Authors: Priya Jaiswal

Abstract: This study presents the development and evaluation of an Environmental Quality Index (EQI) for Lucknow city, aimed at assessing the environmental factors that influence public health outcomes. The EQI is designed to integrate three critical environmental components—air quality, water quality, and green cover—which are known to have direct and indirect effects on human health. Data spanning from 2020 to 2024 were collected from reputable government sources, including the Central Pollution Control Board (CPCB), Central Ground Water Board (CGWB), and the Forest Survey of India (FSI). These datasets were systematically processed, analyzed, and normalized to create a composite index that represents the overall environmental condition of the city in relation to public health risks. The results indicate that Lucknow’s environmental quality generally falls within the moderate to poor range, reflecting significant challenges for maintaining population health. Rising levels of air pollutants, persistent water contamination, and limited improvement in urban green spaces collectively contribute to increased vulnerability to respiratory, cardiovascular, and waterborne diseases. Year-wise analysis reveals gradual deterioration in air and water quality, highlighting the urgent need for targeted public health interventions and environmental management strategies. The EQI developed in this study provides a valuable tool for policymakers, health authorities, and urban planners to identify high-risk areas, prioritize interventions, and monitor the effectiveness of measures aimed at reducing environmental health hazards.

DOI: https://doi.org/10.5281/zenodo.17098074

 

Published by: vikaspatanker

Authors: Amir Shabbir Patel, Sahil AA Khan, Dhairya Suryawanshi, Rohan Karchuli

Abstract: The real estate industry is currently experiencing a rapid digital transformation, largely fueled by the integration of artificial intelligence (AI) technologies. Among the most promising applications is the use of AI-powered recommendation systems, which aim to redefine how buyers, sellers, and agents interact with property platforms. These intelligent systems are designed to analyze large volumes of property data and user preferences, offering highly personalized recommendations that improve the overall user experience. By leveraging data-driven insights, AI has the potential to simplify property discovery, reduce the complexity of decision-making, and enhance overall market efficiency. This study explores the implementation of different AI models, including machine learning algorithms, deep learning techniques, and natural language processing (NLP), within the context of real estate platforms. We evaluate their ability to process structured and unstructured data such as location, price, amenities, and even user reviews or natural language queries. A prototype recommendation system was developed and tested using real user behavioral data, including browsing history, clicks, and saved properties. The case-based experiment demonstrated that AI- enabled recommendations not only improved engagement but also significantly reduced search time, making the property- hunting process more efficient and user-centric. In addition to the technical benefits, this paper also examines the broader challenges and ethical considerations associated with AI adoption in real estate. Issues such as data privacy, algorithmic bias, and transparency in recommendations are highlighted as key areas that require careful attention. Furthermore, the study identifies opportunities for future research, such as integrating predictive analytics for market trends, enhancing trust through explainable AI, and expanding personalization by considering emotional and lifestyle factors. By addressing these challenges and advancing the current models, AI-driven recommendation systems can play a transformative role in shaping the future of the real estate industry. [4].

Published by: vikaspatanker

Authors: Saiyed Aiyatullah Kalimullah, Malek Mohammadarsh Mohammedasif, Jethava Shyam Hiteshbhai, Chudasma Dhruv Dineshbhai

Abstract: This project presents Care Smart AI, a compre- hensive Hospital Management System (HMS) integrated with artificial intelligence to improve healthcare delivery and op- erational efficiency. The system leverages modern full-stack technologies including Flask for backend API services, MongoDB for data persistence, React and TailwindCSS for responsive user interfaces, and machine learning for symptom assessment and diagnostic report summarization. Care Smart AI enables secure, efficient patient management with role-based access for patients, doctors, and administrators. It demonstrates a scalable, acces- sible, and intelligent platform that enhances clinical decision- making, automates administrative tasks, and improves patient care quality across healthcare institutions.

Published by: vikaspatanker

Authors: Mohamed Asiq .A, Santhosh M, Vishal.S, D.Jeevanantham,B.E

Abstract: Wastewater treatment is essential for safeguarding public health, protecting ecosystems, and supporting sustainable urban development. This report presents insights from anacademic field visit to the Government Wastewater Treatment Plant (WWTP) at Pollachi, Tamil Nadu. The plant is based on Sequential Batch Reactor (SBR) technology, which provides an efficient and compact solution for secondary treatment of municipal sewage. During the visit, students observed the general layout of the facility, including preliminary units (receiving chamber, screens, grit chambers), secondary biological treatment (SBR reactors, decanters), tertiary treatment (chlorination chambers, contact tanks), and sludge handling units (sludge well, centrifuge building). The plant also houses supporting infrastructure such as laboratory facilities, blower rooms, and landscaped green belts that enhance both aesthetics and environmental protection. The visit provided practical exposure to treatment operations, sludge management, effluent quality monitoring, and safety protocols. It also highlighted the broader significance of WWTPs in ensuring sustainable sanitation, preventing water pollution, and promoting wastewater reuse. This report connects classroom knowledge of environmental engineering with real-world field practice, emphasizing the critical role of wastewater treatment plants in urban infrastructure.

Published by: vikaspatanker

Authors: Sangeeth Kumar.J, Janarthanan.V, Logeshwaran.S, D.Jeevanantham,B.E

Abstract: Water treatment plants (WTPs) play a fundamental role in delivering safe and reliable drinking water to urban and rural populations. This journal paper documents a field visit to the Pollachi Water Treatment Plant (WTP) located at Kolathur Village, Pollachi Taluk, Coimbatore District, which is part of the Combined Water Supply Scheme (CWSS) supplying Pollachi North, Pollachi South, Kinathukadavu, Gudimangalam, and adjoining habitations. The scheme sources water from the Aliyar River, with an intake well and raw water pump house that lifts water for treatment. The plant consists of headworks, aerator, stilling chamber, flash mixers, dividing chambers, clariflocculators, filter beds, clear water sump, and chemical treatment units for coagulation, flocculation, and disinfection. During the visit, the operation of raw water pumping mains, filter media layers, chlorination arrangements, laboratory facilities, booster pumping stations, and service reservoirs were observed. With a designed treatment capacity of 26.38 MLD, the scheme ensures reliable water supply to urban wards and more than 200 rural habitations. This field exposure enabled students to understand the engineering design and operational aspects of drinking water treatment and distribution, bridging theoretical knowledge with field practice

Published by: vikaspatanker

Authors: Manoj Kumar Yadav, Deepak Kumar Singh

Abstract: Air pollution has emerged as one of the most serious environmental and public health challenges in South Asia, with fine particulate matter (PM2.5) identified as the most pernicious pollutant due to its ability to penetrate deep into the human respiratory system. Uttar Pradesh, the most populous state in India, frequently records PM2.5 concentrations that exceed national and international standards. This study presents an integrated framework that combines geostatistical interpolation and machine learning regression to predict PM2.5 levels across ten non-attainment cities in Uttar Pradesh. Daily PM2.5 data for the period 2021–2024 were obtained from continuous monitoring stations and subjected to rigorous preprocessing. Spatial interpolation using Ordinary Kriging was implemented to generate high-resolution exposure surfaces, while machine learning algorithms including Random Forest, Gradient Boosting Regressor, Extreme Gradient Boosting, Support Vector Regression, and K-Nearest Neighbour were trained to capture temporal and spatial variability. Results demonstrate that PM2.5 concentrations consistently exceeded permissible limits, with pronounced seasonal peaks in winter and relative minima during monsoon months. Kriging revealed spatial clustering of pollution hotspots in Ghaziabad, Kanpur, and Lucknow, while peripheral cities exhibited lower but still concerning levels. Among machine learning models, XGBoost achieved the highest predictive performance with R² values above 0.74, followed by Gradient Boosting. Integration of Kriging-derived features into machine learning workflows improved prediction accuracy by 8–12%. The study demonstrates that hybrid geostatistical–machine learning approaches provide reliable and high-resolution PM2.5 predictions, enabling early-warning systems, spatially targeted interventions, and evidence-based policy planning.

Published by: vikaspatanker

Authors: Anish Kumar, Sourav ray, Ambrose Henrey Mwikwabe, Shreya Gandh, Rohit Kumar Singh

Abstract: The New Technology File System (NTFS) is the default file system for modern Windows and contains rich metadata (journaling, security descriptors, etc.) that aids forensic investigations. Its Master File Table (MFT) holds records for every file (even deleted ones), while transactional logs ($LogFile and $UsnJrnl) record detailed changes . However, NTFS also offers covert storage (alternate data streams, directory $DATA, and boot record slack) and exhibits known integrity flaws. This paper reviews current NTFS forensic methods – including MFT parsing, journal analysis, and hidden-data detection 3 4 – and identifies weaknesses (e.g. limited $MFTMirror backup, unexamined boot sector areas 6). We propose novel recovery techniques: an enhanced boot-sector reconstruction algorithm (combining backup boot data with $LogFile-derived geometry) and an improved metadata restoration process that leverages $LogFile and signature scanning when the MFT is damaged. We demonstrate these on synthetic NTFS images and show improved recovery of system structures and hidden content compared to baseline tools. The contributions include new forensic workflows and illustrative diagrams of NTFS layout and analysis steps.

DOI: http://doi.org/10.5281/zenodo.17083346

Published by: vikaspatanker

Authors: Praveen Kumar Yadav, Kundan Bhushan, Er. Manoj Kumar Yadav

Abstract: Rapid urbanization is a primary driver of local climate change, leading to the formation of the Surface Urban Heat Island (SUHI) effect, which poses significant environmental and public health challenges. This study presents a comprehensive spatiotemporal analysis of the SUHI phenomenon in Lucknow, India, over a decade (2014–2024) by leveraging the analytical power of the Google Earth Engine (GEE) platform and ArcGIS. Using annual mean Land Surface Temperature (LST) derived from Landsat 8 thermal imagery, we employed two distinct metrics to quantify the SUHI effect: statistical Urban Hot Spot (UHS) analysis and the Urban Thermal Field Variance Index (UTFVI). SUHI hotspots were identified as areas with LST exceeding two times standard deviations above the regional mean (LST > μ + 2σ), while the UTFVI was used to classify the urban environment into six levels of thermal comfort. The results reveal a significant intensification and spatial expansion of the SUHI effect over the study period. The total area identified as a Urban hotspot increased from 25 km² in 2014 to 26 km² in 2024, a growth of over 4%. Concurrently, the area experiencing the worst ecological conditions ("Worst" UTFVI zone) expanded from 1,038 km² to 1,050 km² a growth of 1.16% . These high-temperature zones are predominantly concentrated in the city's central commercial core and newly developed residential areas, correlating with the expansion of impervious surfaces. This research provides quantitative evidence of Lucknow's escalating thermal risk and underscores the utility of GEE and geospatial indices for monitoring urban environmental health. The findings offer critical insights for policymakers and urban planners to develop targeted heat mitigation strategies, such as the strategic implementation of green infrastructure.

Published by: vikaspatanker

Authors: Nikhil Gaur, Dr. Jyoti Yadav

Abstract: To investigate the lateral ultimate bearing capacity of cantilever rigid piles subjected to large horizontal displacement, this paper analyzes the distribution characteristics of soil resistance along the pile side and explores calculation methods for lateral bearing capacity of pile foundations using both numerical simulation and theoretical approaches. The results indicate that, under large displacement conditions, the soil in front of the pile yields progressively from top to bottom. Once the soil adjacent to the pile reaches its limit displacement, the lateral soil resistance no longer increases with further displacement. The ultimate lateral bearing capacity of cantilever rigid piles under large horizontal displacement is determined based on the ultimate displacement of the side soil. Among the tested approaches, the modified “m” method demonstrates the best fitting accuracy. However, further investigation is required to define the applicable range of foundation coefficient distribution.

DOI: https://doi.org/10.5281/zenodo.17081470

 

 

Published by: vikaspatanker

Authors: Nikhil Gaur, Dr. Jyoti Yadav

Abstract: Most of the sub-structures of new railway river bridges in India are built with solid mass concrete gravity piers and abutments. These piers, designed without steel reinforcement, rely on the assumption that they are not subjected to tensile stresses under regular loading. However, during high-magnitude earthquakes, their safety becomes a critical concern, particularly in seismically active regions of India. This study assesses the seismic vulnerability of solid gravity bridge piers, which are key components of railway bridges, since they transfer loads between the substructure and the superstructure. Seven existing piers from the state of Gujarat were analyzed using free vibration analysis and nonlinear static (pushover) analysis in ABAQUS. Free vibration analysis revealed that the fundamental mode mass participation was always below 50%, while the cumulative participation of the first six modes remained under 80%, demonstrating significant contributions from higher vibration modes. Pushover analysis results confirmed the limited ductility of solid piers and highlighted their susceptibility under seismic excitations. The study emphasizes the need for seismic strengthening strategies to ensure the safety and serviceability of such piers.

DOI: https://doi.org/10.5281/zenodo.17081435

 

 

Published by: vikaspatanker