The Experts below are selected from a list of 69 Experts worldwide ranked by ideXlab platform
Ivan Frollo - One of the best experts on this subject based on the ideXlab platform.
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Measurement Science is the Science of Sciences - There is no Science without Measurement
Measurement Science Review, 2020Co-Authors: Viktor Witkovský, Ivan FrolloAbstract:AbstractOmnia in mensura et numero et pondere disposuisti is a famous Latin phrase from Solomon’s Book of Wisdom, dated to the mid first century BC, meaning that all things were ordered in measure, number, and weight. Naturally, the wisdom is appearing in its relation to man. The Wisdom of Solomon is understood as the perfection of knowledge of the righteous as a gift from God showing itself in action. Consequently, a natural and obvious conjecture is that measurement science is the science of sciences. In fact, it is a basis of all experimental and theoretical research activities. Each measuring process assumes an object of measurement. Some science disciplines, such as quantum physics, are still incomprehensible despite complex Mathematical interpretations. No phenomenon is a real phenomenon unless it is observable in space and time, that is, unless it is a subject to measurement. The science of measurement is an indispensable ingredient in all scientific Fields. Mathematical foundations and interpretation of the measurement science were accepted and further developed in most of the scientific Fields, including physics, cosmology, geology, environment, quantum mechanics, statistics, and metrology. In this year, 2020, Measurement Science Review celebrates its 20th anniversary and we are using this special opportunity to highlight the importance of measurement science and to express our faith that the journal will continue to be an excellent place for exchanging bright ideas in the field of measurement science. As an illustration and motivation for usage and further development of Mathematical methods in measurement science, we briefly present the simple least squares method, frequently used for measurement evaluation, and its possible modification. The modified least squares estimation method was applied and experimentally tested for magnetic field homogeneity adjustment.
John T. Gerig - One of the best experts on this subject based on the ideXlab platform.
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Pulsed field gradients in simulations of one- and two-dimensional NMR spectra.
Journal of Magnetic Resonance, 1999Co-Authors: Ghirmai Meresi, Miroslava Cuperlovic, William E. Palke, John T. GerigAbstract:Abstract A method for the inclusion of the effects ofz-axis pulsed field gradients in computer simulations of an arbitrary pulsed NMR experiment with spin 1 2 nuclei is described. Recognizing that the phase acquired by a coherence following the application of az-axis pulsed field gradient bears a fixed relation to its order and the spatial position of the spins in the sample tube, the sample is regarded as a collection of volume elements, each phase-encoded by a characteristic, spatially dependent precession frequency. The evolution of the sample's density matrix is thus obtained by computing the evolution of the density matrix for each volume element. Following the last gradient pulse, these density matrices are combined to form a composite density matrix which evolves through the rest of the experiment to yield the observable signal. This approach is implemented in a program which includes capabilities for rigorous inclusion of spin relaxation by dipole–dipole, chemical shift anisotropy, and random field mechanisms, plus the effects of arbitrary RF Fields. Mathematical procedures for accelerating these calculations are described. The approach is illustrated by simulations of representative one- and two-dimensional NMR experiments.
Boris Gutkin - One of the best experts on this subject based on the ideXlab platform.
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The simulation of addiction: pharmacological and neurocomputational models of drug self-administration.
Drug and Alcohol Dependence, 2007Co-Authors: Serge H. Ahmed, Georgiy Bobashev, Boris GutkinAbstract:: In an attempt to better understand the pharmacological and neurobiological determinants of drug addiction, researchers have begun to build Mathematical and computational models of drug self-administration. Quantitative pharmacological models have been developed to describe the acquisition of drug self-administration behavior and the maintenance of drug use behavior. However, models that describe behavior acquisition are unable to explain behavior maintenance, and vice versa. Recently, computational modeling has been used to develop more neurobiologically realistic models with the potential to explain drug self-administration across all major behavioral stages (i.e., initiation, escalation and maintenance)and across a wide range of reinforcement contingencies. Intrigued by the apparent irrational behavior of drug addicts, researchers from a wide range of scientific disciplines have formulated a plethora of theoretical schemes over the years to understand addiction. However, most of these models are qualitative in nature and are formulated using terms that are often ill-defined. As a result, the empirical validity of these models has been difficult to test rigorously, which has served to generate more controversy than clarity. In this context, as in other scientific Fields, Mathematical and computational modeling may contribute to the development of more testable and rigorous models of addiction. Recently, several researchers have begun to simulate drug self-administration behavior in an attempt to better understand the pharmacological and neurobiological determinants of drug addiction. This article stems from a 2006 College on Problems of Drug Dependence (CPDD) workshop and includes a few examples of Mathematical and computational models of drug self-administration in rodents that illustrate the contribution of Mathematical modeling to our understanding of the pharmacological and neurobiological factors in drug self-administration.
Chaeshin Chu - One of the best experts on this subject based on the ideXlab platform.
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A tale of two Fields: Mathematical and statistical modeling of infectious diseases.
Osong public health and research perspectives, 2011Co-Authors: Hae-wol Cho, Chaeshin ChuAbstract:The managing editor of Osong Public Health and Research Perspectives (PHRP) attended the Casablanca International Workshop in Mathematical Biology: Analysis and Control, Morocco, June 20–24, 2011. PHRP was welcomed by the community, especially by the infectious disease-modeling group. They welcomed a new public health journal that contains articles for applied Mathematical modelers. It is an asset to have such strong supporters for PHRP. Mathematical models for public health experts are varied. The classical disease-transmission model was created by Kermack-McKendrick in 1927 [1]. In this model, disease transmission is conveniently conceptualized as passage among members of a population by moving among compartments. Actually, this is a special case of the Susceptible-Infectious-Removed (SIR) compartmental model. SIR functions well for infectious or communicable diseases with immunity against re-infection or disease with no immunity. Another model divides I-class into two classes with an exposed period between being infected and becoming infectious, which yields Susceptible-Exposed-Infectious-Removed (SEIR) and Susceptible-Exposed-Infectious-Susceptible (SEIS). We can consider Susceptible-Infectious-Removed-Susceptible (SIRS) models with temporary immunity [1]. In the simple Susceptible-Infected-Susceptible model, the deterministic dynamic structure shows us a great deal about the behavior of stochastic paths and brings to our attention questions that pertain to the stochastic model: What is the nature of the stochastic path as it varies near the deterministic equilibrium? Starting from the deterministic equilibrium, what is the distribution of time until the stochastic hits zero? We should point out that the stochastic models that we have discussed here are simple ones, involving no more than two linked stochastic equations [2]. The basic reproduction number R0 is the number of secondary infections caused by a single infective agent introduced in an entirely susceptible population over the course of the infection of this single infective and determines whether there is an epidemic in a population [1]. Stochastic modeling with compartmental stochastic models is often utilized. The dynamics of an Ordinary Differential Equation system informs us of the deterministic skeleton on which the behavior of corresponding stochastic systems are built [2]. Statistical modeling has two tracks to reach conclusions from data. One assumes that the data are generated by a given stochastic data model, whereas the other uses algorithmic models and treats the data mechanism as unknown. Logistic regression is frequently used in public health because it produces a linear combination of the variables with weights that give an indication of the variable importance. The end is a neat result of how the prediction variables affect the response variable plus confidence intervals for the weights. Assume also that each one with a different approach to data modeling fits a model to the same dataset and that each one applies standard goodness-of-fit test, looks at residuals, etc, and is convinced that their model fits the data. Yet the two models give different pictures of nature’s mechanism and lead to different conclusions [3]. Computer-generated simulation is becoming increasingly important in inference of given data, but still one of the most hackneyed methods to estimate disease-related phenomena is multiple regression [4]. In this issue two modeling approaches are presented. A compartmental deterministic Mathematical model was introduced to predict the evolution of obesity in a population and to propose strategies to reduce its incidence. The authors took obesity as an epidemic and developed a model based on Susceptible-Infected-Susceptible transmission. Obesity was regarded as an infectious disease caused by social peer pressure or social contact. These social contacts had influences on the probability of transmission of a sedentary lifestyle and unhealthy nutritional habits. From these considerations they proposed an epidemiologic-type model to study the epidemic evolution of obesity [5]. Statistical modeling was adopted to estimate seasonal influenza patients in Korea using sentinel surveillance data. This report followed the traditional statistical modeling track. The authors present two regression models: first, they estimated monthly reporting rates (W1) from sentinel clinics of the influenza surveillance system, and then estimated hospital-visit patient numbers for the sentinel clinics. They also estimated weight of scale of treatment of national hospital/clinics to sentinel hospital/clinics (W2). The weight by month and province (W3) was estimated as the last step. The authors obtained the final number of influenza patients with multiple linear regression models using the above weights [6].
Viktor Witkovský - One of the best experts on this subject based on the ideXlab platform.
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Measurement Science is the Science of Sciences - There is no Science without Measurement
Measurement Science Review, 2020Co-Authors: Viktor Witkovský, Ivan FrolloAbstract:AbstractOmnia in mensura et numero et pondere disposuisti is a famous Latin phrase from Solomon’s Book of Wisdom, dated to the mid first century BC, meaning that all things were ordered in measure, number, and weight. Naturally, the wisdom is appearing in its relation to man. The Wisdom of Solomon is understood as the perfection of knowledge of the righteous as a gift from God showing itself in action. Consequently, a natural and obvious conjecture is that measurement science is the science of sciences. In fact, it is a basis of all experimental and theoretical research activities. Each measuring process assumes an object of measurement. Some science disciplines, such as quantum physics, are still incomprehensible despite complex Mathematical interpretations. No phenomenon is a real phenomenon unless it is observable in space and time, that is, unless it is a subject to measurement. The science of measurement is an indispensable ingredient in all scientific Fields. Mathematical foundations and interpretation of the measurement science were accepted and further developed in most of the scientific Fields, including physics, cosmology, geology, environment, quantum mechanics, statistics, and metrology. In this year, 2020, Measurement Science Review celebrates its 20th anniversary and we are using this special opportunity to highlight the importance of measurement science and to express our faith that the journal will continue to be an excellent place for exchanging bright ideas in the field of measurement science. As an illustration and motivation for usage and further development of Mathematical methods in measurement science, we briefly present the simple least squares method, frequently used for measurement evaluation, and its possible modification. The modified least squares estimation method was applied and experimentally tested for magnetic field homogeneity adjustment.