The book has undergone three editions, several reprints, and revised reprints in a span of 13 years. There are many biochemistry textbooks in the market. Some of them are purely basic while others are applied, and there are very few books which cover both these aspects together. For this reason, the students learning biochemistry in their undergraduate courses have to depend on multiple books to acquire a sound knowledge of the subject. This book, ‘Biochemistry’ is unique with a simultaneous and equal emphasis on basic and applied aspects of biochemistry. This textbook offers an integration of medical and pure sciences, comprehensively written to meet the curriculum requirements of undergraduate courses in medical, dental, pharmacy, life-sciences and other categories (agriculture, veterinary, etc.). This book is designed to develop in students a sustained interest and enthusiasm to learn and develop the concepts in biochemistry in a logical and stepwise manner. It incorporates a variety of pedagogic aids, besides colour illustrations to help the students understand the subject quickly and to the maximum. The summary and biomedical/clinical concepts are intended for a rapid absorption and assimilation of the facts and concepts in biochemistry. The self-assessment exercises will stimulate the students to think rather than merely learn the subject. In addition, these exercises (essays, short notes, fill in the blanks, multiple choice questions) set at different difficulty levels, will cater to the needs of all the categories of learners.New to This Edition The book offers an integration of medical and pure sciences, and is comprehensively written, revised and updated to meet the curriculum requirements of Medical, Pharmacy, Dental, Veterinary, Biotechnology, Agricultural Sciences, Life Sciences, and others studying Biochemistry as one of the subjects. It is the first text book on Biochemistry in English with multi-colour illustrations by an author from Asia. The use of multicolours is for a clearer understanding of the complicated biochemical reactions. It is written in a lucid style with the subject being presented as an engaging story growing from elementary information to the most recent advances, and with theoretical discussions being supplemented with illustrations, flowcharts, and tables for easy understanding of Biochemistry. It has each chapter beginning with a four-line verse followed by the text, biomedical concepts, a summary, and self-assessment exercises. The lively illustrations and text with appropriate headings and sub-headings in bold type faces facilitate reading path clarity and quick recall. It provides the most recent and essential information on Molecular Biology and Biotechnology, Diabetes, Cancer, Free Radicals, Free radicals and Antioxidants, Prostaglandins, etc. It describes a wide variety of case studies and biochemical correlations and several newer biomedical aspects- Metabolic syndrome, Therapeutic diets, Atkins diet, Trans fatty acids, Epigenetics, Nutrigenomics, Recombinant ribozymes, Membrane transport disorders, Pleural fluid etc. It contains the basics (Bioorganic and Biophysical Chemistry, Tools of Biochemistry, Immunology, and Genetics) for beginners to learn easily Biochemistry, origins of biochemical words, confusables in Biochemistry, principles of Practical Biochemistry, and Clinical Biochemistry Laboratory.

    

    Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

    This text provides a simple and straightforward introduction to econometrics for the beginner. The book is designed to help students understand econometric techniques through extensive examples, careful explanations, and a wide variety of problem material. In each of the editions, I have tried to incorporate major developments in the field in an intuitive and informative way without resort to matrix algebra, calculus, or statistics beyond the introductory level. The fourth edition continues that tradition.

    Provides a review of introductory noncalculus-based physics for those who do not have a strong background in mathematics.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    This supplement will cater to the requirements of students by covering all important topics of Laplace transformation, Matrices, Numerical Methods. Further enhanced is its usability by inclusion of chapter end questions in sync with student needs. Table of contents: 1. Basic Concepts 2. An Introduction to Modeling and Qualitative Methods 3. Classification of First-Order Differential Equations 4. Separable First-Order Differential Equations 5. Exact First-order Differential Equations 6. Linear First-Order Differential Equations 7. Applications of First-Order Differential Equations 8. Linear Differential Equations: Theory of Solutions 9. Second-Order Linear Homogeneous Differential Equations with Constant Coefficients 10. nth-Order Linear Homogeneous Differential Equations with Constant Coefficients 11. The Method of Undetermined Coefficients 12. Variation of Parameters 13. Initial-Value Problems for Linear Differential Equations 14. Applications of Second-Order Linear Differential Equations 15. Matrices 16. eAt 17. Reduction of Linear Differential Equations to a System of First-Order Equations 18. Existence and Uniqueness of Solutions 19. Graphical and Numerical Methods for Solving First-Order Differential Equations 20. Further Numerical Methods for Solving First-Order Differential Equations 21. Numerical Methods for Solving Second-Order Differential Equations Via Systems 22. The Laplace Transform 23. Inverse Laplace Transforms 24. Convolutions and the Unit Step Function 25. Solutions of Linear Differential Equations with Constant Coefficients by Laplace Transforms 26. Solutions of Linear?Systems by Laplace Transforms 27. Solutions of Linear Differential Equations with Constant Coefficients by Matrix Methods 28. Power Series Solutions of Linear Differential Equations with Variable Coefficients 29. Special Functions 30. Series Solutions N

    He proposes his solution.

     99.99% of the world’s mysteries are yet to be discovered and/or solved. Why not… It’s time for you to rediscover science? One of the most compelling draws of the sciences for many people is the potential of discovering something that was not known before. Whether someone’s doing it for fame, for fortune, or just for the fun of it, discovering something new, leaving your own personal mark for the rest of humanity’s time in the universe, is a tempting prospect for many. How would you feel about naming a star, and for others to know that you named it? That star would be visible in the sky for the rest of your lifetime, and more than likely for your great-great-great-grandchildren’s lifetimes. Your discovery would be immortalized above for the life of the star. Inside this book you will discover: -String theory and how it came about -Black holes and quantum gravity -If Schrödinger’s Cat is really a cat? -Disagreements between Einstein and Bohr -The double slit experiment Attention! Quantum Physics is NOT for everyone! This book is not for people: -Who doesn’t want to impress their girl with science -Who are not curious about the universe -Who isn’t inspired to name their own science theory If you are ready to learn about quantum physics, Scroll Up And Click On The “BUY NOW” Button Now!

    Offering time-tested problems, conceptual and visual pedagogy, and a state-of-the-art media package, this 11th edition looks to the future of university physics, in terms of both content and approach.

    He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the "prediction paradox": The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good-or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary-and dangerous-science.Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.

    In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

     Offers extensively updated coverage, new problem sets, and chapter-ending material to enhance the book’s relevance to today’s engineers and scientists. Includes new problem sets demonstrating updated applications to engineering as well as biological, physical, and computer science. Emphasizes key ideas as well as the risks and hazards associated with practical application of the material. Includes new material on topics including: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; “dummy” variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting. Introduces Bayesian statistics, including its applications to many fields. For those interested in learning more about probability and statistics.

    New features include an enhanced reader-friendly approach, new color illustrations, and a greater emphasis on clinical applications. A new, interactive CD-ROM offers three-dimensional animations of masticatory movements - including tooth contact relationships and temporomandibular joint movements - that bring the text's illustrations to life. Plus, an interactive mock examination mimics the National Board exam for outstanding review and practice.