Authors: Sambasiva Rao Madamanchi

Abstract: Disaster recovery (DR) planning for hybrid infrastructures that combine Solaris and Linux poses unique challenges due to differences in tooling, system architecture, and operational practices. Solaris often supports legacy, mission-critical applications, while Linux drives modern, scalable workloads. This document provides a comprehensive guide to building a resilient DR strategy across both platforms. Key areas include risk assessment, backup and recovery tooling, system state preservation, and application/database restoration. Emphasis is placed on automation through Ansible, shell, and Python scripts, as well as configuration management and monitoring integration. The guide also highlights best practices such as maintaining consistent time and user IDs, isolating recovery zones, and leveraging enterprise backup solutions. Through clear documentation, defined team roles, and routine testing, organizations can achieve a DR framework that is platform-aware, repeatable, and aligned with evolving operational and compliance requirements.

DOI: https://doi.org/10.5281/zenodo.15771601

 

Published by: vikaspatanker

Authors: Research Scholar Mitali Khandelwal, Dr. Kshmasheel Mishra

Abstract: Road traffic accidents are a leading cause of fatalities and injuries worldwide, with their severity influenced by numerous dynamic and interrelated factors. Predicting the severity of such accidents is critical for timely emergency response, infrastructure planning, and overall road safety enhancement. This study explores advanced machine learning (ML) techniques to accurately predict accident severity using diverse datasets comprising weather conditions, road characteristics, vehicle information, and driver behavior. A novel hybrid model is proposed, integrating classical pre-trained models with ensemble learning and deep neural networks to capture complex nonlinear relationships within the data. To address class imbalance a common issue in accident datasets, strategies such as data augmentation and cost-sensitive learning are incorporated. The proposed architecture undergoes rigorous performance evaluation using metrics like accuracy, precision, recall, and F1-score, demonstrating superior predictive capability compared to traditional models. Further, the study emphasizes model interpretability through tools like SHAP and LIME to enhance transparency and trust. By leveraging real-world traffic data and scalable ML infrastructure, the developed model offers a reliable solution for severity prediction, aiding emergency services and policy decisions. Ultimately, this research contributes to the reduction of road casualties and the advancement of intelligent transport systems.

Published by: vikaspatanker

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Published by: vikaspatanker

Authors: Joyshree Saikia. Assistant Professor

Abstract: The representation of North East India has been colonial and ethnographically reductive and there has been a gross misrepresentation of the heterogeneous traits of the region under the faulty homogeneous label of 'Northeast'. It is undeniable that the region is a treasure house of unique traditions, cultures, languages and dialects. However, the only obvious trait of homogeneity among them is reclaiming their indigenous oral tradition/s in present time. Within any colonial discourse, the oral form “is generally identified with the illiterate and even the uncivilized” (Ao 104). Hence, tribal oral culture has oftentimes been misinterpreted by colonial discourse, which leads to identifying the communities as illiterate, ignorant, and backward. The study will examine how the concept of writing orality is accomplished in Boats on Land, a short story collection by contemporary Khasi writer Janice Pariat. The stories in this collection attempt to disrupt the hierarchy of the textual over the spoken, a binary that Pariat believes is a colonial construct. The study aims to show how the valorisation of the Khasi oral tradition in the text challenges the pre-established dominance of written literature.

DOI:

 

Published by: vikaspatanker

Authors: Riya Dadarao Gawai

Abstract: India's wedding traditions are deeply rooted in cultural rituals, symbolic gestures, and spatial expressions. With the rise of destination weddings in India, there's an increasing demand for architectural spaces that not only cater to luxury and comfort but also honor the rituals, aesthetics, and materiality of Indian traditions. This research investigates how contemporary wedding venues can reflect India's cultural diversity through regional architectural language, use of traditional materials, and planning of ritual-centric spaces. The goal is to bridge modern hospitality design with traditional Indian values.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: A. S. Bagawan, C. S. Katageri, S. N. Benal

Abstract: Biodegradable plastics have emerged as a potential solution to mitigate the environmental impacts of conventional plastics, which persist in ecosystems for centuries. This article evaluates the environmental safety of biodegradable plastics, focusing on their degradation mechanisms, ecological impacts, and lifecycle sustainability. Through a mixed-methods approach combining literature review and experimental analysis, we assess the performance of common biodegradable plastics like polylactic acid (PLA) and polyhydroxyalkanoates (PHA) under various environmental conditions. Findings indicate that while biodegradable plastics offer reduced persistence compared to conventional plastics, their environmental safety depends on proper waste management infrastructure and environmental conditions conducive to biodegradation. Challenges such as incomplete degradation, microplastic formation, and high production costs underscore the need for improved materials and policies. This study highlights the potential of biodegradable plastics to enhance environmental safety while identifying critical areas for future research and development.

DOI: http://doi.org/10.5281/zenodo.15754651

Published by: vikaspatanker

Authors: Shriti Malviya, Professor Shashikant B. Dhobale

Abstract: The present study demonstrates the study and modelling of the geo polymer and Geo-Jute Fabric Pavement. The main idea is to reduce the Water Infiltration and the Erosive property of the traditional bitumen roads. In this project we have introduced 2 layers of Geo-Jute Fabric which are placed between the Subgrade and the Base course and another one is placed between the Binder course and the Surface course. The Geo-Jute layer between the Subgrade and Base course will reduce the Water Infiltration property whereas the Geo-Jute Fabric between the Binder course and the Surface course will reduce the progression of wear and tear underneath the Surface course. The table compares the key properties of two materials: GeoJute and Geopolymer, focusing on their CBR value, water absorption, and cost. GeoJute shows a higher California Bearing Ratio (CBR) value of 4.6, compared to 3.5 for Geopolymer. This indicates that GeoJute provides better strength and load-bearing capacity in subgrade soil reinforcement applications. GeoJute exhibits significantly higher water absorption, with a rate of 200%, while Geopolymer has a much lower absorption rate of 10%. This suggests that GeoJute retains more moisture, which might affect its performance in wet conditions.

 

 

Published by: vikaspatanker

Authors: Dr. Ayonija Pathre, Bishnu Kumar

Abstract: In wireless sensor networks (WSNs), many sensor devices are spread throughout the environment with the goal of collecting data and sending them to a base station (BS) for further studies. The issue of their limited battery power has aroused the interest of researchers, and several protocols were developed to optimize energy use and thus increase the network’s lifetime. The present research enhances the well-known low-energy adaptive clustering hierarchy (LEACH) protocol with a new artificial intelligence (AI) protocol named energy distance NN LEACH. For this purpose, an innovative clustering strategy built on the machine learning NN algorithm is used in WSNs to improve the cluster formation process and maximise network stability. By implementing an objective function that considers each node’s residual energy and distance from the cluster centre when selecting the cluster head (CH) of each cluster, LEACH also eliminates the inherent randomness in LEACH during the CH election process. The proposed protocol has the advantage of ensuring better CH distribution throughout the network surface with a balanced load across all network nodes. In comparison with the known LEACH, the simulation results demonstrate the efficiency of our approach: the lifetime of the network is extended and the energy consumption is reduced.

Published by: vikaspatanker

Authors: Prathibha M, Dr. Manjunath M, Dr. Evangelin Geetha D

Abstract: This study introduces an intelligent waste detection and sorting system driven by artificial intelligence and deep learning models such as YOLO and TensorFlow. It efficiently classifies waste into categories like recyclable, organic, and hazardous using real-time image analysis. A tailored model trained on waste datasets enables accurate recognition across varying environments. Developed with Python and OpenCV, the system also recommends appropriate disposal methods. Its compact design supports integration into smart bins and industrial workflows. The approach promotes automation in waste segregation, offering a sustainable and scalable solution for effective waste management.

DOI: http://doi.org/

 

Published by: vikaspatanker

Authors: Bhagwan, Assistant Professor Ashwini Sangam

Abstract: Conjunctivitis, a frequent eye illness characterized by inflammation of the conjunctiva – the delicate tissue lining the inner eyelids and covering the eye's white region, may occur from numerous causes such as bacterial or viral agents, as well as allergies. While often non-severe and self-resolving after a few of weeks, it might cause pain and hamper good vision. Harnessing the power of Convolutional Neural Networks (CNNs), a type of artificial intelligence, provides a revolutionary way to diagnose conjunctivitis in ocular pictures. CNNs excel at recognizing subtle picture patterns by learning from an enormous dataset covering both healthy and sick eyes. Trained CNN models exhibit amazing skill in detecting conjunctivitis within fresh photos with great accuracy

DOI: https://doi.org/10.5281/zenodo.15753155

Published by: vikaspatanker