Volume 13 | Issue 4

 Abstract

The present study attempts to look into the Atmanirbhar Bharat Abhiyaan and Skill India Mission in the light of the Endogenous Growth Model and the New Growth Theory. I employ the Ordinary Least Squares (OLS)-based Auto Regressive Distributed Lag (ARDL) approach to estimate the determinants of economic growth of India in the long-run for the period 1960 to 2014. The choice of the study period is based on data availability and also on the fact that 2014 has been threshold year for India. A number of institutional reforms have been undertaken in the country since the year 2014. Human capital followed by physical capital and openness are found to be main determinants of growth for the period 1960-2014. The institutional variables are insignificant during the study period. Since 2014 there has been a shift in political regime which has ushered a new wave of institutional reforms. The study tries to justify the ongoing reforms undertaken to address the weak institutional quality observed in our period of study.

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Atmanirbhar Bharat Abhiyaan, Skill India Mission, Endogenous Growth, New Growth Theory, Time- Series Model

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As an alternative to lipid-solid carriers (like liposomes), niosomes are vesicular nanocarriers that are stable, non-toxic, biodegradable, and reasonably priced. The instability, rapid disintegration, bioavailability, and solubility of some drugs or natural substances may be improved by niosomes. When it comes to the targeted administration of antibacterial, antimicrobial, anti-inflammatory, antioxidant, and anticancer compounds, niosomes have the potential to be incredibly powerful systems. This essay will provide a summary of their makeup, the most popular methods for formulation, and their present application as delivery systems for cancer treatments.

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Niosomes, Drug delivery, Nanocarrier, Vesicles, Nano medicine.

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The bilateral relationship between India and Saudi Arabia has traditionally been rooted in energy interdependence, with crude oil dominating trade exchanges for decades. However, the landscape is rapidly evolving. Saudi Arabia's Vision 2030 seeks to transform the Kingdom into a diversified economy with thriving sectors such as tourism, renewable energy, and technology. Parallelly, India is emerging as a global economic power with growing energy needs and a burgeoning middle class, positioning itself as a strategic partner for Riyadh beyond hydrocarbons. This research explores the evolving contours of India-Saudi Arabia trade, analysing how both nations are transitioning from an oil-dependent trade framework to a multifaceted economic partnership. Drawing upon official trade data, government declarations, and academic literature, this paper examines the diversification strategies of both countries, key sectors for potential cooperation, and the role of institutional mechanisms in fostering sustainable trade relations. It highlights emerging sectors such as green hydrogen, fintech, infrastructure, pharmaceuticals, and defence collaboration as arenas of mutual growth. Additionally, the study identifies challenges like regulatory bottlenecks, geopolitical shifts, and economic uncertainties that may affect future trade trajectories. The paper concludes with policy recommendations aimed at enhancing economic synergy and mutual strategic interests. In an era defined by global decarbonization and economic realignment, the India-Saudi Arabia trade partnership is poised to move from oil to opportunities--offering a blueprint for diversified, resilient, and mutually beneficial bilateral relations.

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India-Saudi Arabia Trade, Diversification, Vision 2030, Energy Security, Economic Partnership, Renewable Energy, Bilateral Relations

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The Automatic Waste Segregation System aims to enhance waste management practices by efficiently sorting waste into three categories: wet waste, dry waste, and metallic waste. Utilizing advanced sensor technologies, this system leverages an LM393 IC moisture sensor to detect the moisture content in waste, effectively identifying wet organic materials such as food scraps. Additionally, an infrared (IR) sensor is employed to distinguish dry waste, including paper, plastics, and textiles, based on the reflective properties of the materials. To further enhance the sorting process, a metal detector is integrated to identify and segregate metallic waste, ensuring that recyclables are collected accurately. The automation of waste segregation not only reduces the manual labor involved in traditional waste sorting methods but also increases the accuracy and efficiency of the process. This system is designed to operate autonomously, with real-time monitoring and feedback mechanisms that allow for seamless integration into existing waste management infrastructures.

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Arduino Mega, IR Sensor, DC Motor, Rotary Indexer, Container, etc.

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This abstract describes how kidney stone detection has progressed significantly, evolving from traditional imaging techniques such as ultrasound, X-rays, and CT scans to modern, technology-driven methods. While conventional approaches remain valuable, they often suffer from limitations in accuracy, sensitivity, and reliance on operator expertise. Recent developments in artificial intelligence-- particularly machine learning, deep learning, and image processing--have revolutionized medical diagnostics by enabling faster, more accurate, and automated detection of kidney stones. Deep learning models, especially convolutional neural networks (CNNs), are capable of analyzing medical images with high precision, minimizing human error and reducing diagnosis time. This project explores the transition from traditional diagnostic methods to AI-powered techniques, emphasizing the enhanced ability of modern systems to accurately identify and localize kidney stones.

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Kidney stone detection, Deep learning, Convolutional Neural Networks (CNNs), Ultrasound image analysis.

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This paper explores the concept of AI Creativity, emphasizing the collaborative interaction between human intelligence and artificial intelligence within the creative domain. While the rapid advancement of AI technologies has sparked extensive debates concerning ethical issues, job displacement, and societal impacts, there remains a paucity of systematic research on how AI can be leveraged to enhance human creativity rather than supplant it. Drawing upon an analysis of over 1,600 application cases across more than 45 fields and supported by scholarly literature, this study advocates for a paradigm shift--from competing with AI to collaborating with it. AI serves as a substantial augmentation to human cognition, amalgamating collective human knowledge and facilitating collaborative creation across diverse temporal and spatial contexts.

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AI Creativity, Human-AI Co-Creation, Artificial Intelligence, Generative AI, Creative Collaboration.

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Knowledge sharing plays a crucial role in fostering academic excellence, innovation, and professional growth within higher educational institutions. Faculty members, as key contributors to knowledge creation and dissemination, are expected to engage in continuous knowledge-sharing activities. This study aims to identify and analyze the critical factors that influence knowledge-sharing behaviors among faculty members working in Arts and Science Colleges in Trichy. A structured questionnaire was administered, and data from 50 faculty members were analyzed using Principal Component Analysis (PCA) to uncover underlying constructs that shape knowledge-sharing practices. The analysis revealed six significant components: Technological Adaptation, Collaborative Knowledge Sharing, Cognitive and Pedagogical Development, Career and Institutional Growth, Institutional Support & Policies, and Personal Motivation & Attitudes. The reliability of the instrument was confirmed with a Cronbach's Alpha value of 0.902, indicating a high level of internal consistency. The findings suggest that the effective use of digital tools, supportive institutional frameworks, and intrinsic motivation are vital in promoting knowledge sharing among faculty. However, disparities in engagement levels point to the need for targeted interventions and supportive strategies. The study provides practical insights for educational policymakers and institutional leaders to design programs and policies that foster a more collaborative and knowledge-driven academic environment. It also underscores the importance of aligning personal and institutional objectives to enhance faculty participation in knowledge-sharing activities.

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Knowledge Sharing, Technological Adaptation, Institutional Support, Academic Development, Higher Education, Faculty Engagement.

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The effectiveness of educators largely depends on their character, competence, and dedication to their profession. It is undeniable that a person's attitude towards their profession plays a crucial role in achieving desirable success. An individual's attitude is influenced by their emotions and passions. Panday and Tripathy (2006) conducted a study on effective school teachers, highlighting that quality educators have a direct impact on shaping students' personalities and contribute significantly to the educational outcomes. Therefore, the effectiveness of education hinges on the presence of skilled and dedicated teachers working within educational institutions. The attitude of educators significantly impacts student outcomes within the classroom. Teachers spend most of the school day in close proximity to their students, and consequently, their initial interactions, behaviour, preferences, and temperament greatly influence their students. The research is significant to two major areas of human resources, namely, identifying the various factors that affecting/influencing Professional Attitude along with the level of Professional Attitude of woman teachers and examining their impact on Work Commitment of woman teachers working in private higher secondary schools in Thanjavur district

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Women School Teachers, Professional Attitude, Teaching Profession, Work Commitment

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The Zeliangrong community is among the major Naga communities and has been widely known for its vibrant festivals and ritual practices that reflect their spiritual faith, social and cultural identity. The Zeliangrong people observe festivals almost every season which are highly associated with the cycle of agriculture. This article focuses on the various festivals and rituals practices devoted by the community such as "Gaan-Ngai", "Pok-phangai". The study also focuses on three Zeliangrong inhabited states of Northeast India - Manipur, Nagaland, and Assam. The paper will be examining the case of "Tingkao Ragwang Chapriak" (TRC) in particular. Some of the concern rituals include animal sacrifices, traditional dances, feast, and tribute that are crucial for strengthening the social bond and the relationship between the people and the divine or natural forces. The dormitory, which is a social institution also plays a fundamental role responsible for the smooth activities and success of the festivals. The paper also investigates the importance and meaning or beliefs of the existing practices of the community during the festivals. Within the context of Gaan-Ngai, the paper highlights, behind its colourful embellishments, the deeper meaning of the festival which is the expression of love, joy, and blessings.

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Zeliangrong, Gaan Ngai, Tingkao Ragwang Chapriak, Festival, Rituals, Dormitory

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The Morung refers to a traditional youth dormitory or a tribal club serving as a centre for social life, education and cultural transmission for unmarried male members. The system has been the central point of all varied activities of a Naga Village. It has been rightly called as the fulcrum of village democracies. It is the centre for social, religious and political activities. It is the club, the public school, the military training centre, the hostel for boys and the meeting centre for village elders. It served as an institution to transmit the tradition, customs and laws from generation to generation and also served as the centre for defense and training in the art of warfare. Its proximity to the working of a village council is significant in the polity of the village. Despite the dwindling of this traditional institution, its relevance to the culture of the Nagas cannot be underestimated. The spirit of Morung can still be imbibed to the present context which can contribute towards harmony, welfare and peace. Thus this paper will revisit this unique and significant institution of Morung of Nagaland in general and focus on the Khangchiu (Morung) institution for a better understanding of this unique and powerful institution in Naga culture and society.

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Nagas, Culture, Morung, Khangchiu, Zeliangrong

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The National Education Policy (NEP) 2020 is a comprehensive framework that aims to transform the Indian education system to meet the challenges of the 21st century. It introduces several reforms and innovative approaches to enhance the quality and inclusivity of education. Education plays a key role in shaping and developing student's intellect and skill set that leads them to be independent thinkers and prepares them for future challenges. Since the post independence period the higher education system in India has grown remarkably, to become one of the largest educational foundations in the world. However, the Indian education system has varied issues of concern, including access, equity and quality of higher education together with the assessment of institutions and their accreditation. Our objective is to determine the attitude of college students towards National Education Policy 2020. Through our survey, we have analyzed the attitude of 54 college students with the help of a questionnaire consisting of 24 items. The results reveal that the overall attitude towards NEP2020 is positive. Among groups that exhibited positive attitude towards NEP 2020, females were on the fore front compared to males though the difference was not statistically significant.

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National Educational policy 2020, Attitude, College Students.

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To provide real-time bus tracking and transit information for rural passengers using GPS, GTFS, and third-party APIs to enhance efficiency, reliability, and safety in public transportation.

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GTFS , Mapping , Tracker

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Secure communication is crucial in the digital age. Image steganography embeds secret information within digital images, making it undetectable to human vision. This paper uses the Least Significant Bit (LSB) technique to hide data in images using Python. It evaluates the effectiveness of LSB embedding by analyzing image quality, payload capacity, and resistance to steganalysis. The research discusses the benefits and limitations of image steganography for secure data transfer and potential improvements for future security threats and real-time communication.

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Steganography, LSB Technique, Image Processing, Information Security, Data Hiding, Python, Secret Communication, Digital Image, Encoding, Stego Image.

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Menstrual irregularities can significantly impact the quality of life, particularly in females experiencing conditions like dysmenorrhea and heavy bleeding. These irregularities are often linked to Body Mass Index (BMI). This descriptive correlational study aimed to assess the prevalence of menstrual irregularities and examine their relationship with BMI. Conducted at Desh Bhagat University, Mandi Gobindgarh, data was collected from 187 female students, of who 100 reported menstrual irregularities. A self-structured checklist was used to assess menstrual patterns, while BMI was measured using a weighing scale and measuring tape. The study found that 69% of participants experienced menstrual irregularities, with 81% reporting mild symptoms. Additionally, 71% of the students had a normal BMI. Correlation analysis revealed a moderate positive correlation between BMI and menometrorrhagia, and a strong negative correlation between BMI and hypomenorrhea. Chi-square tests indicated a significant association between menstrual irregularities and demographic factors such as age, education, marital status, family income, and age at menarche. BMI also showed significant associations with age, education, marital status, and menstrual bleeding duration. The findings suggest that menstrual irregularities are more prevalent among obese women, highlighting the importance of lifestyle modifications, including proper diet and regular exercise, to maintain a healthy BMI and support menstrual health.

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Body Mass Index, Menstrual irregularities

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In the highly competitive business environment of today, firms must continuously evaluate and enhance employee performance in order to achieve strategic goals. Traditional performance evaluation methods usually lack efficiency, consistency, and transparency, which leads to management issues and employee dissatisfaction. This study proposes the design and implementation of an Employee Performance Appraisal System (EPAS), which provides a comprehensive, automated, and user-friendly platform for performance appraisals, in order to address these issues. The recommended features--which are all made to satisfy the needs of companies with large workforces--include a thorough employee profile, established evaluation standards, secure feedback channels, and real-time performance tracking. The system was created with PHP, MySQL, HTML, CSS, and JavaScript to ensure seamless user interaction while safeguarding the confidentiality and precision of appraisal data. By combining qualitative and quantitative evaluation criteria, the EPAS makes objective performance reviews easier and promotes ongoing improvement. Additionally, the system has strong analytics and reporting capabilities that let decision-makers and HR managers track trends, find top performers, and assist with strategic planning using accurate performance data. EPAS enhances overall productivity, increases corporate transparency, and improves employee happiness by minimizing manual intervention and guaranteeing a fair appraisal process

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Employee Performance Appraisal, Performance Management System, HR Management, Talent Development

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Carbon capture and utilization (CCU) has risen as a transformative green innovation frontier, addressing the dual challenge of moderating climate change and fostering sustainable development. CCU technologies capture carbon dioxide (CO?) emissions outflows from industrial sources and the atmosphere transforms them into profitable and valuable products such as fuels, chemicals, and construction materials. This paper investigates the most recent advancements in CCU technologies, their environmental and economic benefits, and their potential to contribute to a global net-zero emission goals. However, CCU technologies face many challenges, including high costs, scalability issues and energy demands energy which limit their widespread adoption. This paper aims to explore the role of CCU as a sustainable innovation, examining its technological advancements, economic and environmental benefits, and the barriers to its implementation. This study provides actionable insights for researchers, policymakers, and industry pioneers to accelerate the selection of CCU technologies as a key pillar of green innovation.

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Keywords: CCU, Green innovation and Sustainable development.

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This project explores the feasibility of using deep learning to detect blood groups from fingerprint images. Traditional blood group detection. methods require invasive blood sample collection and laboratory analysis, which can be time-consuming and resource-intensive. In contrast, this approach aims to provide a non-invasive, rapid, and accessible alternative. By leveraging a Convolutional Neural Network (CNN) architecture, we analyze fingerprint patterns to predict blood groups (A, B, AB, O). A dataset of fingerprint images with corresponding blood group labels is collected and preprocessed, including normalization and augmentation techniques.

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Transfusion, blood group prediction, artificial intelligence, image processing, segmentation, feature extraction, pattern recognition

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This study analyzes the medicinal importance and allergenic hazards of Azadirachta indica (neem), a plant that is widely used in traditional health practices like Ayurveda and Unani. Neem has been found to possess antimicrobial, anti-inflammatory, and immune-enhancing activities, which are attributed to bioactive compounds like azadirachtin, salannin, nimbin, flavonoids, and phenolic acids. Though beneficial, neem is potential allergen that can cause reactions, especially triggered by compounds like azadirachtin, leading to conditions such as contact dermatitis and respiratory trouble. The review highlights the importance of identifying allergenic compounds using techniques like chromatography and patch tests. It emphasizes the need for safer formulation and greater awareness among the public to counteract allergic risks while preserving neem's therapeutic advantages.

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This study analyzes the medicinal importance and allergenic hazards of Azadirachta indica (neem), a plant that is widely used in traditional health practices like Ayurveda and Unani. Neem has been found to possess antimicrobial, anti-inflammatory, and immune-enhancing activities, which are attributed to bioactive compounds like azadirachtin, salannin, nimbin, flavonoids, and phenolic acids. Though beneficial, neem is potential allergen that can cause reactions, especially triggered by compounds

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The paper proposes a machine learning-based and computer vision-based real-time driver drowsiness detection system to reduce 30% of road accidents caused by driver fatigue. The system uses OpenCV for video capture and Dlib for detection of facial landmarks, i.e., 68 facial points for calculation of Eye Aspect Ratio using Euclidean distance. Temporal behavior and statistic attributes obtained through EAR values are utilized by machine learning classifiers for discriminating among alert and sleepy conditions. For detection of sleepiness, real-time audio alarm output is presented. Experiment testing was conducted on various subjects subjected to varying conditions of light, as well as road conditions, and providing a measure of drowsiness detectability with a measure of accuracy at 94% and a number of zero false alarms. The system's response time averaged 0.3 seconds from detection to alert, giving drivers sufficient reaction time to prevent accidents. Our method incorporates a novel adaptive thresholding mechanism that dynamically adjusts according to the baseline EAR values of individual users, resulting in significant performance under diverse facial structures and eye shapes. The system also includes an additional temporal analysis window that is utilized to analyze EAR patterns across adjacent frames for canceling spurious alarms due to natural blinking or temporary closures. With support for Python 3.8 and MySQL databases, this non-intrusive monitor solution promises high potential to enhance road safety via early detection of drowsiness. The suggested method combines commonly employed computer vision techniques with novel feature extraction methods to offer robust detection performance under varying lighting conditions and face orientations.

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 Keywords

Driver Drowsiness Detection, Eye Aspect Ratio (EAR), Computer Vision, Machine Learning Classification, Road Safety, OpenCV, Dlib, Facial Landmark Detection, Real-time Monitoring, Adaptive Thresholding, Neural Networks, Support Vector Machine, Random Forest, Euclidean Distance, Temporal Analysis, Non-intrusive Monitoring.

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