Volume 14 | Issue 5 |

 Abstract

The increasing complexity of modern aircraft systems, coupled with the demand for higher operational efficiency and safety, has driven the evolution of maintenance strategies from reactive and preventive approaches toward predictive, data-driven methodologies. Traditional maintenance practices, which rely on scheduled inspections and failure-based interventions, often lead to unnecessary component replacements or unexpected system failures, resulting in increased operational costs and reduced aircraft availability. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies capable of leveraging large volumes of aircraft operational data to enable accurate prediction of system health and remaining useful life. This article presents a comprehensive data-driven framework for AI-based predictive maintenance in aircraft systems, integrating sensor data acquisition, health monitoring systems, and advanced analytics to detect anomalies, predict failures, and optimize maintenance scheduling. The study explores the application of machine learning techniques, including supervised learning, time-series analysis, and anomaly detection, to model complex system behavior across critical aircraft subsystems such as engines, avionics, and hydraulic systems. A scalable system architecture is outlined, encompassing onboard data collection, edge processing, secure data transmission, and cloud-based model training and deployment integrated with Maintenance, Repair, and Overhaul (MRO) operations. Furthermore, the article focuses key challenges associated with real-time deployment, including data quality, class imbalance due to rare failure events, model interpretability, and compliance with stringent aerospace certification standards. The operational benefits of predictive maintenance, including reduced unscheduled downtime, improved safety, and cost optimization, are critically examined. Finally, emerging research directions such as digital twin integration, federated learning across aircraft fleets, and explainable AI are discussed as pathways toward next-generation intelligent maintenance systems. By combining domain-specific knowledge with advanced AI methodologies, this article establishes a structured approach for implementing predictive maintenance solutions that enhance reliability, efficiency, and safety in modern aerospace operations.

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 Keywords

Artificial Intelligence, Predictive Maintenance, Aircraft Systems

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Creative Commons Attribution 4.0 and The Open Definition

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The evolution of e-commerce has significantly reshaped modern consumer behavior, offering unparalleled convenience and access to global marketplaces. However, a major drawback of traditional online shopping is the inability to perceive products in a real-world context, leading to uncertainty, misjudgment of size, placement issues, and frequent product returns. Augmented Reality (AR) offers a transformative solution by enabling customers to visualise products in their own environment through mobile devices. This study presents the development of an AR-based e commerce system that integrates optimised 3D product models, Firebase-based secure authentication, and Google Scene Viewer for AR visualisation. The methodology emphasises true-to-scale model creation, mobile-optimised rendering, gesture-based interaction, and cloud-linked product retrieval. The proposed system significantly enhances user clarity, improves product understanding, and increases purchase confidence. Test results demonstrate that AR visualisation can reduce product-related ambiguity and strengthen the overall shopping experience. In addition to furniture and home products, the system is extended to support virtual clothing try on and accessories visualisation, enabling users to preview apparel, glasses, watches, and jewellery on their own body using AR-based overlay techniques.

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 Keywords

--Augmented Reality, E-commerce, Google Scene Viewer, 3D Models, Firebase Authentication, Mobile AR.

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Creative Commons Attribution 4.0 and The Open Definition

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In the modern business environment, organizations are increasingly focusing on sustainability along with profitability. Growing environmental issues such as climate change, pollution, and resource depletion have encouraged companies to adopt eco-friendly practices. Green finance plays an important role by supporting investments in renewable energy, energy efficiency, and waste management. It helps organizations balance economic growth with environmental responsibility. This study examines the impact of green finance initiatives on sustainability and environmental responsibility in the corporate sector, with reference to Weikfield Foods Pvt. Ltd... The research focuses on practices such as solar energy adoption, resource conservation, and efficient operations However, challenges such as high initial cost and lack of awareness still exist. Overall, green finance proves to be an effective tool for sustainable development and long-term business growth.

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 Keywords

Green Finance, Sustainability, Environmental Responsibility, Renewable Energy Financial Performance.

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 Abstract

: Gond art is a significant tribal art form that originates from the Gond community in central India. Initially practiced as a ritualistic and decorative expression on walls and floors, it reflects the community's close relationship with nature, folklore, and spiritual beliefs. Over time, this art form has adapted to modern contexts, transitioning onto paper, canvas, and commercial platforms. This study examines how Gond art has evolved while maintaining its traditional identity. It explores the incorporation of new materials, changing themes, and the influence of contemporary markets. The paper also discusses the challenges of preserving authenticity in the face of modernization. Overall, the research highlights how Gond art continues to exist as both a cultural tradition and a dynamic form of contemporary expression.

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 Keywords

: Gond Art, Tribal Art, Contemporary Art, Traditional Art, Indian Folk Art, Cultural Heritage

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Creative Commons Attribution 4.0 and The Open Definition

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ABSTRACT BACKGROUND: Radiculopathy or nerve root pain arise from disc herniation, spinal stenosis radiating down the leg in a dermatomal pattern. This study focuses on disability, pain and range of motion of spine The study evaluates "The effectiveness of McKenzie technique versus Lumbar stabilization on reducing symptoms and disability of Lumbar radiculopathy. OBJECTIVE: The purpose of the study is to evaluate the effectiveness of McKenzie technique and Lumbar stabilization for the patients with Lumbar radiculopathy. METHODOLOGY: This study included 30patients, they were divided into Group A and Group B. Group A given McKenzie technique and Group B given Lumbar stabilization exercise. Pain was assessed using Visual analogue scale (VAS), Functional disability was measured using Oswestry disability index and Range of motion by Schober test. RESULT: The results between pre-treatment and post-treatment scores of pain and disability are considered. The mean and standard deviation are taken for both the Group A and Group B. The Group A showed better improvement in mean &standard deviation using Visual Analogue Scale, Oswestry disability index scores and Range of motion value than the Group B. CONCLUSION: Both groups showed better improvement in reducing pain and functional disability. This study proved that Group A showed better improvement in reducing pain and reducing functional disability when compared to Group B.

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KEYWORDS: Radiculopathy, McKenzie technique, chronic low back pain, Oswestry disability index, Lumbar stabilization exercise, Visual analogue scale

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The Right to Information (RTI) Act, which came into force on October, 12th 2005 marked a historical step towards transparency and accountability in administration in India. Nearly, two decades, the Act has been instrumental in exposing corruption and various scams, empowering citizens to demand information from public authorities. However, while the Act has undoubtedly brought about remarkable change, certain amendments to the Right to Information rules are necessary to prevent misuse, streamline the process, and reduce the burden on the courts.

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 Keywords

The Right to Information Act - 2005, Constitution, Indian Parliament, The Central Information Commission, Transparency, Accountability, Good governance.

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Creative Commons Attribution 4.0 and The Open Definition

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Attendance management within academic institutions has historically relied on passive recording mechanisms that merely capture presence and absence without extracting deeper insights or enabling proactive decision making. The proposed Smart Attendance Prediction and Alert System introduces a comprehensive analytical layer on top of conventional tracking by examining the dynamics of total classes conducted, classes attended, absence frequency, and recurring behavioral tendencies such as selective absenteeism and clustered bunk patterns. By synthesizing historical trajectories with current progression, the system estimates the probability of a student falling below institutional thresholds and surfaces this risk early enough for meaningful intervention. The design assumes conceptual integration with institutional ERP platforms through secure API mediated data exchange so that attendance streams are ingested automatically and processed continuously. The output is not limited to a percentage but expands into interpretable risk categories accompanied by contextual explanations and recommended corrective actions. A dual notification mechanism communicates these insights to both the student and the parent, thereby strengthening accountability loops and reducing information asymmetry. In effect, the system repositions attendance from a retrospective metric into a forward looking signal that can influence behavior, improve consistency, and minimize last minute academic penalties.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Attendance, Prediction, Alert, Analysis, API, ERP.

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Creative Commons Attribution 4.0 and The Open Definition

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The present device is indigenously prepared for all healthcare practitioners to support manufacturing in India. Additionally, it can be used in other sectors where there is a danger that contaminated air may cause respiratory disorders. Healthcare workers (HCWs) are at particular risk during pandemics and epidemics of highly virulent diseases with significant morbidity and a case fatality rate. These diseases include severe acute respiratory syndrome coronaviruses, SARS- CoV-1 and SARS-CoV-2, Middle Eastern Respiratory Syndrome (MERS), and Ebola. With the current (SARS-CoV-2) global pandemic, it is critical to delineate appropriate contextual respiratory protection for HCWs. All locally available components can easily be serviced and replaced, making it a cheaper device than all other Powered air-purifying respirators (PAPRs) available. The major components are the helmet and filter casing. There is moderate quality evidence for PAPR. Its use is associated with higher thermotolerance but lower mobility and communication ability scores. There has been a trend toward improved self-reported comfort with PAPR technology in low-quality observational simulation studies. Due to its two-layer filtering layers, it can be used in adversely contaminated environments. Field observational studies have shown no difference in infections among healthcare workers using the PAPR device compared to other matched ventilators. The PAPR was confirmed to have low mobility and high thermostability with audibility. However, more practical research is needed to explain the effectiveness of PAPR technology and provider satisfaction.

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 Keywords

Covid-19, PAPR, Respiratory Protection, Healthcare worker

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Creative Commons Attribution 4.0 and The Open Definition

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The present study titled "Impact Study on Tamil Nadu Chief Minister Breakfast Scheme" aims to examine the effectiveness of the breakfast scheme introduced by the Government of Tamil Nadu in improving the nutritional status, school attendance, and academic performance of primary school children. The scheme was launched to address classroom hunger and enhance student participation in government schools. This study adopts a descriptive research design to analyze the impact of the scheme among beneficiaries in Coimbatore District. A total of 60 respondents including students, parents, and teachers were selected using convenient sampling method. Primary data were collected through structured questionnaires and interviews, while secondary data were gathered from journals, books, and government reports. The findings reveal that the scheme has significantly improved students' attendance, punctuality, energy levels, and concentration in classrooms. It also reduced the financial burden on parents and improved the nutritional intake of children. However, certain challenges such as need for menu variety and limited community participation were observed. The study concludes that the Chief Minister Breakfast Scheme is a highly effective social welfare initiative contributing to child development and educational outcomes. It recommends strengthening awareness, improving food variety, and enhancing monitoring mechanisms for better implementation.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Impact study, Tamil Nadu scheme, Chief Minister Breakfast scheme

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid spread of misinformation and fake news through online social networks has become a major societal concern, leading to political instability, social conflicts, and financial fraud. This project aims to develop a defensive machine learning model that can effectively detect and mitigate fake news propagation. The proposed system employs Natural Language Processing (NLP) techniques to analyze textual features and classify news articles as real or fake. Additionally, graph-based analysis is used to examine user behavior and detect bot accounts that contribute to the spread of misinformation. The methodology involves data collection from reliable fake news datasets (e.g., Fake Newsnet, LIAR, and Kaggle Fake News datasets), text preprocessing using TF-IDF and Word Embeddings, and classification using machine learning algorithms such as Logistic Regression, Random Forest, and LSTM (Long Short-Term Memory networks). Furthermore, network analysis using Graph Neural Networks (GNNs) and Network helps identify suspicious user activity and misinformation propagation patterns.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

defensive machine learning model ,Natural Language Processing,graph-based analysis ,-IDF and Word Embeddings.journalism integrity

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Creative Commons Attribution 4.0 and The Open Definition