National Engineering College (NEC), Kovilpatti, Tamil Nadu, India is a self financing Autonomous Institution. NEC was established in the year 1984, approved by All India Council for Technical Education (AICTE), New Delhi, India, and the courses offered are accredited by National Board of Accreditation (NBA), New Delhi. NEC is affiliated to Anna University, Chennai. NEC offers six undergraduate degree programmes and five post-graduate degree programmes in Engineering and Technology. The institution runs under a trust formed by the Chairman Kalvi Thanthai Thiru. K. Ramasamy.More than 10,000 engineers have graduated during the past 35 years. NEC has Research Centre Status to run Doctoral research programmes approved by Anna University, Chennai, through which more than 300 researchers have either completed or are pursuing research in diverse fields.
Improving the thermal energy conversion efficiency of solar stills is still a key challenge to accelerating clean and sustainable desalination technologies to combat worldwide water scarcity. In this paper, an innovative Twin Wedge Solar Still (TWSS) design is experimentally investigated with two new modifications: (i) a graphene nanoplatelet (GNP)-coated absorber plate to increase solar absorption, and (ii) a mixed nano-composite phase change material (nPCM) based on aluminium oxide (Al2O3) and graphene oxide (GO) for enhanced thermal energy storage. This coupled combination has not been used previously for TWSS applications. The new system exhibits improved performance by enhancing solar absorptivity, thermal conductivity, and storage capacity, resulting in a cumulative productivity of 6.133 L/m2/day. The developed modified system shows 139.4 %, 153.2 %, and 230.7 % greater productivity, energy efficiency, and exergy efficiency compared to the existing TWSS. The evaporative heat transfer rate becomes almost double (235.89 W/m2K compared to 99.15 W/m2K), and the economic cost of distilled water decreases to $0.012/L from $0.024/L for the traditional system. The outcomes verify that the integration of the proposed GNP-coated absorber and GO-Al2O3-based nPCM provides a new and economical path to enhance solar desalination performance, making it a promising strategy for sustainable freshwater production.
Artificial intelligence (AI) and learning analytics are transforming higher education by enabling data-driven personalization, adaptive assessment, and informed instructional decision-making. Conventional learning management systems (LMSs) primarily support content delivery but lack mechanisms for providing actionable insights to educators or personalized guidance to learners. To address these limitations, this study introduces an AI-Supported Learning Analytics Platform that integrates three key functions: (i) personalized learner support through intelligent matching and recommendations, (ii) automated quiz generation and evaluation using large language models, and (iii) conversational assistance for real-time interaction. The platform not only enhances student engagement but also collects interaction and performance data, which are trans- formed into analytics for educators to inform course design and pedagogical strategies. A mixed-method research design, combining system development with quasi-experimental evaluation, was employed in a higher education context. Findings indicate that students using the proposed platform achieved higher academic performance, greater engagement, and improved satisfaction compared to those using traditional LMSs. Moreover, the analytics generated provided educators with valuable feedback for evidence-based decision-making. This study contributes to the field of educational technology by (1) demonstrating how AI- driven personalization, assessment, and conversational support can be embedded in higher education platforms, (2) highlighting the role of learning analytics in supporting both student learning and educator decision-making, and (3) offering a framework for integrating AI-supported analytics into institutional teaching and learning practices. The results affirm the potential of AI-enhanced systems to strengthen both learner experiences and institutional decision-making in higher education.
This article presents a smart shopper trolley system that lets shoppers see their entire haul of purchases. Each product is scanned with autonomy, and its price is added to the overall cost using the trolley's scanner and display unit. Because the display shows the present total cost, consumer may easily manage their spending before they reach the billing section. Additionally, a Firebase database receives the detected product details and displays the list of items in an Excel-like sheet format for reference. The system is powered by a microprocessor that handles pricing, calculation, product scanning, and data transfer to the cloud. This project's main objective is to make the buying procedure more transparent and to raise consumer awareness of purchases while they are shopping. This study shows how simple trolley-based tactics can improve consumer comfort without changing the present billing process. Finally, the approach suggested makes it easier to create smart retail environments by combining rudimentary automation with cloud storage.
Neonatal sepsis is a serious, perhaps fatal illness that needs to be identified early and treated with antibiotics right away. Conventional blood culture tests, on the other hand, take 24 to 72 hours, which increases the risk of death and delays clinical decision-making. This study uses routinely obtained clinical and demographic data from Neonatal Intensive Care Unit (NICU) records to propose a machine learning-based approach to predict neonatal sepsis before blood culture confirmation. To stop data leaking, the dataset was preprocessed, and variables related to blood culture results were removed from the input features. Accuracy, ROC-AUC, precision, recall, and F1-score measures were used to assess the implementation and performance of advanced machine learning models, such as Light Gradient Boosting Machine (LightGBM) and CatBoost. Because of its adept handling of missing values, class imbalance, and categorical variables, the CatBoost model showed excellent predictive performance. By assisting doctors in identifying high-risk newborns early on, the suggested methodology can improve neonatal outcomes by facilitating prompt antibiotic administration. The potential of artificial intelligence-based decision support systems to improve newborn sepsis therapy in clinical settings is demonstrated by this study.
In the modern world, using nano-materials has become a new area of technological advancement and their effective functionalities have a significant impact on various fields including the construction field by providing insulation, improving strength, and resisting crack formation. Graphene oxide (GO) is poised to make impact on the construction industry in the coming years due to its oxygenated functionalities that enhance dispersibility, surpassing other graphene-based materials. The quality of dispersion is crucial when utilizing GO as a modifier for cement-based products. The abundance of calcium ions in fresh cement paste can hinder the efficacy of GO, underscoring the necessity to investigate the dispersion mechanism of GO. Graphene oxide (GO) is produced as a result of oxidization of the strongest material graphene which imparts strength, toughness, and higher thermal properties, this study was intended to examine the concrete properties for strength and durability properties, made with silica fume (SF) as the partial replacement to cement by 5
