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Группа авторов Computational Pharmaceutical Solid State Chemistry


This book is the first to combine computational material science and modeling of molecular solid states for pharmaceutical industry applications. • Provides descriptive and applied state-of-the-art computational approaches and workflows to guide pharmaceutical solid state chemistry experiments and to support/troubleshoot API solid state selection • Includes real industrial case examples related to application of modeling methods in problem solving • Useful as a supplementary reference/text for undergraduate, graduate and postgraduate students in computational chemistry, pharmaceutical and biotech sciences, and materials science

14288.36 RUR

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Группа авторов New Approaches to Preventing Suicide

Группа авторов Anthropological Approaches to Psychological Medicine

Группа авторов Synthetic Approaches to Nonaromatic Nitrogen Heterocycles

Группа авторов Experimental Approaches to Conservation Biology

Группа авторов Mechanical Engineering in Uncertainties From Classical Approaches to Some Recent Developments

Группа авторов Approaches to Needs Assessment in Children's Services

Группа авторов Forensic Approaches to Death, Disaster and Abuse

Группа авторов Computational Approaches to Energy Materials


The development of materials for clean and efficient energy generation and storage is one of the most rapidly developing, multi-disciplinary areas of contemporary science, driven primarily by concerns over global warming, diminishing fossil-fuel reserves, the need for energy security, and increasing consumer demand for portable electronics. Computational methods are now an integral and indispensable part of the materials characterisation and development process. Computational Approaches to Energy Materials presents a detailed survey of current computational techniques for the development and optimization of energy materials, outlining their strengths, limitations, and future applications. The review of techniques includes current methodologies based on electronic structure, interatomic potential and hybrid methods. The methodological components are integrated into a comprehensive survey of applications, addressing the major themes in energy research. Topics covered include: • Introduction to computational methods and approaches • Modelling materials for energy generation applications: solar energy and nuclear energy • Modelling materials for storage applications: batteries and hydrogen • Modelling materials for energy conversion applications: fuel cells, heterogeneous catalysis and solid-state lighting • Nanostructures for energy applications This full colour text is an accessible introduction for newcomers to the field, and a valuable reference source for experienced researchers working on computational techniques and their application to energy materials.

14984.57 RUR

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Группа авторов Large-Scale Inverse Problems and Quantification of Uncertainty


This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods. Key Features: • Brings together the perspectives of researchers in areas of inverse problems and data assimilation. • Assesses the current state-of-the-art and identify needs and opportunities for future research. • Focuses on the computational methods used to analyze and simulate inverse problems. • Written by leading experts of inverse problems and uncertainty quantification. Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.

12317.55 RUR

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Группа авторов Vascular Disease: Diagnostic and Therapeutic Approaches

Группа авторов OMICS-Based Approaches in Plant Biotechnology

Группа авторов Dimensional Approaches in Diagnostic Classification

Группа авторов Writing the Visual


Classroom-focused articles on a variety of theoretically sound approaches to helping students write about visual rhetoric.

1514.39 RUR

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Ingvar Eidhammer Computational and Statistical Methods for Protein Quantification by Mass Spectrometry


The definitive introduction to data analysis in quantitative proteomics This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistical approaches to pursue the planning, design and analysis of quantitative proteomics experiments. The author’s carefully constructed approach allows readers to easily make the transition into the field of quantitative proteomics. Through detailed descriptions of wet-lab methods, computational approaches and statistical tools, this book covers the full scope of a quantitative experiment, allowing readers to acquire new knowledge as well as acting as a useful reference work for more advanced readers. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry: Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs. Is illustrated by a large number of figures and examples as well as numerous exercises. Provides both clear and rigorous descriptions of methods and approaches. Is thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work. Features detailed discussions of both wet-lab approaches and statistical and computational methods. With clear and thorough descriptions of the various methods and approaches, this book is accessible to biologists, informaticians, and statisticians alike and is aimed at readers across the academic spectrum, from advanced undergraduate students to post doctorates entering the field.

8750.82 RUR

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Группа авторов Introduction to Computational Chemistry


Introduction to Computational Chemistry Second Edition provides a comprehensive account of the fundamental principles underlying different methods, ranging from classical to the sophisticated. Although comprehensive in its coverage, this textbook focuses on calculating molecular structures and (relative) energies and less on molecular properties or dynamical aspects. No prior knowledge of concepts specific to computational chemistry are assumed, but the reader will need some understanding of introductory quantum mechanics, linear algebra, and vector, differential and integral calculus.

21727.28 RUR

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Riccardo Boero Behavioral Computational Social Science


This book is organized in two parts: the first part introduces the reader to all the concepts, tools and references that are required to start conducting research in behavioral computational social science. The methodological reasons for integrating the two approaches are also presented from the individual and separated viewpoints of the two approaches.The second part of the book, presents all the advanced methodological and technical aspects that are relevant for the proposed integration. Several contributions which effectively merge the computational and the behavioral approaches are presented and discussed throughout

6929.96 RUR

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Группа авторов Computational Models of Brain and Behavior


A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

15102.39 RUR

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Rense Corten Computational Approaches to Studying the Co-evolution of Networks and Behavior in Social Dilemmas


Computational Approaches to Studying the Co-evolution of Networks and Behaviour in Social Dilemmas shows students, researchers, and professionals how to use computation methods, rather than mathematical analysis, to answer research questions for an easier, more productive method of testing their models. Illustrations of general methodology are provided and explore how computer simulation is used to bridge the gap between formal theoretical models and empirical applications.

7176.31 RUR

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Группа авторов Performing History


Designed for a broad readership including scholars, performers and amateurs of musicIncorporates the most recent approaches of scholars doing groundbreaking work in the fieldUseful for students considering approaches to the study of musicWritten in an accessible and engaging way, suitable for non-specialists

8257.58 RUR

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