A Whole Foods Primer Volume 1 Of 2 Easyread Super Large 24pt Edition

A Whole Foods Primer demonstrates the wisdom of selecting whole foods for optimal intake of the nutrients essential for good health. In this book, readers will learn about the basic nutritional components of each whole food group and their specific functions and beneficial roles in the body. The text presents nutrient profiles and surprising facts about some hundred whole foods, their historical uses as food and medicine, and the latest research that identifies their health-bestowing qualities.

In its second, extensively revised second edition, Semiconducting Silicon Nanowires for Biomedical Applications reviews the fabrication, properties, and biomedical applications of this key material. The book begins by reviewing the basics of growth, characterization, biocompatibility, and surface modification of semiconducting silicon nanowires. Attention then turns to use of these structures for tissue engineering and delivery applications, followed by detection and sensing. Reflecting the evolution of this multidisciplinary subject, several new key topics are highlighted, including our understanding of the cell-nanowire interface, latest advances in associated morphologies (including silicon nanoneedles and nanotubes for therapeutic delivery), and significantly, the status of silicon nanowire commercialization in biotechnology. Semiconducting Silicon Nanowires for Biomedical Applications is a comprehensive resource for biomaterials scientists who are focused on biosensors, drug delivery, and the next generation of nano-biotech platforms that require a detailed understanding of the cell-nanowire interface, along with researchers and developers in industry and academia who are concerned with nanoscale biomaterials, in particular electronically-responsive structures. Reviews the growth, characterization, biocompatibility, and surface modification of semiconducting silicon nanowires Describes silicon nanowires for tissue engineering and delivery applications, including cellular binding & internalization, tissue engineering scaffolds, mediated differentiation of stem cells, and silicon nanoneedles & nanotubes for delivery of small molecule / biologic-based therapeutics Highlights the use of silicon nanowires for detection and sensing Presents a detailed description of our current understanding of the cell-nanowire interface Covers the current status of commercial development of silicon nanowire-based platforms

More Than Sixty Course Syllabi That Bring the New Complexity of Graphic Design to Light All graphic designers teach, yet not all graphic designers are teachers. Teaching is a special skill requiring talent, instinct, passion, and organization. But while talent, instinct, and passion are inherent, organization must be acquired and can usually be found in a syllabus. Teaching Graphic Design, Second Edition, contains syllabi that are for all practicing designers and design educators who want to enhance their teaching skills and learn how experienced instructors and professors teach varied tools and impart the knowledge needed to be a designer in the current environment. This second edition is newly revised to include more than thirty new syllabi by a wide range of professional teachers and teaching professionals who address the most current concerns of the graphic design industry, including product, strategic, entrepreneurial, and data design as well as the classic image, type, and layout disciplines. Some of the new syllabi included are: Expressive Typography Designer as Image Maker Emerging Media Production Branding Corporate Design Graphic Design and Visual Culture Impact! Design for Social Change And many more Beginning with first through fourth year of undergraduate courses and ending with a sampling of graduate school course options, Teaching Graphic Design, Second Edition, is the most comprehensive collection of courses for graphic designers of all levels.

The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

Molecular imprinting is a rapidly growing field with wide-ranging applications, especially in the area of sensor development, where the process leads to improved sensitivity, reliability, stability, and reproducibility in sensing materials. Molecularly Imprinted Sensors in Analytical Chemistry addresses the most recent advances and challenges relating to molecularly imprinted polymer sensors, and is the only book to compile this information in a single source. From fundamentals to applications, this material will be valuable to researchers working in sensing technologies for pharmaceutical separation and chemical analysis, environmental monitoring and protection, defense and security, and healthcare. Provides a systematic introduction to the different types of MIP-based sensors and reviews the basic principles behind each type of sensor Includes state-of-the-art methodology supported by comparisons and discussions from leading experts in the field Covers all types of sensing modes (optical, electrochemical, thermal, acoustic, etc.), materials and platforms Appeals to a multidisciplinary audience of scientists and graduate students in a wide variety of fields, including chemistry, biology, biomedical science and engineering, and materials science and engineering

Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.