Daily Archives: September 11, 2025

Authors: Himanshu Ranjan, Manoj Kumar Yadav, Sushant Kumar

Abstract: The Indo-Gangetic Plain's Lucknow, a metropolis that is quickly urbanising, is seeing dangerously high levels of tiny particulate matter (PM2.5), which endanger both the environment and human health. In contrast to Delhi, which has seen a great deal of study on air quality, Lucknow has not received as much attention despite its increasing industrial emissions, biomass burning, and vehicle traffic. This study looks at the temporal and geographical trends of PM2.5 in Lucknow, identifies the main sources of emissions, and uses exposure-response relationships to assess the health risks associated with these findings. Data from state monitoring stations and the CPCB were used to evaluate the daily and seasonal variations in PM2.5 concentrations. in addition to meteorological factors. According to the findings, steady atmospheric conditions and biomass burning cause PM2.5 levels to peak throughout the winter months, with concentrations frequently above both national and WHO guidelines. Significant attributable hazards for cardiovascular and respiratory morbidity are suggested by epidemiological studies, especially for older and paediatric groups. The critical need for integrated mitigation strategies such as switching to cleaner fuels, reducing vehicle emissions, and increasing green cover is highlighted in this article. The results give policy interventions under India's National Clean Air Programme (NCAP) an evidence-based basis, which is important given Lucknow's geographic location and population susceptibility.

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Published by: vikaspatanker

Authors: Deepak Kumar Mishra, Professor Boskee Sharm

Abstract: Concrete, a fundamental material in construction, is increasingly being modified to incorporate sustainable alternatives that enhance performance while minimizing environmental impact. This study investigates the effects of partially replacing cement with marble powder and adding steel fibres in varying proportions (0%, 0.5%, 1%, 1.5%, and 2.0%) on the mechanical properties of M25 grade concrete. Results show that a mix containing 15% marble powder and 1% steel fibre achieves optimal compressive, split tensile, and flexural strength at 28 days. The marble powder improves workability due to its smooth texture and spherical shape, while the addition of steel fibre, though reducing workability, enhances bonding and overall strength. The findings suggest that the combination of marble powder and steel fibres can be effectively used in structural applications such as multistoried buildings and bridges. A recommended optimal mix of 15% marble powder and 1% steel fibre offers the best performance, though further long-term studies are advised to assess durability and field performance

 

Published by: vikaspatanker

Authors: Poonam Mishra, Neeraj Gupta

Abstract: The rapid acceleration of scientific publication rates has created unprecedented challenges in tracking the evolution of research trends and identifying emerging paradigms within academic disciplines. This paper presents a novel computational framework for temporal trend evolution mapping in scientific literature that combines advanced natural language processing techniques with dynamic network analysis to capture and visualize the progression of scientific concepts over time. Our methodology integrates transformer-based document embeddings, temporal clustering algorithms, and graph-based trend propagation models to create comprehensive maps of knowledge evolution across multiple time scales. The framework employs a multi-dimensional approach that analyzes citation patterns, semantic similarity evolution, and author collaboration networks to identify trend emergence, maturation, and decline phases. Experimental validation on large-scale datasets from PubMed, arXiv, and Web of Science demonstrates the framework's effectiveness in detecting significant research trends up to 18 months before they become mainstream, with precision scores exceeding 0.89 for trend prediction tasks. The system successfully identified the emergence of CRISPR gene editing research, COVID-19 therapeutic developments, and artificial intelligence applications in drug discovery as major trending topics months before their widespread recognition. Our contribution provides researchers, funding agencies, and academic institutions with powerful tools for strategic research planning, early trend identification, and comprehensive understanding of scientific knowledge evolution patterns.

DOI: http://doi.org/10.5281/zenodo.17099295

Published by: vikaspatanker

Authors: Vipin Kumar, Manvika Chaudhary, Manoj Kumar Yadav

Abstract: Air pollution is a pressing concern in Indian cities, particularly due to increasing vehicular load, industrial expansion, and urban activities. This study assessed the ambient air quality of Prayagraj city during the post-monsoon season of 2024 across residential, commercial, and industrial zones. The monitoring focused on PM₁₀, PM₂.₅, SO₂, NO₂, and trace metals (Pb and Ni), and the results were compared with the National Ambient Air Quality Standards (NAAQS).The analysis revealed that PM₁₀ levels in residential areas ranged between 112–125 µg/m³, with Rambagh and Georgetown recording the highest values, nearly double the NAAQS limit of 60 µg/m³. Commercial a 150 µg/m³, while industrial zones such as the Naini Industrial Area peaked above reas exhibited even higher concentrations, with CMT and Johnstonganj exceeding 160 µg/m³, indicating severe exceedances. Similarly, PM₂.₅ concentrations ranged from 38–52 µg/m³ in residential areas, while commercial locations consistently surpassed 80 µg/m³, far above the NAAQS standard of 40 µg/m³. Industrial sites again recorded the highest PM₂.₅ levels, approaching 100 µg/m³.In contrast, gaseous pollutants showed moderate levels. SO₂ concentrations remained between 20–35 µg/m³ in residential and commercial zones and around 40 µg/m³ in industrial areas, all within the permissible limit of 80 µg/m³. NO₂ levels averaged 25–40 µg/m³ in residential areas, with commercial hotspots reaching 45 µg/m³, and industrial sites recording around 50 µg/m³, still below the standard of 80 µg/m³ but indicative of vehicular and industrial influence.Overall, the study demonstrates that particulate matter (PM₁₀ and PM₂.₅) is the most critical pollutant in Prayagraj, with concentrations 2–3 times higher than the standards, while SO₂ and NO₂ remained within safe limits. These findings highlight the urgent need for targeted emission control strategies focusing on traffic management, industrial regulation, and dust mitigation.

DOI: https://doi.org/10.5281/zenodo.17099110

 

Published by: vikaspatanker

Authors: Shivendra Singh, Manoj Yadav

Abstract: Tannery effluents are a major source of chromium contamination, particularly hexavalent chromium [Cr(VI)], which is highly toxic, carcinogenic, and persistent in aquatic systems. Conventional treatment methods for chromium removal are often costly and environmentally unsustainable. This study investigates the potential of low-cost, eco-friendly agro-waste materials—sawdust, clay, and used tea leaves—as adsorbents for the removal of both total chromium and hexavalent chromium from tannery effluent. Batch adsorption experiments were carried out to evaluate the effect of parameters such as pH, contact time, adsorbent dosage, and initial chromium concentration. The results demonstrate that all three materials exhibit significant adsorption capacities, with efficiency varying across the different adsorbents. Sawdust and used tea leaves showed higher affinity towards Cr(VI), while clay exhibited better overall performance in reducing total chromium levels. The adsorption process was found to follow pseudo-second-order kinetics and fit well with the Langmuir isotherm model, suggesting monolayer adsorption on homogeneous surfaces. This study highlights the feasibility of employing locally available agro-waste adsorbents as sustainable alternatives to conventional methods for the treatment of tannery wastewater, thereby contributing to cost-effective and environmentally friendly wastewater management

DOI: http://doi.org/10.5281/zenodo.17098961

Published by: vikaspatanker

Authors: Mrs. Kocherla Jayanthi

Abstract: The rapid evolution of cyber threats demands effective and adaptive intrusion detection systems to protect critical network infrastructures. This study seeks to evaluate the efficacy of supervised machine learning models in detecting network intrusions using the NSL-KDD dataset. The NSL-KDD dataset, a well-established benchmark for intrusion detection, undergoes thorough pre-processing, including handling missing values, feature normalization, and categorical encoding to ensure high-quality input data. We implement a range of supervised machine learning algorithms Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbours (KNN), Gradient Boosted Trees, and Support Vector Machine (SVM) to classify network traffic as either benign or malicious. The process involves splitting the dataset into training and testing subsets, followed by hyperparameter optimization through grid search to enhance model performance. We evaluate the models using key metrics such as accuracy, confusion matrix, Receiver Operating Characteristic (ROC) curve, and Area Under the Curve (AUC). Our findings reveal that Random Forest and Gradient Boosted Trees achieve superior accuracy and lower false positive rates compared to other classifiers. The comparative analysis provides practical insights into each algorithm’s strengths and limitations for cybersecurity applications.

DOI: http://doi.org/10.5281/zenodo.17098664

Published by: vikaspatanker

Authors: Vishal Yadav, Manas Mishra, Aditya verma

Abstract: In Ayodhya, Uttar Pradesh, India, the Saryu River is revered as a sacred river. However, the quality of the water is declining as a result of human anthropogenic activities. The goal of the current study was to use established techniques to evaluate the quality of river water by analyzing bacterial populations and physicochemical characteristics with seasonal fluctuations. The majority of the physicochemical parameters, primarily pH, DO, BOD, and TDS, were found to be within the allowable levels that regulatory bodies had suggested. Other parameters, such as Alkalinity or Fluorite and chemical oxygen demand (COD), were marginally above the allowable limits. Microbial investigations revealed the existence of both fungal and bacterial communities. The rainy season has the highest bacterial concentration, followed by the summer and winter seasons. The results of this study may help with irrigation and drinking water quality monitoring throughout the year

DOI: https://doi.org/10.5281/zenodo.17098581

 

Published by: vikaspatanker

Authors: Miss. Mounika Lokavarapu, Dr.G. Sharmila Sujatha

Abstract: Object recognition plays a crucial role in computer vision applications, particularly in assisting visually impaired individuals for safe and independent navigation. Despite its significance, existing techniques often face limitations in recognizing multiple objects efficiently and accurately. The aim of this work is to develop a robust multi-label object recognition framework capable of detecting and classifying surrounding objects in real time to enhance situational awareness for visually impaired users. The proposed system takes real-world images as input and processes them using machine learning and advanced computer vision algorithms. A multi-label classification approach is employed to simultaneously detect and group objects, reducing detection time while improving recognition accuracy. By leveraging deep learning models with optimized type/grouping techniques, the system achieves faster execution with best-in-class time complexity. Experimental analysis demonstrates that the framework not only improves detection performance but also provides reliable object recognition in both indoor and outdoor environments, making it highly effective for real-world navigation assistance. The proposed framework, “AI-Powered Assistive Vision: A Novel Deep Learning Framework for Object Detection and Recognition for the Visually Impaired,” is developed using Python with TensorFlow/Keras and OpenCV libraries, and implemented under embedded hardware with camera and processing units, enabling real-time deployment for assistive navigation applications.

DOI: http://doi.org/10.5281/zenodo.17098418

Published by: vikaspatanker

Authors: Ms. S. Sushma Rawath

Abstract: The rapidly evolving global scenario, characterized by technological advancements, geopolitical shifts, and socio-economic transformations, has led to the emergence of "new markets." These markets are driven by a combination of digital innovations, environmental imperatives, demographic changes, and evolving consumer preferences. Opportunities in areas like renewable energy, digital finance, sustainable agriculture, and advanced healthcare define new markets that are no longer constrained by traditional industrial or geographic boundaries. Furthermore, the globalization of technology and digital platforms has enabled businesses to access previously untapped regions and demographics, particularly in developing economies. This paper explores the drivers behind these emerging markets, their implications for global trade, and strategies businesses can adopt to thrive in this dynamic environment. It also highlights the challenges associated with navigating regulatory complexities, cultural differences, and technological disparities. Understanding and adapting to these new markets is crucial for fostering inclusive and sustainable economic growth in the 21st century.

DOI: http://doi.org/

Published by: vikaspatanker

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