PV panels are installed in an open-spaced setting and then exposed to dust, dirt, and debris which significantly reduce their power output, making regular cleaning essential. Therefore, this research developed an automatic cleaning system for solar panels to enhance their efficiency and performance. The developed system utilizes an Arduino microcontroller, a lead screw mechanism, and a cleaning arm to automate the cleaning process. The system is designed to automatically control the cleaning system wirelessly using a Wi-Fi module that has been integrated on the Arduino board, and when the solar panels require cleaning, it activates the cleaning arm to remove the accumulated dirt. This research project involves the design, development, and implementation of the automatic cleaning system. The components used in the system include a PC817 optocoupler, C815 limit switch, Nodemcu microcontroller, DC wiper motor (12V), screw mechanism, metallic frame, solar panels, and a DC power supply (12V). These components are carefully selected to ensure efficient and reliable operation of the cleaning system. The system performance for both cleaning and dusty panels has been evaluated and it was found that the efficiency for the cleaning system is higher with output power of 53.69W. The developed system can be used to enhance the PV module performance areas where the weather can be classified as dusty and the pollutants are increasing day by day as a result of smokes, industrial work and new building construction.

In the modern organizational landscape, information technology plays a pivotal role in shaping business processes. The increasing reliance on IT necessitates a focus on the confidentiality, availability, and integrity of both enterprise and customer data, making information security a paramount concern. This study delves into the challenges faced by Small and Medium-sized Enterprises (SMEs) in Fort Portal Central Division during their information security governance efforts, highlighting issues such as limited resources, budget constraints, time limitations, and a lack of expertise in drafting and ensuring compliance with security policies. To address these challenges, a comprehensive framework for improving Information Security Governance in SMEs was developed and evaluated. Primary data were collected from 351 respondents, including Proprietors, Directors, CEOs, Managers, and operations personnel, shedding light on the specific hurdles faced by SMEs. The proposed framework underwent rigorous evaluation based on design science parameters, demonstrating efficiency and usability. The results of the evaluation revealed that the developed framework effectively addressed the identified challenges, fulfilling the study's objective. The study recommends SMEs in Fort Portal City to implement the framework to enhance their Information Security Governance efforts. Additionally, policy makers in Uganda, including the National Information Technology Authority Uganda (NITA-U) and Uganda Investments Authority (UIA), can leverage the designed framework to make informed decisions regarding SMEs and information security management and governance. This research contributes valuable insights to the broader discourse on information security governance in SMEs, particularly within the context of Fort Portal City.

The integration of advanced analytics in retail supply chains has transformed operational efficiency, demand forecasting, and risk mitigation. This study examines the impact of predictive analytics, machine learning, and AI-driven risk intelligence on supply chain performance. Using a mixed-methods approach, including case study analysis and statistical modeling, the research highlights key improvements in inventory accuracy, logistics optimization, and fraud detection success rates. Findings indicate that retailers leveraging real-time analytics have experienced a 25-40% increase in supply chain efficiency, with major gains in demand forecasting precision and supplier risk assessments. Despite these advantages, challenges remain, including high implementation costs, data security vulnerabilities, and algorithmic biases. Smaller retailers face significant barriers in adopting AI-powered analytics due to infrastructure limitations and workforce constraints. The study emphasizes the importance of ethical AI governance, cybersecurity protocols, and regulatory compliance in ensuring responsible analytics adoption. Future research should focus on scalable AI frameworks, blockchain-enhanced supply chain security, and quantum computing applications in predictive analytics. The findings underscore the need for a multi-stakeholder approach that integrates technological innovation with ethical considerations to achieve sustainable, transparent, and resilient supply chain ecosystems. This research contributes to the ongoing discourse on data-driven retail transformation, offering strategic insights for industry leaders, policymakers, and researchers in supply chain management.

Background: Rural infrastructure, agro-processing, and decentralized energy systems in the Osmanabad district of Maharashtra utilize a significant quantum of rotating machinery (e.g., centrifugal pumps, turbines, and compressors). Regular mechanical failures and erratic equipment breakdowns in these facilities result in substantial loss of productivity and maintenance problems, which can be particularly challenging in resource-poor settings with limited technical support. Objectives: The purpose of this work is to evaluate the effectiveness of AI-based PdM models in detecting faults and preventing machine malfunctions for rotating machinery. This paper aims to design context-sensitive, affordable, and understandable AI solutions that meet rural deployment requirements, to satisfy fault detection accuracy, maintenance cost savings, and stakeholders' trust. Methods: Employing a concurrent mixed-methods approach, the study integrated 6 weeks of multi-sensor data (vibration, temperature, acoustic signals) collected from five rural machinery sites in Osmanabad, with qualitative interviews with technicians and plant managers. Machine learning algorithms (CNNs, LSTMs, Isolation Forests, hybrid TCN-Autoencoders) were trained and validated under the supervised and unsupervised paradigms. The performance measures were the classification accuracy, mean squared error, and stakeholders' usability rating. Results: The fault detection accuracies were all higher than 95% for all the models. CNNs had the best performance with 99.89% for impeller blade faults, and LSTMs had 98.5% for turbine vibration anomalies. The total maintenance costs were decreased by 31% and the downtime was reduced by up to 70%. Technicians had high trust in AI systems, particularly if they were provided with explainable outputs such as fault heatmaps and predictive dashboards. Conclusions: AI-supported PdM systems are capable of generating impactful improvements in equipment reliability and operational efficiency when co-designed with community stakeholders and adjusted for a rural setting. This study adds to mechanical engineering and equitable AI adoption in underserved areas.

Background: CO₂ emissions related to the production of concrete contribute significantly to the global footprint, resulting in approximately 8% of the total anthropogenic CO₂ output. Even as India races to build its rural infrastructure, districts like Osmanabad, with its pervasive climate vulnerability and construction-induced emissions, must grapple with a potent toxic brew of the two. Carbon-neutral concrete especially through CO₂ mineralization or the use of carbon-capturing additives presents a viable way to decarbonize construction while enhancing the material's properties. Objectives: This study aims to assess the feasibility, environmental impact, and stakeholder opinions regarding carbon-neutral concrete technology in Osmanabad. More specifically, the study explores the possibility of using CO₂ mineralization during curing and locally available carbon-capturing additives to reduce embodied carbon in rural infrastructure projects. Methods: A mixed-methods exploratory design was employed, incorporating semi-structured interviews, field observations, focus groups, and technical performance tasks. The research population consisted of 80 informants: engineers, masons, municipal officers, vendors, and teachers. Thematic analysis of qualitative data was conducted through NVivo, and comparable quantitative indicators, including compressive strength and carbonation depth, were benchmarked across pilot sites. Results: There were improvements in the strength of carbon-neutral mixes of up to 25% higher and 30 to 50% in the carbonation depth than the equivalent conventional concrete. Technical professionals had a high level of stakeholder awareness; however, this was lower among field workers. Obstacles were the healing infrastructure, additional expense, and training deficiencies. Considering the local availability versus cost, fly ash and biochar were identified as potential amendments. Conclusion: Carbon-neutral concrete could be an alternative for climate-resilient construction in Osmanabad. Its scale-up relies on policy and support, regionally specific supply chains, and capacity development. Environment: Through the combination of environmental innovation and rural development, Osmanabad is demonstrative of what low-carbon infrastructure could look like in a resource-stripped context.