Port Planning and Management Simulation examines port planning simulation applications, showing how they supports better port decision-making. Using a clear organizational format based on actual port system structure and operation processes, the book provides practical and theoretical insights on port planning and management. The book describes the water, land, collecting and distributing components of the port system, focusing on management, development, and risk mitigation. It examines the key challenges based on discrete system simulation theory that is less affected by local or national regulations. It compares various simulation scenarios for optimal port operational strategy. It quantifies port emissions, analyzes the impact of different reduction strategies, and presents operational strategies for green port planning developmentmand management. Port Planning and Management Simulation provides guidance for carrying out deep analysis in a complex and dynamic system, providing an integrated solution framework based on simulation techniques for improving efficiency and cost savings of the port system.

This book is about reasoning with causal associations during diagnostic problem-solving. It formalizes several currently vague notions of abductive inference in the context of diagnosis. The result is a mathematical model of diagnostic reasoning called parsimonious covering theory. Within this diagnostic, problems and important relevant concepts are formally defined, properties of diagnostic problem-solving are identified and analyzed, and algorithms for finding plausible explanations in different situations are given along with proofs of their correctness. Another feature of this book is the integration of parsimonious covering theory and probability theory. Based on underlying cause-effect relations, the resulting probabilistic causal model generalized Bayesian classification to diagnostic problems where multiple disorders (faults) may occur simultaneously. Both sequential best-first search algorithms and parallel connectionist (neural network) algorithms for finding the most probable hypothesis are provided. This book should appeal to both theoretical researchers and practitioners. For researchers in artificial intelligence and cognitive science, it provides a coherent presentation of a new theory of diagnostic inference. For engineers and developers of automated diagnostic systems or systems solving other abductive tasks, the book may provide useful insights, guidance, or even directly workable algorithms.

Exploring the thorny issues of industrial organisation, competition policy and liberalization in the Asia-Pacific region, this book examines the ways in which governments regulate business. Using case studies from China, the USA, New Zealand, Thailand, Malaysia and Japan, the authors take a comparative look at the evolution of policies and their implementation on the ground.
With a specific focus on the energy, transport and telecommuncations sectors, this book represents the most up-to-date analysis of the ways in which governments in the Asia-Pacific are coping with rapid industrial and economic change.

Exploring the thorny issues of industrial organisation, competition policy and liberalization in the Asia-Pacific region, this book examines the ways in which governments regulate business. Using case studies from China, the USA, New Zealand, Thailand, Malaysia and Japan, the authors take a comparative look at the evolution of policies and their implementation on the ground.
With a specific focus on the energy, transport and telecommuncations sectors, this book represents the most up-to-date analysis of the ways in which governments in the Asia-Pacific are coping with rapid industrial and economic change.

This book is about reasoning with causal associations during diagnostic problem-solving. It formalizes several currently vague notions of abductive inference in the context of diagnosis. The result is a mathematical model of diagnostic reasoning called parsimonious covering theory. Within this diagnostic, problems and important relevant concepts are formally defined, properties of diagnostic problem-solving are identified and analyzed, and algorithms for finding plausible explanations in different situations are given along with proofs of their correctness. Another feature of this book is the integration of parsimonious covering theory and probability theory. Based on underlying cause-effect relations, the resulting probabilistic causal model generalized Bayesian classification to diagnostic problems where multiple disorders (faults) may occur simultaneously. Both sequential best-first search algorithms and parallel connectionist (neural network) algorithms for finding the most probable hypothesis are provided. This book should appeal to both theoretical researchers and practitioners.For researchers in artificial intelligence and cognitive science, it provides a coherent presentation of a new theory of diagnostic inference. For engineers and developers of automated diagnostic systems or systems solving other abductive tasks, the book may provide useful insights, guidance, or even directly workable algorithms.