Tushar Sharma - Böcker

Awareness of design smells - indicators of common design problems - helps developers or software engineers understand mistakes made while designing, what design principles were overlooked or misapplied, and what principles need to be applied properly to address those smells through refactoring. Developers and software engineers may "know" principles and patterns, but are not aware of the "smells" that exist in their design because of wrong or mis-application of principles or patterns. These smells tend to contribute heavily to technical debt - further time owed to fix projects thought to be complete - and need to be addressed via proper refactoring.Refactoring for Software Design Smells presents 25 structural design smells, their role in identifying design issues, and potential refactoring solutions. Organized across common areas of software design, each smell is presented with diagrams and examples illustrating the poor design practices and the problems that result, creating a catalog of nuggets of readily usable information that developers or engineers can apply in their projects. The authors distill their research and experience as consultants and trainers, providing insights that have been used to improve refactoring and reduce the time and costs of managing software projects. Along the way they recount anecdotes from actual projects on which the relevant smell helped address a design issue. Contains a comprehensive catalog of 25 structural design smells (organized around four fundamental designprinciples) that contribute to technical debt in software projects Presents a unique naming scheme for smells that helps understand the cause of a smell as well as pointstoward its potential refactoring Includes illustrative examples that showcase the poor design practices underlying a smell and the problemsthat result Covers pragmatic techniques for refactoring design smells to manage technical debt and to create and maintainhigh-quality software in practice Presents insightful anecdotes and case studies drawn from the trenches of real-world projects

Nanotechnology for CO2 Utilization in Oilfield Applications delivers a critical reference for petroleum and reservoir engineers to learn the latest advancements of combining the use of CO2 and nanofluids to lower carbon footprint. Starting with the existing chemical and physical methods employed for synthesizing nanofluids, the reference moves into the scalability and fabrication techniques given for all the various nanofluids currently used in oilfield applications. This is followed by various, relevant characterization techniques. Advancing on, the reference covers nanofluids used in drilling, cementing, and EOR fluids, including their challenges and implementation problems associated with the use of nanofluids.

Finally, the authors discuss the combined application of CO2 and nanofluids, listing challenges and benefits of CO2, such as carbonation capacity of nanofluids via rheological analysis for better CO2 utilization. Supported by visual world maps on CCS sites and case studies across the industry, this book gives today's engineers a much-needed tool to lower emissions.

Covers applications for the scalability and reproducibility of fabrication techniques for various nanofluids used in the oilfield, including visual world maps that showcase current stages and future CCS sites Helps readers understand CO2 case studies for subsurface applications, including CO2 injection into depleted reservoirs Provides knowledge on the existing challenges and hazards involved in CO2 for safer utilization

Oracle Certified Professional Java SE 7 Programmer Exams 1Z0-804 and 1Z0-805 is a concise, comprehensive, step-by-step, and one-stop guide for the Oracle Certified Professional Java SE 7 Programmer Exam.

This book is a comprehensive, step-by-step and one-stop guide for the Java SE 8 Programmer II exam (IZ0-809). Salient features of this book include: 100% coverage of the exam topics, a full-length mock exam, practice exam questions, exam notes and tips. Oracle Certified Professional Java SE 8 Programmer II Guide (Exam IZ0-809) is a comprehensive guide for the OCPJP 8 exam. The book starts by answering frequently asked questions about the OCPJP 8 exam (Chapter 1). The book maps each exam topic into a chapter and covers 100% of the exam topics (next 12 Chapters). Exam topics are discussed using numerous programming and real-world examples. Each chapter ends with practice exam questions and a quick summary that revises key concepts covered in the chapter from exam perspective. After reading the main chapters, you can take the full-length mock exam to ensure that you have enough practice before actually taking the exam (Chapter 14).  If you are an OCPJP 8 exam aspirant, this book is certainly for you. This book assumes that you are already familiar with Java fundamentals (that is in line with the prerequisite of having a OCAJP 8 certification before you take up the OCPJP 8 exam). This book will be a delectable read to you because of its simple language, example driven approach, easy-to-read style, and complete focus towards the exam. Salient Features •In-depth and 100% coverage of all 12 exam topics for the certification •Numerous illustrative programming and real-world examples•Hundreds of practice exam questions (including a full-length mock exam)What you will learn: • Have the necessary knowledge to clear the exam since 100% of the exam topics are covered to the required depth • clearly understand the scope and objectives of the exam, the technical topics covered in the exam, and type and level-of-difficulty of the exam questions (in short, you will clearly know what’s exactly required for passing the exam) • get into an “exam mindset” by trying out hundreds of practice exam questions.

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries andframeworks are also covered.Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment.Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!What You'll LearnExecute end-to-end machine learning projects and systemsImplement hands-on examples with industry standard, open source, robust machine learning tools and frameworksReview case studies depicting applications of machine learning and deep learning on diverse domains and industriesApply a wide range of machine learning models including regression, classification, and clustering.Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.Who This Book Is ForIT professionals, analysts, developers, data scientists, engineers, graduate students