流體力學概論 (5版)

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<姆斯>流體力學概論<文京> 5/e 蔡豐欽 9789861505374

COMPUTATIONAL FLUID DYNAMICS

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書名：COMPUTATIONAL FLUID DYNAMICS 作者：ANDERSON 出版社：McGraw-Hill ISBN：9780071132107

流體力學 (1版)

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作(編/譯)者 : 徐瑞堂‧徐貴新‧陳鴻輝‧劉張源 編譯／吳中興 總校閱 出版年份 : 2007 ISBN : 9789864122196 類別 : 流體力學 書號 : 1031A4 幾色 : 2 規格 : 16K 發行公司 : 高立 版權日期 : 2007/08/25 頁數 : 532 分別 : 授權書 第一章 流體性質與流體力學之研究 第二章 流體黏度 第三章　壓力量測 第四章　靜止流體的作用力 第五章 浮力與穩定性 第六章 流體的流動和柏努利方程式 第七章 一般能量方程式 第八章 雷諾數、層流和紊流 第九章 摩擦形成的能量損失 第十章 次要損失 第十一章 串聯管路系統 第十二章 並聯管路系統 第十三章 泵的選擇和應用 第十四章 明渠流 第十五章 流的量測 第十六章 流體流動所產生的力 第十七章 阻力和升力 第十八章 風扇、鼓風機、空氣壓縮機及氣體的流體 第十九章 風管中空氣的流體 附 錄 英中文索引

Fundamentals of Aerodynamics (6版)

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【原文書】 書名：Fundamentals of Aerodynamics 6/e 作者：ANDERSON 出版社：McGraw-Hill 出版日期：2017/00/00 ISBN：9781259251344

Fundamentals of Aerodynamics (7版)

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Description The new edition of Fundamentals of Aerodynamics follows in the same tradition as the previous editions: it is for students—to be read, understood, and enjoyed. It is consciously written in a clear, informal, and direct style to talk to the reader and gain their interest in the challenging and yet beautiful discipline of aerodynamics. Table of Contents Part One - Fundamental Principles 1) Aerodynamics: Some Introductory Thoughts 2) Aerodynamics: Some Fundamental Principles and Equations Part Two - Inviscid, Incompressible Flow 3) Fundamentals of Inviscid, Incompressible Flow 4) Incompressible Flow over Airfoils 5) Incompressible Flow over Finite Wings 6) Three-Dimensional Incompressible Flow Part Three - Inviscid, Compressible Flow 7) Compressible Flow: Some Preliminary Aspects 8) Normal Shock Waves and Related Topics 9) Oblique Shock and Expansion Waves 10) Compressible Flow Through Nozzles, Diffusers, and Wind Tunnels 11) Subsonic Compressible Flow over Airfoils: Linear Theory 12) Linearized Supersonic Flow 13) Introduction to Numerical Techniques for Nonlinear Supersonic Flow 14) Elements of Hypersonic Flow Part Four - Viscous Flow 15) Introduction to the Fundamental Principles and Equations of Viscous Flow 16) A Special Case: Couette Flow 17) Introduction to Boundary Layers 18) Laminar Boundary Layers 19) Turbulent Boundary Layers 20) Navier-Stokes Solutions: Some Examples

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【簡介】 Fundamentals of Cloud Security offers a structured, framework-based approach to understanding, implementing, and securing cloud environments. Built around the NIST Cybersecurity Framework, it connects identification, protection, detection, response, and recovery to practical applications across AWS, Azure, and Google Cloud. This First Edition text guides readers through readiness assessments, shared responsibility, governance, compliance, and architectural considerations that shape secure, scalable cloud systems. Content explores the cloud threat ecosystem, principles of security and trust, and the strategies, tools, and controls that drive resilient operations. Case studies and real-world examples illustrate how to bridge theory and practice, while coverage of emerging security roles, automated tools, and AI-driven techniques highlights the evolution of cloud protection. Designed to align with cybersecurity and cloud computing programs, it supports modern learning outcomes and prepares learners to address current and future challenges in the field. Features and Benefits Organized around the NIST Cybersecurity Framework, linking key lifecycle stages to practical cloud security strategies. Applies real-world scenarios and provider specifics for AWS, Azure, and Google Cloud to strengthen practical understanding. Covers readiness, governance, and compliance, providing tools to evaluate cloud migration and manage ongoing alignment. Highlights emerging roles and shift-left/shift-right security, guiding modern approaches to multi-cloud threat management. Explores AI-driven and automated security approaches that enhance monitoring, response, and governance across deployments. Offers immersive, scenario-based Cloud Labs that reinforce concepts through real-world, hands-on virtual practice. Instructor resources include PowerPoint slides, test bank, sample syllabi, instructor manual, and time-on-task documentation.

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【簡介】 This one- or two-term calculus-based basic probability text is written for majors in mathematics, physical sciences, engineering, statistics, actuarial science, business and finance, operations research, and computer science. It presents probability in a natural way: through interesting and instructive examples and exercises that motivate the theory, definitions, theorems, and methodology. This book is mathematically rigorous and, at the same time, closely matches the historical development of probability. Whenever appropriate, historical remarks are included, and the 2096 examples and exercises have been carefully designed to arouse curiosity and hence encourage students to delve into the theory with enthusiasm. 【目錄】 1. Axioms of Probability 2. Combinatorial Methods 3. Conditional Probability and Independence 4. Distribution Functions and Discrete Random Variables 5. Continuous Random Variables 6. Special Discrete Distributions 7. Special Continuous Distributions 8. Understanding Relationships: Covariance, Correlations, and Conditional Distributions 9. More Bivariate and Multivariate Topics 10. Inequalities and Limit Theorems Appendix Tables

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【簡介】 An introductory textbook for undergraduate or beginning graduate students that integrates probability and statistics with their applications in machine learning.Most curricula have students take an undergraduate course on probability and statistics before turning to machine learning. In this innovative textbook, Ethem Alpaydın offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydın accessibly connects machine learning to its roots in probability and statistics, starting with the basics of random experiments and probabilities and eventually moving to complex topics such as artificial neural networks. With a practical emphasis and learn-by-doing approach, this unique text offers comprehensive coverage of the elements fundamental to an empirical understanding of machine learning in a data science context. Consolidates foundational knowledge and key techniques needed for modern data scienceCovers mathematical fundamentals of probability and statistics and ML basicsEmphasizes hands-on learningSuits undergraduates as well as self-learners with basic programming experienceIncludes slides, solutions, and code