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Gesamtkatalog

Catalogue complet

Python Experiments in Physics and Astronomy acts as a resource for science and engineering students or faculty who would like to see how a diverse selection of topics can be analyzed and simulated using Python programs.

Houlahan P: Python Experiments in
Physics and Astronomy

Python Experiments in Physics and Astronomy

1. Python and Object Oriented Design Notes. 2. Exploring Data. 3. Signals and Trends. 4. Gravity Fields and Mass Distributions. 5. Spiral Galaxies and Dark Matter. 6. Sampling a Distribution. 7. Projectiles - The German 88. 8. Rocket Launches. 9. Building a star catalog from an image. 10. Photometry: Measuring object brightness. 11. Aligning Images and Finding Targets. 12. The Saha Equation and the Balmer Spectrum. 13. Isochrons - The Ages of Rocks.

Snap! Solar System

Data-Driven Modeling

Originating from the lattice Gas Automaton, LBM has reshaped the field with its straightforward approach and high efficacy. Despite its widespread use, navigating the scattered literature can be daunting for newcomers. Fluid Dynamics with the Lattice Boltzmann Method presents a clear path from novice to expert. This comprehensive guide bridges gaps in understanding, covering essential principles and exploring advanced topics like compressible flows and combustion. Authored by experts, this book consolidates diverse models and recent advancements, ensuring you're equipped to navigate the latest developments in computational fluid dynamics. Whether you're just starting out or aiming to stay ahead of the curve, this book is your essential companion in mastering LBM.

Fluid Dynamics with the Lattice Boltzmann Method

Artificial Intelligence for Smart Internet of Things Applications

Quantum Machine Learning

Space and Planetary Advances

Exoplanet Detection Methods: Uncovering the Evolution of Planetary Systems is a comprehensive guide that delves into modern techniques for detecting exoplanets. It explores current methods while unravelling the fascinating story of planetary system evolution. Each chapter is meticulously structured to introduce detection methods, offer step-by-step instructions, analyze datasets, and present available instrumentation. This book caters to both beginners and seasoned researchers, providing them with the means to understand the characteristics and evolutionary timelines of different planetary populations. Beyond methodological insights, the book includes online proposal case studies, curated lists of resources, and further detailed step-by-step methods. This makes it an indispensable companion for those venturing into exoplanetary studies or working on planetary demographics.

Uncovering the Evolution of Planetary Systems

Exoplanet Detection Methods

Science And Religion

Multi-Agent Control Systems

A complete and up-to-date overview of popular and practical erasure codes in distributed storage In Distributed Storage in Practice, a team of distinguished researchers delivers a comprehensive discussion on distributed storage coding and distributed storage systems. Divided into two parts, the book first explores distributed storage coding technology based on Maximum Distance Separable (MDS) codes, including array codes, Reed-Solomon codes, locally repairable codes, and regenerating codes. It then goes on to examine the challenges presented by repairing distributed data in real-world scenarios. Distributed Storage in Practice uses two perspectives: practical optimization of distributed storage coding and emerging technologies such as blockchain. It discusses the technical foundations of blockchain and integrates blockchain into distributed storage systems. It also offers an overview of several popular blockchain-based storage systems. It also includes: <ul> <li>A thorough introduction to the current development of quantum technology, including its fundamentals, quantum memory, quantum computers, quantum security, and quantum networks</li> <li>Comprehensive explorations of data recovery methods for specific networked distributed storage scenarios</li> <li>Practical integrations of theory and practice, including classic techniques and the most recent advancements in storage coding</li> <li>A practical example of a distributed secure storage system integrated with blockchain technology</li> </ul> Perfect for researchers and undergraduate and graduate students studying computer science, Distributed Storage in Practice will also benefit blockchain professionals.

Distributed Storage in Practice

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.

Python Experiments in Physics and Astronomy

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