Category Archives: Uncategorized

Authors: Denish Ocira, Edward Ssemakula

Abstract: The continuous rise of urbanization has led to an overwhelming increase in waste generation with serious consequences for the environment and humans. Most waste disposal methods are inefficient, with little accountability or participation from the community, hence we propose a Smart Waste Management System (SWMS) built on AI technologies that employs computer vision and cloud computing to track on a real-time basis, facilitating improved waste sorting and the complaint making towards upcycling. The system allows the community to upload pictures of items to be reused and are identified as categories using an artificial intelligence model through which there is a trgging of the item for appropriate action. The platform also enables conversations on tracking complaints and donations of reusable items, thereby enabling data emergence for urban waste management authorities in making decisions. This paper explains the system design and implementation and is sustainability implications.

DOI: https://doi.org/10.5281/zenodo.17119882

 

Published by: vikaspatanker

Authors: Sai Rithwik Nooguri

Abstract: Credit card fraud detection is a challenge in the financial sector, where the rarity of fraudulent transactions makes accurate classification particularly difficult. This study presents a comprehensive approach that integrates data preprocessing, resampling techniques, traditional machine learning models, anomaly detection methods, and deep reinforcement learning for effective fraud detection. Initially, extensive exploratory data analysis (EDA) was conducted, followed by handling missing values and applying Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. A variety of supervised models, including Logistic Regression, Random Forest, XGBoost, and Multi-Layer Perceptron (MLP), as well as unsupervised anomaly detection methods like Isolation Forest and Local Outlier Factor, were evaluated. Subsequently, a Deep Q-Learning Network (DQN) was implemented to model fraud detection as a sequential decision-making problem, allowing the system to dynamically learn fraud patterns. The experimental results demonstrate that DQN achieved high precision, recall, and F1- score, outperforming several traditional classifiers. This study highlights the importance of combining classical and modern learning paradigms to enhance information assurance in credit card transaction systems. The code supports reproducibility and future research.

DOI: https://doi.org/10.5281/zenodo.17119185

 

Published by: vikaspatanker

Authors: Podapala Siva Reddy, Ch. V. Radhika, Gadiraju Parvathi

Abstract: Public-private partnerships (PPPs) have changed over centuries, notably since the Roman Empire, to modern forms of infrastructure production and public service delivery. There has been a resurgence of interest beginning in the late twentieth century in seeing PPPs as a way to engage in infrastructure development via viable financing alternatives and efficient risk sharing. This study examined whether PPPs are effective in both mobilizing private-sector capital for infrastructure development, whether they do so through-efficient risk allocation, and also whether this risk allocation model for service delivery yields improved and responsive public service delivery within the context of infrastructure production. The study considered international examples of implementation, the most salient contractual and other governance characteristics of PPPs, and critically examined factors that impact sustainability of PPPs. The study was mixed-methods and underscored how PPPs can enhance infrastructure production/quality and that future research should focus on sectoral frameworks, the socio-economic implications of PPPs for communities, and advancements of the governance framework for PPPs.

Published by: vikaspatanker

Authors: Mrs. Vandana Rahul Shah

Abstract: Concrete, though the most widely used construction material, is prone to cracking, which compromises durability, service life, and sustainability of structures. Conventional repair methods are temporary, costly, and labor-intensive. In recent years, self-healing concrete has emerged as a promising alternative, capable of autonomously repairing cracks and enhancing long-term performance. This review focuses on two major self-healing approaches: bacterial concrete, where microorganisms such as Bacillus subtilis precipitate calcium carbonate within cracks, and crystalline admixture-based concrete, where chemical additives react with unhydrated cement particles and moisture to form insoluble crystals. A comparative analysis of past studies indicates that bacterial concrete can effectively heal cracks up to 0.8 mm, providing superior strength and durability improvements, though at higher cost. Crystalline admixtures, on the other hand, are economical, commercially available, and suitable for healing micro-cracks up to 0.5 mm, particularly in water-retaining structures. The paper highlights the mechanisms, advantages, limitations, and applications of both approaches, and identifies future research directions including hybrid systems, large-scale field trials, and cost optimization. Findings suggest that self-healing concrete technologies have significant potential to reduce maintenance needs and promote sustainable infrastructure development.

Published by: vikaspatanker

Authors: Safaan Shawl

Abstract: In an era increasingly dominated by algorithmic precision and data-driven decision-making, the question of whether an artisanal product such as white wine can be priced through a deterministic model seems both audacious and tantalising. This paper embarks on precisely that odyssey—an independent attempt to formulate an original pricing algorithm for white wines by reverse-engineering the latent relationships between their physicochemical properties and their market value. Drawing from publicly available datasets and deploying statistical intuition rather than merely machine learning brute force, this research proposes a novel, human-designed formula that accurately estimates the price of white wines. The formula integrates variables such as acidity, sulphates, residual sugar, and volatile acidity—each weighted with philosophical and economic significance—into a predictive framework that is both interpretable and intuitive. Unlike conventional black-box regressions, the methodology underscores transparency, causal inference, and domain-sensitive calibration. This work is not only a tribute to the enduring relevance of analytical thinking in a machine age but also a call for more interdisciplinary bridges between oenology and economics, chemistry and computation, palate and price. It aims to empower connoisseurs, traders, and vineyards alike to understand, forecast, and perhaps demystify the economics swirling within every bottle. The findings reveal a striking congruence between predicted and actual price tiers, suggesting that white wine pricing, far from being capricious or arbitrary, often adheres to a hidden logic that this paper attempts to uncover and articulate.

DOI: https://doi.org/10.5281/zenodo.17104035

 

Published by: vikaspatanker

Authors: Aditya Verma, Himanshu Ranjan, Manoj Kumar Yadav, Sushant Kumar

Abstract: The Indo-Gangetic Plain of India continues to face a serious environmental and public health problem due to air pollution, as particulate matter (PM2.5 and PM10) continuously surpasses permissible limits. In order to predict particulate matter concentrations in Lucknow, India, this study creates a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model utilising an eight-year dataset (2017–2024) of meteorological and air quality indicators. To guarantee dependability, the data underwent preprocessing using min–max normalisation, wind vector reconstruction, interpolation, and outlier correction. Through the integration of CNN's feature extraction and LSTM's sequential learning, the CNN–LSTM model is able to capture temporal relationships as well as spatial correlations. R2, RMSE, MAE, and MAPE were used to compare performance to standalone models. According to the results, the hybrid method successfully reproduced seasonal variability, including winter peaks and monsoon-driven falls, with the maximum accuracy (R2 = 0.658 for PM2.5; R2 = 0.754 for PM10). The CNN–LSTM outperformed other models in terms of robustness and generalisability, although somewhat underestimating intense episodic surges. Under India's National Clean Air Programme (NCAP), the results highlight the model's potential as a decision-support tool for early warning systems and policy actions. The importance of deep learning hybrids for long-term air quality control in heavily polluted metropolitan areas is demonstrated by this work.

DOI: https://doi.org/10.5281/zenodo.17103728

 

Published by: vikaspatanker

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