Intermittent distributed energy resources (DERs) add challenges to the modern power system network. On the other hand, information and communication technology (ICT) is changing traditional electricity grids into smart grids, which facilitates a decentralized system in which prosumers may participate in energy trading. Smart grids, DER integration, and network connectivity are adding complexity to the power system network day by day; Blockchain technology might be a great tool to manage the network’s operational complexity. The Blockchain provides for quicker, frictionless, secure, and transparent transactions. With the addition of smart contracts, it may be utilized to manage the expanding complexity of the contemporary power system. In this study, the authors focus on the scope, challenges, and potential future direction of Blockchain technology application in the power system. Blockchain has received interest and has been used for decentralized power system applications in recent years, but it is still young and has scalability, decentralization, and security concerns. This article discusses the interfaces and the possibilities that can assure trust, security, and transparency in decentralized power system applications and make a decentralized power system and power market possible.

The rapid advancements in Artificial Intelligence, specifically Machine Learning (ML) and Deep Learning (DL), have opened new prospects in medical sciences for improved diagnosis, prognosis, and treatment of severe health conditions. This paper focuses on the development of an ML model with high predictive accuracy to classify arrhythmic electrocardiogram (ECG) signals. The ECG signals datasets utilized in this study were sourced from the PhysioNet and MIT-BIH databases. The research commenced with binary classification, where an optimized Bidirectional Long Short-Term Memory (Bi-LSTM) model yielded excellent results in differentiating normal and atrial fibrillation signals. A pivotal aspect of this research was a survey among medical professionals, which not only validated the practicality of AI-based ECG classifiers but also identified areas for improvement, including accuracy and the inclusion of more arrhythmia types. These insights drove the development of an advanced Convolutional Neural Network (CNN) system capable of classifying five different types of ECG signals with better accuracy and precision. The CNN model's robust performance was ensured through rigorous stratified 5-fold cross validation. A web portal was also developed to demonstrate real-world utility, offering access to the trained model for real-time classification. This study highlights the potential applications of such models in remote health monitoring, predictive healthcare, assistive diagnostic tools, and simulated environments for educational training and interdisciplinary collaboration between data scientists and medical personnel.

Over the past years, global electricity consumption has surged significantly, highlighting the critical need for effective energy management strategies. These strategies are essential for conserving resources, reducing costs, and promoting sustainable energy use. Buildings alone account for approximately 60% of global energy consumption. Optimally utilizing current technologies can potentially reduce this consumption by 30 to 80%. Collecting extensive building energy consumption data enables the application of data-driven approaches, such as statistical and machine learning techniques, over traditional physics-based methods. An energy management system (EMS) automates data collection and visualization, aiding users in monitoring, analyzing, and managing energy resources. Historical data can be leveraged to develop strategies for optimized energy use, identifying peak and off-peak usage, thereby reducing costs. Commercially available building management solutions are often costly for small and medium-sized buildings, IoT technologies offer a cost-effective alternative. IoT systems connect devices and appliances to a network, facilitating seamless communication and data exchange. Essential components include sensors, actuators, data processing units, and communication networks. Various IoT architectures aim to enhance communication, scalability, security, and efficiency. This paper reviews key literature on IoT-based energy management systems, highlighting research gaps, system architectures, communication standards, real-world implementations. Based on this review a suitable system was developed at Kathmandu University, and the data obtained from the system is analyzed to highlight the critical need for effective energy management strategies.