This title investigates the trends and challenges that ports, logistics and supply chains have tackled in recent decades and the way forward. A new concept, port focal logistics is introduced which appreciates the efforts by previous studies in this field, but simultaneously recognize the limitations, and the need for further improvements.

Tough Test Questions? Missed Lectures? Not Enough Time? Textbook too pricey? Fortunately, there's Schaum's. This all-in-one-package includes more than 2,400 fully solved problems, examples, and practice exercises to sharpen your problem-solving skills. Plus, you will have access to the revised online Schaum's.com website-- it's just like having your own virtual tutor! You'll find everything you need to build confidence, skills, and knowledge for the highest score possible. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. Helpful tables and illustrations increase your understanding of the subject at hand. Schaum's Outline of Mathematical Handbook of Formulas and Tables, Fifth Edition features: * More than 2,400 formulas and tables * Clear explanations for all mathematical formulas and procedures * Formulas and tables for elementary to advanced topics * A complete index to all topics * Access to revised Schaums.com website

A collection of standard and cutting-edge techniques for using Xenopus oocytes and oocytes/egg extracts to reconstitute biological and cellular processes. These readily reproducible methods take advantage of the oocyte's impressive protein abundance, its striking protein translation capacity, and its breathtaking possibilities for the assembly of infectious viral particles by single cell injection of multiple RNAs. The authors focus on the versatility of frog oocytes and egg extracts in cell biology and signal transduction, and cover all the major uses of oocytes/extracts as experimental models.

The enterprise-focused framework of supply chain, which an overwhelming majority of books on supply chain management (SCM) have adopted, falls short in explaining recent developments in the real world, especially the so-called Wal-Mart model, in which a 'factory' is a virtual logistics network of multiple international manufacturing firms. The book fills the gap and examines supply chain and transport logistics. The success of the Wal-Mart model rests on dynamic innovations in two key dimensions, namely, all-mode logistics service facilitation and industrial organization of supply chains, on which existing SCM textbooks have little coverage. For example, managing transport utility and facility, such as seaports and airports, has become expected parts of logistics and SCM, especially in an international orientation; which, however, are seldom covered in the textbooks on SCM and logistics. Supply chain and transport logistics as termed in this book is precisely based on this intriguing interrelationship, referring to supply-chain centered logistics of enterprise-crossing characteristics, including both service facilitation and industrial organization (IO) aspects of logistics. This book also includes the development of a unified methodological framework which underpins all the characteristics of the intriguing interrelationship between supply chain management and logistics. It covers many aspects of the important and innovative developments well. The book offers a unique coverage of integrated logistics of navigation, aviation and transportation. The book not only answers the urgent need for a book on supply chain management and transport logistics but also highlights the central role of supply chain logistics in the emerging fields of sustainable (green), humanitarian and maritime supply chains and the importance of studying supply chain management together with transport logistics. It also explains the difference between supply chain

This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMU This book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning. --Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU This is a wonderful book! I'm pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I've seen that has up-to-date and well-rounded coverage. Thank you to the authors! --Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level. Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist. Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

This book investigates the trends and challenges that ports, logistics and supply chains have tackled in recent decades and the way forward. A new concept, port focal logistics is introduced which appreciates the efforts by previous studies in this field, but simultaneously recognize the limitations, and the need for further improvements.

A collection of standard and cutting-edge techniques for using Xenopus oocytes and oocytes/egg extracts to reconstitute biological and cellular processes. These readily reproducible methods take advantage of the oocyte's impressive protein abundance, its striking protein translation capacity, and its breathtaking possibilities for the assembly of infectious viral particles by single cell injection of multiple RNAs. The authors focus on the versatility of frog oocytes and egg extracts in cell biology and signal transduction, and cover all the major uses of oocytes/extracts as experimental models.

The enterprise-focused framework of supply chain, which an overwhelming majority of books on supply chain management (SCM) have adopted, falls short in explaining recent developments in the real world, especially the so-called Wal-Mart model, in which a 'factory' is a virtual logistics network of multiple international manufacturing firms. The book fills the gap and examines supply chain and transport logistics. The success of the Wal-Mart model rests on dynamic innovations in two key dimensions, namely, all-mode logistics service facilitation and industrial organization of supply chains, on which existing SCM textbooks have little coverage. For example, managing transport utility and facility, such as seaports and airports, has become expected parts of logistics and SCM, especially in an international orientation; which, however, are seldom covered in the textbooks on SCM and logistics. Supply chain and transport logistics as termed in this book is precisely based on this intriguing interrelationship, referring to supply-chain centered logistics of enterprise-crossing characteristics, including both service facilitation and industrial organization (IO) aspects of logistics. This book also includes the development of a unified methodological framework which underpins all the characteristics of the intriguing interrelationship between supply chain management and logistics. It covers many aspects of the important and innovative developments well. The book offers a unique coverage of integrated logistics of navigation, aviation and transportation. The book not only answers the urgent need for a book on supply chain management and transport logistics but also highlights the central role of supply chain logistics in the emerging fields of sustainable (green), humanitarian and maritime supply chains and the importance of studying supply chain management together with transport logistics. It also explains the difference between supply chain

This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMU This book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning. --Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU This is a wonderful book! I'm pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I've seen that has up-to-date and well-rounded coverage. Thank you to the authors! --Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level. Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist. Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.