Other books that might interest you

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  Optimal: Understanding...

  Dr Jeff Foster

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  A myth-busting guide on testosterone from leading male health expert Dr Jeff Foster to help men feel stronger, healthier, and get back to their best selves. 
  Testosterone: it’s a powerhouse hormone that fuels men’s energy, focus, mood, and vitality at every age. Yet despite its importance, testosterone is surrounded by confusion, stigma, and half-truths. 
  In this accessible, myth-busting guide, leading expert in male health Dr Jeff Foster sets the record straight. Drawing on years of clinical experience as an NHS GP Partner and a Private GP with a specialist interest in men's health, he explains how testosterone really works; why deficiency is far more common than most people think, and how it can impact everything from sleep to mental health, strength to sex, and overall wellbeing. 
  Learn how to spot the signs of low testosterone; what modern treatments can (and can’t) do, and why looking after your hormone health is one of the best investments you can make for your future. 
  Testosterone is not simply about muscles and libido. It’s about health, confidence, and living life to the fullest. 
  This is the men's health book all men need to read: 
  ‘Essential reading for every man’ – James Haskell 
  ‘No-one knows more about testosterone and men’s health than Dr Jeff’ – Tom Hopper 
  ‘Dr Jeff is the ultimate expert in men’s health – you need this book’ – Jason Fox 
  ‘Eye-opening, informative and genuinely useful – a must-read for every man’ – Dr Naomi Potter 
  ‘I found Dr Jeff’s book so insightful and enriching on the subject of testosterone, I had an instant intellectual tumescence’ – Russell Kane 
  For fans of Rotimi Akinsete (This Book Could Help), Mind (Take a Moment), and James Armstrong (Keto Diet for Weight Loss).

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  Big Data Frameworks -...

  Mark Jackson

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  In the era of data-driven decision-making, managing and analyzing large-scale datasets has become a critical challenge for organizations across industries. Big Data Frameworks: Architectures, Tools, and Techniques for Managing Large-Scale Data.Comprehensive review of Apache Hadoop, Spark and Flink offers a comprehensive exploration of the essential frameworks and tools that enable effective big data management and analysis. 
  This book provides a deep dive into the architectures and technologies that power modern big data solutions. Beginning with foundational concepts, readers will gain an understanding of data lifecycle management and the core principles of big data ecosystems.  
  Beyond individual frameworks, the book addresses the integration of big data technologies with machine learning, explores cloud-based big data solutions, and emphasizes best practices for security and governance. 
  Each chapter is enriched with practical examples, real-world case studies, and actionable insights, making this book a valuable resource for data professionals, architects, and anyone involved in big data projects. Whether you're a beginner looking to understand the basics or an experienced practitioner seeking advanced techniques, Big Data Frameworks provides the knowledge and tools necessary to tackle the challenges of large-scale data management and analysis. 
  Unlock the power of big data and stay ahead in the rapidly evolving field with Big Data Frameworks: Architectures, Tools, and Techniques for Managing Large-Scale Data. Comprehensive review of Apache Hadoop, Spark and Flink. 
   

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  Artificial Intelligence -...

  Bright Mills

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  Artificial Intelligence (AI) is a transformative field of computer science dedicated to building systems that can perform tasks typically requiring human intelligence. These tasks include learning, reasoning, problem-solving, perception, language understanding, and decision-making. This comprehensive guide explores the foundational principles of AI, its historical evolution, methodologies, real-world applications, ethical challenges, and the future it holds. The document begins by defining AI, its goals, and its place in the broader field of computer science. It discusses the major branches, such as machine learning, natural language processing, computer vision, robotics, and expert systems. Following that, the paper delves into the historical timeline of AI—from early philosophical concepts to the rise of neural networks and deep learning in the 21st century. The core methodologies are explored in-depth, including supervised, unsupervised, and reinforcement learning, as well as symbolic AI, heuristic approaches, and hybrid models. It further illustrates how AI is integrated across industries like healthcare, finance, manufacturing, education, transportation, and defense. The guide also addresses the critical ethical and societal questions surrounding AI: data privacy, algorithmic bias, surveillance, job displacement, and the need for regulation and responsible AI development. Finally, it speculates on the future trajectory of AI, including artificial general intelligence (AGI), quantum AI, and human-AI symbiosis. It highlights both the immense opportunities AI offers and the serious challenges it poses. This 10,000-word exposition aims to serve students, researchers, policymakers, and enthusiasts by offering a detailed, accessible, and balanced perspective on one of the most powerful technologies of the 21st century.

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  Management of Water Utility...

  Jason Watson

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  It introduces alternative optimization, simulation, and statistical methods useful for project identification, design, siting, operation and evaluation and for studying post-planning issues. The authors cover both basin-wide and urban water issues and present ways of identifying and evaluating alternatives for addressing multiple-purpose and multi-objective water quantity and quality management challenges. Reinforced with cases studies, exercises, and media supplements throughout, the text is ideal for upper-level undergraduate and graduate courses in water resource planning and management as well as for practicing planners and engineers in the field.

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  Data Virtualization

  Mark Jackson

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  In the age of digital transformation, managing and integrating data effectively is crucial for organizational success. "Mastering Data Virtualization" is your comprehensive guide to understanding, implementing, and optimizing data virtualization, a revolutionary approach that simplifies data access and integration without the need for physical data replication. 
  This book provides a deep dive into the fundamentals of data virtualization, from its core concepts and architecture to its real-world applications and future trends. 
  "Mastering Data Virtualization" equips you with the knowledge and tools needed to harness the power of data virtualization. Transform your data management practices, enhance decision-making, and drive innovation with this essential guide to modern data integration. 
   

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  The UFO Experience - Evidence...

  J. Allen Hynek, Paul Hynek

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  The case against UFOs and unidentified aerial phenomena (UAP) has not been put to rest. Although UFOs "officially" did not exist for decades according to the government, reports of sightings continue to be made, and the latest releases from the government and related hearings have surprised the world. While the scientific community has put UFOs out to pasture, the evidence used to dismiss them is rare and unscientific. 
   
   
    
  Dr. Hynek, a scientist himself, and the only government-paid ufologist in history, looks at the decisions made by officialdom in the early days of ufology and how these decisions have held us back—to the point that we are still naively talking about UFOs as we were in the 1950s. Has seventy years of research made no difference at all in our understanding? 
   
   
    
  Dr. Hynek proves that there is a conspiracy afoot to hide the facts and that there are many cases that still need to be explained by mainstream science—not dismissed with facile jokes and stupid logic. Citing specific cases, Hynek challenges those in the ivory tower by raising questions that have still not been answered and refuting mainstream arguments that have yet to be proven.

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