In the context of the energy transition, optimizing photovoltaic solar systems with charge controllers plays a crucial role in managing the energy produced by solar panels and its storage in batteries. Two dominant technologies are used in this field: MPPT (Maximum Power Point Tracking) and PWM (Pulse Width Modulation). This paper presents an in-depth comparative study of these two technologies, focusing on their efficiency, cost, and suitability for small-scale solar installations, particularly in rural African contexts where budget constraints are significant. The study begins with a detailed literature review, analyzing the operating principles of MPPT and PWM controllers, their respective advantages and disadvantages, and performance under various environmental conditions. Previous studies are examined to identify the conditions under which each type of controller offers the best performance. Empirical data and existing case studies are reviewed to establish a solid comparison base. This analysis is accompanied by tables and graphs illustrating the performance of both types of controllers. Based on the results of this analysis, the paper proposes the design of a PWM charge controller suitable for small solar installations in rural areas with budget constraints. This solution aims to promote energy accessibility while minimizing costs, offering a viable alternative for rural communities with limited resources.

The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a Graphical User Interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed Air Passenger Demand Prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

This paper presents the findings of a probabilistic evaluation of a doubly symmetric I-steel beam's bending, shear, and deflection limit states. The design adhered to BS 5950, Part 1, 2000. Failure equations for flexure, shear, and deflection were derived, while random variable probabilistic models were sourced from the literature. Optimization using the First-Order Reliability Method (FORM) yielded design points, reliability indices, and sensitivity analyses. The results revealed that the reliability index decreased as beam span increased, with negative indices observed at a load ratio of 1.0 and beam span of 8.5m. Moreover, increasing the beam span to an overall depth ratio above 42 compromised reliability. The design achieved material savings in the plastic section modulus for a target reliability index of 3.0 but increased the modulus for a target index of 3.80 over a 50-year period. The design proved critical in bending, safe in deflection, and satisfactory in shear.

The current research is centered on the optimization and prediction of non-elastic performance factors crucial for imprοving the struϲtural integrity and strength of pipeline weldments, with a specific emphasis on the period of immersion in an HCl solution. The research investigates the results of welding factors on immersion period. Utilizing Design Expert software, the study employs Central Composite Design (CCD) methodology to generate an experimental matrix and develop models. Additionally, Respοnse Surfaϲe Methodοlogy (RSM) and Artifiϲial Neural Networks (ANN) are utilized for the prediϲting and optimizing these parameters. The research concludes that optimal welding parameters, 160 amps current, 21.28 volts voltage, and 14.67 liters/min gas flow rate, which results in an immersion period of 18.067 days in the HCl solution. The study shows that both the RSM and ANN are effective for optimization and prediction, with RSM demonstrating slightly superior predictive capabilities.

Artificial Intelligence (AI) expeditiously transmutes from a specialized area of study to a key component of contemporary technology, propelling breakthroughs in a wide range of industries. AI Unleashed, Pioneering Trends and Future Directions in Artificial Intelligence is a study examining the current developments influencing the field's progress and future course. This study explores essential fields, including autonomous systems, machine learning, and natural language processing, showcasing new developments and present uses. It also considers AI's ethical and societal ramifications, including issues with prejudice, privacy, and the necessity of robust governance systems. Exploring the confluence of artificial intelligence (AI) with other cutting-edge technologies, such as quantum computing and the Internet of Things (IoT), highlights the potential for unparalleled capabilities. With a perspective beyond the future, this overview highlights the significant obstacles and possibilities that will shape artificial intelligence (AI), from improving human-machine interaction to expanding general intelligence. This assessment offers insights into the cutting-edge trends propelling AI forward and the future paths that will mold the next wave of AI innovation through an extensive examination.