Idaho State University

About: Idaho State University is a education organization based out in Pocatello, Idaho, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 3527 authors who have published 6477 publications receiving 150602 citations. The organization is also known as: ISU & Academy of Idaho.

Recent developments in GEANT4

Abstract: Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Over the past several years, major changes have been made to the toolkit in order to accommodate the needs of these user communities, and to efficiently exploit the growth of computing power made available by advances in technology. The adaptation of Geant4 to multithreading, advances in physics, detector modeling and visualization, extensions to the toolkit, including biasing and reverse Monte Carlo, and tools for physics and release validation are discussed here.

Optimal Control Systems

Abstract: INTRODUCTION Classical and Modern Control Optimization Optimal Control Historical Tour About This Book Chapter Overview Problems CALCULUS OF VARIATIONS AND OPTIMAL CONTROL Basic Concepts Optimum of a Function and a Functional The Basic Variational Problem The Second Variation Extrema of Functions with Conditions Extrema of Functionals with Conditions Variational Approach to Optimal Systems Summary of Variational Approach Problems LINEAR QUADRATIC OPTIMAL CONTROL SYSTEMS I Problem Formulation Finite-Time Linear Quadratic Regulator Analytical Solution to the Matrix Differential Riccati Equation Infinite-Time LQR System I Infinite-Time LQR System II Problems LINEAR QUADRATIC OPTIMAL CONTROL SYSTEMS II Linear Quadratic Tracking System: Finite-Time Case LQT System: Infinite-Time Case Fixed-End-Point Regulator System Frequency-Domain Interpretation Problems DISCRETE-TIME OPTIMAL CONTROL SYSTEMS Variational Calculus for Discrete-Time Systems Discrete-Time Optimal Control Systems Discrete-Time Linear State Regulator Systems Steady-State Regulator System Discrete-Time Linear Quadratic Tracking System Frequency-Domain Interpretation Problems PONTRYAGIN MINIMUM PRINCIPLE Constrained Systems Pontryagin Minimum Principle Dynamic Programming The Hamilton-Jacobi-Bellman Equation LQR System using H-J-B Equation CONSTRAINED OPTIMAL CONTROL SYSTEMS Constrained Optimal Control TOC of a Double Integral System Fuel-Optimal Control Systems Minimum Fuel System: LTI System Energy-Optimal Control Systems Optimal Control Systems with State Constraints Problems APPENDICES Vectors and Matrices State Space Analysis MATLAB Files REFERENCES INDEX

Model selection for ecologists: the worldviews of AIC and BIC

Abstract: Ecologists frequently ask questions that are best addressed with a model comparison approach. Under this system, the merit of several models is considered without necessarily requiring that (1) models are nested, (2) one of the models is true, and (3) only current data be used. This is in marked contrast to the pragmatic blend of Neyman-Pearson and Fisherian significance testing conventionally emphasized in biometric texts (Christensen 2005), in which (1) just two hypotheses are under consideration, representing a pairwise comparison of models, (2) one of the models, H0, is assumed to be true, and (3) a single data set is used to quantify evidence concerning H0. As Murtaugh (2014) noted, null hypothesis testing can be extended to certain highly structured multi-model situations (nested with a clear sequence of tests), such as extra sums of squares approaches in general linear models, and drop in deviance tests in generalized linear models. This is especially true when there is the expectation that higher order interactions are not significant or nonexistent, and the testing of main effects does not depend on the order of the tests (as with completely balanced designs). There are, however, three scientific frameworks that are poorly handled by traditional hypothesis testing. First, in questions requiring model comparison and selection, the null hypothesis testing paradigm becomes strained. Candidate models may be non-nested, a wide number of plausible models may exist, and all of the models may be approximations to reality. In this context, we are not assessing which model is correct (since none are correct), but which model has the best predictive accuracy, in particular, which model is expected to fit future observations well. Extensive ecological examples can be found in Johnson and Omland (2004), Burnham and Anderson (2002), and Anderson (2008). Second, the null hypothesis testing paradigm is often inadequate for making inferences concerning the falsification or confirmation of scientific claims because it does not explicitly consider prior information. Scientists often do not consider a single data set to be adequate for research hypothesis rejection (Quinn and Keough 2002:35), particularly for complex hypotheses with a low degree of falsifiability (i.e., Popper 1959:266). Similarly, the support of hypotheses in the generation of scientific theories requires repeated corroboration (Ayala et al. 2008). Third, ecologists and other scientists are frequently concerned with the plausibility of existing or default models, what statistician would consider null hypotheses (e.g., the ideal free distribution, classic insular biogeography, mathematic models for species interactions, archetypes for community succession and assembly, etc.). However, null hypothesis testing is structured in such a way that the null hypothesis cannot be directly supported by evidence. Introductory statistical and biometric textbooks go to great lengths to make this conceptual point (e.g., DeVeaux et al. 2013:511, 618, Moore 2010:376, Devore and Peck 1997:300–303).

The Roles of Harsh and Fluctuating Conditions in the Dynamics of Ecological Communities

Abstract: Harsh conditions (e.g., mortality and stress) reduce population growth rates directly; secondarily, they may reduce the intensity of interactions between organisms. Near‐exclusive focus on...

Resource pulses, species interactions and diversity maintenance in arid and semi-arid environments

Abstract: Arid environments are characterized by limited and variable rainfall that supplies resources in pulses. Resource pulsing is a special form of environmental variation, and the general theory of coexistence in variable environments suggests specific mechanisms by which rainfall variability might contribute to the maintenance of high species diversity in arid ecosystems. In this review, we discuss physiological, morphological, and life-history traits that facilitate plant survival and growth in strongly water-limited variable environments, outlining how species differences in these traits may promote diversity. Our analysis emphasizes that the variability of pulsed environments does not reduce the importance of species interactions in structuring communities, but instead provides axes of ecological differentiation between species that facilitate their coexistence. Pulses of rainfall also influence higher trophic levels and entire food webs. Better understanding of how rainfall affects the diversity, species composition, and dynamics of arid environments can contribute to solving environmental problems stemming from land use and global climate change.