Authors: U.Meri Kishore, C.Aswini, S.Mahammad Thousif, V.Manasa, P.Bhargavi, Y.Vishnu Govardhan

Abstract: This paper presents the design and implementation of a Shadow Mode Robotic Arm, a high- precision teleoperation system designed to replicate human arm movements in real-time. The system leverages an ESP32-CAM module for vision-based pose estimation, utilizing the MediaPipe framework to track key upper-body landmarks. The extracted joint coordinates are processed and mapped to a multi-degree-of-freedom (DOF) robotic arm actuated by MG995 metal-gear servos. An ESP32microcontroller serves as the secondary control unit, generating Pulse Width Modulation (PWM) signals for synchronized actuation. The system features a dual-mode operation: a direct mimicry "Shadow Mode" and a remote web- dashboard control mode. Experimental results demonstrate a low-latency response (<100ms) and high kinematic fidelity, offering a cost-effective solution for hazardous environment handling, telemedicine, and industrial automation.

DOI: https://doi.org/10.5281/zenodo.18695456

Published by: vikaspatanker

Authors: Monica Lakshmi R, Parvathareddy Kavya Reddy, Prasiddhi S, Keerthana Devi S

Abstract: Small and marginal farmers in developing economies face challenges such as delayed crop disease detection, subjective quality assessment, and non-transparent pricing due to intermediary-dominated markets. To address these issues, this paper presents Agri-Connect, an AI-driven digital platform that integrates automated crop disease detection, quality grading, and a direct farmer–consumer marketplace. The proposed system employs a hybrid deep learning architecture, combining UNet-based semantic segmentation for precise diseased region extraction and a Vision Transformer (ViT) for robust disease classification and severity analysis. Experimental evaluation was conducted on a combined dataset consisting of ICAR images, drone-captured imagery, and real- world field images under varying environmental conditions. The proposed framework achieved a disease classification accuracy of approximately 94% and reliable quality grading performance across multiple produce categories, outperforming conventional CNN-based approaches. A multilingual, voice-enabled interface and AI- powered chatbot enhance usability for low-literacy users, while an integrated real-time marketplace enables transparent, quality-based pricing and direct trade. Agri-Connect demonstrates the practical potential of linking AI-verified crop analysis with digital market access to improve farmer income, transparency, and sustainable agricultural practices.

DOI: https://doi.org/10.5281/zenodo.18679741

Published by: vikaspatanker

Authors: Dr. S. Roslin

Abstract: In recent years, Systematic Investment Plans (SIPs) have emerged as a preferred investment strategy among Indian investors due to their disciplined structure, affordability, and ability to mitigate market volatility. Simultaneously, gold continues to retain its significance as a safe-haven asset, offering protection against inflation and economic uncertainty. This study aims to evaluate the performance of SIP investments in gold by empirically examining the Axis Gold Fund Regular–Growth scheme over a ten-year period from 2015 to 2025.The research adopts a quantitative research methodology and relies on secondary data to analyze SIP performance across multiple investment horizons, namely 10 years, 5 years, 3 years, 2 years, and 1 year. Key performance indicators such as total investment, latest value, absolute return, annualized return, Return on Investment (ROI), Investment Growth Factor, and Wealth Gain are employed to assess the effectiveness of gold SIPs as both short-term and long-term investment options. Overall, the study concludes that SIPs in gold mutual funds represent a reliable and structured investment avenue for wealth creation and portfolio diversification. The results offer valuable insights for investors, financial planners, and academicians by highlighting the relevance of gold SIPs in long-term financial planning and risk management strategies.

DOI: https://doi.org/10.5281/zenodo.18678948

Published by: vikaspatanker

Authors: Anurag Kumar, Dr Ashish Kumar Rai

Abstract: Arc faults present a significant risk to electrical power systems, potentially causing equipment damage, fires, and service interruptions if not detected quickly. Traditional protection methods often struggle to detect arc faults because of their nonlinear, low-current, and nonstationary behaviour. Wavelet-based analysis has proven effective due to its strong time–frequency resolution. By decomposing voltage and current signals into multiple frequency bands, wavelet transforms extract transient features linked to arc initiation and extinction. Indicators such as wavelet coefficients, high-frequency energy, and entropy help distinguish arc faults from normal conditions and disturbances. Discrete, continuous, and wavelet packet transforms, combined with intelligent classifiers, enhance detection accuracy, speed, and robustness in modern distribution systems.

Published by: vikaspatanker

Authors: Shabbir I. Tamboli

Abstract: Water quality plays a important role in maintaining ecological balance and human health. Rapid urbanization, industrial discharge, and agricultural runoff have significantly affected surface and groundwater resources in many local regions of India. The present study evaluates the physicochemical parameters of water samples collected from selected locations in the local area. Parameters such as pH, turbidity, total dissolved solids (TDS), hardness, chloride content, and dissolved oxygen (DO) were analyzed using standard laboratory methods. The results were compared with BIS (Bureau of Indian Standards) drinking water standards. The study reveals that certain parameters such as TDS and hardness exceeded permissible limits in some locations, indicating potential environmental and health risks. The findings highlight the need for continuous monitoring and effective water management strategies to protect environmental sustainability.

Published by: vikaspatanker

Authors: Khaleel Khan Mohammed

Abstract: Data engineering is now an essential subject for handling, processing, and analysing big data as the amount of data collected is increasing exponentially. This paper gives a future-focused overview of data engineering. The creation, building, upkeep, and optimization of data architecture, infrastructure, and pipelines are all essential components of data engineering, a field within data science. This paper presents a systematic study of data engineering pipelines with a focus on leakage-safe data splitting, preprocessing order, evaluation protocols, and reproducibility practices. We outline a canonical preprocessing workflow that enforces strict separation between training and evaluation data while ensuring that all data-dependent transformations are learned exclusively from training partitions. The paper further discusses suitable validation strategies for both static and time-dependent data, emphasizes the role of nested and repeated cross-validation, and highlights the importance of ablation and stability analysis in assessing model robustness. Finally, we examine provenance-aware logging and experiment tracking as essential components for reproducible and auditable machine learning systems. The proposed guidelines aim to support the development of trustworthy, scalable, and reproducible ML pipelines across data-intensive domains.

DOI: https://doi.org/10.5281/zenodo.18677279

Published by: vikaspatanker

Authors: Vivek Menon

Abstract: Enterprise-scale modernization has evolved from a strategic option to an operational imperative in the contemporary digital economy. Organizations that continue to rely on legacy applications and rigid, hardware-centric network infrastructures face mounting challenges in sustaining competitiveness, operational efficiency, and security resilience. Rapid technological innovation, evolving customer expectations, intensifying cloud-native competition, and increasingly sophisticated cyber threats are collectively reshaping the enterprise IT landscape. Systems originally designed for stability and centralized control now struggle to support modern requirements such as real-time analytics, elastic scalability, distributed workforce enablement, continuous deployment cycles, and AI-driven automation. As a result, modernization initiatives are becoming foundational to long-term enterprise sustainability and growth.This review provides a comprehensive examination of enterprise modernization strategies across both application and network domains. On the application side, modernization approaches such as cloud migration, microservices adoption, API-first design, containerization, DevOps integration, and Infrastructure as Code (IaC) are analyzed for their impact on scalability, agility, and maintainability. Transitioning from monolithic architectures to modular, loosely coupled systems enables organizations to accelerate innovation cycles, improve fault isolation, and enhance operational efficiency. Simultaneously, adopting cloud-native frameworks facilitates resource elasticity, cost optimization, and global service delivery.From a networking perspective, the paper explores the transformation from traditional perimeter-based infrastructures to software-defined networking (SDN), software-defined wide area networking (SD-WAN), and Zero Trust security architectures. These paradigms introduce centralized control, programmable network policies, identity-based access enforcement, and continuous monitoring capabilities. By decoupling control and data planes and embedding security mechanisms directly into network layers, enterprises can enhance visibility, reduce lateral threat movement, and support distributed cloud environments.Furthermore, the review evaluates automation-driven infrastructure and AI-enabled operations (AIOps) as critical enablers of modernization at scale. Automated provisioning, predictive monitoring, anomaly detection, and self-healing systems reduce operational complexity while improving service reliability. Governance frameworks, compliance integration, risk mitigation strategies, and cultural transformation are also discussed as essential components of successful modernization initiatives.The paper highlights both the tangible benefits—such as improved agility, cost reduction, resilience, and competitive advantage—and the inherent technical and organizational challenges associated with modernization, including data migration complexity, legacy integration risks, skill gaps, and change resistance. Finally, emerging trends such as AI-native architectures, edge computing integration, 5G-enabled connectivity, platform engineering, and sustainable green IT practices are examined as shaping forces of next-generation enterprise IT ecosystems.Overall, enterprise-scale modernization is framed not merely as a technological transition but as a strategic, organizational transformation that redefines how enterprises design, secure, deploy, and manage digital systems in an increasingly complex and interconnected world.

DOI: http://doi.org/10.5281/zenodo.18677080

Published by: vikaspatanker

Authors: Meera Krishnan

Abstract: Distributed systems have evolved into the foundational infrastructure supporting modern digital services, enabling cloud-native applications, microservices-based architectures, big data platforms, and globally distributed enterprise ecosystems. By leveraging geographically dispersed computing resources, distributed systems provide scalability, high availability, and fault tolerance. However, as system scale and architectural complexity increase, operational management becomes significantly more challenging. Organizations must address issues related to dynamic resource provisioning, configuration consistency, dependency management, automated scaling, continuous updates, and security enforcement across heterogeneous environments. Traditional manual administration approaches are insufficient for handling such complexity, often leading to configuration drift, deployment failures, environment inconsistencies, and increased operational risk. To overcome these limitations, automation-driven paradigms such as Infrastructure-as-Code (IaC) and Continuous Integration/Continuous Deployment (CI/CD) have emerged as essential components of modern distributed system management. Infrastructure-as-Code transforms infrastructure provisioning and configuration into machine-readable, version-controlled definitions, enabling reproducibility, consistency, and rapid environment replication. Simultaneously, CI/CD frameworks automate application build, testing, validation, and deployment processes, ensuring continuous delivery of reliable software updates across distributed architectures. The integration of IaC and CI/CD establishes a unified automation pipeline in which infrastructure and application lifecycles are managed cohesively, promoting operational efficiency, traceability, and resilience. This review comprehensively examines the conceptual foundations, architectural frameworks, and practical implementations of integrating IaC with CI/CD for distributed system automation. It analyzes declarative and imperative infrastructure models, automated deployment strategies, immutable infrastructure principles, and cloud-native orchestration practices. Furthermore, the paper evaluates the operational benefits of automation—including scalability optimization, reduced configuration drift, accelerated recovery, enhanced collaboration, and improved compliance management—while critically assessing associated challenges such as state management complexity, security vulnerabilities in automation scripts, pipeline debugging difficulties, and cost governance concerns. In addition, emerging paradigms such as GitOps, policy-as-code, DevSecOps, AI-driven pipeline optimization, and self-healing infrastructure mechanisms are discussed to highlight the ongoing evolution toward intelligent and autonomous system management. By synthesizing current practices and research directions, this review provides a structured perspective on how integrated automation frameworks enhance reliability, scalability, and security in distributed environments, while outlining future research opportunities aimed at achieving more adaptive, predictive, and cost-efficient distributed system operations.

DOI: http://doi.org/10.5281/zenodo.18677076

Published by: vikaspatanker

Authors: Arjun Rao

Abstract: Cloud-native system engineering has fundamentally transformed the way modern software applications are architected, deployed, and managed across distributed computing environments. Unlike traditional monolithic models that rely on tightly coupled components and static infrastructure, cloud-native approaches embrace modularity, elasticity, and automation as core design principles. Built around technologies such as containerization, microservices architecture, declarative infrastructure, and automated orchestration, cloud-native systems are specifically engineered to operate efficiently in dynamic public, private, and hybrid cloud ecosystems. These systems are designed not only to scale horizontally in response to fluctuating workloads but also to maintain operational continuity in the presence of hardware failures, network disruptions, and unpredictable traffic surges. A primary objective of cloud-native engineering is to achieve high availability (HA)—ensuring minimal service downtime—and high performance (HP)—delivering low latency, high throughput, and efficient resource utilization. High availability is accomplished through architectural strategies such as redundancy, replication, self-healing mechanisms, intelligent load balancing, and fault isolation. High performance, on the other hand, is supported by horizontal scalability, caching strategies, observability-driven optimization, and automated resource management. Together, these characteristics enable resilient and adaptive distributed systems capable of sustaining mission-critical workloads. This review provides a comprehensive examination of the foundational architectural principles, including microservices decomposition and container orchestration; the enabling technologies that support scalability and resilience; and the operational frameworks that integrate continuous integration and continuous deployment (CI/CD). It further explores advanced performance optimization techniques, such as predictive auto-scaling and edge computing, alongside established resilience strategies, including circuit breaker patterns, chaos engineering, and service mesh architectures. Emphasis is placed on practical design patterns, reliability engineering practices, and the cultural integration of DevOps methodologies to achieve sustained operational excellence. By synthesizing current advancements and emerging trends, this review highlights how cloud-native system engineering is evolving toward autonomous, self-optimizing infrastructures. These infrastructures combine intelligent automation, real-time observability, and predictive resilience to meet the growing demands of large-scale, distributed applications.

DOI: http://doi.org/10.5281/zenodo.18670345

Published by: vikaspatanker

Authors: Ananya Iyer

Abstract: The rapid evolution of cloud computing paradigms has significantly accelerated the adoption of distributed, cloud-native systems grounded in microservices architecture, containerization, dynamic orchestration, and continuous delivery pipelines. Unlike traditional monolithic systems that rely on tightly coupled components and static infrastructure, cloud-native applications are deliberately engineered to leverage elastic scalability, resource abstraction, and automated infrastructure provisioning within highly dynamic cloud environments. Foundational platforms such as Docker and Kubernetes have enabled the development of portable, resilient, and self-healing workloads capable of operating consistently across heterogeneous infrastructures. These technologies facilitate container image standardization, declarative orchestration, automated scaling, and fault recovery. However, as deployments extend to multi-cluster, hybrid-cloud, and multi-cloud ecosystems, system complexity increases exponentially, making comprehensive lifecycle governance a significant technical and organizational challenge. End-to-end lifecycle management therefore encompasses not only architectural design and containerized development but also automated CI/CD pipelines, runtime orchestration, observability engineering, security integration, performance tuning, cost governance (FinOps), and systematic service decommissioning. This review synthesizes contemporary methodologies, architectural patterns, and operational frameworks that support lifecycle governance within large-scale cloud-native ecosystems. It critically examines cross-cutting paradigms including DevSecOps integration, Infrastructure as Code (IaC), GitOps workflows, service mesh architectures, policy-as-code enforcement, FinOps optimization, and AI-driven operations (AIOps). These paradigms collectively emphasize automation, declarative configuration management, continuous validation, and compliance-aware deployment strategies. Particular attention is devoted to runtime observability engineering, integrating metrics, logs, and distributed tracing to enable proactive monitoring and rapid fault isolation. Additionally, the review addresses emerging security imperatives such as software supply chain integrity, container image signing, zero-trust networking models, and runtime threat detection. By embedding governance mechanisms directly into CI/CD and orchestration pipelines, organizations can mitigate configuration drift, reduce operational risk, and enhance resilience in highly dynamic distributed environments. Furthermore, emerging directions such as platform engineering, internal developer platforms (IDPs), serverless-native orchestration models, eBPF-based deep observability, and autonomous remediation frameworks are analyzed as transformative drivers of next-generation lifecycle management. These innovations aim to abstract operational complexity, improve developer productivity, and enable predictive, self-optimizing infrastructure behavior. The study concludes that holistic lifecycle integration—rather than isolated adoption of discrete tools—is essential for achieving sustained operational resilience, regulatory compliance, energy-efficient infrastructure utilization, and continuous innovation in large-scale distributed ecosystems. By consolidating architectural principles, operational best practices, and forward-looking research trajectories, this review provides a comprehensive conceptual and practical framework for researchers and practitioners seeking to advance end-to-end lifecycle management strategies in modern cloud-native systems.

DOI: http://doi.org/10.5281/zenodo.18670338

Published by: vikaspatanker