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Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues
In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.
Journal Article
Antecedents and Consequences of Employer Branding
This study reviewed and analysed the phenomenon of employer branding. We began with a review of recent research in employer branding. Next, drawing the theoretical knowledge from OB, HRM, and marketing, a framework is developed depicting the antecedents of employer branding and its impact on the company performance. For this, primary data were collected administering a questionnaire survey on 347 top-level executives in 209 companies in India, and secondary data were collected on financial performance. The results revealed that realistic job previews, perceived organizational support, equity in reward administration, perceived organizational prestige, organizational trust, leadership of top management, psychological contract obligations, and corporate social responsibility influence employer branding, which in turn impact non-financial and financial performance of companies. Furthermore, leadership of top management is the most potent predictor of employer branding. Greater deviation of the existing state from the ideal state of antecedents adversely affects employer branding. Management can use this framework for developing strategy towards implementation of employer branding.
Journal Article
Mathematical modeling for intelligent systems : theory, methods, and simulation
\"Mathematical Modeling for Intelligent Systems: Theory, Methods and Simulation aims to provide a reference for the applications of mathematical modeling using intelligent techniques in various unique industry problems in the era of Industry 4.0. Providing a thorough introduction to the field of soft computing techniques, the book covers every major technique in artificial intelligence in a clear and practical style. It also highlights current research and applications, addresses issues encountered in the development of applied systems, and describes a wide range of intelligent systems techniques, including neural networks, fuzzy logic, evolutionary strategy, and genetic algorithms. The book demonstrates concepts through simulation examples and practical experimental results. The book offers a well-balanced mathematical analysis of modelling physical systems. Summarizes basic principles in differential geometry and convex analysis as needed. The book covers a wide range of industrial and social applications, and bridges the gap between core theory and costly experiments through simulations and modelling. The focus of the book is manifold ranging from stability of fluid flows, nano fluids, drug delivery, and security of image data to Pandemic modeling etc. The book is primarily aimed at advanced undergraduates and postgraduate students studying computer science, mathematics and statistics. Researchers and professionals will also find this book useful\"-- Provided by publisher.
BOOK
Role of Oral Microbiome Signatures in Diagnosis and Prognosis of Oral Cancer
Despite advancement in cancer treatment, oral cancer has a poor prognosis and is often detected at late stage. To overcome these challenges, investigators should search for early diagnostic and prognostic biomarkers. More than 700 bacterial species reside in the oral cavity. The oral microbiome population varies by saliva and different habitats of oral cavity. Tobacco, alcohol, and betel nut, which are causative factors of oral cancer, may alter the oral microbiome composition. Both pathogenic and commensal strains of bacteria have significantly contributed to oral cancer. Numerous bacterial species in the oral cavity are involved in chronic inflammation that lead to development of oral carcinogenesis. Bacterial products and its metabolic by-products may induce permanent genetic alterations in epithelial cells of the host that drive proliferation and/or survival of epithelial cells. Porphyromonas gingivalis and Fusobacterium nucleatum induce production of inflammatory cytokines, cell proliferation, and inhibition of apoptosis, cellular invasion, and migration thorough host cell genomic alterations. Recent advancement in metagenomic technologies may be useful in identifying oral cancer–related microbiome, their genomes, virulence properties, and their interaction with host immunity. It is very important to address which bacterial species is responsible for driving oral carcinogenesis. Alteration in the oral commensal microbial communities have potential application as a diagnostic tool to predict oral squamous cell carcinoma. Clinicians should be aware that the protective properties of the resident microflora are beneficial to define treatment strategies. To develop highly precise and effective therapeutic approaches, identification of specific oral microbiomes may be required. In this review, we narrate the role of microbiome in the progression of oral cancer and its role as an early diagnostic and prognostic biomarker for oral cancer.
Journal Article
Computational intelligence aided systems for healthcare domain
\"The text covers recent advances in artificial intelligence, smart computing, and their applications in augmenting medical and health care systems. It will serve as an ideal reference text for graduate students and academic researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical\"-- Provided by publisher.
Book
A Review of the Gate-All-Around Nanosheet FET Process Opportunities
In this paper, the innovations in device design of the gate-all-around (GAA) nanosheet FET are reviewed. These innovations span enablement of multiple threshold voltages and bottom dielectric isolation in addition to impact of channel geometry on the overall device performance. Current scaling challenges for GAA nanosheet FETs are reviewed and discussed. Finally, an analysis of future innovations required to continue scaling nanosheet FETs and future technologies is discussed.
Journal Article
Prediction of nanofluid viscosity using multilayer perceptron and Gaussian process regression
More than a decade, a numerous experimental and theoretical studies of thermophysical properties of nanofluids are conducted to reveal its heat transfer characteristics. Due to nanofluid unique thermal properties, it is broadly used in various applications from automobile applications to biomedical applications. Despite that various experimental and theoretical studies of nanofluids are developed, the accordance between them is very little and also it is tiresome and expensive. To predict the thermal properties in an easy way, soft computing tools are utilized. In this research work, dynamic viscosity ratio of Al2O3/H2O is predicted using machine learning techniques like multilayer perceptron and Gaussian process regression. In the proposed multilayer perceptron—artificial neural network model, varying a range of neurons in the hidden layer and using Levenberg–Marquardt as training function, it is found that 6 neurons in the hidden layer give less root mean square error value of 0.01118. Different kernel functions are opted to train the proposed Gaussian process regression model, and it is found that Matern kernel function shows the best performance with less root mean square error value of 0.018, and regression coefficient value of both the models is 0.99. This research work will reduce the experimental test run cost, and the models are accurate in prediction.
Journal Article