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Schilling P: Parametric Modeling with
SOLIDWORKS 2025

Parametric Modeling with SOLIDWORKS 2025

This third edition of Designing Tall Buildings is an enhanced and accessible reference that guides the reader through the fundamental principles of designing high-rise buildings.This new edition includes four new chapters, including initial concepts for tall building design, techniques for optimization, considerations for carbon and the use of alternative materials such as timber. The third edition also includes 300 additional illustrations, and current code requirements and equations. Each chapter focuses on a theme central to tall-building design, giving a comprehensive overview of the related architectural and structural engineering concepts. Author Mark Sarkisian, PE, SE, NAE, LEED, provides clear definitions of technical terms and important equations, gradually developing the reader's knowledge. Projects drawn from SOM's vast portfolio of built high-rises, many of which Sarkisian engineered, demonstrate these concepts. The book considers the influence of particular site geology, wind conditions, and seismicity. With this contextual knowledge, the reader can develop structural systems best suited for a tower that are not only safe, but constructible and economical. Sarkisian specifically addresses the influence of nature in design, urging the integration of structure and architecture for building of superior performance, sustainability, and aesthetic excellence.This third edition is a must-have reference for students and designers.

Structure as Architecture

Designing Tall Buildings

Handbooks in Communication and Media

"The Handbook of Artificial Intelligence and Journalism will be the first of its kind in seeking to unveil the algorithmic transformation of journalism and define this new research field by highlighting the interplay between journalism and technological advancements. The rise of AI and its pervasive reach has ushered in a new era of practice and research in the field of communication, particularly in the realm of journalism, where it is reshaping not only the organizational structure of the news but the way it is produced, distributed, and consumed. This Handbook delves into the AI's potential for the Journalism field, as well as possible outcomes of the integration of intelligent systems into news journalism. Incorporating studies from around the world, including the global south, chapters will provide an inclusive and holistic understanding of the algorithmic transformation in journalism across different cultural, economic, and societal contexts. Topics covered include AI and mis/disinformation, ethical implications, emerging business models in algorithm-driven journalism, and AI governance. For students and scholars of communication and media studies or journalism, this handbook offers a theoretical framework, key concepts, and new approaches to algorithm-driven journalism, aiding them in the comprehension of the potentials and impact of the use of intelligent technologies in journalism"--

The Handbook of Artificial Intelligence and Journalism

Wendepunkte in der Analogen und Digitalen Welt

An expert and contemporary presentation of computational analysis and design tools for professionals studying or applying the principles of aerodynamics In Aerodynamics: A Computational Introduction, a distinguished aerospace engineer and researcher Krzysztof Fidkowski delivers an up-to-date and authoritative reference text covering the essentials of aerodynamics. The book covers key topics, including basic fluid dynamics, potential flow, airfoil theory, boundary layers, wing theory, and compressible flow. Fidkowski takes a computation-first approach to the subject matter, presenting aerodynamics in a way that is compatible with the way the subject is handled in contemporary businesses and research labs. Aerodynamics offers numerical demonstrations, codes, and theory comparisons that make important derivations and conclusions easier to understand and apply. Readers will also find: <ul> <li>A self-contained introduction to the thermodynamics, mathematics, and numerical methods central to the subject of aerodynamics</li> <li>Comprehensive explorations of kinematics, dynamics, and potential flow</li> <li>Practical discussions of panel methods and airfoil theory, including design tools</li> <li>Numerical and analytical tools for boundary-layer analysis</li> <li>Complete treatment of finite-wing theory, including horseshoe vortices, lifting-line theory, and the vortex-lattice method.</li> </ul> Perfect for engineering students who want to learn how to apply basic numerical methods to large-scale aerodynamics problems, Aerodynamics: A Computational Introduction will also benefit professionals who wish to better understand aerodynamics and computational fluid dynamics.

A Computational Introduction

Aerodynamics

Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction. The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google's CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoreticalfoundations to practical applications. The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks. Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including: <ul> <li>Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selection</li> <li>Intelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generation</li> <li>Deepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI's greater impact on information integrity</li> <li>Security attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacks</li> <li>Privacy-preserving AI techniques including federated learning and homomorphic encryption</li> </ul> Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility.

Triangulating AI Security

How statistical invariances will help us build AI systems exhibiting human-like performance by following human-like strategies. Current machine learning systems crumble when the distributions of training and testing examples differ in spurious correlations. This is a major roadblock toward the development of advanced machine intelligence, which demands not only human-like performance but the deployment of human-like strategies. The prevalent approach in AI, fixated on recklessly minimizing average training error, falls short in producing AI systems capable of authentic out-of-distribution generalization. This book introduces the Invariance Principle, a new epistemological tool to unearth correlations invariant across diverse collections of empirical data. The Invariance Principle, encapsulated in the axiom “frame your problem so its answer matches across circumstances," will not only find its practical incarnation in the family of Invariant Risk Minimization algorithms, but also illuminate our understanding of causation. It will permeate topics such as environment discovery, large-language models, self-supervised learning, mixing data augmentation, uncertainty estimation, and fairness. The author argues that the Invariance Principle is a central inductive bias fueling advances across fields of knowledge, such as physics, metaphysics, and cognitive science. The final chapter includes personal examples of how invariance has shaped the author’s understanding of his own subjective experience, as well as how he has interpreted both Eastern and Western philosophical traditions.

The Invariance Principle

Energy Efficient Internet of Things-Based Wireless Sensor Network

A Path to Sustainable Development Goals

AI and ML Techniques in IoT-based Communication

Woodhead Publishing Series in Civil and Structural Engineering

Data Science for Advanced Civil Engineering Applications

Data-Centric Civil Engineering

Data-Driven Modeling

Advances in Nonlinear Dynamical Systems and Robotics (ANDC)

Swarm Robotics Solutions for National Security and Marine Forensics explores the cutting-edge applications of swarm robotics in safeguarding national security and advancing marine forensics. This book delves into the innovative use of collaborative robotic systems inspired by nature, where multiple autonomous agents work together seamlessly to address complex challenges. From enhancing surveillance and reconnaissance capabilities for national defence to revolutionizing marine forensic investigations, this comprehensive guide highlights the transformative impact of swarm robotics on critical domains. A must-read for researchers, practitioners, and enthusiasts in robotics, artificial intelligence, and security, this book illuminates the future possibilities of swarm robotics in shaping a safer and more secure world.

Swarm Robotics Solutions for National Security and Marine Forensics

Practical Bioinformatics

Parametric Modeling with SOLIDWORKS 2025

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