The solar photovoltaic system for domestic application rapidly increased in India. Several studies have already proved that the efficiency range of the photovoltaic units is only 15-25%. Also a detail studies have been carried out on the factors affecting power output of PV System. Among various factors like dust, temperature, humidity, the Temperature has major influence on power output. This paper detailed about recovering the waste heat, (the heat generated in the panel) near achieving concurrent benefits for Water Heating and Cooling of Panels and increased the power output.

Indian power sector having the target of 175GW of power from renewable energy, out of that 40 GW from Rooftop solar Photovoltaic system by 2022. The installed capacity of solar photovoltaic until 2017 is about 14.77 GW only. Even though in depth study has been carried out on the performance analysis, there is no bench marking has been arrived for the Indian Roof Top Solar PV system. Hence this paper has made an attempt by calculating the various parameters like performance ratio, plant capacity utilization ratio for the two 20KWp grid connected rooftop power plant in Telangana state. In addition to the above, it needs to do in depth analysis of power quality issues on roof top grid tied system.

Student diversity plays a crucial role in fostering inclusive education, which enhances innovation, creativity, and problem-solving. In India, while the student mobility is increasing, disparities across regions and institutions continue to influence students’ decision in choosing an institution. Understanding student’s decision making will enhance equal opportunity for quality education. This study aims to identify the factors affecting interstate student diversity in Indian higher education. Secondary data from a national ranking body were analysed using statistical techniques and supervised machine learning algorithms using tool OriginPro 2024 and MATLAB’s R2024a respectively. Statistical analyses, including Pearson correlation, Spearman correlation, and Kruskal–Wallis tests, were employed to analyse relationships among factors, while machine learning models such as Gaussian Process Regression, Ensemble methods, Support Vector Machines, and Kernel-based approaches were used to assess feature importance. The findings show that the factors of student diversity vary across institution types. In Central Government Funded Institutions (CGFIs), location (23%) and course offerings (9%), while in State Government Funded Institutions (SGFIs), international student proportion (17%), were significant factors. For Self-Financed Institutions (SFIs), institutional rank (13%) and placement opportunities (10%) were key factors. These insights can guide policies and strategies to enhance student diversity and promote inclusive education in alignment with National Education Policy (NEP) 2020 and quality education Sustainable Development Goals (SDG) 4.

Time series-based modeling provides a fundamental understanding of process fluctuations in an anaerobic digestion process. However, such models are scarce in literature. In this work, a dynamic model was developed based on modified Hill’s model using MATLAB, which can predict the biomethane production with time series. This model can predict the biomethane production for both batch and continuous process, across substrates and at diverse conditions such as total solids, loading rate, and days of operation. The deviation between literature and the developed model was less than ±7.6%, which shows the accuracy and robustness of this model. Moreover, statistical analysis showed there was no significant difference between literature and simulation, verifying the null hypothesis. Finding a steady and optimized loading rate was necessary to an industrial perspective, which usually requires an extensive experimental data. With the developed model, a stable and optimal methane yield generating loading rate could be identified at minimal input.