Qing Zhao

Comprehensive resource on the fundamentals, recent research developments, and applications of triboelectric nanogenerators (TENGs) Triboelectric Nanogenerators delivers a comprehensive overview of a lightweight, low cost, and high-efficiency sustainable mechanical energy harvesting technique that draws from the ambient environment and has high output and extremely flexible structural designs. The book starts with an introduction on triboelectric nanogenerator technology, then continues to discuss the latest fundamental studies about TENGs, including models of triboelectric effect, the discharge effect, and standardized evaluation. The next part explains new output performance promotion of TENGs for power generation applications, including output promotion through multiple methods, as well as power management and effective energy storage. The last part explores new concepts inspired by the field of TENGs, including applications in sensing, actuation, data science, artificial intelligence (AI), the Internet of Things (IoT), and smart cities. Written by a team of highly qualified academics with significant research experience in the field, Triboelectric Nanogenerators includes information on: Triboelectric charge generation and discharge, highlighting how electrostatic energy is often wastedEnergy potential of TENGs in comparison to other sources such as lithium-ion batteries and other types of batteriesMethods to promote and configure output performance, such as through gas environments, liquid-solid interfaces, and electromechanical structuresHigh-voltage output from TENGs and its potential for new applications in areas such as breakdown charge triggering, electrospraying, field emissions, and others Triboelectric Nanogenerators is an essential reference for scientists, engineers, students, and professionals in related fields seeking to understand this exciting energy harvesting technique and gain all of the knowledge needed to help further progress in the field.

This book systematically clarifies the casing deformation prevention and control technologies based on the integration of research area of both geology and engineering. These technologies incorporate reservoir geomechanics, casing deformation mechanics and hydraulic fracturing mechanics. It covers fluid channel-fault activation model and casing deformation mechanisms, types and formation conditions of fluid channels, casing deformation risk prediction technology, casing deformation early warning technology based on microseismic and fracturing treatment curves, casing deformation control technology based on hydraulic fracturing simulation and casing deformation prevention strategy. This book is a valuable reference for both technical personnel and graduate students working in petroleum engineering, rock mechanics and reservoir geomechanics.

The book introduces a series of state-of-art Bayesian models that can be used to understand and predict spatially structured population dynamics in our changing world. Several chapters are devoted to introducing models that utilize detection/non-detection data, count data, combined count and capture-recapture data, and spatial capture-recapture data, respectively. The book provides R code of Metropolis-Hastings algorithms that allow efficient computing of these complex models. The book is aimed at graduate students and researchers who are interested in using and further developing these models.

Multi-armed bandit problems pertain to optimal sequential decision making and learning in unknown environments. Since the first bandit problem posed by Thompson in 1933 for the application of clinical trials, bandit problems have enjoyed lasting attention from multiple research communities and have found a wide range of applications across diverse domains. This book covers classic results and recent development on both Bayesian and frequentist bandit problems. We start in Chapter 1 with a brief overview on the history of bandit problems, contrasting the two schools—Bayesian and frequentist—of approaches and highlighting foundational results and key applications. Chapters 2 and 4 cover, respectively, the canonical Bayesian and frequentist bandit models. In Chapters 3 and 5, we discuss major variants of the canonical bandit models that lead to new directions, bring in new techniques, and broaden the applications of this classical problem. In Chapter 6, we present several representative application examples in communication networks and social-economic systems, aiming to illuminate the connections between the Bayesian and the frequentist formulations of bandit problems and how structural results pertaining to one may be leveraged to obtain solutions under the other.