Authors: Harshita Sharma, Saniya, Dr. Rishabh Bhardwaj

Abstract: Heavy metal contamination in groundwater represents a critical environmental and public health challenge globally. This study assessed the concentrations and spatial distribution of four priority heavy metals—Nickel (Ni), Arsenic (As), Mercury (Hg), and Uranium (U)—in water sources across Muzaffarnagar and Shamli districts of western Uttar Pradesh, India. Water samples were collected from diverse sources including residential borewells, hand pumps in agricultural areas, and surface water bodies during 2025-26. Physicochemical parameters (pH, hardness, dissolved organic carbon, chemical oxygen demand) and heavy metal concentrations were analyzed using standardized methods including EDTA titration, UV-persulfate oxidation, and atomic absorption spectroscopy. Results revealed that Nickel concentrations ranged from 0.12 to 0.35 mg/L (50% exceeding WHO limit of 0.07 mg/L), Arsenic from 0.005 to 0.030 mg/L (75% exceeding 0.01 mg/L limit), Mercury from 0.003 to 0.007 mg/L, and Uranium from 0.01 to 0.05 mg/L (50% exceeding 0.03 mg/L limit). Surface water and urban groundwater showed the highest contamination levels. The findings indicate significant anthropogenic influence from industrial effluents, agricultural runoff, and domestic sewage, compounded by natural geogenic sources. Statistical analysis revealed moderate correlations between metals, suggesting common mobilization mechanisms. This study provides critical baseline data for water resource management and highlights the urgent need for monitoring, treatment infrastructure, and public health interventions in the study region.

DOI: http://doi.org/10.5281/zenodo.20322000

Published by: vikaspatanker

Authors: Sanju, Rahul Kumar Jha, Shivam Kumar, Sumit kumar, Ashish Juneja

Abstract: This study focuses on developing eco-friendly fly ash bricks using silica fume and alkali activators (NaOH and Na₂SiO₃) as a sustainable alternative to traditional clay bricks. The objective is to utilize industrial waste effectively while reducing environmental degradation caused by clay brick production. Fly ash was used as the primary material, with silica fume added to enhance mechanical strength and alkaline chemicals to initiate geopolymerization. Bricks were prepared by mixing materials, molding, and proper curing. Tests including compressive strength, water absorption, and visual inspection were conducted. Results indicate that alkali-activated fly ash bricks with silica fume exhibit superior strength and durability compared to conventional clay bricks. This approach promotes waste utilization, low pollution, and energy-efficient construction, offering a promising solution for sustainable building practices.

Published by: vikaspatanker

Authors: Purushotam Naidu k, R.Srilatha, S.Gayathri, U.N. Harshitha, P. Siri Chandana

Abstract: Urban population growth creates new challenges related to civic infrastructure, and there is a need for efficient and smart complaint management systems. This paper describes the SocioSphere, which is an AI-based civic issue management platform that uses Natural Language Processing (NLP), machine learning, and high-performance web technologies to automatically process and route complaints. A report verification module (Fake/Real) built with Logistic Regression and engineered textual features can filter out spam and low-quality complaints. Valid complaints use a transformer model (RoBERTa) to identify the multi-class categories to which the complaint belongs. Furthermore, we have added a method of estimating the urgency of a complaint through the use of VADER-based sentiment analysis and heuristics for engagement, thus allowing for priority-based decisions. FastAPI is used to develop the backend API layer, offering high-speed (asynchronous/low latency) performance for model inferences and data processing. Complaints will be stored in the system's database and dynamically routed to appropriate authorities for final resolution. The experimental results demonstrate that the approach is effective for both classification and validation, as well as improving transparency and reducing manual work through the use of data-driven governance within smart city systems.

DOI: https://doi.org/10.5281/zenodo.20321890

Published by: vikaspatanker

Authors: Ullas M K, Vishal Kumar B N, Yashwanth A S, Dr. S V. Anil Kumar, Dr. S V. Anil Kumar

Abstract: The Agri Bot Solar Tracker is a smart agricultural robot designed to support modern farming through automation and renewable energy. It performs agricultural tasks such as seed sowing and field monitoring using sensors to measure soil moisture, humidity, and temperature. The system uses Wi-Fi communication for remote monitoring and control through a monitoring and control station. Powered by solar energy with an integrated solar tracking system, the robot maximizes energy efficiency by adjusting the solar panel according to sunlight direction. This sustainable system reduces manual labor, improves operational efficiency, and promotes eco-friendly farming practices.

Published by: vikaspatanker

Authors: Meenal Maan, Priyanshi Gupta, Dr. Rishabh Bhardwaj

Abstract: Enzymatic browning represents one of the most significant post-harvest challenges affecting fresh fruits and vegetables, leading to rapid deterioration in visual appeal, flavor, texture, and nutritional quality. This phenomenon primarily occurs due to the activity of polyphenol oxidase (PPO), which catalyzes the oxidation of phenolic compounds into quinones that subsequently polymerize into brown pigments. Such changes not only reduce consumer acceptability but also contribute to substantial economic losses in the food industry. In recent years, increasing consumer awareness regarding food safety and the demand for clean-label products have led to a shift away from synthetic antioxidants such as BHA and BHT toward natural alternatives. Natural antioxidants derived from plant sources are considered safer, environmentally friendly, and beneficial for health due to their additional bioactive properties. The present study focuses on evaluating the comparative effectiveness of selected natural antioxidants—ascorbic acid, citric acid, fresh lemon juice, sodium chloride, and calcium chloride—in inhibiting enzymatic browning in fresh-cut apple slices (cv. Fuji). The samples were treated with different concentrations of these agents and monitored over a period of 120 minutes under controlled conditions. Browning intensity was assessed using a standardized visual scale, and percentage inhibition was calculated relative to untreated controls. Additionally, in-vitro antioxidant assays such as DPPH and ABTS were performed to validate the radical scavenging capacity of the tested compounds. The results demonstrated that ascorbic acid exhibited the highest efficacy, completely inhibiting browning at higher concentrations. Lemon juice also showed strong antioxidant activity due to the synergistic presence of ascorbic acid, citric acid, and flavonoids. Citric acid displayed moderate effectiveness by reducing pH and chelating metal ions, while sodium chloride and calcium chloride showed comparatively lower inhibitory effects.

DOI: http://doi.org/10.5281/zenodo.20321255

Published by: vikaspatanker

Authors: Harshita Sharma, Sakshi Rani, Meenal Maan

Abstract: Laboratories are significant generators of diverse hazardous waste streams including chemical, biological, radioactive, sharps, and electronic waste. Improper management of such waste endangers human health, occupational safety, and environmental integrity. This study examines sustainable laboratory waste management practices through the framework of Green Chemistry, as established by Anastas and Warner (1998). The study reviews the classification of laboratory waste, current segregation and treatment protocols, and the effectiveness of sustainability interventions including microscale experimentation, green solvent substitution, solvent recovery, and digital laboratory management. Data analysis from published institutional case studies reveals that laboratories implementing green chemistry principles achieve waste reductions of 50–70%, E-factor improvements of 60–90%, and annual cost savings of approximately 50%. Key challenges including institutional inertia, cost barriers, and inadequate training are identified, alongside evidence-based recommendations for overcoming them. The study concludes that sustainable laboratory practice is simultaneously an environmental imperative, a safety strategy, and an economic advantage.

DOI: http://doi.org/10.5281/zenodo.20321194

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Authors: Dr. Deepika, Abhinav K G, Adithya Verma M A, Chiranth S Shetty, G Suhas Kartik

Abstract: Dairy farms generate substantial quantities of methane gas through enteric fermentation and manure decomposition. Elevated methane concentrations in enclosed or poorly ventilated cowsheds adversely affect cattle health, reduce milk productivity, and pose safety hazards to farm workers. Conventional gas-monitoring systems are reactive and threshold-based, generating alerts only after dangerous concentrations have already been reached. This paper presents an IoT and Machine Learning (ML)-based framework for real-time methane detection and bovine health risk classification. MQ-4 (methane), MQ-135 (air quality/ammonia), and DHT22 (temperature and humidity) sensors interface with an ESP32 microcontroller to collect continuous environmental readings that are transmitted to Firebase cloud storage via Wi-Fi using MQTT/HTTP protocols. Five supervised ML classifiers — Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression, and K-Nearest Neighbors (KNN) — are trained and evaluated for three-class bovine health risk classification (Low, Moderate, High). Random Forest achieved the highest performance with 96.8% accuracy, 96.5% precision, 96.8% recall, and an F1-score of 96.6% at the 90-10 train-test split, outperforming SVM (91.3%), Decision Tree (84.1%), KNN (79.6%), and Logistic Regression (76.9%). Automated alerts are delivered to farmers via a real-time Arduino IoT Cloud dashboard, email, and mobile push notifications. The proposed system is scalable and cost-effective.

DOI: https://doi.org/10.5281/zenodo.20321084

Published by: vikaspatanker

Authors: Ms. K. Divarshini, Dr. P. Poornima

Abstract: The rapid growth of e-commerce and smartphone usage has significantly transformed consumer buying behaviour. Online shopping platforms provide convenience, variety, and easy access to products, influencing consumer decisions. This study aims to analyse the buying behaviour of consumers towards the Flipkart mobile application. Primary data was collected through a structured questionnaire from 100 respondents. The findings reveal that factors such as discounts, product variety, user-friendly interface, and delivery services play a major role in influencing consumer decisions. The study concludes that Flipkart is widely preferred due to its convenience and attractive offers, but improvements in delivery speed and product quality can further enhance customer satisfaction.

DOI: http://doi.org/10.5281/zenodo.20309209

Published by: vikaspatanker

Authors: Shubham sahu, Dr. Dharmbir Yadav

Abstract: The rapid growth of modern telecommunication technologies such as 2G, 4G LTE, and 5G has significantly increased the complexity of telecom network management. Telecom operators continuously generate massive amounts of network data related to traffic usage, bandwidth utilization, latency, throughput, and user activity. Traditional telecom monitoring systems mainly rely on manual analysis and threshold-based alert mechanisms, which are often unable to predict future network congestion, performance degradation, or operational failures effectively. To overcome these limitations, intelligent and automated monitoring solutions are required for efficient telecom network management. This research work, titled “Telecom Network Intelligence System using AI,” proposes an Artificial Intelligence (AI) and Machine Learning (ML) based framework for intelligent telecom network monitoring, traffic prediction, congestion detection, and performance analysis. The proposed system integrates telecom KPI analytics, predictive Machine Learning models, and real-time dashboard visualization to support proactive telecom operations and data-driven decision-making. The system analyzes important telecom Key Performance Indicators (KPIs) such as Call Setup Success Rate (CSSR), Call Drop Rate, LTE Throughput, PRB Utilization, Network Latency, and User Throughput collected from 2G, 4G LTE, and 5G networks. Machine Learning algorithms including Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) are utilized for traffic forecasting, anomaly detection, and congestion prediction. The proposed framework also integrates Python, SQL, Advanced Excel, and Microsoft Power BI for telecom data preprocessing, predictive analytics, and interactive dashboard development. The AI-driven dashboards provide real-time KPI monitoring, network health visualization, congestion alerts, and technology-wise performance comparison. Experimental analysis demonstrates that the proposed system improves prediction accuracy, reduces operational complexity, supports proactive fault management, and enhances telecom network efficiency. The research contributes toward the development of intelligent telecom monitoring systems capable of supporting future AI-driven telecom operations, AIOps integration, self-healing networks, and next-generation 5G/6G communication infrastructures.

Published by: vikaspatanker

Authors: Dr. Mohammad Shah Alam Chowdhury

Abstract: The present study explores the representation of socialism and social realism in the selected works of Aravind Adiga and Rohinton Mistry. Socialism, as a socio-economic ideology, advocates equality, collective welfare, and the reduction of class divisions; however, its practical implementation in India has often revealed contradictions marked by poverty, corruption, exploitation, and social inequality. Contemporary Indian English literature reflects these realities by portraying the struggles of marginalized communities and critiquing the failures of political and economic systems. The study examines how Adiga and Mistry depict the harsh realities of Indian society through themes such as class oppression, poverty, labor exploitation, corruption, identity crisis, and social injustice. Aravind Adiga’s novels, particularly The White Tiger and Last Man in Tower, expose the inequalities of post-liberalization India where capitalist ambitions overshadow socialist ideals. His narratives present a satirical and confrontational critique of economic disparity, moral decay, and institutional corruption. In contrast, Rohinton Mistry’s works, including A Fine Balance and Such a Long Journey, offer a compassionate and humanistic portrayal of ordinary individuals struggling against political oppression, social discrimination, and economic hardships. The research further compares the narrative techniques, ideological perspectives, and social concerns reflected in the writings of both authors. While Adiga emphasizes rebellion, survival, and individual ambition within a corrupt socio-economic structure, Mistry focuses on resilience, human dignity, empathy, and collective suffering. Despite their differing approaches, both writers critically examine the failures of governance and the widening gap between the privileged and marginalized sections of society. The study concludes that the works of Adiga and Mistry serve as powerful critiques of socio-political realities in contemporary India. Their fiction not only reflects the complexities of socialism and capitalism but also highlights the role of literature as a medium for social awareness, resistance, and the representation of marginalized voices.

DOI: https://doi.org/10.5281/zenodo.20305758

Published by: vikaspatanker