Daily Archives: June 27, 2025

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/

 

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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

Authors: Research Scholar Venugopal Burugupally, Dr Ajay Swarup

Abstract: Concrete-filled steel tubular (CFST) sections are widely used in structural applications due to their superior mechanical properties, including high axial load capacity, energy dissipation, and fire resistance. This study presents a finite element analysis (FEA) of CFST columns under axial compression using ANSYS Workbench. The numerical model incorporates nonlinear material behavior, including an elastoplastic model with strain hardening for steel and the Drucker–Prager plasticity model for concrete to account for confinement effects. A structured finite element mesh was employed, with solid elements for concrete and shell elements for steel. The analysis considered realistic boundary conditions, applying displacement-controlled axial loading with fixed base constraints. The FEM results were validated against experimental data from the literature, showing a maximum deviation of less than 5% in peak axial load prediction. Load-displacement curves confirmed that steel confinement enhances concrete performance, delaying local buckling and increasing overall strength. Stress distribution analysis indicated effective load transfer between the steel tube and concrete core, while buckling patterns demonstrated progressive load redistribution, preventing sudden failure. These findings confirm that FEM is an effective tool for optimizing CFST designs and predicting their structural response under varying load conditions.

Published by: vikaspatanker

Authors: Professor Dr. Rajendra Singh Kushwah, Ritesh Kumar

Abstract: The most popular method of forecasting SPs throughout the course of history was to make use of statistical models such as moving averages, ARIMA, and the GARCH. This was the most common strategy. It is difficult for these models to accurately predict future SPs due to the fact that financial markets are characterized by their complexity, non-linearity, and unending change. In spite of the fact that these models are able to effectively recognize particular linear trends within historical data, they often have a difficult time accurately predicting events that will occur in the future. As a result of the fact that price changes are impacted by a large variety of different factors, these models do not allow for the precise estimation of future SPs. These models are not particularly accurate as a consequence of this. In recent years, there has been an increase in interest in the topic of financial forecasting in relation to the capabilities of machine learning and deep learning approaches. This attention has brought about a number of interesting developments. The methodologies in question are able to accurately characterize not only the time-dependent patterns that are present in the data, but also the intricate and non-linear linkages that are there. In the field of time series prediction, recurrent neural networks (RNN) and long short-term memory (LSTM) networks, in addition to generalized recurrent units (GRU), have been shown to be successful tools. When it comes to capturing long-range dependencies in supply chains, it is preferable to traditional methods since it is more accurate.

Published by: vikaspatanker

Authors: Raymond Ashieyi-Ahorgah

Abstract: – Africa's abundant mineral resources present unprecedented opportunities for industrial transformation, yet the continent's industrial development remains constrained by inadequate energy infrastructure. This study examines the critical relationship between energy infrastructure development and mineral-based industrialization in Africa from a United States investment perspective. Drawing on recent empirical evidence and policy developments through 2025, we analyze how strategic energy investments can unlock Africa's mineral wealth while creating sustainable industrial value chains. Our analysis reveals that energy infrastructure serves as a fundamental catalyst for mineral-based industrialization, with renewable energy transitions offering particular promise for long-term sustainable development. The findings suggest that U.S. investments in Africa's energy infrastructure, particularly in renewable energy systems, can generate substantial returns while supporting the continent's industrial transformation. We recommend targeted investment strategies that leverage public-private partnerships and address key barriers including financing constraints, regulatory frameworks, and technical capacity limitations.

DOI: https://doi.org/10.5281/zenodo.17212450

 

Published by: vikaspatanker