Authors: Nisha Gautam

Abstract: Heavy metal contamination in river sediments is a major environmental concern due to rapid urbanization and industrialization. The present study assessed heavy metal contamination in sediment samples collected from selected sites of the Gomti River in Lucknow, Uttar Pradesh, India. Sediment samples were collected from Gaughat, Kudiya Ghat, Daliganj Bridge, and Hanuman Setu during November 2025 and analysed for chromium (Cr), nickel (Ni), arsenic (As), cadmium (Cd), and iron (Fe). Pollution assessment indices including Enrichment Factor (EF), Contamination Factor (CF), and Pollution Load Index (PLI) were used to evaluate contamination levels and anthropogenic influence. The results revealed significant spatial variation in heavy metal concentrations. Iron showed the highest concentration, while cadmium exhibited extremely high concentrations compared to its background value, indicating severe contamination. The concentration pattern followed the order: Fe > Cr > Ni > Cd > As. EF analysis indicated extremely severe enrichment of Cd, whereas Cr showed moderate enrichment. CF results also confirmed very high contamination by Cd. PLI values at all sampling sites were greater than 1, indicating polluted sediment conditions. The study concludes that anthropogenic activities such as sewage discharge, urban runoff, and industrial effluents are major contributors to heavy metal accumulation in the Gomti River sediments.

DOI: http://doi.org/10.5281/zenodo.20268759

Published by: vikaspatanker

Authors: Kamble Abhishek B, Nimbalkar Saurabh S, Patil Abhayrajsinh M.

Abstract: The increasing demand for eco-friendly and automated landscaping solutions has led to the development of a Solar-Powered Grass Cutter with Mobile Remote Control for medium outdoor spaces such as gardens, parks, and institutional campuses. The system harnesses solar energy via photovoltaic panels, which charge a 12V, 7Ah lead-acid battery through an MPPT charge controller. An ESP32 microcontroller receives Bluetooth commands from a mobile application to drive four Johnson DC gear motors (12V, 10 RPM) for mobility and a PMDC motor (12V, 5000 RPM) for blade actuation, interfaced via an L298N motor driver. Field testing demonstrated a cutting efficiency of 98.5% for grass heights of 15–25 mm and 78% for overgrown grass exceeding 60 mm, with operational runtime of 1.5–2 hours per full charge. The total fabrication cost is approximately Rs. 10,955, making it a cost-effective and environmentally sustainable alternative to conventional fuel-powered grass cutters.

Published by: vikaspatanker

Authors: Nitin Mishra

Abstract: Groundwater is the principal source of drinking and irrigation water in the Indo-Gangetic alluvial plains of Uttar Pradesh, India. Rapid urbanization, agricultural intensification, excessive groundwater abstraction, and geogenic contamination have significantly affected groundwater quality in the region. The present study evaluates groundwater quality in Central Uttar Pradesh using hydrogeochemical assessment, entropy-weighted water quality index (EWQI), and machine learning (ML) prediction techniques. A total of 178 groundwater samples were analyzed for major physicochemical parameters including pH, EC, TDS, TH, Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, SO42−, NO3−, F−, SiO2, and CO32−. The entropy weight method was employed to minimize subjectivity in water quality assessment, while hydrogeochemical interpretations were carried out using Piper and Gibbs diagrams. Three machine learning models, namely Classification and Regression Tree (CART), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were implemented to predict groundwater quality conditions. The results revealed that groundwater chemistry is predominantly controlled by rock–water interaction and ion exchange processes, with Ca–HCO3 and mixed hydrochemical facies dominating the study area. The EWQI values indicated that most groundwater samples fall within good to medium drinking water quality categories, although localized fluoride enrichment was observed in several locations. Among the applied models, XGBoost demonstrated superior predictive capability with R2 = 0.9597, RMSE = 2.2376, and MAE = 1.7690, outperforming RF and CART models. The findings highlight the effectiveness of integrating GIS-based hydrogeochemical analysis with machine learning approaches for groundwater quality prediction and sustainable groundwater management in Central Uttar Pradesh.

DOI: http://doi.org/10.5281/zenodo.20268302

Published by: vikaspatanker

Authors: Prof. Chetan Kumar V, Hardik Jain, Pranathi B H, Vikas R P, Srinidhi Prabhu M U

Abstract: The deployment of production-grade Multi-Agent Systems (MAS) from natural language specifications remains a significant challenge in software engineering, requiring so- phisticated role decomposition, reliable tool integration, exe- cutable code synthesis, and robust packaging with dependency management. This paper presents MindMeld, a novel tiered LLM orchestration framework that transforms natural language requirements into deployable, containerized multi-agent systems through a three-phase pipeline architecture. MindMeld introduces several key innovations: (1) a formal planning phase that generates machine-verifiable JSON agent specifications with explicit dependency graphs and interface con- tracts; (2) a closed-loop validation tier combining static analysis, dynamic runtime testing in isolated sandboxes, and iterative self-refinement based on structured error feedback; and (3) an automated integration phase that synthesizes orchestration logic, manages inter-agent communication, and produces containerized artifacts with complete dependency resolution. We conduct comprehensive evaluation on 47 diverse natural- language build requests spanning 8 task categories (data pro- cessing, API integration, document analysis, notification systems, workflow automation, content generation, monitoring, and multi- modal processing). Our results demonstrate that MindMeld achieves 78.7% end-to-end build success compared to 34.0% for single-pass generation baselines, with an average of 1.8 validation iterations per sub-agent. Ablation studies reveal that the planning phase contributes 23.4% improvement and the val- idation loop adds 21.3% improvement to overall success rates. A controlled user study with 24 participants shows 3.2× reduction in deployment time and 4.1/5.0 satisfaction scores. These results establish MindMeld as a practical framework for bridging the gap between natural language intent and production-ready multi- agent systems.

Published by: vikaspatanker

Authors: Amina Farooq

Abstract: Digital transformation in enterprise IT has become a strategic priority for organizations seeking to improve operational efficiency, enhance customer experience, and maintain competitiveness in an increasingly digital economy. It involves the integration of advanced technologies such as cloud computing, artificial intelligence, big data analytics, Internet of Things (IoT), and automation into traditional IT systems and business processes. This study explores the key components of digital transformation, including infrastructure modernization, application modernization, data-driven decision-making, and agile development practices. It also examines how enterprises are shifting from legacy systems to cloud-native and service-oriented architectures to improve scalability and flexibility. Furthermore, the paper highlights major challenges such as legacy system integration, cybersecurity risks, organizational resistance, and skill gaps. Emerging trends such as AI-driven automation, DevOps adoption, and intelligent enterprise systems are also discussed. The findings emphasize that digital transformation is essential for enabling innovation, improving efficiency, and achieving long-term business sustainability in modern enterprises.

DOI: http://doi.org/10.5281/zenodo.20283651

 

Published by: vikaspatanker

Authors: Nur Aisyah Karim

 

Abstract: Artificial intelligence (AI) has become a transformative technology in enhancing enterprise network monitoring systems by enabling intelligent, automated, and real-time analysis of network activities. Traditional monitoring approaches often struggle to manage the increasing complexity, scale, and dynamic nature of modern enterprise networks. AI-based monitoring systems address these limitations by leveraging machine learning, deep learning, and data analytics to detect anomalies, predict network failures, and optimize performance. These systems continuously analyze network traffic, system logs, and user behavior to identify security threats, performance bottlenecks, and operational inefficiencies. The study explores the architecture of AI-driven monitoring systems, including data collection, processing, analytics, and response layers integrated with cloud infrastructure. It also highlights applications in cybersecurity, network optimization, and predictive maintenance. Furthermore, the paper discusses key challenges such as data volume, false positives, model accuracy, and integration complexity. Emerging trends such as autonomous network management, edge AI, and real-time predictive analytics are also examined. The findings emphasize that AI-based monitoring significantly enhances network reliability, security, and operational efficiency in enterprise environments.

DOI: http://doi.org/10.5281/zenodo.20283684

 

Published by: vikaspatanker

Authors: Sana Rahman

 

Abstract: Cloud infrastructure optimization has become a critical area of research and development as organizations increasingly rely on cloud computing for scalable, flexible, and cost-effective IT services. Efficient utilization of cloud resources is essential to reduce operational costs, improve performance, and ensure high availability of services. This study reviews various cloud infrastructure optimization techniques, including resource allocation, load balancing, auto-scaling, virtualization, and energy-efficient computing strategies. It also examines the role of artificial intelligence and machine learning in enhancing optimization through predictive analytics and intelligent decision-making. The paper highlights how cloud providers manage computing, storage, and network resources to achieve optimal performance under dynamic workloads. Furthermore, it discusses key challenges such as resource wastage, latency, workload unpredictability, and security constraints. Emerging trends such as serverless computing, edge-cloud integration, and AI-driven cloud management are also explored. The findings emphasize that effective optimization techniques are essential for improving efficiency, scalability, and sustainability in modern cloud infrastructures.

DOI: http://doi.org/10.5281/zenodo.20283703

 

Published by: vikaspatanker

Authors: Chandra Perera

Abstract: Distributed systems have become a foundational technology in modern computing, enabling organizations to build scalable, reliable, and efficient applications across multiple interconnected nodes. These systems distribute computation, storage, and processing tasks across different machines, improving performance, fault tolerance, and resource utilization. This study explores the fundamental concepts of distributed systems, including communication models, consistency mechanisms, fault tolerance, and concurrency control. It also examines how distributed architectures are applied in various industries such as finance, healthcare, e-commerce, telecommunications, and cloud computing. The paper highlights key technologies supporting distributed systems, including microservices, containerization, distributed databases, and cloud platforms. Furthermore, it discusses major challenges such as network latency, data consistency, security risks, and system complexity. Emerging trends like edge computing, serverless architectures, and blockchain-based distributed systems are also analyzed. The findings emphasize that distributed systems are essential for supporting large-scale, high-performance applications in today’s interconnected digital world.

DOI: http://doi.org/10.5281/zenodo.20283802

 

Published by: vikaspatanker

Authors: Takeshi Nakamura

Abstract: Application Programming Interfaces (APIs) have become a fundamental component of modern software systems, enabling seamless communication and integration between applications, services, and platforms. With the rapid growth of cloud computing, microservices architectures, and mobile applications, API usage has increased significantly, making API management and security a critical concern. This study explores key aspects of API management, including API lifecycle management, rate limiting, authentication, monitoring, and version control. It also examines security challenges such as unauthorized access, data exposure, injection attacks, and misuse of API endpoints. The paper highlights essential security mechanisms such as OAuth, API gateways, encryption, token-based authentication, and access control policies. Furthermore, it discusses best practices for ensuring secure and efficient API deployment in distributed systems. Emerging trends such as API-first design, zero trust security models, and AI-driven API monitoring are also analyzed. The findings emphasize that effective API management and security are essential for maintaining system integrity, performance, and trust in modern digital ecosystems.

DOI: http://doi.org/10.5281/zenodo.20283978

 

Published by: vikaspatanker

Authors: Priya Narayanan

Abstract: Machine learning has emerged as a powerful approach for detecting anomalies in modern network environments, where traditional rule-based security systems often fail to identify evolving and sophisticated cyber threats. With the exponential growth of network traffic and the increasing complexity of distributed systems, ensuring real-time threat detection has become a critical requirement. This study explores the application of machine learning techniques for anomaly detection in network systems, focusing on supervised, unsupervised, and semi-supervised learning methods. These techniques enable the identification of unusual patterns in network traffic that may indicate intrusions, malware activity, or unauthorized access. The paper also examines the integration of machine learning models with network monitoring tools, intrusion detection systems, and cloud-based security platforms. Furthermore, it discusses key challenges such as high false-positive rates, data imbalance, concept drift, and scalability issues. Emerging solutions including deep learning models, autoencoders, and real-time streaming analytics are also highlighted. The findings indicate that machine learning significantly enhances the accuracy, adaptability, and efficiency of network anomaly detection systems, making them essential for modern cybersecurity frameworks.

DOI: http://doi.org/10.5281/zenodo.20284075

 

Published by: vikaspatanker