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This study presents a compact educational photovoltaic/thermal (PV/T) system designed for thorough performance assessment under simulated weather conditions. As an affordable educational tool, the system offers significant pedagogical value. The PV/T system features two photovoltaic modules: a thermally enhanced module and a standard one. The thermally enhanced module uses water as a coolant, which transfers heat from the PV cells to a fan-operated heat exchanger, with the coolant being recirculated to maintain optimal conditions. A halogen lamp, placed between the modules, simulates solar radiation to ensure effective electrical current generation. The system's remote-control capabilities, managed via the Message Queuing Telemetry Transport (MQTT) protocol, enable real-time adjustments to the coolant flow rate, heat exchanger efficiency, and lamp brightness, as well as monitoring of electrical parameters. Experimental findings indicate that the PV/T module achieves a 7.71% increase in power output compared to the standard PV module and offers a 17.41% improvement in cooling efficiency over scenarios without cooling. Additionally, the numerical methods used in the study show a maximum deviation of 4.29% from the experimental results, which is considered acceptable. This study showcases a best practice model for solar training, applicable from elementary to university levels, and suggests innovative approaches to enhancing solar energy education. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) management has become a highly challenging issue for many countries at different levels of development since the growth of population and urbanisation has resulted in a large increase in MSW generation. It is difficult to forecast the solid waste generated and its characteristics due to the non-linear nature of the MSW system, hence the importance of introducing artificial intelligence (AI) techniques and machine learning (ML) methods. This paper discusses a systematic literature review (SLR) on the application of AI techniques in MSW management, including waste generation prediction, waste collection and transportation and waste treatment and final disposal. The study reviewed and analysed the research studies published between 2000 and 2021, and investigated the current challenges faced by researchers in implementing AI approaches in the MSW system. It was concluded that artificial neural networks are the most used approach in various MSW-related problems. However, the lack and reliability of data are limiting the advancement of AI techniques in this field. Additionally, most studies claimed that their results are accurate and can be implemented in real-life scenarios, with an absence of a clear baseline to assess the performance of the adopted approaches. The detailed gaps and future suggestions for AI techniques in MSW systems are also discussed for further research. [ABSTRACT FROM AUTHOR]