Authors: Ritesh Vinod Deshmukh

Abstract: Indigenous communities across India have evolved construction systems that respond beautifully to their surroundings, traditions, and material availability. This research explores the tribal building techniques of the Gond community in Maharashtra—techniques shaped by centuries of climate interaction, local materials, and symbolic customs. Through on-site documentation, literature studies, and direct interactions with tribal members, the study uncovers the relevance of these methods in sustainable and context-sensitive architecture. This paper emphasizes how integrating tribal techniques into modern design, like in the proposed Gond Tribal Cultural Centre near Tadoba, can restore ecological balance and cultural identity in rural development.

Published by: vikaspatanker

Authors: Eric Sifuna Siunduh, Zachary Mwangi, Dr. Anselemo Peters Ikoha

Abstract: Climate change poses unprecedented challenges to African nations, necessitating sophisticated analytical approaches to understand and predict its multifaceted impacts. This study evaluates the effectiveness of mechanistic data analysis methods in machine learning applications for assessing climate change effects across Africa. Through a comprehensive analysis of temperature, precipitation, and socioeconomic data from 2020-2024, the study compared traditional statistical approaches with mechanistic machine learning models including physics-informed neural networks (PINNs), causal inference frameworks, and hybrid mechanistic-statistical models. The methodology integrated satellite data, ground-based observations, and socioeconomic indicators from 54 African countries, employing cross-validation techniques and mechanistic validation approaches. Results demonstrate that mechanistic methods significantly outperform traditional approaches in prediction accuracy (RMSE improved by 23-31%) and interpretability. Physics-informed models showed superior performance in temperature prediction (R² = 0.89) while causal inference frameworks excelled in understanding precipitation-agriculture relationships. The study reveals critical insights into drought patterns, agricultural vulnerability, and urban heat island effects across different African climatic zones. Key findings indicate that mechanistic approaches provide more robust predictions for policy-relevant scenarios, particularly in data-sparse regions common across Africa. However, computational complexity and data requirements present implementation challenges. The study recommends the integration of mechanistic methods with traditional approaches for comprehensive climate impact assessment, emphasizing the need for capacity building and infrastructure development to support widespread adoption of these advanced analytical techniques in African climate research

DOI: http://doi.org/10.5281/zenodo.15869058

Published by: vikaspatanker

Authors: Prof. Ar. Gulfam Shaikh, Vedant Mundiwale, Prof. Ar. Dilip Jade

Abstract: The evolution of sports complexes has undergone a significant transformation over the last few decades, driven by advances in construction technologies, materials, and design philosophies. Modern sports infrastructure demands multifunctionality, sustainability, spectator comfort, and technological integration. This study explores the advanced construction techniques employed in modern sports complexes, including long-span roofing systems, modular construction, precast technologies, tensile membrane structures, and smart building systems. These approaches contribute to faster construction, cost efficiency, structural integrity, and enhanced user experience. Case studies such as Jawaharlal Nehru Stadium, Delhi and Tottenham Hotspur Stadium contextualize these practices. Challenges like cost overruns, design complexity, and sustainability mandates are also examined. The study concludes by emphasizing innovation, interdisciplinary collaboration, and future-ready design strategies for sports architecture.

 

 

Published by: vikaspatanker

Authors: Mr. Eric Sifuna Siunduh, Mr. Victor Mony Otieno, Professor Samuel Mbugua

Abstract: Fault-tolerant software architecture has become increasingly critical in modern distributed systems, where system failures can result in significant economic losses and service disruptions. This research paper provides a comprehensive analysis of fault-tolerant software architecture design patterns, implementation strategies, and performance evaluation methodologies. Through a systematic literature review of 45 peer-reviewed articles published between 2020-2024, this study identifies key architectural patterns including redundancy-based approaches, checkpoint-restart mechanisms, and self-healing systems. The methodology employed includes comparative analysis of fault tolerance techniques, performance benchmarking, and case study evaluation of real-world implementations. Data analysis reveals that hybrid approaches combining multiple fault tolerance strategies achieve 99.99% system availability with 15-30% performance overhead. Results demonstrate that micro services architectures with circuit breaker patterns and service mesh implementations provide superior fault isolation compared to monolithic systems. The discussion includes detailed analysis of trade-offs between fault tolerance levels and system performance, supported by empirical data from 12 case studies. Key findings indicate that automated recovery mechanisms reduce mean time to recovery (MTTR) by 65% compared to manual intervention approaches. This research contributes to the field by providing a comprehensive framework for evaluating fault-tolerant architectures and offers practical guidelines for system architects. Future research directions include exploration of AI-driven fault prediction, quantum-resistant fault tolerance mechanisms, and edge computing fault tolerance strategies.

DOI: http://doi.org/10.5281/zenodo.15868925

Published by: vikaspatanker

Authors: Eric Sifuna Siunduh, Zachary Mwangi, Dr. Alice Nambiro Wechuli,

Abstract: The increasing adoption of cloud computing and Web 3.0 technologies offers transformative potential for public governance in Africa, particularly in enhancing citizen participation. Despite various efforts to digitize public services, many governments still struggle to ensure inclusive, transparent, and interactive participation frameworks. This paper examines how cloud computing and Web 3.0 technologies can be harnessed to empower citizens and strengthen e-participation in the African context. It explores the integration of semantic web, blockchain, and machine learning to facilitate interactive e-governance platforms. By employing an ex post facto research design, the study synthesizes empirical and theoretical insights to develop a model for citizen empowerment. Findings show that cloud-based platforms significantly increase accessibility and engagement, while Web 3.0 tools foster real-time collaboration and personalization. The proposed empowerment model emphasizes decentralization, transparency, and inclusivity. The study concludes with policy recommendations to foster digital literacy, improve infrastructure, and safeguard data governance for sustainable civic engagement.

DOI: http://doi.org/10.5281/zenodo.15868757

Published by: vikaspatanker

Authors: Eric Sifuna Siunduh, Victor Mony Otieno

Abstract: Green computing has emerged as a critical strategy for achieving sustainable technological growth globally. In Africa, this approach offers an essential path to mitigating pressing issues such as energy scarcity, poor e-waste management, and environmental degradation. Green computing encompasses environmentally friendly practices in the design, usage, and disposal of information and communication technology (ICT) infrastructure. This paper explores trends, drivers, challenges, and future directions of green computing adoption in Africa, emphasizing the role of data centers, cloud computing, and policy enforcement. A mixed-methods approach was employed, utilizing secondary sources such as journal articles, case studies, and institutional reports published between 2019 and 2024. Key findings reveal that renewable energy integration in ICT operations, policy support, and international collaborations are driving adoption in countries like Kenya, South Africa, and Rwanda. However, persistent barriers—such as limited infrastructure, high initial costs, and a shortage of skilled professionals—continue to hinder widespread implementation. The study recommends coordinated stakeholder action, increased investment in education and renewable energy, and the development of regional green ICT hubs. This paper contributes to understanding how Africa can align digital transformation with sustainability, advancing both environmental goals and socio-economic development.

DOI: http://doi.org/10.5281/zenodo.15868375

Published by: vikaspatanker

Authors: Amit Sharma

Abstract: As the printing industry transitions into the era of Industry 4.0, traditional quality assurance methods—centered on manual inspection and reactive defect handling—are increasingly inadequate for the speed, complexity, and customization demands of modern pressrooms. This paper explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in real-time monitoring and quality assurance (QA) across print production workflows. Leveraging technologies such as computer vision, IoT sensor networks, and predictive analytics, AI-enabled systems enable proactive defect detection, automated correction, and dynamic process optimization. Applications include in-line visual inspection, root cause analysis, intelligent alerting, and traceable compliance logging. Case studies demonstrate significant gains in defect reduction, throughput, and client satisfaction. However, adoption remains hindered by challenges such as legacy equipment integration, data infrastructure gaps, workforce readiness, and cybersecurity concerns. Future directions emphasize the role of digital twins, federated learning, cloud-based QA hubs, and sustainability-aware defect prevention. Ultimately, AI transforms quality assurance from a reactive function into a strategic enabler—advancing efficiency, brand protection, and environmental responsibility in next-generation print operations.

DOI: https://doi.org/10.5281/zenodo.15868325

 

Published by: vikaspatanker

Authors: Rupsa Sarkar

Abstract: This work introduces a new energy-based model based on cross product torque analysis for the generalization of the tipping condition of rigid bodies of general shape. Classical mechanics employs torque to find rotational balance, but my method introduces the percent contact area (PCA) to define the extent to which the object is supported on a surface and how this influences tipping. The article presents a formula for computing the minimum amount of external energy to cause tipping by considering torque through the cross product and accounting for geometric distribution and weight. The PyBullet simulations yield high correlation, affirming the model's ability to make predictions.

DOI: https://doi.org/10.5281/zenodo.15867962

 

Published by: vikaspatanker

Authors: Dr. Sanjeev Puri, Sandeep Keshav

Abstract: The evolution of Edge-AI technologies has created new opportunities in pediatric healthcare, allowing for real-time monitoring of infants while maintaining privacy. This research introduces an innovative multi-modal Edge-AI framework that combines video, audio, and physiological data to anticipate potential mental health issues in infants. The proposed system processes information locally on edge devices, minimizing latency, enhancing privacy, and enabling continuous monitoring in both clinical and home settings. By employing lightweight AI models for on-device processing, the system promotes early identification of neurodevelopmental challenges and encourages timely interventions. This approach aims to shift healthcare from a reactive stance to a preventive one, ultimately aiming to foster long-term enhancements in mental health. The paper outlines the system's architecture, techniques for optimizing AI models, and prospective applications in pediatric healthcare environments.

DOI: https://doi.org/10.5281/zenodo.15867669

 

Published by: vikaspatanker

Authors: Research Scholar Sandeep Keshav, Professor Dr. Sanjeev Puri

Abstract: The evolution of Edge-AI technologies has created new opportunities in pediatric healthcare, allowing for real-time monitoring of infants while maintaining privacy. This research introduces an innovative multi-modal Edge-AI framework that combines video, audio, and physiological data to anticipate potential mental health issues in infants. The proposed system processes information locally on edge devices, minimizing latency, enhancing privacy, and enabling continuous monitoring in both clinical and home settings. By employing lightweight AI models for on-device processing, the system promotes early identification of neurodevelopmental challenges and encourages timely interventions. This approach aims to shift healthcare from a reactive stance to a preventive one, ultimately aiming to foster long-term enhancements in mental health. The paper outlines the system's architecture, techniques for optimizing AI models, and prospective applications in pediatric healthcare environments.

Published by: vikaspatanker