Category Archives: Uncategorized

Authors: Samuel N Nimaful, Augustine Hanyabui, Joel Holison, Faith Esther Holison, Laureta Tatenda Nyamsutswa, Gloria O. Darkoh

Abstract: Older buildings constitute the vast majority of the world’s building stock and typically have poor energy performance. With an estimated 75% of 2050 buildings already in existence today[1], deep energy retrofits are critical to reducing carbon emissions and energy costs. Retrofit strategies must begin with a comprehensive audit to identify inefficiencies such as poor insulation, air infiltration, outdated HVAC systems, and inefficient lighting or controls. Common retrofit measures include upgrading the building envelope (insulation, windows, sealing), modernizing HVAC and ventilation, installing efficient lighting and controls, and adding on-site renewables like solar PV[2][3]. Cost-benefit and life-cycle analyses are essential to evaluate each measure’s payback period and savings. For instance, New York State’s Buildings of Excellence program found that passive-house envelope retrofits can reduce site EUI by ~62% with paybacks of ~5.5 years (with incentives)[4]. However, achieving deep savings often requires integrated packages; one Swedish case achieved 53% energy demand reduction by combining wall insulation, high-performance glazing, and heat-recovery ventilation[5]. Global case studies demonstrate success across building types and climates. For example, 345 Hudson (a high-rise office in NYC) will use a novel “thermal network” to share waste heat between floors, targeting >50% energy reduction and 85% carbon reduction[6]. In New York City, recladding the Manhattan West office tower with a self-shading high-performance facade and upgrading its HVAC yielded substantial cooling load reductions while allowing continued partial occupancy[7][8]. Meanwhile, multifamily housing projects (e.g. NYSERDA’s Buildings of Excellence) have demonstrated average EUI drops of ~62% by applying Passive-House-style envelopes, ductless heat pumps, and energy-recovery ventilation[9][4].

DOI: https://doi.org/10.5281/zenodo.19413993

 

Published by: vikaspatanker

Authors: Priya Gupta, Uttam Kumar, Vansh Tyagi, Ankur Kaushik

Abstract: Agriculture faces challenges including unpredictable weather, plant diseases and their treatment, soil classification with crop recommendation and limited agricultural expertise access. GenZ AgriTech addresses these through an integrated AI platform leveraging machine learning and deep learning. The system includes seven core modules: weather forecasting, plant disease detection (99.17% accuracy), soil type classification(99.63% accuracy), AI chatbot support, government scheme information portal, crop recommendation, and yield prediction — all delivered through a user-friendly frontend with advanced visualizations. This platform implements a comprehensive web-based agricultural assistance system utilizing artificial intelligence and machine learning technologies to support Indian farmers. By integrating multiple AI-powered services, it provides intelligent decision-making tools for sustainable agriculture, contributing to food security and farmer empowerment across the nation.

Published by: vikaspatanker

Authors: Dr. Amit Kumar Awasthi

Abstract: Fluoride in drinking water presents a paradoxical public health challenge; while essential in trace amounts for dental health, its excessive intake leads to debilitating fluorosis. A selected study region in the Gangetic plain of northern India, situated within the fluoride-endemic alluvial belt and host to significant industrial activity, is a critical area for investigating this geogenic and anthropogenic contaminant. This comprehensive review paper synthesizes existing data and hypotheses to assess the extent and sources of fluoride contamination in the region's drinking water, evaluate its health impacts on the local population, and propose integrated mitigation strategies. Analysis suggests widespread contamination exceeding the WHO (1.5 mg/L) and BIS (1.0 mg/L) permissible limits in groundwater, particularly in deeper aquifers. The primary source is geogenic, attributed to the dissolution of fluoride-bearing minerals (e.g., fluorite, apatite) in the subsurface geology under alkaline, high-bicarbonate, and low-calcium conditions. Anthropogenic contributions from local industrial clusters, especially leather tanneries and chemical units, may exacerbate the problem. The health impacts are severe and visible, with high prevalence rates of dental fluorosis among children and adolescents, and advanced cases of skeletal fluorosis leading to pain, stiffness, and crippling deformities in adults. Non-skeletal manifestations, including gastrointestinal, neurological, and endocrine disruptions, are also indicated. The review concludes that fluoride contamination is a silent, chronic public health emergency in the study region, disproportionately affecting rural and socio-economically disadvantaged communities reliant on untreated groundwater. Urgent, coordinated action encompassing alternative water sourcing, defluoridation technology deployment, robust monitoring, intensive public health campaigns, and supportive healthcare is recommended. This paper underscores the necessity of a "One Health" approach, integrating hydrogeology, public health, and social policy to address this multifaceted crisis.

DOI: https://doi.org/10.5281/zenodo.19413616

 

Published by: vikaspatanker

Authors: Nimsha A, Dr Vandana yadav

Abstract: The invasion of cancer is a complicated biological process that is regulated by the interactions between different types of cells and the microenvironment of the tumor. Traditional models of local diffusion sometimes fail to account for long-range cell migration and nonlocal interactions, both of which play an important part in the evolution of tumors because of their importance. As part of this research, nonlocal diffusion models are developed and analyzed in order to provide a description of cancer cell invasion. These models incorporate integral operators in order to reflect spatially extended interactions between cells and the extracellular matrix. In this study, we evaluate the effect of nonlocal diffusion factors on tumor spread patterns by employing mathematical analytic techniques such as stability, well-posedness, and numerical simulations. In addition to providing a greater understanding of the dynamics of cancer progression, the findings reveal that nonlocal impacts have the potential to drastically affect invasion speed, morphology, and the establishment of diverse tumor fronts. In light of these discoveries, the potential of nonlocal mathematical models as predictive tools for understanding and managing cancer invasion has been brought to light. This lays the groundwork for more precise therapeutic tactics.

Published by: vikaspatanker

Authors: Prof. Maske.P.P, Aditya Tangade, Sanskar Mulik, Rushabh Pachpute, Darpan Rathod

Abstract: AgriMart – Smart Agricultural Marketplace Mobile Application is an Android-based digital platform developed to simplify the process of purchasing agricultural products for farmers and agricultural buyers. The application provides a centralized mobile marketplace where users can easily browse a wide range of farming supplies such as seeds, fertilizers, pesticides, and agricultural equipment. The primary objective of the system is to reduce dependency on traditional purchasing methods and improve accessibility to essential agricultural resources through a user-friendly digital interface. The application addresses common challenges faced by farmers, including lack of transparent pricing, limited product availability in local markets, and difficulty in comparing products from multiple suppliers. By integrating modern mobile technologies and cloud-based database services, the system enables real-time product updates, efficient cart management, and secure order placement. The inclusion of multilingual support and simplified navigation ensures that users from rural and semi-urban backgrounds can easily interact with the application. AgriMart is developed using Android Studio and Java for application logic, Firebase Realtime Database for data storage and management, and Razorpay payment gateway for secure digital transactions. These technologies ensure system reliability, scalability, and smooth performance during real-time usage. The application also includes administrative functionalities that allow product management, order monitoring, and marketplace analytics. Overall, the AgriMart application contributes to the digital transformation of agricultural commerce by providing a convenient, transparent, and efficient platform for farmers to access agricultural products. The system aims to improve purchasing efficiency, save valuable time, and promote the adoption of modern digital solutions in the agricultural sector.

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Published by: vikaspatanker

Authors: Mayuresh Shastri, Prathamesh Jawalkar, Tanaya Inpure, Shrushti Rakh, Priti Borate

Abstract: Artificial Intelligence (AI) is transforming the field of architecture by enhancing design processes, improving efficiency, and enabling data-driven decision-making. This project explores the integration of AI technologies in architectural practices, focusing on their applications in design generation, building performance analysis, and construction management. AI-powered tools can analyze large datasets, optimize spatial planning, and generate innovative design solutions that respond to environmental, social, and functional requirements. The study highlights how machine learning algorithms and generative design techniques assist architects in creating sustainable and energy-efficient structures. Additionally, AI enables predictive analysis for structural safety, cost estimation, and maintenance planning, reducing risks and improving project outcomes. The project also examines real-world case studies where AI has been successfully implemented in architectural projects. Despite its advantages, the adoption of AI in architecture presents challenges such as ethical concerns, data dependency, and the need for skilled professionals. This research aims to provide a comprehensive understanding of AI’s potential and limitations, emphasizing its role as a collaborative tool rather than a replacement for human creativity. Overall, the integration of AI in architecture represents a significant shift towards smarter, more adaptive, and sustainable built environments.

Published by: vikaspatanker

Authors: Mr. V. Gopinath, V. Aasritha Devi, P. Deekshitha, V. Pragna, P. Siva Sankara Rao

Abstract: Global food security is currently challenged by a dual-front crisis: a non-linear surge in the global population and the concurrent, unpredictable degradation of arable land, as highlighted by the United Nations [18]. Traditional agricultural methodologies frequently depend on generalized fertilizer applications that fail to account for site-specific soil chemistry, leading to nutrient runoff or stunted growth (Wolfert et al. [19]). Building upon the foundational web-based and mobile frameworks established by Agri Vision Pro [1] and AgroVision et al. [2], this research introduces AgroVision Pro. AgroVision Pro is a high-fidelity, multi-stage machine learning framework designed to eliminate guesswork by integrating classification and regression pipelines into a cohesive decision-support ecosystem. Utilizing state-of-the-art algorithms, including XGBoost (Chen et al. [9]) and Random Forest (Breiman [10]), the platform achieves a 93.2% accuracy in crop selection and an R^2 score of 0.89 in yield quantification. This research demonstrates how localized soil data, processed through an innovative "Feature-Chaining" architecture, transitions agriculture from a reactive industry to a proactive, precision-driven powerhouse.

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Published by: vikaspatanker

Authors: Kushal B D, Kirankumar B, Hani Firdous, Priyanka H S, Dr. M J Anand

Abstract: This project presents the development of an autonomous line-following and obstacle-avoidance robot using the ESP32 microcontroller. Infrared sensors detect and follow the predefined path, while an ultrasonic sensor measures distance and identifies obstacles to prevent collisions. The ESP32 processes real-time sensor data to control the motor driver, ensuring smooth and stable navigation. A GSM module is integrated to send alerts during critical situations. The system is designed to be low-cost, scalable, and suitable for educational and automation applications. The prototype demonstrates consistent path tracking and efficient obstacle avoidance, making it adaptable for real-world industrial logistics and warehouse environments. The overall design highlights the importance of combining multi-sensor integration with wireless communication to enhance the robustness and usability of autonomous robotic systems.

Published by: vikaspatanker

Authors: Krishna Prisad Bajgai, Netra Prasad Joshi, Niraj Kumar Shah, Dr. Bhojraj Ghimire

Abstract: Microfinance institutions (MFIs) play a crucial role in promoting financial inclusion in developing economies such as Nepal. However, the increasing rate of non-performing loans (NPLs) threatens the sustainability of the microfinance sector. Traditional credit monitoring methods are often reactive and lack predictive capabilities for early detection of loan defaults. MThis study proposes an Agentic AI-based Early Warning System (EWS) for predicting non-performing loans in Nepalese microfinance institutions. The proposed framework integrates machine learning algorithms, autonomous AI agents, and explainable AI mechanisms to analyze borrower data and generate real-time risk alerts The system utilizes financial transaction data, borrower demographic profiles, repayment histories, and behavioral indicators to predict loan default probability. Experimental evaluation using ensemble machine learning models demonstrates improved predictive accuracy compared to traditional credit scoring approaches. The proposed framework contributes to FinTech innovation by enabling proactive credit risk management, improving loan portfolio quality, and supporting regulatory oversight within Nepal’s microfinance ecosystem.

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Published by: vikaspatanker

Authors: Ms. Lungsanghungle Newme, Dr. Arup Barman

Abstract: Artificial Intelligence is fundamentally transforming the discipline of data engineering. This paper examines how AI is reshaping core data engineering functions including relational and cloud database management, data warehousing, enterprise analytics, digital analytics platforms such as Adobe Analytics, and cloud-native platforms such as Snowflake. Drawing on current industry practices and emerging platform capabilities, this paper analyzes the impact of AI on pipeline development, data quality management, automated metadata governance, and real-time analytics. This paper further discusses how the role of the data engineer is evolving from manual code writing to strategic architecture and AI-assisted orchestration. The paper also addresses key challenges including data privacy in regulated financial environments, skill evolution requirements, and the governance of AI-generated outputs. Paper findings indicate that organizations which invest in AI-ready data infrastructure, establish strong governance frameworks, and upskill their engineering teams will gain significant competitive advantages in the next decade.

DOI: https://doi.org/10.5281/zenodo.19396647

Published by: vikaspatanker