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Smooth Hand Offs is for every parent, grandparent, church leader, teenager, and other individual who wants to leave a legacy of faith for the generations that follow. A free online study guide is available at http://alturl.com/m44ty.

Lose weight and feel great with juicing and smoothies For those of us who don't have time to cut up or cook fruits and vegetables with every meal, juices and smoothies are a fast and easy way to consume them at home or on the go. Packed with over 100 recipes, Juicing & Smoothies For Dummies covers the most up-to-date information on incorporating this healthy lifestyle into your everyday routine. From how to safely cleanse the body of toxins to the hottest ingredients to bolster juices and smoothies—including chia seeds, coconut oil, hemp seeds, bee pollen, and more—it arms you with everything you need to sip your way to a healthier, happier you. There are many health benefits to drinking freshly juiced fruits and vegetables. These tasty and nutrition-packed beverages can help protect you against cardiovascular disease, cancer, cellular damage, and various inflammatory diseases, such as rheumatoid arthritis. Plus, it's great for weight loss because juices and smoothies have hunger-reducing properties, on top of being filling. In this friendly and accessible guide, you'll find expert guidance on how to use juices and smoothies to reap all of these excellent rewards, while getting the recommended daily amount of fruits and vegetables—in a glass! Concoct more than 120 juicing and smoothie recipes using the hottest, most nutritious ingredients Find the best juicers and blenders for the job Ward off colds and migraines, promote longevity, and shed pounds Get a month's worth of grocery items to have on hand to make healthy juices and smoothies in minutes Whether you want to lose weight, cleanse, or simply add more healthy fruits and veggies to your diet, Juicing & Smoothies For Dummies makes it easy.

Sip your way to ultimate nutrition and feel better than ever Whether you're looking to detox, lose weight, or just add more veggies to your diet, green smoothies are the way to go. Easy to prepare, portable, and endlessly customizable, green smoothies are the trendy new beverage in everyone's cup. Think you don't like kale, collard greens, or watercress? Try them in a smoothie and you'll never see them the same way again. Green smoothies are the easiest, most painless way to add more nutrients to your diet, so you can feel better than ever before. Green Smoothies For Dummies is your beginner's guide to the world of drinkable greens. Author and international smoothie guru Jennifer Thompson explains the benefits of green smoothies, and provides over 90 recipes that will make you start craving your vegetables. You'll get to know the flavors and properties of each ingredient, and how to combine ingredients for complete nutrition. Replace meals with green smoothies without sacrificing nutrients Boost your nutrition even higher with protein and fiber supplements Reduce hunger and feel full longer with the right smoothie blends Customize your smoothies to your personal nutritional needs Before too long, you'll be experimenting and coming up with your own favorite combinations. Your vegetable intake will skyrocket, and you'll look and feel fantastic. How often does something so good for you taste so delicious? Green smoothies help you fill the nutrient gaps in your diet so you can experience optimal health and well-being. Green Smoothies For Dummies is your guide to all things smoothie, and will get you started now.

Comprehensive theoretical overview of kernel smoothing methods with motivating examples Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of contexts, considering independent and correlated data e.g. with short-memory and long-memory correlations, as well as non-Gaussian data that are transformations of latent Gaussian processes. These types of data occur in many fields of research, e.g. the natural and the environmental sciences, and others. Nonparametric density estimation, nonparametric and semiparametric regression, trend and surface estimation in particular for time series and spatial data and other topics such as rapid change points, robustness etc. are introduced alongside a study of their theoretical properties and optimality issues, such as consistency and bandwidth selection. Addressing a variety of topics, Kernel Smoothing: Principles, Methods and Applications offers a user-friendly presentation of the mathematical content so that the reader can directly implement the formulas using any appropriate software. The overall aim of the book is to describe the methods and their theoretical backgrounds, while maintaining an analytically simple approach and including motivating examples—making it extremely useful in many sciences such as geophysics, climate research, forestry, ecology, and other natural and life sciences, as well as in finance, sociology, and engineering. A simple and analytical description of kernel smoothing methods in various contexts Presents the basics as well as new developments Includes simulated and real data examples Kernel Smoothing: Principles, Methods and Applications is a textbook for senior undergraduate and graduate students in statistics, as well as a reference book for applied statisticians and advanced researchers.

The edition introduces a new class of invariant derivatives and shows their relationships with other derivatives, such as the Sobolev generalized derivative and the generalized derivative of the distribution theory. This is a new direction in mathematics. i-Smooth analysis is the branch of functional analysis that considers the theory and applications of the invariant derivatives of functions and functionals. The important direction of i-smooth analysis is the investigation of the relation of invariant derivatives with the Sobolev generalized derivative and the generalized derivative of distribution theory. Until now, i-smooth analysis has been developed mainly to apply to the theory of functional differential equations, and the goal of this book is to present i-smooth analysis as a branch of functional analysis. The notion of the invariant derivative (i-derivative) of nonlinear functionals has been introduced in mathematics, and this in turn developed the corresponding i-smooth calculus of functionals and showed that for linear continuous functionals the invariant derivative coincides with the generalized derivative of the distribution theory. This book intends to introduce this theory to the general mathematics, engineering, and physicist communities.

This book contains theoretical and application-oriented methods to treat models of dynamical systems involving non-smooth nonlinearities. The theoretical approach that has been retained and underlined in this work is associated with differential inclusions of mainly finite dimensional dynamical systems and the introduction of maximal monotone operators (graphs) in order to describe models of impact or friction. The authors of this book master the mathematical, numerical and modeling tools in a particular way so that they can propose all aspects of the approach, in both a deterministic and stochastic context, in order to describe real stresses exerted on physical systems. Such tools are very powerful for providing reference numerical approximations of the models. Such an approach is still not very popular nevertheless, even though it could be very useful for many models of numerous fields (e.g. mechanics, vibrations, etc.). This book is especially suited for people both in research and industry interested in the modeling and numerical simulation of discrete mechanical systems with friction or impact phenomena occurring in the presence of classical (linear elastic) or non-classical constitutive laws (delay, memory effects, etc.). It aims to close the gap between highly specialized mathematical literature and engineering applications, as well as to also give tools in the framework of non-smooth stochastic differential systems: thus, applications involving stochastic excitations (earthquakes, road surfaces, wind models etc.) are considered. Contents 1. Some Simple Examples. 2. Theoretical Deterministic Context. 3. Stochastic Theoretical Context. 4. Riemannian Theoretical Context. 5. Systems with Friction. 6. Impact Systems. 7. Applications–Extensions. About the Authors Jerome Bastien is Assistant Professor at the University Lyon 1 (Centre de recherche et d'Innovation sur le sport) in France. Frederic Bernardin is a Research Engineer at Departement Laboratoire de Clermont-Ferrand (DLCF), Centre d'Etudes Techniques de l'Equipement (CETE), Lyon, France. Claude-Henri Lamarque is Head of Laboratoire Geomateriaux et Genie Civil (LGCB) and Professor at Ecole des Travaux Publics de l'Etat (ENTPE), Vaulx-en-Velin, France.