انجنيئرنگ اينڊ ٽيڪنالاجيMehran University of Engineering & Technology (Sindhi: انجنيئرڱ ۽ ٽيڪنيڀياس جي جامعہ مهراڻ) (Often referred as Mehran University or MUET) is a public research university located in Jamshoro, Sindh, Pakistan focused on STEM education.Established in July 1976, as a campus of the University of Sindh, and a year later was chartered as an independent university. The academician S.M. Qureshi was appointed as the founding Vice Chancellor of the university. It was ranked sixth in engineering category of Higher Education Institutions in the "5th Ranking of Pakistani Higher Education Institutions" in 2016.
Conventional desalination methods require significant energy and pose environmental challenges, whereas systems powered by renewable energy face issues related to efficiency and cost-effectiveness. Direct contact membrane distillation in hybrid photovoltaic-thermal systems shows potential, but thorough techno-economic and environmental evaluations are still scarce. A validated numerical model is necessary to accurately represent thermal and electrical behavior across various meteorological conditions. The performance of the system was enhanced through the application of metaheuristic algorithms, specifically Particle Swarm Optimization and Genetic Algorithm, focusing on three critical parameters: outlet fluid temperature, permeate flux, and electrical efficiency. Annual optimization results indicate that May is the peak-performing month, characterized by maximum solar irradiance, which allows a one-panel system to attain a permeate flux of 13.92 kg/m2 & sdot;h and electrical efficiency of 12.7%. The model was expanded to multi-panel configurations (ranging from 2 to 10 panels), with optimal flow rates identified to maintain thermal stability (Tfout <= 343 K). The 10-panel configuration attained a maximum permeate flux of 305.36 kg/m2 & sdot;h and produced 1404.09 m3/year of freshwater. Economic analysis indicated a decrease in the levelized cost of energy from 0.6769 USD/m3 for one panel to 0.2102 USD/m3 for ten panels, alongside a reduction in the payback period from 102 days to 33.3 days. The profitability of the highest configuration over a 25-year project lifespan surpassed $300,000. The environmental assessment indicated a possible carbon offset of 26.57 metric tons of COQ per year, with carbon credit revenue estimated at $13,700 annually based on current emissions trading values. The findings confirm the Photovoltaic-Thermal solar-driven Direct Contact Membrane Distillation desalination system as a scalable and economically feasible desalination option, particularly for off-grid areas with limited water resources. The system integrates renewable electricity generation, low-grade thermal energy recovery, and sustainable freshwater production effectively.
This study develops and empirically evaluates an Adaptive Gamification Design Model (AGDM) to address programming learning difficulties (PLDs). A three-phase mixed-methods design was employed. Phase I used a PRISMA-guided systematic review of studies published from 2010 to 2025 (N = 112) to construct a multidimensional Programming Learning Difficulty Taxonomy (PLDT) encompassing cognitive, affective, and instructional challenges. In Phase II, the taxonomy was validated through exploratory and confirmatory factor analyses of survey data from 842 undergraduate computer science and software engineering students at four public universities. Phase III comprised a 14-week quasi-experimental intervention involving a control group (n = 142) and an adaptive-gamification group (n = 144). Cognitive load, syntax anxiety, and self-efficacy deficit significantly predicted course failure. Compared with traditional instruction, the AGDM environment produced higher academic performance and intrinsic motivation and reduced the dropout rate from 34% to 12%. Structural equation modeling indicated that engagement and self-efficacy mediated the relationship between adaptive gamification and academic performance. The framework is operationalized through the Adaptive Gamification Optimization Algorithm (AGOA), which dynamically adjusts task difficulty, feedback scaffolding, and motivational incentives according to each learner's PLDT profile. The study contributes a validated taxonomy and a scalable adaptive-gamification framework that can be integrated into computing curricula to support competence, retention, and personalized learning.
As wind energy capacity surpasses 1136 GW globally, ML technologies prove essential for achieving renewable energy targets and grid stability requirements. Machine learning (ML) has emerged as a transformative technology for wind energy systems, revolutionizing forecasting accuracy, operational efficiency, and system reliability. This comprehensive review synthesizes recent advances across 500+ peer-reviewed studies from 2020 to 2025, revealing 15–40% performance improvements over traditional methods across all major applications. Deep learning approaches achieve up to 99% accuracy in fault detection while optimized forecasting systems reduce Mean Absolute Percentage Error (MAPE) to 5–12% and demonstrate 20% increases in energy value through advanced prediction capabilities. The review examines ML applications spanning wind power forecasting, turbine control optimization, predictive maintenance, and emerging technologies including digital twins and physics-informed neural networks. Critical challenges including data availability, model interpretability, and cross-site generalization are addressed, while future research directions emphasize physics-informed ML, federated learning, and explainable AI approaches.
Climate change is affecting ecosystems, communities, and human health worldwide. These changes pose risks to global energy systems so there is a dire need to combat climate change and limit global warming to 1.5 degrees C. This study undertake global energy systems and forecasted total energy consumption, production and greenhouse gas (GHG) emissions worldwide for the study period 2021-2050 by taking the input data from 1970 to 2020 using the four algorithm's namely, Holt Winter (HW), Exponential Smoothing (ES), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA) implemented in Python. It is found that HW and ES have same forecast results globally with energy consumption of 236,285 TWh which can easily meet by 475,980 TWh generation until 2050. Renewables and fossil fuels contributed to 250,106 TWh units and 225,874 TWh units with 48 billion metric tons of GHG emissions until 2050. The global forecast of ARIMA model suggested that 232,878 TWh energy consumption is noticed which can easily meet by 446,126 TWh generation with 213,052 TWh share of renewables and 233,074 TWh of fossil fuels with 49 billion metric tons of GHG emissions produced until 2050. SARIMA model forecast is very much valuable for limiting global mean temperature to 1.5 degrees C. The global energy consumption is forecasted to be 231,022 TWh which easily meet by 350,054 TWh green energy generation potential with almost zero emissions until 2050 and it is found that SARIMA model has 98% of accuracy.
This study offers a computational framework that analyzes the escape characteristics of transcendental complex maps by utilizing the AK iteration scheme. The well-known polynomial map of the form zn+c is generalized to the form zn+sin(z)+log(cm), with m≥1 and c∈C\{0}, allowing the creation of complex fractal structures. A precise escape criterion is developed for the AK iteration scheme, ensuring the numerical stability of the scheme when applied to the construction of the Mandelbrot set and the Julia set. In order to validate the effectiveness of the developed framework, a comparative analysis is performed between the AK iteration scheme and the CR iteration scheme, focusing on the first parametric case of the Mandelbrot set and the Julia set. The average escape time, average number of iterations, non-escaping area index, and fractal dimension are analyzed with respect to the two iteration schemes. The numerical results indicate that the fractal structure obtained by the AK iteration scheme is different from the fractal structure obtained by the CR iteration scheme, showing the effectiveness of the AK iteration scheme as a powerful tool in the study of complex systems.
