model greatly affects the level of acreage allocation, crop rotation and animal production level of the farm. (sun.ac.za)
- The computed responses compared well with the Centrifuge test results.This paper deals with the effects of free field ground motion analysis on seismic non linear behavior of embedded piles. (ac.ir)

#### process

- We propose a model that allows the description of a general studio mixing process as a linear stationary process of "generalized source image signals" considered as individual tracks. (aes.org)
- A process model can be defined as a strategy (also known as software engineering paradigm), comprising process, methods, and tools layers as well as the generalphases for developing the software. (ecomputernotes.com)
- A process model for software engineering depends on the nature and application of the software project. (ecomputernotes.com)
- Thus, it is essential to define process models for each software project. (ecomputernotes.com)
- A process model reflects the' goals of software development such as developing a high quality product and meeting the schedule on time. (ecomputernotes.com)
- Every software development process model takes requirements as input and delivers products as output. (ecomputernotes.com)
- A linear time layout algorithm for business process models. (uni-trier.de)
- Faster and Better Business Process Modeling with the IBM Pattern-based Process Model Accelerators. (uni-trier.de)
- Applying Patterns during Business Process Modeling. (uni-trier.de)

#### data

- To appear in the Proceedings of the Second International Workshop on Model-Oriented Data Analysis, Springer-Verlag, Wien. (dml.cz)
- You can find technical data about this model in a separate brochure. (docplayer.net)
- Yes, there are issues associated with the maximum likelihood estimators for the Logit and Probit models when the data are mis-classified, but these can be addressed. (blogspot.com)
- Another useful application for large amounts of data is to solve dynamic macroeconomic models over many periods to allow for exploratory analysis. (carleton.edu)

#### approach

- A linear approach was used for seismic free field ground motion analysis. (ac.ir)

#### Introduction

- An Introduction to Generalized Linear Models. (dtu.dk)

#### build

- We often need to build a predictive model that estimates rates. (uncertainaffairs.com)

#### example

- For example, the effect of parameter changes on models like the Cobb-Douglas function, the Keynesian Cross model , the IS-LM model and Solow growth model may be easily found numerically. (carleton.edu)
- Consider the screen capture from Keynesian cross model example to the right. (carleton.edu)

#### several

- As stated earlier, the waterfall model comprises several phases, which are listed below. (ecomputernotes.com)
- Models with several equations or non-linear equations may be explored numerically by Excel. (carleton.edu)

#### Systems

- His research involved the development of dynamical, algebraic and geometrical aspects of the fundamental concepts of poles and zeros of Linear Systems. (city.ac.uk)

#### often

- Scientific audio scene analysis rather focuses on "natural" mixtures and most often uses linear (convolutive) models of point sources placed in the same acoustic space. (aes.org)
- One could try linear regression, but specialized tools often do much better. (uncertainaffairs.com)

#### structure

- The term 'system structure' is linked to system invariants and the basic topology of interconnections expressed in terms of graphs and structured models. (city.ac.uk)
- The structure of compound 10 revealed a distorted trans - trans conformation for the PN skeleton, in contrast to the cis - trans arrangement generally believed to exist for linear phosphazene high polymers. (rsc.org)

#### comprehensive

- Combining Pattern Languages and Reusable Architectural Decision Models into a Comprehensive and Comprehensible Design Method. (uni-trier.de)

#### allows

- this would be a tedious exercise without Excel, and allows for more model exploration. (carleton.edu)

#### simple

- This model is simple to understand and represents processes which are easy to manage and measure. (ecomputernotes.com)

#### description

- 2 Linear actuators Econom 0 Description Applications Facts The users of the steel or stainless steel Econom 0 come from many different industries: From architects and planners of contemporary façade architecture up to plant construction and mechanical engineering specialists. (docplayer.net)

#### special

- One special Econom 0 model is UL certified and can be used without restrictions in the USA and Canada. (docplayer.net)

#### options

- and check the Trend box and Linear or Growth options as shown in the image on the right (click to enlarge). (carleton.edu)

#### different

- In contrast, the sound engineer can mix musical signals of very different nature and belonging to different acoustic spaces, and exploits many audio effects including non-linear processes. (aes.org)
- The waterfall model comprises different phases and each phase has its distinct goal. (ecomputernotes.com)

#### Real

- Real linear algebra -- 3. (gwu.edu)

#### easily

- Excel can easily insert a linear regression line and display associated equation in the chart that is an estimate of the growth rate version of Okun's Law, as shown on the right (click to enlarge). (carleton.edu)

#### program

- This model was incorporated into a Finite Element program (ANSYS), which was used to compute the response of laterally excited piles. (ac.ir)

#### software

- In the waterfall model (also known as the classical life cycle model), the development of software proceeds linearly and sequentially from requirement analysis to design, coding, testing, integration, implementation, and maintenance. (ecomputernotes.com)

#### Large

- [ 1 ] The main challenge was adapting the existing models whose primary focus was containing a hazardous material release to one that reflected the chaos of a large-scale disaster involving a large number of affected individuals. (medscape.com)

#### production

- Model solution with risk indicated that crop rotation strategy and animal production level is sensitive to risk levels considered. (sun.ac.za)
- Linear models of production. (gwu.edu)

#### examples

- 1. Linear programming: examples, definitions, and statements of the principal theorems -- 2. (gwu.edu)
- The Solow growth model is a difficult topic to tackle in undergraduate classes, partly because its abstract and technical nature makes it difficult to work through numerical examples. (carleton.edu)