The treatment of multiple trauma is continuously updated to provide the latest treatments, and receiving great attention of surgeons, doctors, researchers, residents students, and other health practitioners. In addition, to provide optimal treatment and prevent pitfalls in management of multiple trauma cases, I hope that a scientific forum will be the right forum for sharing ideas and exchange skills on how we can provide optimal and comprehensive management of multiple traumas for society in the future.
Worley provides an analysis of some stumbling blocks on the path of the young and reformed. Those inside and outside the young, restless, and reformed community will profit from this insider's insights.
Avoid data blunders and create truly useful visualizations Avoiding Data Pitfalls is a reputation-saving handbook for those who work with data, designed to help you avoid the all-too-common blunders that occur in data analysis, visualization, and presentation. Plenty of data tools exist, along with plenty of books that tell you how to use them—but unless you truly understand how to work with data, each of these tools can ultimately mislead and cause costly mistakes. This book walks you step by step through the full data visualization process, from calculation and analysis through accurate, useful presentation. Common blunders are explored in depth to show you how they arise, how they have become so common, and how you can avoid them from the outset. Then and only then can you take advantage of the wealth of tools that are out there—in the hands of someone who knows what they're doing, the right tools can cut down on the time, labor, and myriad decisions that go into each and every data presentation. Workers in almost every industry are now commonly expected to effectively analyze and present data, even with little or no formal training. There are many pitfalls—some might say chasms—in the process, and no one wants to be the source of a data error that costs money or even lives. This book provides a full walk-through of the process to help you ensure a truly useful result. Delve into the "data-reality gap" that grows with our dependence on data Learn how the right tools can streamline the visualization process Avoid common mistakes in data analysis, visualization, and presentation Create and present clear, accurate, effective data visualizations To err is human, but in today's data-driven world, the stakes can be high and the mistakes costly. Don't rely on "catching" mistakes, avoid them from the outset with the expert instruction in Avoiding Data Pitfalls.
“Don’s book is a very good addition both to the testing literature and to the literature on quality assurance and software engineering... . [It] is likely to become a standard for test training as well as a good reference for professional testers and developers. I would also recommend this book as background material for negotiating outsourced software contracts. I often work as an expert witness in litigation for software with very poor quality, and this book might well reduce or eliminate these lawsuits....” –Capers Jones, VP and CTO, Namcook Analytics LLC Software and system testers repeatedly fall victim to the same pitfalls. Think of them as “anti-patterns”: mistakes that make testing far less effective and efficient than it ought to be. In Common System and Software Testing Pitfalls, Donald G. Firesmith catalogs 92 of these pitfalls. Drawing on his 35 years of software and system engineering experience, Firesmith shows testers and technical managers and other stakeholders how to avoid falling into these pitfalls, recognize when they have already fallen in, and escape while minimizing their negative consequences. Firesmith writes for testing professionals and other stakeholders involved in large or medium-sized projects. His anti-patterns and solutions address both “pure software” applications and “software-reliant systems,” encompassing heterogeneous subsystems, hardware, software, data, facilities, material, and personnel. For each pitfall, he identifies its applicability, characteristic symptoms, potential negative consequences and causes, and offers specific actionable recommendations for avoiding it or limiting its consequences. This guide will help you Pinpoint testing processes that need improvement–before, during, and after the project Improve shared understanding and collaboration among all project participants Develop, review, and optimize future project testing programs Make your test documentation far more useful Identify testing risks and appropriate risk-mitigation strategies Categorize testing problems for metrics collection, analysis, and reporting Train new testers, QA specialists, and other project stakeholders With 92 common testing pitfalls organized into 14 categories, this taxonomy of testing pitfalls should be relatively complete. However, in spite of its comprehensiveness, it is also quite likely that additional pitfalls and even missing categories of pitfalls will be identified over time as testers read this book and compare it to their personal experiences. As an enhancement to the print edition, the author has provided the following location on the web where readers can find major additions and modifications to this taxonomy of pitfalls: http://donald.firesmith.net/home/common-testing-pitfalls Please send any recommended changes and additions to dgf (at) sei (dot) cmu (dot) edu, and the author will consider them for publication both on the website and in future editions of this book.
Ideal intermediate-level book for programmers to turn to once they have read the introductory books. Identifies what can go wrong and provides refactored solutions for each pitfall complete with code. All of the pitfalls are cross-referenced within the text as well as outlined in a summary table in the back of the book. Includes background information and troubleshooting tips so programmers can avoid other errors that may also occur. The Web site contains all of the refactored code solutions and links to appropriate tool downloads.
This book provides a unique visual and comprehensive approach to intra-operative technical errors and covers identification, consequences, repair and prevention of those errors. Detailed analyses of all reported complications for more than 80 major operations help you minimize the risk of errors in surgical procedures ranging from general, thoracic, vascular, and pediatric...to colorectal, endocrine, breast and trauma. A practical approach provides you with the essential guidance you need to make the best clinical decisions. Offers in-depth guidance on the prevention, management, and consequences of complications and pitfalls that occur before, during, and after surgery-all in one convenient resource. Organizes sections according to area of surgery for fast reference. Features a templated outline for specific procedures, allowing you to quickly review the associated pitfalls. Presents over 800 illustrations-including full-color intraoperative and postoperative photos-which enable you to follow the progression of a surgery and watch out for "problem areas,” while color line drawings help you visualize complex procedures.
The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures.
Accurate diagnosis of neurological disorders can often be difficult due to the complex nature of the nervous system. Whilst technological advances have greatly improved diagnostic and interpretation techniques, errors can still occur, which consequently result in mistakes in therapeutic care. This book is a guide to diagnostic strategies for a multitude of common and less common neurological disorders. Scenarios are set in both a clinical and an intensive care setting. Divided into 13 sections, the text begins with an overview of general neurology. Each of the following sections examines disorders in a different part of the nervous system. Diagnostic processes are evaluated and potential areas where a clinician may make a mistake, are examined and explained in depth. The final section presents 15 authentic cases covering diagnostic challenges in critical care units, all provided by leading experts from the University of Texas Southwestern Medical Centre, USA. With contributions from internationally recognised neurologists, this comprehensive text is highly illustrated with neurological images, and many chapters feature additional editorial notes and appendices. Access to a selection of clinical videos via a QR code is also provided with this book.
Being aware of thesis and dissertation pitfalls can help the graduate student make efficient use of resources available to him or her and bring precision to research and writing of that important project. The authors present 61 cases cast as an envisioned conversation between a student and a professor whom the student consults about a problem. The cases are presented within ten chapters that proceed through a sequence of typical stages in the production of a thesis or dissertation. Chapter titles include Choosing and Defining a Research Topic, Searching the Professional Literature, Developing a Proposal, Getting Help, Devising Data-Collection Procedures, Organizing the Collected Information, Interpreting the Results, Writing the Report, Defending the Finished Product, and Publishing the Study.
The craft of designing mathematical models of dynamic objects offers a large number of methods to solve subproblems in the design, typically parameter estimation, order determination, validation, model reduc tion, analysis of identifiability, sensi tivi ty and accuracy. There is also a substantial amount of process identification software available. A typi cal 'identification package' consists of program modules that implement selections of solution methods, coordinated by supervising programs, communication, and presentation handling file administration, operator of results. It is to be run 'interactively', typically on a designer's 'work station' . However, it is generally not obvious how to do that. Using interactive identification packages necessarily leaves to the user to decide on quite a number of specifications, including which model structure to use, which subproblems to be solved in each particular case, and in what or der. The designer is faced with the task of setting up cases on the work station, based on apriori knowledge about the actual physical object, the experiment conditions, and the purpose of the identification. In doing so, he/she will have to cope with two basic difficulties: 1) The com puter will be unable to solve most of the tentative identification cases, so the latter will first have to be form11lated in a way the computer can handle, and, worse, 2) even in cases where the computer can actually produce a model, the latter will not necessarily be valid for the intended purpose.
