Category Archives: Uncategorized

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.

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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.

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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

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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.

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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

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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.

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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

 

 

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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

 

 

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Authors: ER Sandeep Tandon

Abstract: This paper presents a detailed simulation and analysis of unsymmetrical faults (LG, LL, LLL) in a three-phase grid interconnection using MATLAB/Simulink. The model includes two voltage sources representing grid ends, connected via a two-pi section transmission line to simulate realistic power transfer conditions. The system response to each fault type is analyzed in terms of voltage and current distortions. Separate fault simulations are carefully modeled using practical parameters. Output data is collected using Workspace blocks and statistically analyzed to extract minimum, maximum, and mean values. Comparative tables and waveform plots illustrate the behavior of each fault type. Additionally, the paper discusses the theoretical basis of fault currents using symmetrical components. The study aims to serve as a base model for educators, researchers, and developers of AI-based fault detection systems.

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Authors: Sharma Pankaj Kanhaiya, Professor Devendra Sharma, Professor Saurabh Gupta

Abstract: The integration of photovoltaic (PV) systems with battery and supercapacitor storage in DC microgrids demands efficient energy management to enhance system stability, reliability, and operational efficiency. This research presents an optimized neural network-based energy management approach tailored for a standalone DC microgrid incorporating PV panels, lithium-ion batteries, and supercapacitors. The neural network model is specifically designed to handle the nonlinear characteristics of the microgrid, optimize power flow, and maintain the state of charge (SoC) of energy storage devices within safe limits. By utilizing advanced training algorithms inspired by optimization techniques such as artificial rabbit optimization, the proposed system achieves improved prediction accuracy and load balancing. The approach also integrates a fuzzy logic control mechanism to facilitate real-time adaptive responses to dynamic load changes and renewable generation variability. Simulation results demonstrate enhanced voltage stability, reduced power fluctuations, and efficient energy distribution compared to conventional methods. This optimized neural network strategy effectively mitigates the challenges inherent in hybrid energy storage management, promoting longer battery life, quicker response times from supercapacitors, and overall system resilience. The study contributes significant insights toward the development of intelligent energy management systems for sustainable and autonomous DC microgrid applications ((PDF) Artificial Rabbits Optimized Neural Network-Based Energy …, 2024).

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