There is a broad range of novel Coronaviruses (CoV) such as the common cold, cough, and severe lung infections. The mutation of this virus, which originally started as COVID-19 in Wuhan, China, has continued the rapid spread globally. As the mutated form of this virus spreads across the world, testing and screening procedures of patients have become tedious for healthcare departments in largely populated countries such as India. To diagnose COVID-19 pneumonia by radiological methods, high-resolution computed tomography (CT) of the chest has been considered the most precise method of examination. The use of modern artificial intelligence (AI) techniques on chest high-resolution computed tomography (HRCT) images can help to detect the disease, especially in remote areas with a lack of specialized physicians. This article presents a novel metaheuristic algorithm for automatic COVID-19 detection using a least square support vector machine (LSSVM) classifier for three classes namely normal, COVID, and pneumonia. The proposed model results in a classification accuracy of 87.2% and an F1-score of 86.3% for multiclass classifications from simulations. The analysis of information transfer rate (ITR) revealed that the modified quantum-based marine predators algorithm (Mq-MPA) feature selection algorithm reduces the classification time of LSSVM by 23% when compared to the deep learning models.

Efficient and reliable energy systems are key to progress of society. High performance batteries are essential for widely used technologies like Electric Vehicles (EVs) and portable electronics. Additionally, an effective Battery Management System (BMS) is crucial to oversee vital parameters of battery. However, BMS can experience cell imbalance due to charging/discharging dynamics, which reduce battery capacity, lifespan, and efficiency, and raise critical safety concerns. This calls for effective cell-balancing techniques. Notably, the existing literature on cell balancing is limited, urgently necessitating a thorough survey to pinpoint key research gaps and suggest prompt solutions. In this article, cell balancing and corresponding techniques are reviewed. Initially, we detail comparison of passive cell balancing techniques and assess their respective advantages, drawbacks, and practical applications. Then, we discuss the strengths and weaknesses of active cell balancing methods and applicability of cell balancing for both, series and parallel-connected cells. Additionally, we examine the need for cell balancing in commonly used batteries, and applications in EVs. Lastly, we present detailed prospects which include challenges and directions for future research.

In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy, which has limited their use mainly to high-capability devices such as network nodes. However, with many advancements in technologies such as the Internet of Things (IoT) and edge computing, it is desirable to incorporate ML techniques into resource-constrained embedded devices for distributed and ubiquitous intelligence. This has motivated the emergence of the TinyML paradigm which is an embedded ML technique that enables ML applications on multiple cheap, resource- and power-constrained devices. However, during this transition towards appropriate implementation of the TinyML technology, multiple challenges such as processing capacity optimization, improved reliability, and maintenance of learning models' accuracy require timely solutions. In this article, various avenues available for TinyML implementation are reviewed. Firstly, a background of TinyML is provided, followed by detailed discussions on various tools supporting TinyML. Then, state-of-art applications of TinyML using advanced technologies are detailed. Lastly, various research challenges and future directions are identified.

The therapeutic efficacy and safety of Tamra Bhasma (Copper calyx), a widely used Ayurvedic metallic formulation, depend on thorough incineration and the absence of residual free metal. Although effective, conventional quality assessment methodologies such as Nirutthikarana (test for quality assessment) are labour-intensive, time-consuming, and require elevated temperature processing. This study aimed to assess the quality of Tamra Bhasma using an innovative electrolysis-based technique and compare its efficacy with that of the traditional Nirutthikarana protocol. The experimental design incorporated three commercially sourced Tamra Bhasma samples. Using a contemporary approach, 1 g of each sample was solubilized in dilute hydrochloric acid and subsequently subjected to electrolysis using silver electrodes. The experimental parameters of voltage, current, and duration were standardized, followed by the observation and analysis of cathode deposition. The mass of metallic precipitation on the Rajata (Silver foil) served as a quantitative metric of free metals. The experimental outcomes demonstrated that sample I exhibited substantial deposition at elevated voltages (8v–10v), indicative of incompletely processed metallic constituents. In contrast, samples II and III displayed minimal and stable deposition at intermediate voltages (4v–6v), suggesting properly processed Bhasma formulations. These findings align with the classical Nirutthikarana test results, thereby validating the analytical precision of the electrolysis methodology. The electrolysis-based analytical framework represents a dependable, efficient, and quantifiable alternative to traditional Nirutthikarana assessment. This approach reduces procedural complexities, enhances precision, and provides a scalable method for systematic Bhasma quality control, effectively integrating traditional Ayurvedic principles with contemporary analytical science.