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This book can provide the basics for understanding how swarm intelligence algorithms work, and aid readers in programming these algorithms to solve various computational problems. It is useful for students studying nature-based optimization algorithms, and can be a helpful for learning the basics of these algorithms efficiently.

With issues of equity at the forefront of mathematics education research and policy, Mathematics Teaching, Learning, and Liberation in the Lives of Black Children fills the need for authoritative, rigorous scholarship that sheds light on the ways that young black learners experience mathematics in schools and their communities. This timely collection significantly extends the knowledge base on mathematics teaching, learning, participation, and policy for black children and it provides new framings of relevant issues that researchers can use in future work. More importantly, this book helps move the field beyond analyses that continue to focus on and normalize failure by giving primacy to the stories that black learners tell about themselves and to the voices of mathematics educators whose work has demonstrated a commitment to the success of these children. Danny Bernard Martin is Chair of the Department of Curriculum and Instruction in the College of Education and Associate Professor of Mathematics at the University of Illinois at Chicago. Preface Acknowledgements Section I: Mapping A Liberatory Research and Policy Agenda 1. Liberating the Production of Knowledge About African American Children and Mathematics, Danny Bernard Martin Section II: Pedagogy, Standards, and Assessment 2. Researching African American Mathematics Teachers of African American Students: Conceptual and Methodological Considerations, Lawrence M. Clark, Whitney Johnson & Daniel Chazan 3. \"This Little Light of Mine!\" Entering Voices of Cultural Relevancy into the Mathematics Teaching Conversation, Lou Edward Matthews 4. Instructional Strategies and Dispositions of Teachers Who Help African American Students Gain Conceptual Understanding, Carol E. Malloy 5. Contrasting Pedagogical Styles and Their Impact on African American Students, Robert Q. Berry III & Oren L. McClain 6. More than Test Scores: How Teachers’ Classroom Practice Contributes to and What Student Work Reveals about Black Students’ Mathematics Performance and Understanding, Erica N. Walker Section III: Socialization, Learning, and Identity 7. The Social Construction of Youth and Mathematics: The Case of a Fifth-Grade Classroom, Kara J. Jackson 8. Identity at the Crossroads: Understanding the Practices and Forces that Shape African American Success and Struggle in Mathematics, Joi A. Spencer 9. Wrestling with the Legacy of Stereotypes: Being African American in Math Class, Na’ilah Suad Nasir, Grace Atukpawu, Kathleen O’Connor, Michael Davis, Sarah Wischnia & Jessica Tsang 10. Opportunities to Learn Geometry: Listening to the Voices of Three African American Students High School Students, Marilyn E. Strutchens & S. Kathy Westbrook 11. Negotiating Sociocultural Discourses: The Counter-Storytelling of Academically and Mathematically Successful African American Male Students, David W. Stinson 12. \"Come Home, Then\": Two Eighth-Grade Black Female Students’ Reflections on their Mathematics Experiences, Yolanda A. Johnson 13. \"Still Not Saved\": The Power of Mathematics to Liberate the Oppressed, Jacqueline Leonard Section IV: Collaboration and Reform 14. University/K-12 Partnerships: A Collaborative Approach to School Reform, Martin L. Johnson & Stephanie Timmons Brown Contributors Index \"I have a deep appreciation for this volume and understand its value for the educational enterprise in general and Black children in particular. Even those who do not share my same sentiments will find this book to be insightful, informative, and thought provoking concerning the mathematics teaching and learning of Black children.\"-- Christopher C. Jett, Journal of Urban Mathematics Education \"This book is a rich resource for anyone who is concerned about mathematics learning of black and other minority children. Recommended for university libraries and researchers involved in mathematics education of minority children...Recommended.\"-- CHOICE \"This is a book that I heartily recommend to anyone who cares about equity and tackling racism, and it is a book that appropriately and refreshingly put teaching at its center. The book presents many different forms of research and writing, colorful and engaging accounts, insightful and chilling accounts of racism, and powerful new lenses and theories to consider the issues. This may be the first collection of its kind in mathematics education that brings different authors together to focus exclusively on African American children.\"-- Teachers College Record

This book considers the so-called Unlikely Intersections, a topic that embraces well-known issues, such as Lang's and Manin-Mumford's, concerning torsion points in subvarieties of tori or abelian varieties. More generally, the book considers algebraic subgroups that meet a given subvariety in a set ofunlikelydimension. The book is an expansion of the Hermann Weyl Lectures delivered by Umberto Zannier at the Institute for Advanced Study in Princeton in May 2010. The book consists of four chapters and seven brief appendixes, the last six by David Masser. The first chapter considers multiplicative algebraic groups, presenting proofs of several developments, ranging from the origins to recent results, and discussing many applications and relations with other contexts. The second chapter considers an analogue in arithmetic and several applications of this. The third chapter introduces a new method for approaching some of these questions, and presents a detailed application of this (by Masser and the author) to a relative case of the Manin-Mumford issue. The fourth chapter focuses on the André-Oort conjecture (outlining work by Pila).

Swarm intelligence algorithms are a form of nature-based optimization algorithms. Their main inspiration is the cooperative behavior of animals within specific communities. This can be described as simple behaviors of individuals along with the mechanisms for sharing knowledge between them, resulting in the complex behavior of the entire community. Examples of such behavior can be found in ant colonies, bee swarms, schools of fish or bird flocks. Swarm intelligence algorithms are used to solve difficult optimization problems for which there are no exact solving methods or the use of such methods is impossible, e.g. due to unacceptable computational time. This set comprises two volumes: Swarm Intelligence Algorithms: A Tutorial and Swarm Intelligence Algorithms: Modifications and Applications. The first volume thoroughly presents the basics of 24 algorithms selected from the entire family of swarm intelligence algorithms. It contains a detailed explanation of how each algorithm works, along with relevant program codes in Matlab and the C ++ programming language, as well as numerical examples illustrating step-by-step how individual algorithms work. The second volume describes selected modifications of these algorithms and presents their practical applications. This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. It contains a short description along with a pseudo-code showing the various stages of its operation. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem.

This chapter presents a nature-inspired ant colony optimization (ACO) technique, along with its modified variants. The improved versions of this optimization technique are slightly different and effective than that of its standard version. ACO has inspired from the foraging behavior of ant colony and its capability to seek the shortest path between their nest and food source. This optimization method is based on a natural phenomena known as pheromone trails, a substance laid down by ants especially when carrying food so that their fellow ants can sense and follow this path. The new ants entering into the ant system will follow the path with highest pheromone concentration. In this chapter, a brief overview of standard ACO is presented, followed by different variants of ACO since its development. The application of the ACO technique for real-life optimization problems is demonstrated by solving an optimal shunt capacitor allocation problem of 33-bus test distribution system for power loss minimization.

The Second-Order Adjoint Sensitivity Analysis Methodology generalizes the First-Order Theory presented in the author’s previous books published by CRC Press. This breakthrough has many applications in sensitivity and uncertainty analysis, optimization, data assimilation, model calibration, and reducing uncertainties in model predictions. The book has many illustrative examples that will help readers understand the complexity of the subject and will enable them to apply this methodology to problems in their own fields. Highlights: • Covers a wide range of needs, from graduate students to advanced researchers • Provides a text positioned to be the primary reference for high-order sensitivity and uncertainty analysis • Applies to all fields involving numerical modeling, optimization, quantification of sensitivities in direct and inverse problems in the presence of uncertainties. About the Author: Dan Gabriel Cacuci is a South Carolina SmartState Endowed Chair Professor and the Director of the Center for Nuclear Science and Energy, Department of Mechanical Engineering at the University of South Carolina. He has a Ph.D. in Applied Physics, Mechanical and Nuclear Engineering from Columbia University. He is also the recipient of many awards including four honorary doctorates, the Ernest Orlando Lawrence Memorial award from the U.S. Dept. of Energy and the Arthur Holly Compton, Eugene P. Wigner and the Glenn Seaborg Awards from the American Nuclear Society. MOTIVATION FOR COMPUTING FIRST- AND SECOND-ORDER SENSITIVITIES OF SYSTEM RESPONSES TO THE SYSTEM’S PARAMETERS The Fundamental Role of Response Sensitivities for Uncertainty Quantification The Fundamental Role of Response Sensitivities for Predictive Modeling Advantages and Disadvantages of Statistical and Deterministic Methods for Computing Response Sensitivities ILLUSTRATIVE APPLICATION OF THE SECOND-ORDER ADJOINT SENSITIVITY ANALYSIS METHODOLOGY (2 nd -ASAM) TO A LINEAR EVOLUTION PROBLEM Exact Computation of the 1 st -Order Response Sensitivities Exact Computation of the 2 nd -Order Response Sensitivities Computing the 2 nd -Order Response Sensitivities Corresponding to the 1 st -Order Sensitivities Discussion of the Essential Features of the 2 nd -ASAM Illustrative Use of Response Sensitivities for Predictive Modeling THE SECOND-ORDER ADJOINT SENSITIVITY ANALYSIS METHODOLOGY (2 nd -ASAM) FOR LINEAR SYSTEMS Mathematical Modeling of a General Linear System The 1 st -Level Adjoint Sensitivity System (1 st -LASS) for Computing Exactly and Efficiently 1 st -Order Sensitivities of Scalar-Valued Responses for Linear Systems The 2 nd -Level Adjoint Sensitivity System (2 nd -LASS) for Computing Exactly and Efficiently 1 st -Order Sensitivities of Scalar-Valued Responses for Linear Systems APPLICATION OF THE 2 nd -ASAM TO A LINEAR HEAT CONDUCTION AND CONVECTION BENCHMARK PROBLEM Heat Transport Benchmark Problem: Mathematical Modeling Computation of First-Order Sensitivities Using the 2 nd -ASAM Computation of first-order sensitivities of the heated rod temperature Computation of first-order sensitivities of the coolant temperature Verification of the \"ANSYS/FLUENT Adjoint Solver\" Applying the 2 nd -ASAM to Compute the Second-Order Sensitivities and Uncertainties for the Heat Transport Benchmark Problem APPLICATION OF THE 2 nd -ASAM TO A LINEAR PARTICLE DIFFUSION PROBLEM Paradigm Diffusion Problem Description Applying the 2 nd -ASAM to Compute the First-Order Response Sensitivities to Model Parameters Applying the 2 nd -ASAM to Compute the Second-Order Response Sensitivities to Model Parameters Role of Second-Order Response Sensitivities for Quantifying Non-Gaussian Features of the Response Uncertainty Distribution Illustrative Application of First-Order Response Sensitivities for Predictive Modeling APPLICATION OF THE 2 nd -ASAM FOR COMPUTING SENSITIVITIES OF DETECTOR RESPONSES TO UNCOLLIDED RADIATION TRANSPORT The Ray-Tracing Form of the Forward and Adjoint Boltzmann Transport Equation Application of the 2 nd -ASAM to Compute the First-Order Response Sensitivities to Variations in Model Parameters Application of the 2 nd -ASAM to Compute the Second-Order Response Sensitivities to Variations in Model Parameters THE SECOND-ORDER ADJOINT SENSITIVITY ANALYSIS METHODOLOGY (2 nd -ASAM) FOR NONLINEAR SYSTEMS Mathematical Modeling of a General Nonlinear System The 1 st -Level Adjoint Sensitivity System (1 st -LASS) for Computing Exactly and Efficiently the 1 st -Order Sensitivities of Scalar-Valued Responses The 2 nd -Level Adjoint Sensitivity System (2nd-LASS) for Computing Exactly and Efficiently the 2 nd -Order Sensitivities of Scalar-Valued Responses for Nonlinear Systems APPLICATION OF THE 2 nd -ASAM TO A NONLINEAR HEAT CONDUCTION PROBLEM Mathematical Modeling of Heated Cylindrical Test Section Application of the 2 nd -ASAM for Computing the 1st-Order Sensitivities Application of the 2 nd -ASAM for Computing the 2nd-Order Sensitivities Dan Gabriel Cacuci is a South Carolina SmartState Endowed Chair Professor and the Director of the Center for Nuclear Science and Energy, Department of Mechanical Engineering at the University of South Carolina. He has a Ph.D. in Applied Physics, Mechanical and Nuclear Engineering from Columbia University. He is also the recipient of many awards including four honorary doctorates, the Ernest Orlando Lawrence Memorial award from the U.S. Dept. of Energy and the Arthur Holly Compton, Eugene P. Wigner and the Glenn Seaborg Awards from the American Nuclear Society.

Zero indicates the absence of a quantity or a magnitude.It is so deeply rooted in our psyche today that nobody will possibly ask \"What is zero?\" From the beginning of the very creation of life, the feeling of lack of something or the vision of emptiness/void has been embedded by the creator in all living beings.

Fundamentals of Technical Mathematics introduces key, applied mathematics for engineering technologists and technicians.Through a simple, engaging approach, the book reviews basic mathematics, including whole numbers, fractions, mixed numbers, decimals, percentages, ratios, and proportions.The book covers conversions to different units of measure.

The notion of swarm intelligence was introduced for describing decentralized and self-organized behaviors of groups of animals. Then this idea was extrapolated to design groups of robots which interact locally to cumulate a collective reaction. Some natural examples of swarms are as follows: ant colonies, bee colonies, fish schooling, bird flocking, horse herding, bacterial colonies, multinucleated giant amoebae Physarum polycephalum, etc. In all these examples, individual agents behave locally with an emergence of their common effect. An intelligent behavior of swarm individuals is explained by the following biological reactions to attractants and repellents. Attractants are biologically active things, such as food pieces or sex pheromones, which attract individuals of swarm. Repellents are biologically active things, such as predators, which repel individuals of swarm. As a consequence, attractants and repellents stimulate the directed movement of swarms towards and away from the stimulus, respectively. It is worth noting that a group of people, such as pedestrians, follow some swarm patterns of flocking or schooling. For instance, humans prefer to avoid a person considered by them as a possible predator and if a substantial part of the group in the situation of escape panic (not less than 5%) changes the direction, then the rest follows the new direction, too. Some swarm patterns are observed among human beings under the conditions of their addictive behavior such as the behavior of alcoholics or gamers. The methodological framework of studying swarm intelligence is represented by unconventional computing, robotics, and cognitive science. In this book we aim to analyze new methodologies involved in studying swarm intelligence. We are going to bring together computer scientists and cognitive scientists dealing with swarm patterns from social bacteria to human beings. This book considers different models of simulating, controlling, and predicting the swarm behavior of different species from social bacteria to humans. The book analyzes new methodologies involved in studying swarm intelligence. It brings together computer scientists and cognitive scientists dealing with swarm patterns from social bacteria to human beings. Topics include swarm computing, soldier crabs computing, social insects computing, ad hoc and sensor wireless network, bio-molecular computing.