The degradation of solar photovoltaic (PV) modules is caused by a number of factors that have an impact on their effectiveness, performance, and lifetime. One of the reasons contributing to the decline in solar PV performance is the aging issue. This study comprehensively examines the effects and difficulties associated with aging and degradation in solar PV applications. In light of this, this article examines and analyzes many aging factors, including temperature, humidity, dust, discoloration, cracks, and delamination. Additionally, the effects of aging factors on solar PV performance, including the lifetime, efficiency, material degradation, overheating, and mismatching, are critically investigated. Furthermore, the main drawbacks, issues, and challenges associated with solar PV aging are addressed to identify any unfulfilled research needs. Finally, this paper provides new directions for future research, best practices, and recommendations to overcome aging issues and achieve the sustainable management and operation of solar energy systems. For PV engineers, manufacturers, and industrialists, this review’s critical analysis, evaluation, and future research directions will be useful in paving the way for conducting additional research and development on aging issues to increase the lifespan and efficiency of solar PV.

The next generation of wireless communication networks will rely heavily on machine learning and deep learning. In comparison to traditional ground-based systems, the development of various communication-based applications is projected to increase coverage and spectrum efficiency. Machine learning and deep learning can be used to optimize solutions in a variety of applications, including antennas. The latter have grown popular for obtaining effective solutions due to high computational processing, clean data, and large data storage capability. In this research, machine learning and deep learning for various antenna design applications have been discussed in detail. The general concept of machine learning and deep learning is introduced. However, the main focus is on various antenna applications, such as millimeter wave, body-centric, terahertz, satellite, unmanned aerial vehicle, global positioning system, and textiles. The feasibility of antenna applications with respect to conventional methods, acceleration of the antenna design process, reduced number of simulations, and better computational feasibility features are highlighted. Overall, machine learning and deep learning provide satisfactory results for antenna design.

In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.