Daily Archives: January 10, 2026

Authors: Tushar Kadam, Dhiraj Gadhave, Nirzara Sarole, Shital Shinde

Abstract: Landfills are a potential threat to human health and the environment, especially from the detrimental and toxic heavy metals. This study focuses on the assessment of heavy metals contamination in leachate and surface soils from different landfills in Pune. The impacted soils showed high heavy metal concentrations especially at non-sanitary unlined landfills, as compared to background values, and natural soil nearby the landfills. Leachate possesses potential risk to surface and groundwater aquifer within the area surrounding the landfill site. The aim of this chapter is to assess the physical parameters and heavy metal levels in leachate. Heavy metals are one of the important pollutants in landfill leachate. Plants and soil near the landfill may be contaminated by leachate. In this study, by evaluating the heavy metals in the leachate of three landfills, the amount of pollution caused by the leachate in the environment around the landfills in Pune was investigated.

Published by: vikaspatanker

Authors: Angel Lalu, Dr Prakash R, Shreyas Sunil, Nandhakumar S, Divya Bharti

Abstract: Self-healing distribution systems are one of the foundational requirements for future smart grids that are built to withstand disturbances, accommodate bidirectional power flow, and also maintain reliability despite the threat of in- creasing renewable penetration. Traditional FLISR (Fault Location, Isolation, and Service Restoration) solutions used currently depend mostly on SCADA, PMUs, and other high- cost protection relays. This infrastructure is usually not un- available in low-voltage networks, microgrids, and academic environments for teaching purposes. Our work proposes a novel low-cost, microcontroller-based self-healing grid pro- totype that uses ACS712 current sensors, ESP32/ESP8266 wireless sensing nodes communicating via ESP-NOW, and an STM32 Nucleo 64 (F446RE) microcontroller executing an embedded Random Forest classifier through the Eloquent- TinyML library. This system automatically and autonomously detects, classifies, and isolates faults based on a real-time multi- feature current signature. Our experimental setup and further validation shows an overall classification accuracy of 92.76%, ESP-NOW latency of 12-to 18 ms over 22 metres, and a pro- tection response time under 200 ms. Compared to other con- ventional schemes, our proposed architecture provides an inex- pensive yet robust platform similar to SCADA-like self-healing behaviours.

Published by: vikaspatanker

Authors: Mrs, Seema Amol More, Professor Dr. Swati Nitin Sayankar

Abstract: Education 5.0 represents a paradigm shift toward human-centric, ethical, and sustainable learning ecosystems by synergizing advanced digital technologies with societal needs. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers in transforming higher education through personalized learning, predictive analytics, and intelligent decision support. However, the absence of a unified and scalable framework often leads to fragmented adoption and ethical concerns. This paper proposes a comprehensive AI–ML framework tailored for higher education institutions from an Education 5.0 perspective. The framework integrates data-driven learning analytics, adaptive instructional systems, student performance prediction, and automated academic administration while emphasizing transparency, inclusivity, and data privacy. The proposed architecture consists of layered modules encompassing data acquisition, intelligent processing, decision intelligence, and stakeholder interaction. A conceptual case study demonstrates the applicability of the framework in a university environment. Comparative analysis highlights improvements in academic outcomes, operational efficiency, and learner engagement. The proposed framework provides a structured pathway for institutions seeking sustainable and ethical AI adoption, contributing to the evolving discourse on next-generation higher education systems.

DOI: http://doi.org/10.5281/zenodo.18204804

Published by: vikaspatanker

Authors: Ms Ruchi Natekar

Abstract: In Mumbai’s fast-growing private real estate construction sector, persistent challenges—cost overruns, schedule delays, and inconsistent quality—continue to limit project performance despite rising demand and increasing urban pressures. Artificial Intelligence (AI) has emerged globally as a transformative tool capable of reshaping construction planning, execution, and monitoring. Yet, in Mumbai, AI adoption remains at a formative stage, shaped by a complex interplay of technological limitations, cultural resistance, and organisational readiness. This study explores how AI is currently being used, where it creates value, and what barriers must be overcome for meaningful transformation. A mixed-methods research design was employed to capture both the breadth and depth of AI adoption. Quantitative insights were gathered through a structured survey of 99 construction professionals, spanning developers, engineers, consultants, and project managers. To complement this, qualitative interviews and focus group discussions were conducted with industry experts to understand their lived experiences, perceptions, and concerns regarding AI-enabled practices. Data were analysed using descriptive statistics, factor analysis, and thematic coding to produce an integrated, evidence-based understanding of AI’s real-world impact within Mumbai’s construction environment. Findings reveal that while AI adoption is still emerging, its footprint is steadily expanding. The most recognised and frequently applied AI tools include predictive analytics for cost estimation, automated scheduling systems, and computer-vision-based quality inspections. Respondents involved in AI-enabled projects reported heightened confidence in the technology’s potential to enhance efficiency, reduce rework, and improve decision-making. However, this optimism exists alongside significant obstacles. The study identifies notable barriers such as low digital literacy, fragmented data systems, regulatory ambiguity, and organisational cultural resistance. Many firms struggle to integrate AI into legacy workflows, and small and medium-sized enterprises face higher financial and technical hurdles. The discussion highlights that successful AI-enabled transformation requires more than just technological investment—it demands structural, cultural, and behavioural shifts within organisations. AI’s impact is therefore as socio-technical as it is operational, requiring alignment across people, processes, and platforms. This research confirms that AI holds strong promise for reducing chronic inefficiencies in Mumbai’s real estate construction sector. Yet, the gap between theoretical potential and on-ground performance remains wide. To bridge this divide, organisations must adopt a phased, context-appropriate strategy that prioritises digital literacy, data standardisation, regulatory clarity, and targeted workforce upskilling. The study offers a practical implementation roadmap tailored to Mumbai’s unique ecosystem, serving as a valuable resource for developers, project managers, policymakers, and technology providers. Ultimately, AI is positioned not as a replacement for human expertise, but as a powerful enabler of smarter, safer, and more resilient urban development.

DOI: https://doi.org/10.5281/zenodo.18204579

 

Published by: vikaspatanker