Xin Zhou

This book primarily introduces innovative multi-physical plastic modeling for geomaterials in unsteady environments and advanced numerical methods for elastoplastic models, topics not systematically covered in other literature. The constitutive models in the form of stress-strain-environment will be interesting to students and researchers in geotechnical engineering and rock and soil mechanics. The book provides a thorough exploration of modeling techniques and algorithmic flowcharts, facilitating a deeper understanding of both theoretical models and numerical methods. It also guides readers in applying these tools to describe the mechanical behavior of geomaterials in various unsteady environments and addresses computational challenges in elastoplastic numerical analysis.

This book introduces a cutting-edge hybrid modeling framework that integrates both knowledge-based and data-driven techniques to tackle the challenges faced by the simulation of intelligent combat systems. Simulation has long been the primary method for evaluating the effectiveness of combat systems, often through the creation of virtual test environments. However, the increasing complexity and dynamic nature of modern battlefield environments present significant challenges for traditional simulation modeling techniques. Combat Simulation offers readers valuable insights into the latest advancements in simulation and data engineering to overcome these challenges in the modeling of complex combat systems. Covering a wide range of topics, from ontological modeling to dynamic data modeling, this book addresses the ongoing digital transformation of combat systems. It serves as a comprehensive guide to real-world applications in the field. Intended for graduate students, researchers, and professional engineers in simulation, systems, and computer engineering, this book equips readers with the tools to teach, apply, and innovate in simulation-based defense technologies.

Blockchain is a distributed ledger, offering an innovative approach to establishing trust in a public and open environment. However, it bears many security attacks in sustaining data/status consistency in the process of data storing and exchanging, and the long-delay attack is one of the most challenging ones. Many researchers have adopted various methods in analyzing the blockchain system security by considering a upper-bounded and static network latency. In a realistic scenario, the network latency is always changing dynamically, resulting in the unreliability and inaccuracy of the existing analysis approaches. In this book, we propose a blockchain security analysis model based on partially observable Markov decision process (POMDP) against the long delay attack by capturing the dynamic network delay. In our model, an observation function about the network delay is learned and updated based on a clustering algorithm according to the real-time network status. With the support of the observation function, a POMDP model is constructed for attackers to maximize their expected rewards. To analyze the security of a blockchain system against the long delay attack, the utility of the attackers and normal miners with the same mining power is calculated and compared. The system is safe as the utility of the normal miners is no less than that of the attackers. Extensive experiments are conducted to show the effectiveness of the proposed analysis model, where Bitcoin system is evaluated and the safe ranges of the system network parameters are analyzed.