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After careful study of this chapter, students should be able to do the following:
LO1: Define scalar, vector, and tensor.
LO2: Describe strain tensor.
LO3: Describe normal and shear strain in an arbitrary direction.
LO4: Define principal strain and principal axes.
LO5: Describe strain invariants.
LO6: Recognize rotation.
LO7: State compatibility equations.
LO8: Understand the experimental method for strain measurement.
2.1 MATHEMATICAL PRELIMINARIES [LO1]
In any scientific or engineering field of study, knowledge of some mathematical techniques and methods are essential. Solid mechanics is no exception. To develop proper formulation methods and solution techniques for elasticity problems, it is necessary to have an appropriate mathematical background. In this chapter, we shall discuss Cartesian tensors, which have a special significance in the discussion of stress, strain, and displacement fields, and their manipulation. Other mathematical details will be discussed as and when they are required in solving different problems.
Tensors may be defined in a number of ways. One simple definition is that a tensor is a physical quantity that is governed by certain transformation laws when the coordinate system is changed. A tensor is invariant under any change of coordinate system, but its components along the coordinate axes change with the changed coordinate system. Tensors of order zero are called scalars. Common examples of scalars are temperature, density, Young's modulus, or Poisson's ratio. They have a single magnitude at each point in space, and they are invariant with coordinate transformations. A typical example of scalars is often taken as temperature T at a point in space with coordinates (x, y, z) represented as T(x, y, z). Temperature at the same point does not change if we choose a different coordinate system (x′, y′, z′) represented as T′(x′, y′, z′) and we may say
T=T′. (2.1.1)
Tensors of first order are vectors, and we know that a vector has a magnitude and a direction. A typical example of a vector is a velocity vector V. It is sometimes taken as a convention to represent a vector by a bold letter. Consider the velocity vector V in (x, y, z) coordinate system.
After careful study of this chapter, students should be able to do the following:
LO1: Define stress at a point.
LO2: Describe stresses on an oblique plane.
LO3: Define principal stresses, hydrostatic, and deviatorial stress tensor.
LO4: Calculate shear stresses.
LO5: Construct Mohr's circle.
LO6: Analyze equations of equilibrium.
3.1 STATE OF STRESS AT A POINT [LO1]
When a body is subjected to external forces, its behavior depends on the magnitude and distribution of forces and properties of the body material. Depending on these factors, the body may deform elastically or plastically, or it may fracture. The body may also fail by fatigue when subjected to repetitive loading. Here we are primarily interested in elastic deformation of materials.
In order to establish the concept of stress and stress at a point, let us consider a straight bar of uniform cross-section of area A and subjected to uniaxial force F as shown in Figure 3.1. Stress at a typical section A - A′ is normally given as σ = F/A. This is true only if the force is uniformly distributed over the area A, but this is rarely true. Therefore, definition of stress must be considered by progressively reducing the area until it is small enough such that the force may be considered to be uniformly distributed.
To understand this, consider a body subjected to external forces P1, P2, P3, and P4 as shown in Figure 3.2. If we now cut the body in two pieces,
Internal forces f1, f2, f3, etc. are developed to keep the pieces in equilibrium. Now consider an infinitesimal element of area ΔA Dat the cut section and let the resultant force on the element be Δf.
This textbook focuses on general topology. Meant for graduate and senior undergraduate mathematics students, it introduces topology thoroughly from scratch and assumes minimal basic knowledge of real analysis and metric spaces. It begins with thought-provoking questions to encourage students to learn about topology and how it is related to, yet different from, geometry. Using concepts from real analysis and metric spaces, the definition of topology is introduced along with its motivation and importance. The text covers all the topics of topology, including homeomorphism, subspace topology, weak topology, product topology, quotient topology, coproduct topology, order topology, metric topology, and topological properties such as countability axioms, separation axioms, compactness, and connectedness. It also helps to understand the significance of various topological properties in classifying topological spaces.
Emphasizing how and why machine learning algorithms work, this introductory textbook bridges the gap between the theoretical foundations of machine learning and its practical algorithmic and code-level implementation. Over 85 thorough worked examples, in both Matlab and Python, demonstrate how algorithms are implemented and applied whilst illustrating the end result. Over 75 end-of-chapter problems empower students to develop their own code to implement these algorithms, equipping them with hands-on experience. Matlab coding examples demonstrate how a mathematical idea is converted from equations to code, and provide a jumping off point for students, supported by in-depth coverage of essential mathematics including multivariable calculus, linear algebra, probability and statistics, numerical methods, and optimization. Accompanied online by instructor lecture slides, downloadable Python code and additional appendices, this is an excellent introduction to machine learning for senior undergraduate and graduate students in Engineering and Computer Science.
This book offers a rich analysis of many aspects of human rights law in the UK and the European legal framework while also including critiques of human rights and the varying conceptions of rights. This book has the advantage of engaging with both Strasbourg caselaw, domestic jurisprudence and the academic scholarship. The issues covered are the right to life, the prohibition of torture, inhuman and degrading treatment, abortion and assisted dying, modern slavery and human trafficking, terrorism, immigration, privacy, hate speech, protest, religion, equality and non-discrimination.
The fourth edition of Explaining the History of American Foreign Relations reconceptualizes this long-established classic to focus squarely on methods: not what we do, but how we do what we do. It presents revised, sharply focused essays on methods for researching national security, development, political economy, gender, religion, race, emotion, and nongovernmental organizations, alongside entirely new contributions on digital resources, spatial analysis, technology, materials, the natural world, the interaction of race and empire, US-Indigenous relations, ideology, and culture. The chapters are bracketed with an essay that assesses changes in the conception of US foreign relations history, and with an overview of how US foreign relations history is practiced in China. The essays, by scholars who have made a significant contribution in their areas of specialization, highlight conceptual approaches and methods that, taken together, offer an innovative and practical 'how-to' manual for both experienced scholars and newcomers to the field.
Designed specifically for class use, this text guides students through developing their own full, working constructed language. It introduces basic concepts and the decisions students need to make about their conlang's speakers and world, before walking them through the process of conlanging in incremental stages, from selecting a language's sounds to choices about its grammar. It includes hundreds of examples from natural and constructed languages, and over seventy end-of-chapter exercises that allow students to apply concepts to an in-progress conlang and guide them in developing their own conlang. Ideal for undergraduates, the text is also suitable for more advanced students through the inclusion of clearly highlighted sections containing advanced material and optional conlang challenges. Instructor resources include an interactive slideshow for selecting stress patterns, an exercise answer guide and a sample syllabus, and student resources include a 'select-a-feature' conlang adventure, a spreadsheet of conlang features, and supplementary documentation for the exercises.
Presenting a concise overview of astrophysical concepts, the second edition of this textbook bridges the gap between introductory astronomy books and advanced astrophysics texts. Designed for one-semester astrophysics courses, the textbook is aimed at science and engineering students with college-level calculus-based physics. The new edition features both revisions and additions, with the extension of topics such as luminosity distance and the inclusion of notable developments such as the James Webb and Roman Space Telescopes. As before, the chapters are organized into five parts, covering: stellar properties; stellar structure and evolution; the interstellar medium and star/planet formation; our Milky Way and other galaxies; and cosmology. The exposition guides students toward a comprehensive fundamental understanding, using 'Quick Questions' to spur practice in basic computations, and multi-part exercises that offer a greater challenge. The solutions to the questions are freely accessible online, with exercise solutions and lecture slides available for instructors.
This textbook provides students with basic literacy on key issues related to Justice, Equity, Diversity, and Inclusion in the United States. Over twelve chapters, it employs critical race theory and intersectionality to promote critical thinking and civic engagement on issues such as American culture, gendered racism, and Black reparations. Each chapter employs interactive and engaging opportunities to learn, making it the ideal introductory resource for undergraduate students. The text is structured around real-world stories, which exemplify the humanity of each person and the complexity of these issues. Causadias presents questions for further discussion or to enhance comprehension, defines key concepts, debunks popular myths, summarizes evidence from trusted sources that challenge misinformation and disinformation, and proposes in-class exercises. Curated reading lists can be found at the end of every chapter for readers to expand their understanding of different topics. This book is also available as Open Access on Cambridge Core.
Students have an almost insurmountable task in understanding statistics in the psychological sciences and applying them to a research study. This textbook tackles this source of stress by guiding students through the research process, start to finish, from writing a proposal and performing the study, to analysing the results and creating a report and presentation. This truly practical textbook explains psychology research methods in a conversational style, with additional material of interest placed in focus boxes alongside, so that students don't lose their way through the steps. Every step is detailed visually with processes paralleled in both SPSS and R, allowing instructors and students to learn both statistical packages or to bridge from one to the other. Students perform hands-on statistical exercises using real data, and both qualitative and mixed-methods research are covered. They learn effective ways to present information visually, and about free tools to collect and analyse data.
With an emphasis on timeless essential mathematical background for optimization, this textbook provides a comprehensive and accessible introduction to convex optimization for students in applied mathematics, computer science, and engineering. Authored by two influential researchers, the book covers both convex analysis basics and modern topics such as conic programming, conic representations of convex sets, and cone-constrained convex problems, providing readers with a solid, up-to-date understanding of the field. By excluding modeling and algorithms, the authors are able to discuss the theoretical aspects in greater depth. Over 170 in-depth exercises provide hands-on experience with the theory, while more than 30 'Facts' and their accompanying proofs enhance approachability. Instructors will appreciate the appendices that cover all necessary background and the instructors-only solutions manual provided online. By the end of the book, readers will be well equipped to engage with state-of-the-art developments in optimization and its applications in decision-making and engineering.
This focused textbook demonstrates cutting-edge concepts at the intersection of machine learning (ML) and wireless communications, providing students with a deep and insightful understanding of this emerging field. It introduces students to a broad array of ML tools for effective wireless system design, and supports them in exploring ways in which future wireless networks can be designed to enable more effective deployment of federated and distributed learning techniques to enable AI systems. Requiring no previous knowledge of ML, this accessible introduction includes over 20 worked examples demonstrating the use of theoretical principles to address real-world challenges, and over 100 end-of-chapter exercises to cement student understanding, including hands-on computational exercises using Python. Accompanied by code supplements and solutions for instructors, this is the ideal textbook for a single-semester senior undergraduate or graduate course for students in electrical engineering, and an invaluable reference for academic researchers and professional engineers in wireless communications.
Important concepts from the diverse fields of physics, mathematics, engineering and computer science coalesce in this foundational text on the cutting-edge field of quantum information. Designed for undergraduate and graduate students with any STEM background, and written by a highly experienced author team, this textbook draws on quantum mechanics, number theory, computer science technologies, and more, to delve deeply into learning about qubits, the building blocks of quantum information, and how they are used in quantum computing and quantum algorithms. The pedagogical structure of the chapters features exercises after each section as well as focus boxes, giving students the benefit of additional background and applications without losing sight of the big picture. Recommended further reading and answers to select exercises further support learning. Written in approachable and conversational prose, this text offers a comprehensive treatment of the exciting field of quantum information while remaining accessible to students and researchers within all STEM disciplines.
The fully revised fifth edition of this highly acclaimed undergraduate textbook provides a thought-provoking introduction to evolutionary psychology, while assuming no prior knowledge of evolutionary theory. The authors continue to carefully guide students towards a level of understanding where they can critically apply evolutionary theory to psychological explanation, providing an engaging and balanced discussion of the field. New material has been added on female homosexuality, artificial intelligence and language, cooking and human brain expansion, Covid-19 and rates of evolutionary change, and the effects of digital media on mental health. This edition also has new and revised boxed case studies, many new figures, extra discussion questions, and additional further reading suggestions. The text is accompanied by online resources including an updated test bank and lecture slides, as well as new answers to the end-of-chapter questions. This is essential reading for students taking undergraduate and graduate courses in evolutionary psychology.
Confidently analyze, interpret and act on financial data with this practical introduction to the fundamentals of financial data science. Master the fundamentals with step-by-step introductions to core topics will equip you with a solid foundation for applying data science techniques to real-world complex financial problems. Extract meaningful insights as you learn how to use data to lead informed, data-driven decisions, with over 50 examples and case studies and hands-on Matlab and Python code. Explore cutting-edge techniques and tools in machine learning for financial data analysis, including deep learning and natural language processing. Accessible to readers without a specialized background in finance or machine learning, and including coverage of data representation and visualization, data models and estimation, principal component analysis, clustering methods, optimization tools, mean/variance portfolio optimization and financial networks, this is the ideal introduction for financial services professionals, and graduate students in finance and data science.
Build a firm foundation for studying statistical modelling, data science, and machine learning with this practical introduction to statistics, written with chemical engineers in mind. It introduces a data–model–decision approach to applying statistical methods to real-world chemical engineering challenges, establishes links between statistics, probability, linear algebra, calculus, and optimization, and covers classical and modern topics such as uncertainty quantification, risk modelling, and decision-making under uncertainty. Over 100 worked examples using Matlab and Python demonstrate how to apply theory to practice, with over 70 end-of-chapter problems to reinforce student learning, and key topics are introduced using a modular structure, which supports learning at a range of paces and levels. Requiring only a basic understanding of calculus and linear algebra, this textbook is the ideal introduction for undergraduate students in chemical engineering, and a valuable preparatory text for advanced courses in data science and machine learning with chemical engineering applications.
This textbook reflects the changing landscape of water management by combining the fields of satellite remote sensing and water management. Divided into three major sections, it begins by discussing the information that satellite remote sensing can provide about water, and then moves on to examine how it can address real-world management challenges, focusing on precipitation, surface water, irrigation management, reservoir monitoring, and water temperature tracking. The final part analyses governance and social issues that have recently been given more attention as the world reckons with social justice and equity aspects of engineering solutions. This book uses case studies from around the globe to demonstrate how satellite remote sensing can improve traditional water practices and includes end-of-chapter exercises to facilitate student learning. It is intended for advanced undergraduate and graduate students in water resource management, and as reference textbook for researchers and professionals.
Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.