Volume 13 | Issue 4

 Abstract

This study investigates the effect of partial replacement of natural coarse aggregate with recycled coarse aggregate (RCA) on the compressive strength and workability of concrete. With the increasing demand for sustainable construction materials, the utilization of construction and demolition waste has become a critical strategy for reducing the environmental footprint of the construction industry. In this experimental research, RCA was used to replace natural aggregate at three levels: 10%, 20%, and 30%, by weight. Additionally, silica fume was incorporated at a fixed dosage of 10% by weight of cement to enhance the pozzolanic activity and improve overall concrete performance. Concrete mixes were prepared using a fixed mix ratio of 1:2:4 (cement: sand: aggregate) with a constant water-cement ratio. For each replacement level, mixes were prepared with and without silica fume, resulting in eight different mix combinations. Standard 150 mm concrete cubes were cast, cured for 28 days, and tested as per IS 516:1959 for compressive strength. Slump tests were also conducted to assess the workability of the mixes. Results showed that compressive strength decreased with increasing RCA content in the absence of silica fume. However, mixes containing silica fume showed a significant improvement in strength, recovering up to 90% of the control mix's compressive performance. Workability slightly declined with RCA content but improved with silica fume addition. The study concludes that up to 30% RCA replacement, especially when combined with silica fume, can be effectively used in structural concrete, promoting sustainable practices without significantly compromising strength or workability

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 Keywords

Recycled aggregate, concrete, OPC, Silica Fumes, Compressive strength

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ROOTSENSE offers a proactive IT support model using AI and predictive analytics to minimize system disruptions. By integrating machine learning, real-time monitoring, and automated diagnostics, it preempts failures and boosts operational efficiency. Key features include anomaly prediction, automated ticketing, and a dashboard-driven REI module for performance enhancement. Empirical results show improved reliability and responsiveness, marking a shift toward self-healing IT infrastructures.

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Predictive Analytics, Real-Time Monitoring, Proactive IT Support, Root Cause Analysis, Automated Ticketing, Machine Learning.

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This research PAPER contains an in-depth strategic analysis of Elon Musk, arguably the most prominent entrepreneur of the 21st century, alongside the various companies he actively manages. It takes into account the Musk's strategic vision and leadership, considering the myriad of industries he has influenced from electric vehicles and aerospace, to social media, neural technologies, and beyond. The study captures insights from his key companies, including but not limited to, Tesla, SpaceX, Neuralink, and The Boring Company to establish the business and operational strategies that are driving these ventures forward. Using some strategic management frameworks such as SWOT, leadership theories, and business model canvas, the paper analyzes what key strengths, weaknesses, opportunities, and threats confront the enterprises in constantly changing international markets. The study also looks at Musk's leadership practices which include but are not limited to tolerance of risks, innovation, engagement with the public, and assesses the implications on the performance of the respective businesses and the society at large. In addition, it examines the overall financial performance and market context of his companies, with full consideration of the controversies and debates on sustainability issues.

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 Keywords

Elon Musk , Strategic Leadership, Innovation Strategy, Entrepreneurial Vision , Business Model Analysis ,Tesla ,SpaceX ,Neuralink, The Boring Company, X (formerly Twitter) ,Risk-Taking Entrepreneurship , SWOT Analysis, PESTLE Analysis , Transformational Leadership, Technology Disruption ,Sustainable Energy ,Space Exploration , Brain-Machine Interfaces ,Crisis Management , Financial Performance , Market Positioning ,Strategic Management, Future of Innovation, Corporate Sustainabil

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In this study, cobalt ferrite (CoFe2O4) nanocrystals were synthesized via a green, plant-mediated combustion route using Ocimum sanctum (Tulsi) leaf extract as a natural fuel and chelating agent. The methodology presents a sustainable alternative to conventional chemical synthesis by eliminating the need for toxic reagents, reducing energy consumption, and minimizing waste generation. The synthesized nanocrystals were systematically characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), UV-Vis diffuse reflectance spectroscopy (DRS), photoluminescence (PL) analysis, and Brunauer-Emmett-Teller (BET) surface area measurements. XRD confirmed the formation of a single-phase spinel structure with average crystallite size around 25 nm. Morphological analysis revealed spherical and porous particles with a uniform distribution. The nanocrystals exhibited a band gap of 2.12 eV, indicating good absorption in the visible region. Photocatalytic activity was evaluated using methylene blue (MB) dye under visible light irradiation, where more than 91% degradation was achieved within 90 minutes. The photocatalyst showed excellent reusability and structural stability over four successive cycles. The enhanced photocatalytic efficiency was attributed to a synergistic effect of narrow band gap, high surface area, and efficient charge carrier separation. This study demonstrates a green and efficient approach for developing spinel ferrite nanomaterials for environmental remediation.

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 Keywords

Spinel ferrite nanocrystals; Green synthesis; Cobalt ferrite; Photocatalytic degradation; Visible light; Tulsi extract; Wastewater treatment.

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The concept of sugarcane migrant workers is relevant to understanding the major socio-economic problems faced by these workers after they migrate to the sugarcane cutting fields. The socio-economic issues include wages, income, and savings. And what are the before-migration and after-migration wage rates? Is the wage rate increasing or not? All such questions have arisen. This study identifies the socio-economic problems faced by sugarcane workers at the workplace. To estimate the income earned during the sugarcane cutting period., This paper is based on the primary data collected by questionnaires and field studies. The researcher used Microsoft Excel for data analysis, and the Paired samples test was used for hypothesis testing comparing the results before and after sugarcane migration.

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Socio-economic problems, Sugarcane migrant workers, Seasonal migration, Before and after migration.

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Actinomycetes are a diverse group of Gram-positive bacteria known for their ability to produce a wide variety of bioactive secondary metabolites with significant medicinal properties. These microorganisms play a critical role in the development of antibiotics, anticancer agents, immunosuppressants, antioxidants, and anti-inflammatory compounds. Notably, Actinomycetes such as Streptomyces species have been the source of life-saving antibiotics like streptomycin, erythromycin, and vancomycin, which have revolutionized the treatment of infectious diseases. The ecological and evolutionary significance of Actinomycetes in producing these bioactive compounds for survival and competition further underscores their importance. Despite these advances made, several challenges such as difficulties in culturing, the rise of antibiotic resistance, and limited exploration of Actinomycetes in understudied environments limits its widespread use. This paper delves into the various therapeutic compounds produced by Actinomycetes, ranging from anticancer drugs like doxorubicin and bleomycin to immunosuppressants like rapamycin, demonstrate the far-reaching implications of Actinomycetes in modern medicine. A future research direction includes exploration of the underexplored species, optimizing metabolite production, and developing combination therapies. Overall, Actinomycetes remain an invaluable resource for combating emerging health challenges, and continued investment in research and innovation is essential to harness their full potential for medical and industrial applications.

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Actinomycetes; Medical; Industrial; cancer; diabetes

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This study focuses on applying and comparing various ML algorithms Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF) to classify different types of network attacks such as DoS, Probe, R2L, and U2R. The CICIDS2018 dataset, known for its modern traffic features and attack diversity, was used as the benchmark. The dataset underwent preprocessing and feature selection to optimize model performance. Among the evaluated models, Random Forest outperformed others, achieving 98.76% accuracy . The results validate the effectiveness of ensemble learning methods in intrusion detection, offering a reliable solution for real-time cyber security monitoring.

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Intrusion Detection System, CICIDS2018, Random Forest, Cyber security, Machine Learning, Network Traffic Analysis

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The advancement of Optical Character Recognition (OCR) has significantly accelerated with the integration of deep learning techniques. This review paper presents a consolidated overview of the major developments in text detection and recognition from document images using deep neural networks. Focusing on modern region-based and segmentation-based methods, this paper highlights clustering-based enhancements in OCR workflows that aim to preserve spatial structure and context. By analyzing various state-of-the-art approaches, benchmarking datasets, evaluation metrics, and current system limitations, we offer insight into the strengths and shortcomings of contemporary OCR systems. The review concludes with a discussion of open research problems and future directions to guide further advancements in intelligent document analysis.

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Deep Learning, OCR, Text Detection, Document Image Analysis, Clustering, Neural Networks, Computer Vision

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Women play a significant role in the development of cooperative societies, contributing to economic empowerment, social progress, and community development. In rural and semi-urban areas, cooperative societies provide women with opportunities for financial independence, decision-making, and participation in economic activities.The study examines how cooperative societies serve as platforms for economic and social empowerment of women. Insights gained can help identify ways to strengthen women's leadership and decision-making abilities in cooperative settings

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Co- Operative society, Women empowerment, Economic development

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This study investigates the potential of utilizing waste tyres in road construction as a sustainable solution to two pressing challenges: waste tyre disposal and the need for improved road construction materials. The research examines various applications of waste tyres in road construction, including rubberized asphalt, tyre-derived aggregates, and reinforcement layers. The study analyzes performance benefits such as enhanced durability, reduced cracking, improved skid resistance, and noise reduction. Additionally, environmental benefits including waste reduction, energy conservation, and reduced carbon emissions are discussed. The findings suggest that incorporating waste tyres in road construction offers a technically viable, economically beneficial, and environmentally sustainable approach to infrastructure development while effectively addressing the global waste tyre management crisis.

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Waste tyres, rubberized asphalt, pavement engineering, recycling, sustainable construction, crumb rubber.

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Depression is the most common psychological problem in every student's life. Wherever belongs to you the arts or science stream. The aim of the research paper is to measure depression among students studying arts and science stream at Government Vidarbha Institute of Science & Humanities in Amravati District. The study was conducted over a sample of 60 students (30 Arts students & 30 Science students), age range from 18 to 21 years. The Mental Depression Scale developed by L. N. Dubey is used for data collection. Data was analyzed by means, standard deviation and 't' test. The result shows, There is a gender difference in depression level between the arts and science students. In Arts, girls have slightly higher depression than boys but in Science boys have slightly higher depression than girls. Also found that, there are no significant differences between arts and science students.

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Depression, Arts and Science Students

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Telecom forecasting is an essential task for telecommunication providers who are aiming to reduce customer deductions and improve customer loyalty. In machine learning technology, ensemble methods such as multi-model speech classifiers have shown promising results in this field. This test compares the performance of individual models with an ensemble melody approach using key metrics such as accuracy, accuracy, Recall, F1 score, and ROC-AUC. The results show that individuals using multi-model language classifiers, particularly soft adjustments, can provide more reliable predictions and contribute to communication providers to more effectively identify endangered species customers.

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Prediction by using Voting Classifier, Multi-Model Approach, Predictive Analytics, Classification Models, Supervised Learning.

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Battery packs in electric vehicles and energy storage systems can suffer various faults that pose safety hazards, including cell internal short circuits (ISC), thermal runaway (TR), sensor failures, and battery management system (BMS) malfunctions. Data-driven machine learning methods have shown promise in detecting such faults early, but their black-box nature raises trust and safety concerns. Explainable Artificial Intelligence (XAI) techniques have emerged to bridge this gap by providing insight into model decisions. This review surveys recent research on XAI-based fault diagnosis in multi-cell lithium-ion battery packs. We discuss traditional model-based approaches (using first-principles battery models and state observers) versus data-driven approaches (machine learning classifiers and anomaly detectors), highlighting how XAI methods can augment the latter. Key XAI techniques including feature attribution methods like SHAP and LIME, attention-based deep learning models, and surrogate modeling are explained and compared. Recent studies have applied XAI to identify the sensor signals and features most indicative of faults (e.g. sudden drops in cell voltage or rises in internal resistance). We compile these findings, noting each method's strengths and limitations. Challenges such as the need for large labeled fault datasets, real-time explainability, and validation of XAI outputs are identified as open issues. Overall, XAI can enhance the transparency and reliability of data-driven fault diagnosis in multi-cell battery packs, enabling safer battery management through human-interpretable insights.

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Battery Fault Diagnosis, Explainable AI, Lithium-Ion Battery, Multi-Cell Battery Pack

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The Companies (Amendment) Bill, 2020, introduced by the Indian government, represents a significant step in simplifying corporate laws and fostering a business-friendly environment. This amendment aims to ease the compliance burden on companies, especially startups and small businesses, while decriminalizing minor offenses, streamlining corporate governance, and enabling easier access to capital markets. The reform is part of a larger initiative to boost India's position in the World Bank's Ease of Doing Business rankings, attract foreign investment, and stimulate economic growth. This article provides a comprehensive analysis of the Companies (Amendment) Bill, 2020, examining its key provisions, impact on the business landscape, and the challenges it presents. By comparing the amendment with previous reforms and analyzing its implications for various sectors, this article explores whether the 2020 amendment is a genuine step towards a more conducive business environment or a compromise on corporate governance standards. The discussion also includes an international perspective to assess how India's reforms align with global best practices and what lessons can be drawn for future amendments.

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Companies (Amendment) Bill, 2020, Corporate Law Reform, Ease of Doing Business, Decriminalization of Offenses, Corporate Governance, CSR Flexibility

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Sujata is a significant figure in Buddhist tradition, primarily remembered for offering milk-rice (kheer) to Siddhartha Gautama before his enlightenment. This act of generosity played a crucial role in reviving the Bodhisattva's strength after years of extreme asceticism, ultimately leading him towards the Middle Path and eventual enlightenment. Despite Sujata's prominence in Buddhist lore, there is limited historical clarity regarding her social and caste background. Scholars and historians have debated her origins, with some suggesting she belonged to the Yadava community. This paper explores textual references, historical interpretations, and socio-cultural contexts to examine this claim. P?li Canon and other early Buddhist texts provide limited information about Sujata's lineage but describe her as the daughter of a wealthy householder in the village of Senani, near Uruvela (modern-day Bodh Gaya). The D?ghanik?ya and J?takas mention her in passing, focusing more on her act of offering rather than her ancestry. The term 'Senani' is sometimes interpreted as a title for a village headman or military leader. Some interpretations of these texts argue that since she was a cowherd's daughter, she may have belonged to a pastoral community, which has led to speculations linking her to the Yadava or Gopa (cowherd) lineage. Yadavas, historically associated with cow-rearing and pastoral traditions, have long been identified with Lord Krishna's lineage in Hindu texts. The Mahabharata and Puranas describe them as a warrior and agricultural community, which aligns with descriptions of Sujata's family as wealthy landowners and cattle rearers. Some scholars argue that the association between Sujata and the Yadava community may stem from the broader historical reality that many pastoral and agricultural groups in ancient India had overlapping identities. The fact that her father was referred to as a 'Senani' could indicate an administrative or warrior class background rather than a purely pastoral one. The caste structure in ancient India was fluid, and communities were often categorized based on occupation rather than rigid birth-based hierarchy. If Sujata's family were indeed cowherds, it is very much plausible that they were associated with the Yadava tradition.

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Senani, Yadav, Agricultural, Pastoral, cowherd, Lineage. Hierarchy, Pali-canon

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The rapid growth of electronic waste (e-waste) has emerged as a significant global environmental challenge due to its toxic components, which can harm both the environment and human health if not properly handled. This study examines the dangerous materials in e-waste, their potential environmental and health impacts, and current management approaches in various countries. Advanced nations have implemented strategies such as life cycle assessment (LCA), material flow analysis (MFA), multi-criteria analysis (MCA), and extended producer responsibility (EPR) to address e-waste issues more effectively. Exposure to dangerous chemicals and e-waste can have major negative effects on the environment and human health. Research emphasizes the hazards of organophosphorus chemicals, incineration pollutants, and the existence of harmful materials in carpets. The disposal of e-waste in landfills, illicit imports, and lax laws are the main issues facing the United States and China. While large companies utilize better procedures than smaller ones, informal recycling practices in poor nations like India cause serious harm. Stakeholder participation, formal recycling systems, stricter legislation, and technology improvements are all necessary for effective management in order to minimize harmful exposure and guarantee sustainable waste processing. E-waste is a problem that is being faced by almost all countries globally but more so by developing countries as the e-waste is imported to these countries in bulk. Hence each of us must be committed to cutting down the waste and use, reuse and recycle all e-waste.

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recycling, waste management, e-waste, life cycle assessment , material flow analysis and sustainable waste.

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The rapid advancement of Smart Industrial Internet of Things (IIoT) technologies demands highly secure, scalable, and efficient architectures for data processing and management. Traditional cloud-centric IIoT models face challenges such as high latency, centralized security vulnerabilities, and inefficient resource utilization. To address these limitations, this paper proposes an integrated framework that combines Blockchain, Software-Defined Networking (SDN), and Edge/Fog Computing. Blockchain provides decentralized authentication, immutable data storage, and enhanced trust among IIoT devices. SDN enables dynamic network management, traffic optimization, and real-time policy enforcement, while Edge/Fog Computing reduces dependency on centralized cloud servers by enabling local data processing.

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Blockchain, Software-Defined Networking (SDN), Edge Computing, Fog Computing, Cloud Computing, Industrial Internet of Things (IIoT), Security, Scalability, Low Latency, Decentralized Architecture.

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The real estate industry has long driven economic expansion, yet remains highly exposed to financial irregularities and fraudulent practices. This study examines efforts to enhance fraud prevention and financial transparency among companies in the Mumbai Metropolitan Region (MMR) through the implementation of Ind-AS 8 and CARO 2020. Artificial Intelligence (AI) tools have been applied to evaluate the effectiveness of these standards in identifying and addressing fraud. Findings suggest that AI-driven financial analysis is significantly boosting fraud detection capabilities, strengthening regulatory compliance, and improving operational efficiency.

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Ind-AS 8, CARO 2020, Fraud Prevention, Real Estate in Mumbai Metropolitan Region, AI in Financial Reporting

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: Papilio demoleus, meteorological factors, population dynamics, pest management, correlation analysis.

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Abstract: Stroke remains one of the leading causes of adult disability worldwide, often resulting in significant motor impairments and loss of functional independence. Functional Electrical Stimulation (FES), a modality that uses low-frequency electrical currents to activate paralyzed or weakened muscles, has gained attention as a potential intervention to enhance motor recovery in stroke rehabilitation. This systematic review aimed to evaluate the effectiveness of FES in improving motor function, reducing spasticity, enhancing gait, and promoting overall functional independence in post-stroke individuals. A comprehensive literature search across six databases yielded 856 articles, of which 18 met the inclusion criteria after thorough screening and quality assessment. The findings consistently indicate that FES, either alone or in conjunction with conventional therapy, can significantly improve upper and lower limb function, gait performance, and neuroplasticity. However, the diversity in stimulation parameters, outcome measures, and methodological quality among studies highlights the need for standardized protocols and more rigorous trials. Overall, FES shows promise as an effective adjunctive tool in post-stroke rehabilitation programs, supporting its integration into routine clinical practice.

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Keywords: Functional Electrical Stimulation, Stroke Rehabilitation, Motor Recovery, Neuroplasticity, Gait Training, Spasticity Reduction, Systematic Review

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