Tag: Mathematical

Mathematical Macroevolution in Diatom Research


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by Pappas, Janice L.;

English | 2023 | ISBN: 1119749859 | 532 pages | True PDF EPUB | 170.03 MB
MATHEMATICAL MACROEVOLUTION IN DIATOM RESEARCH Buy this book to learn how to use mathematics in macroevolution research and apply mathematics to study complex biological problems. This book contains recent research in mathematical and analytical studies on diatoms. These studies reflect the complex and intricate nature of the problems being analyzed and the need to use mathematics as an aid in finding solutions. Diatoms are important components of marine food webs, the silica and carbon cycles, primary productivity, and carbon sequestration. Their uniqueness as glass-encased unicells and their presence throughout geologic history exemplifies the need to better understand such organisms. Explicating the role of diatoms in the biological world is no more urgent than their role as environmental and climate indicators, and as such, is aided by the mathematical studies in this book. The volume contains twelve original research papers as chapters. Macroevolutionary science topics covered are morphological analysis, morphospace analysis, adaptation, food web dynamics, origination-extinction and diversity, biogeography, life cycle dynamics, complexity, symmetry, and evolvability. Mathematics used in the chapters include stochastic and delay differential and partial differential equations, differential geometry, probability theory, ergodic theory, group theory, knot theory, statistical distributions, chaos theory, and combinatorics. Applied sciences used in the chapters include networks, machine learning, robotics, computer vision, image processing, pattern recognition, and dynamical systems. The volume covers a diverse range of mathematical treatments of topics in diatom research. Audience Diatom researchers, mathematical biologists, evolutionary and macroevolutionary biologists, paleontologists, paleobiologists, theoretical biologists, as well as researchers in applied mathematics, algorithm sciences, complex systems science, computational sciences, informatics, computer vision and image processing sciences, nanoscience, the biofuels industry, and applied engineering.

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Mathematical Formulas and Scientific Data A Quick Reference Guide


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by C. P. Kothandaraman

English | 2023 | ISBN: 1683928652 | 300 pages | True EPUB | 10.27 MB
Designed as a quick reference guide for engineers, mathematicians, scientists, and industry professionals, this book provides the essential formulas in all areas of mathematics and the key scientific data used in engineering and science. Part One covers the mathematical formulas from algebra, trigonometry, analytic geometry, differential and integral calculus, and vectors. Part Two covers MKS, CGS, and SI units, mechanical units, electrical and magnetic units, gases, elements, acoustics, materials, thermodynamics, radioactivity, geodetic data, astronomical data, optics, solids, organic and inorganic compounds, and more. Technical terms are defined, and theorems and applicable laws are stated and explained in detail. This data book is essential for every scientist, engineering student, and practicing professional.

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Controlling Epidemics With Mathematical and Machine Learning Models


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by Varghese Abraham

English | 2023 | ISBN: 1799883434 | 278 pages | True PDF EPUB | 36.68 MB
Communicable diseases have been an important part of human history. Epidemics afflicted populations, causing many deaths before gradually fading away and emerging again years after. Epidemics of infectious diseases are occurring more often, and spreading faster and further than ever, in many different regions of the world. The scientific community, in addition to its accelerated efforts to develop an effective treatment and vaccination, is also playing an important role in advising policymakers on possible non-pharmacological approaches to limit the catastrophic impact of epidemics using mathematical and machine learning models. Controlling Epidemics With Mathematical and Machine Learning Models provides mathematical and machine learning models for epidemical diseases, with special attention given to the COVID-19 pandemic. It gives mathematical proof of the stability and size of diseases. Covering topics such as compartmental models, reproduction number, and SIR model simulation, this premier reference source is an essential resource for statisticians, government officials, health professionals, epidemiologists, sociologists, students and educators of higher education, librarians, researchers, and academicians.

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Computational and Mathematical Population Dynamics


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by Necibe Tuncer, Maia Martcheva, Olivia Prosper & Lauren Childs

English | 2023 | ISBN: 9811263027 | 470 pages | True PDF | 35.5 MB
This book is a collection of works that represent the recent advancements in computational and mathematical methods applied to population dynamics. It concentrates on both development of new tools as well as on innovative use of existing tools to obtain new understanding of biological systems. The volume introduces new state-of-the-art techniques for defining and solving numerically control problems in mathematical biology in which the control appears linearly. Such problems produce simpler optimal controls that can be implemented in practice. The book further develops tools for fitting multi-scale models to multi-scale data and studying the practical identifiability of the parameters from multi-scale data. Novel model of Zika with Wolbahia infection in mosquitoes suggests that the most suitable control strategy to control Zika in the absence of Wolbahia is killing mosquitoes but the most suitable strategy when mosquitoes are Wolbahia infected is the treatment of humans.A completely novel methodology of developing discrete-continuous hybrid models of multi-species interactions is also introduced together with avantgarde techniques for discrete-continuous hybrid models analysis. A mathematical model leads to new observations of the within-host virus dynamics and its interplay with the immune responses. In particular, it is observed that the parameters promoting CTL responses need to be boosted over parameters promoting antibody production to obtain a biologically relevant steady state. A novel stochastic model of COVID-19 investigates quarantine and lock down as important strategies for control and elimination of COVID-19.

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