Francesco Ricci – författare

Enables you to easily advance from thermodynamics principles to applications Thermodynamics for the Practicing Engineer, as the title suggests, is written for all practicing engineers and anyone studying to become one. Its focus therefore is on applications of thermodynamics, addressing both technical and pragmatic problems in the field. Readers are provided a solid base in thermodynamics theory; however, the text is mostly dedicated to demonstrating how theory is applied to solve real-world problems.This text's four parts enable readers to easily gain a foundation in basic principles and then learn how to apply them in practice: Part One: Introduction. Sets forth the basic principles of thermodynamics, reviewing such topics as units and dimensions, conservation laws, gas laws, and the second law of thermodynamics.Part Two: Enthalpy Effects. Examines sensible, latent, chemical reaction, and mixing enthalpy effects. Part Three: Equilibrium Thermodynamics. Addresses both principles and calculations for phase, vapor-liquid, and chemical reaction equilibrium. Part Four: Other Topics. Reviews such important issues as economics, numerical methods, open-ended problems, environmental concerns, health and safety management, ethics, and exergy. Throughout the text, detailed illustrative examples demonstrate how all the principles, procedures, and equations are put into practice. Additional practice problems enable readers to solve real-world problems similar to the ones that they will encounter on the job.Readers will gain a solid working knowledge of thermodynamics principles and applications upon successful completion of this text. Moreover, they will be better prepared when approaching/addressing advanced material and more complex problems.

An invaluable guide for problem solving in mass transfer operationsThis book takes a highly pragmatic approach to providing the principles and applications of mass transfer operations by offering a valuable, easily accessible guide to solving engineering problems. Both traditional and novel mass transfer processes receive treatment. As with all of the books in this series, emphasis is placed on an example-based approach to illustrating key engineering concepts.The book is divided into two major parts. It starts with the principles underlying engineering problems—showing readers how to apply general engineering principles to the topic of mass transfer operations. It then goes on to provide step-by-step guidance for traditional mass transfer operations, including distillation, absorption and stripping, and adsorption, plus novel mass transfer processes. Essential topics for professional engineering exams are also covered.Geared towards chemical, environmental, civil, and mechanical engineers working on real-world industrial applications, Mass Transfer Operations for the Practicing Engineer features: Numerous sample problems and solutions with real-world applications Clear, precise explanations on how to carry out the basic calculations associated with mass transfer operations Coverage of topics from the ground up for readers without prior knowledge of the subject Overview of topics relevant to the ABET (Accreditation Board for Engineering and Technology) for those taking the Professional Engineering (PE) exams Appendix containing relevant mass transfer operation charts and tables

This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender  systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool.

This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender  systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool.