Container terminals are constantly being challenged to adjust their throughput capacity to match fluctuating demand. Examining the optimization problems encountered in today’s container terminals, Port Automation and Vehicle Scheduling: Advanced Algorithms for Scheduling Problems of AGVs, Third Edition provides advanced algorithms for handling the scheduling of Automated Guided Vehicles (AGVs) in ports. Building on the earlier editions, previously titled Vehicle Scheduling in Port Automation: Advanced Algorithms for Minimum Cost Flow Problems, this book has undergone extensive revisions and includes two new chapters. New material addresses the solutions to the modeling of decisions in Chapter 3, while in Chapter 11 the authors address an emerging challenge in automated container terminals with integrated management. Key Features: Classifies the optimization problems of the ports into five scheduling decisions. For each decision, it supplies an overview, formulates each of the decisions as constraint satisfaction and optimization problems, and then covers possible solutions, implementation, and performance. Explores in Part One of the book the various optimization problems in modern container terminals, while details in Part Two advanced algorithms for the minimum cost flow (MCF) problem and for the scheduling problem of AGVs in ports. Offers complete package that can help readers address the scheduling problems of AGVs in ports. This is a valuable reference for port authorities and researchers, including specialists and graduate students in operation research. For specialists, it provides novel and efficient algorithms for network flow problems. For students, it supplies the most comprehensive survey of the field along with a rigorous formulation of the problems in port automation.
Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.
Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance. Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used. To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This includes algorithmic trading, forecasting, risk analysis portfolio optimization and other less well-known areas in finance. Trading depth for readability, AI for Finance will help readers decide whether to invest more time into the subject.
Infectious diseases are an ever present threat to humans. In recent years, the threat of these emerging viruses has been greater than ever before in human history, due in large part to global travel by larger numbers of people, and to a lesser extent to disruptions in the interface between developed and undeveloped areas. The emergence of new deadly viruses in human populations during recent decades has confirmed this risk. They remain the third leading cause of deaths in the US and the second world-wide. Emerging Viruses in Human Populations provides a comprehensive review of viruses that are emerging or that threaten to emerge among human populations in the twenty-first century. It discusses the apprehension over emerging viruses that has intensified due to concerns about bioterrorism.* Presents the history of emerging viruses * Includes chapters on SARS, Pandemic Threat of Avian Influenza Viruses, West Nile Virus, Monkeypox Virus, Hantavirus, Nipah Virus and Hendra Virus, Japanese Encephalitis Virus, Dengue and Crimean-Congo Hemorrhagic Fever Viruses * Discusses surveillance for newly emerging diseases
For more than 100 years, leaf venation patterns have been considered diagnostic for plants and are thought to reflect plant evolution. Furthermore, venation patterns are considered valuable for taxonomic purposes as well as a criterion for studying phylogenetic developments. The present volume 9 deals with the venation patterns of the subfamilies Acalyphoidea, Crotonoidea and Euphorbioidea of the Euphorbiaceae. Leaves of 135 genera from these three subfamilies were cleared and and examined. The descriptions of venation patterns follows the format used in previous volumes: After the generic name, a short phrase indicates the number of species attributed to the genus, its geographical distribution followed by the nubmer of leaf species collected for the particular genus. The venation patterns observed within the genus are then described with citations of the species used for clearing and description. The descriptions are supplemented by plates showing diagnostic leafs and their venation patterns on 187 high quality plates.
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