Adhiyamaan College of Engineering

This study aims to examine the web presence, visibility, and user engagement of Indian Institutes of Management (IIM) websites using webometric, demographic, and performance indicators. It seeks to identify disparities between older and newer Indian Institutes of Management and assess how institutional maturity influences online impact. The study analyzes 21 official Indian Institutes of Management websites using webometric indicators such as Domain Authority (DA), Page Authority (PA), link structure, Web Impact Factor (WIF), traffic sources, global and national popularity ranks, and user engagement metrics. Audience characteristics, including gender and age distribution, were also examined. Data were systematically tabulated (Tables 3–11) and interpreted using descriptive analysis. All Indian Institutes of Management uniformly use the .ac.in domain. Older IIMs such as Ahmedabad (DA 52; 4,306,800 external links), Bangalore (1.496 million visits), and Kozhikode (bounce rate 30.01%; 5.81 pages/visit) demonstrate stronger web visibility and engagement. Male users dominate (57.53%–72.69%), while the 25–34 age group forms the largest audience (21–36%). Organic search is the primary traffic source (50.93%–76.35%). Newer Indian Institutes of Management show higher link density (e.g., Bodh Gaya SWIF 5.354) but lower overall visibility. The findings suggest the need for newer Indian Institutes of Management to enhance content depth, external linkages, and user engagement strategies to improve digital visibility and academic outreach.

IoT is one of the significant enabling technologies in the contemporary energy landscape. This work addresses intelligent load management under fault, under-load, and overload conditions. Electrical equipment requires automatic and rapid response to avoid damage and prevent service interruption. In contrast to traditional circuit breakers that disconnect the entire system, the proposed architecture employs ESP32-based intelligent control, together with ACS712 current sensors, to provide accurate per-load current measurements and load-specific overcurrent protection. The system is also capable of selective load isolation, meaning that it maintains the operation of healthy circuits and automatically disconnects only the faulty load without affecting other loads. Cloud-based monitoring is implemented via the ThingSpeak platform, enabling real-time remote system surveillance through any internet-connected device. The faulty section of the system is readily identified from the current readings visualised on the cloud dashboard and mobile interface. The proposed system is powered primarily by solar photovoltaic sources and can also operate from the utility grid, providing flexibility across renewable and conventional supply scenarios. Experimental testing demonstrates protection response times in the sub-200 ms range and high-accuracy current monitoring with a mean absolute error below 0.05 A. The key contributions include: selective load protection, real-time IoT-based energy analysis, and a cost-effective open-hardware architecture. The work advances the safety and efficiency of smart energy control systems in distributed renewable energy environments.

Rapid urbanization and rising vehicle density have made urban traffic congestion a significant problem. An integrated framework for intelligent urban traffic flow optimization utilizing mathematical modeling, optimization methods, and AI-driven computational methodologies is presented in this chapter. Adaptive traffic control is made possible by modeling congestion dynamics using graph theory, network flow models, machine learning, and reinforcement learning. The findings of the simulation show increases in traffic throughput, journey time, and delay reduction. The suggested paradigm for intelligent and sustainable transportation systems is practically applicable, as demonstrated by case studies from Indian cities.

Twenty-four new hybrid Pyrrole-tryptamines compounds were synthesized and thoroughly characterized using various techniques such as FT-IR, 1H NMR, 13C NMR, and LC-MS. An evaluation of each compound was conducted based on criteria including their PASS, BBB, ADME, pharmacophore model, and bioactive score. Antibacterial testing of all derivatives indicated that compound 5j exhibited strong activity against MRSA. Investigations into membrane damage, supported by SEM images, cellular content leakage, potassium efflux, and changes in lipid profiles, confirmed the anti-MRSA properties of compound 5j. In an in silico molecular docking analysis, compound 5j achieved a binding score of − 10.02 against the MRSA protein 6FTB, while streptomycin scored − 10.25. Additionally, when compared to standard doxorubicin against 3T3L1 cell lines, compound 5j demonstrated a less toxic IC50 effect of 669.80 µM on normal cell lines. These findings warrant further research on compound 5j for the potential development of a medication to treat MRSA infections.