The book provides a concise focussed guide to the main management areas that are essential to the success of modern construction projects. The concepts, principles and applications in the seven main management areas that are essential to the success of construction projects are presented. It links in with The CIOB's Education Framework is recommended reading for The CIOB.

The book provides a concise focussed guide to the main management areas that are essential to the success of modern construction projects. The concepts, principles and applications in the seven main management areas that are essential to the success of construction projects are presented. It links in with The CIOB's Education Framework is recommended reading for The CIOB.

After one too many late night discussions, football journalist Paul Watson and his mate Matthew Conrad decide to find the world's worst national team, become naturalised citizens of that country and play for them - achieving their joint boyhood dream of playing international football and winning a 'cap'. They are thrilled when Wikipedia leads them to Pohnpei, a tiny, remote island in the Pacific whose long-defunct football team is described as 'the weakest in the world'. They contact Pohnpei's Football Association and discover what it needs most urgently is leadership. So Paul and Matt travel thousands of miles, leaving behind jobs, families and girlfriends to train a rag-tag bunch of novice footballers who barely understand the rules of the game. Up Pohnpei tells the story of their quest to coach the team and eventually, organise an international fixture - Pohnpei's first since a 16-1 defeat many years ago. With no funding, a population whose obesity rate is 90 percent and toad-infested facilities in one of the world's wettest climates, their journey is beset by obstacles from the outset. Part travelogue, part quest, Up Pohnpei shows how the passion and determination of two young men can change the face of football - and the lives of total strangers - on the other side of the world.

The second edition of Chronic Pain now covers a vast scientific and clinical arena, with the scientific background and therapeutic options much expanded. In common with the other titles comprising Clinical Pain Management, the volume gathers together the available evidence-based information in a reader-friendly format without unnecessary detail, and is divided into three parts. The broad coverage under Part One encompasses basic science, including applied psychology, genetics and epidemiology, through societal aspects of chronic pain and disability, to patient assessment, diagnostic procedures and outcome measures. Part Two considers the different therapies available, including pharmacological, psychological, behavioural, interventional and alternative. In Part Three specific and non-specific pain syndromes and their management are described, including pain in neurological disease, in HIV and AIDS patients, and after surgery or spinal cord injury, regional pain in the head, face, neck, back, joints, chest, abdomen and pelvis, and issues related to pain in children, the elderly and in association with substance misuse.

It is hard to deny that today's world can seem downright hostile toward Christians. Some may look down at their iPhones when we mention God, motion for the check when we bring up church, or casually change the subject when we talk about prayer. In a world full of people whose indifference is greater than their desire to know Christ, how can we dream of growing the church?

In "Contagious Disciple Making," David Watson and Paul Watson map out a simple method that has sparked an explosion of homegrown churches in the United States and around the world. A companion to Cityteam's two previous books, "Miraculous Movements" and "The Father Glorified," "Contagious Disciple Making" details the method used by Cityteam disciple-makers. This distinctive process focuses on equipping spiritual leaders in communities where churches are planted. Unlike many evangelism and church-growth products that focus on quick results, contagious disciple-making takes time to cultivate spiritual leadership, resulting in lasting disciple-making movements. Through "Contagious Disciple Making" readers will come to understand that a strong and equipped leader will continue to grow the church long after church planters move on to the next church.

Features include:

• Glossary of terms
• Engagement tools for use in the field
• Practical techniques to equip others to make disciples

Are you about to embark on a research project for the first time? Unsure which data collection methods are right for your study? Don't know where to start? By presenting the reader with a series of key research management questions, this book introduces the novice researcher to a range of research designs and data collection methods. Building an understanding of these choices and how they can impact on the dissertation itself will lead to a more robust and rigorous dissertation study. This book is designed to direct your research choices with informative text and key questions, advice from "virtual supervisors" and reflections from students. Lists of suggested further reading also help to support you on your journey to developing an organised and successful dissertation project. Researchers seeking support on their journey to a successful dissertation will find this book a valuable resource.

This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. The text illustrates the breadth of the modeling and analysis capabilities that are supported by the software and support of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization and operations research problems. The text begins with a tutorial on simple linear and integer programming models. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from data sources like spreadsheets and databases. Chapters describing advanced modeling capabilities for nonlinear and stochastic optimization are also included. The Pyomo software provides familiar modeling features within Python, a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. The software supports a different modeling approach than commercial AML (Algebraic Modeling Languages) tools, and is designed for flexibility, extensibility, portability, and maintainability but also maintains the central ideas in modern AMLs.

This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. In the third edition, much of the material has been reorganized, new examples have been added, and a new chapter has been added describing how modelers can improve the performance of their models. The authors have also modified their recommended method for importing Pyomo. A big change in this edition is the emphasis of concrete models, which provide fewer restrictions on the specification and use of Pyomo models. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions.

Are you about to embark on a research project for the first time? Unsure which data collection methods are right for your study? Don't know where to start? By presenting the reader with a series of key research management questions, this book introduces the novice researcher to a range of research designs and data collection methods. Building an understanding of these choices and how they can impact on the dissertation itself will lead to a more robust and rigorous dissertation study. This book is designed to direct your research choices with informative text and key questions, advice from "virtual supervisors" and reflections from students. Lists of suggested further reading also help to support you on your journey to developing an organised and successful dissertation project. Researchers seeking support on their journey to a successful dissertation will find this book a valuable resource.

This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. In the third edition, much of the material has been reorganized, new examples have been added, and a new chapter has been added describing how modelers can improve the performance of their models. The authors have also modified their recommended method for importing Pyomo. A big change in this edition is the emphasis of concrete models, which provide fewer restrictions on the specification and use of Pyomo models. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions.

Ice Ghosts weaves together the epic story of the Lost Franklin Expedition of 1845—whose two ships and crew of 129 were lost to the Arctic ice—with the tale of the incredible discovery of the flagship’s wreck in 2014. Paul Watson, who was on the icebreaker that led the discovery expedition, tells a fast-paced historical adventure story: Sir John Franklin and the crew of the HMS Erebus and Terror setting off in search of the fabled Northwest Passage, the hazards they encountered and the reasons they were forced to abandon ship hundreds of miles from the nearest outpost of civilization, and the decades of searching that exposed rumours of cannibalism and a few scattered papers and bones—until a combination of Inuit lore and the latest science yielded a discovery for the ages.

How can we engage critically with music video and its role in popular culture? What do contemporary music videos have to tell us about patterns of cultural identity today? Based around an eclectic series of vivid case studies, this fresh and timely examination is an entertaining and enlightening analysis of the forms, pleasures, and politics that music videos offer. In rethinking some classic approaches from film studies and popular music studies and connecting them with new debates about the current 'state' of feminism and feminist theory, Railton and Watson show why and how we should be studying music videos in the twenty-first century. Through its thorough overview of the music video as a visual medium, this is an ideal textbook for Media Studies students and all those with an interest in popular music and cultural studies. Key Features * Provides a framework for how to describe and analyse a music video. * Uses case studies from internationally well-know artists, such as Kylie, Shakira and Beyonce to explore issues of representation of gender, sexuality and ethnicity. * Draws on classic and contemporary videos from a range of musical styles, from Lady Gaga and Christina Aguilera to Gorillaz and Metallica.

This volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8-12, 2007, in Trento, Italy. The motivation for the meeting is related to the current explosion in the number and variety of heuristic algorithms for hard optimization problems, which raises - merous interesting and challenging issues. Practitioners are confronted with the b- den of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter-tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental meth- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the expe- menter, who, in too many cases, is "in the loop" as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of "learning" about the performance of different techniques, and ways of using memory about algorithm behavior in the past to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained from different runs or during a single run can - prove the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained.