Padmanabhan Seshaiyer – författare

Quantitative Biology provides quantitative and data-driven approaches for analyzing biological and bio-inspired systems, covering the foundations of mathematical modeling, analysis, and computation. The book presents a practical mix of both theory and computation for a variety of biological applications, with tied-in, engaging project activities, instruction, programming language, and technological tools. Modeling approaches combine mathematical foundations, statistical reasoning, and computational thinking, with applications in compartmental, agent-based, bio image, biological interaction, and neural network modeling, as well as machine learning, parameter identification, and applications across societal challenges.Each chapter includes exposure to models and modeling, a foundational instructional framework, benchmark applications, and numerical simulations with a literate programming guided style that helps readers go beyond replication models and into prediction and data-driven discovery. A companion website also features interactive code to accompany projects across each chapter.Introduces and demonstrates mathematical modeling, analysis, and computation for biological and bio-inspired systemsPresents and instructs in computation for a variety of biological applications via engaging project activities, benchmark examples, and technology toolsOffers insights into replicative models for biological systems, empowering prediction and data-driven discoveryIncludes a foundational instructional framework, benchmark applications, and numerical simulations with a literate programming guided style across all chaptersFeatures a companion webpage with interactive code to accompany chapter projects

Quantitative Biology provides quantitative and data-driven approaches for analyzing biological and bio-inspired systems, covering the foundations of mathematical modeling, analysis, and computation. The book presents a practical mix of both theory and computation for a variety of biological applications, with tied-in, engaging project activities, instruction, programming language, and technological tools. Modeling approaches combine mathematical foundations, statistical reasoning, and computational thinking, with applications in compartmental, agent-based, bio image, biological interaction, and neural network modeling, as well as machine learning, parameter identification, and applications across societal challenges.Each chapter includes exposure to models and modeling, a foundational instructional framework, benchmark applications, and numerical simulations with a literate programming guided style that helps readers go beyond replication models and into prediction and data-driven discovery. A companion website also features interactive code to accompany projects across each chapter. - Introduces and demonstrates mathematical modeling, analysis, and computation for biological and bio-inspired systems- Presents and instructs in computation for a variety of biological applications via engaging project activities, benchmark examples, and technology tools- Offers insights into replicative models for biological systems, empowering prediction and data-driven discovery- Includes a foundational instructional framework, benchmark applications, and numerical simulations with a literate programming guided style across all chapters- Features a companion webpage with interactive code to accompany chapter projects

Classical and Modern Numerical Analysis: Theory, Methods and Practice provides a sound foundation in numerical analysis for more specialized topics, such as finite element theory, advanced numerical linear algebra, and optimization. It prepares graduate students for taking doctoral examinations in numerical analysis.The text covers the main areas o

Classical and Modern Numerical Analysis: Theory, Methods and Practice provides a sound foundation in numerical analysis for more specialized topics, such as finite element theory, advanced numerical linear algebra, and optimization. It prepares graduate students for taking doctoral examinations in numerical analysis.The text covers the main areas of introductory numerical analysis, including the solution of nonlinear equations, numerical linear algebra, ordinary differential equations, approximation theory, numerical integration, and boundary value problems. Focusing on interval computing in numerical analysis, it explains interval arithmetic, interval computation, and interval algorithms. The authors illustrate the concepts with many examples as well as analytical and computational exercises at the end of each chapter.This advanced, graduate-level introduction to the theory and methods of numerical analysis supplies the necessary background in numerical methods so that students can apply the techniques and understand the mathematical literature in this area. Although the book is independent of a specific computer program, MATLAB® code is available on the authors' website to illustrate various concepts.

Classical and Modern Numerical Analysis: Theory, Methods and Practice provides a sound foundation in numerical analysis for more specialized topics, such as finite element theory, advanced numerical linear algebra, and optimization. It prepares graduate students for taking doctoral examinations in numerical analysis.The text covers the main areas o

Modeling Mathematical Ideas combining current research and practical strategies to build teachers and students strategic competence in problem solving.This must-have book supports teachers in understanding learning progressions that addresses conceptual guiding posts as well as students’ common misconceptions in investigating and discussing important mathematical ideas related to number sense, computational fluency, algebraic thinking and proportional reasoning. In each chapter, the authors opens with a rich real-world mathematical problem and presents classroom strategies (such as visible thinking strategies & technology integration) and other related problems to develop students’ strategic competence in modeling mathematical ideas.

Modeling Mathematical Ideas combining current research and practical strategies to build teachers and students strategic competence in problem solving.This must-have book supports teachers in understanding learning progressions that addresses conceptual guiding posts as well as students’ common misconceptions in investigating and discussing important mathematical ideas related to number sense, computational fluency, algebraic thinking and proportional reasoning. In each chapter, the authors opens with a rich real-world mathematical problem and presents classroom strategies (such as visible thinking strategies & technology integration) and other related problems to develop students’ strategic competence in modeling mathematical ideas.

This book presents the proceedings of the International Conference on Best Innovative Teaching Strategies (ICOBITS) at BITS Pilani on Feb 9-11, 2023. It brings together global academicians, researchers, and industry experts to delve into innovative teaching-learning practices. Meticulously selected and reviewed by professionals, the proceedings offer cross-cutting perspectives and strategies in education and creative learning. It is organized into four parts and covers Innovative Pedagogical Practices for Technology Enhanced Learning, Technology, Society and Industry in Higher Education, STEM Education and Cultural Studies, Gender Studies and Sustainability in Higher Education. It provides insights into assessment tools for effectively evaluating the efficiency of teaching methods. Part one explores ICT, digital classrooms, metaverse-based teaching, gamification, and AI-ML-based practices. Part two delves into the humanistic approach in technical education, social networking as an educational tool, and outcome-based assessment needs. Part three addresses future educators' requirements through multidisciplinary collaboration in STEM education. Part four discusses gender equity, structured social inclusion and cultural skills in teaching-learning practices. This book is a valuable resource for academicians, researchers, policymakers, administrators, technocrats, and developers actively engaged in education technology across disciplines such as Engineering, Sciences, Management, Humanities, and Social Sciences.