Volume 14 | Issue 4

 Abstract

The growing use of digital technology in education has considerably increased the usage of online learning platforms, resulting in higher amounts of daily screen exposure. While digital learning is convenient and flexible, concerns have been raised about its possible impact on cognitive functioning. The current study sought to investigate the association between screen time and cognitive failures among online learners, as well as the function of metacognitive awareness in that relationship. Specifically, the study looked into whether metacognitive awareness mediates the link between screen time and ordinary cognitive failures. A quantitative correlational research methodology was used, with data collected from 147 online learners via standardized self-report measures assessing screen time, metacognitive awareness, and cognitive failures. Statistical descriptive measures and analyses -- including correlation tests and mediation analysis -- were used to explore how the variables interacted together. Reviewing the findings as a whole, it was determined that there is a direct correlation between screen time and cognitive impairments. In other words, if people spend more time using screens, their probability of experiencing cognitive failures will increase. In addition, screen time has a positive correlation to metacognitive awareness. Thus, it can be reasonably assumed that using screens to engage in learning (through digital learning environments) will improve participants' metacognitive awareness. However, there is no statistically significant correlation between metacognitive awareness and cognitive impairment. Mediation Analysis has indicated that an awareness of metacognitive processes alone does not eliminate the relationship between the amount of time spent on screens and cognitive performance. Therefore, while learners may understand how they learn, this understanding cannot negate the negative consequences of excessive screen use on cognitive performance. In conclusion, this study adds to the existing literature examining the effects of screen use on cognitive functioning and investigating the relationship between cognitive failures and screen use. Hence, in order to avoid cognitive interference while using the internet for learning, students must learn to develop skills of good screen use along with metacognitive skills.

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 Keywords

Screen time, cognitive failures, metacognitive awareness, online learners, digital learning, cognitive functioning.

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Menopause is a significant developmental transition characterized by complex biological, psychological, and social changes. The present study examines mental and physical health during perimenopause and menopause using a biopsychosocial framework. It highlights the role of hormonal fluctuations, psychosocial stressors, and cultural influences in shaping women's experiences. The study emphasizes the importance of awareness, early identification of symptoms, and holistic interventions to improve quality of life.

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 Keywords

Menopause; Perimenopause; Mental Health; Depression; Anxiety; Stress; Menopause Rating Scale (MRS); DASS-21; Hormonal Changes; Vasomotor Symptoms; Sleep Disturbances; Biopsychosocial Model; Women's Health; Quality of Life 1. Introd

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India has made remarkable progress in its journey towards becoming a digitally empowered society and knowledge economy. From expanding internet access in remote villages to revolutionising public service delivery through digital platforms, the country has bridged the urban-rural divide like never before. The digital economy, which contributed 11.74% to the national income in 2022-23, is projected to grow to 13.42% by 2024-25, driven by advancements in artificial intelligence, cloud computing and digital infrastructure.

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 Keywords

Digitalization, Internet, Telecom, Digital tools, 5g Connectivity, Rural India, India AI machine

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Stress is a pervasive factor in modern life, strongly linked to mental health disorders such as depression and anxiety, as well as physical illnesses, including cardiovascular disease. Traditional stress assessment methods, such as self-reports, cortisol testing, and EEG monitoring, are limited by subjectivity, invasiveness, or impracticality for continuous use. Advances in artificial intelligence (AI) and machine learning (ML) have created new opportunities for noninvasive, real-time stress detection through facial expression recognition, multimodal monitoring, and hybrid ensemble systems. This review synthesizes literature published between 2021 and 2025 and compares methodologies, datasets, evaluation metrics, and applications. We highlight the strengths, limitations, and future research priorities, with an emphasis on explainable AI, dataset diversity, and ethical deployment. The findings suggest that AI-based stress detection systems hold transformative potential in clinical, workplace, and personal wellness contexts, although further validation and regulation are essential for large-scale adoption.

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 Keywords

Stress detection, Facial expression recognition, Machine learning, Artificial intelligence, Mental health monitoring

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 Abstract

An extensive variety of biological processes and pharmaceutical treatments make use of heterocyclic compounds, which are a huge category of organic molecules that comprise nitrogen and sulfur. These compounds are utilized in a wide number of applications. A vast number of valuable chemicals, both those that occur naturally and those that are created by people, require these heterocycles in order to have a structure that is complete. For instance, antibiotics, antifungal medicines, anticancer pharmaceuticals, anti-inflammatory pharmaceuticals, antiviral pharmaceuticals, and anticonvulsant pharmaceuticals are all examples of compounds that fall under this category. The nitrogen and sulfur atoms that are contained within the ring system have a significant influence on the manner in which the electrons of the molecules are dispersed, the manner in which they react chemically, and the degree to which they attach to biological targets such as enzymes, receptors, and nucleic acids. This influence is exerted by the ring system. The biological relevance of nitrogen and sulfur heterocycles is described in this review, with a special emphasis on the ways in which their structure influences their function, how they act, and the potential that they have as medications. Specifically, the discussion focuses on the ways in which their structure influences their function. In the realm of medicinal chemistry and medication research, the use of these heterocycles has increased even more as a consequence of recent developments in the manufacturing of these heterocycles and the alteration of their functions. It has been determined by the most current research in the field of pharmacology that heterocyclic compounds that include nitrogen and sulfur continue to be a key component in the process of developing possible new drugs.

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 Keywords

Nitrogen, Sulfur heterocycles, Antibacterial, Antifungal, Antimicrobial agents.

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The rapid growth of mobile health and fitness applications has increased user awareness of physical activity and wellness; however, most existing systems function primarily as passive trackers that record historical data rather than actively supporting adaptive fitness planning. These applications commonly rely on rigid schedules and generic recommendations, which fail to accommodate the dynamic and unpredictable nature of modern lifestyles. As a result, users--particularly students and working professionals--often experience reduced adherence and high dropout rates. This review paper examines recent research on fitness goal management systems, with a focus on personalization strategies, adaptive scheduling techniques, and intelligent planning approaches. Traditional fitness trackers, rule-based planners, and artificial intelligence driven recommendation systems are analyzed to evaluate their capabilities and limitations. The review identifies key research gaps, including limited real-time schedule adaptation, insufficient user modeling, and weak integration of dietary planning with physical activity management. Furthermore, existing systems often lack constraint-aware mechanisms to reschedule missed workouts in response to changing user availability. Based on the analysis, this paper highlights the potential of adaptive and personalized dynamic fitness goal systems that leverage constraint satisfaction and intelligent scheduling techniques. Such systems can proactively generate and update workout and meal plans in real time while respecting individual lifestyle constraints. The findings emphasize the need to shift from static fitness tracking toward flexible, user-centered planning systems to improve long-term engagement and sustainability.

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 Keywords

Adaptive Fitness Systems, Personalized Goal Planning, Dynamic Scheduling, Constraint Satisfaction, Health Informatics

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Edge AI, Precision agriculture, IoT-based agricultural monitoring, Real-time sensing, Resource-constrained devices, Event-driven communication, Model compression, On-device inference, Smart farming systems

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Cardiovascular disease continues to be one of the primary contributors to mortality across the globe. The inability to detect its early signs in time leads to delayed treatment and, in many cases, fatal outcomes. This paper presents a machine learning based prediction system for heart disease that makes use of two well-known classification algorithms, namely Decision Tree and K-Nearest Neighbors (KNN). The Decision Tree model is chosen because it provides rule-based outputs that are easy to understand and interpret, making it particularly suitable for medical applications where reasoning behind a decision matters. The KNN model, on the other hand, works by identifying similarities between a new patient record and previously seen data, offering reliable classification results. The system is built on the UCI Heart Disease dataset, which includes clinical attributes such as age, resting blood pressure, serum cholesterol, fasting blood sugar, maximum heart rate, and chest pain type. Before training the models, the dataset is carefully prepared through steps like missing value treatment, feature normalization, and standard scaling. Both classifiers are trained and tested using performance indicators including accuracy, precision, recall, F1-score, and confusion matrix analysis. The system also features a simple web-based interface through which a user can enter patient details and receive an instant prediction. This work aims to support early diagnosis of heart disease and assist healthcare professionals in making faster and more informed decisions.

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 Keywords

Heart Disease Prediction, Decision Tree, K-Nearest Neighbors, Machine Learning, UCI Dataset, Classification, Healthcare, Data Preprocessing

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Parental divorce has emerged as a significant social issue influencing children's emotional stability, educational performance, and social integration. Although divorce rates in India remain comparatively lower than in Western countries, states such as Kerala have experienced a rapid increase in marital dissolution over the last decade. The present study examines the social, economic, and psychological effects of parental divorce among children in Kerala, using empirical data collected from 600 children of divorced parents. Employing a descriptive-analytical research design, the study investigates differences in children's experiences based on gender, education, and religion. The findings indicate that parental divorce significantly affects children's emotional well-being, academic concentration, social relationships, and economic security, with female and school-going children exhibiting higher vulnerability. The study highlights the urgent need for integrated sociological, educational, and psychosocial interventions to support children from divorced families.

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 Keywords

Socio-Economic and Psychological, Parental Divorce

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Over the past ten years, the relationship between organisations and society has been the subject of intense discussion, often accompanied by harsh comments. To meet the requirements of both present and future generations, it is imperative to protect the integrity of natural systems and adhere to international norms that safeguard fundamental, social, and environmental principles. Since employees are strategically important in assisting the company in achieving its requirements, their involvement is crucial to the success and effectiveness of Corporate Social Responsibility (CSR). With employees' active participation, an organisation's chances of accomplishing sustainable goals are greatly reduced, which is why employee environmental empowerment is crucial for CSR projects.

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Corporate Social Responsibility, Pro-Environmental Behaviour, Meta-Analysis, CSR, Subgroup Analysis on Type of Industry

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Abstract This research paper investigates the effectiveness of constructivist learning approaches in higher education by analyzing both existing literature and empirical data. Constructivist learning theory emphasizes active engagement, personal experiences, and social interactions to construct knowledge, contrasting with traditional lecture-based methods. This study examines how constructivist methodologies impact students' academic achievement, retention, engagement, and self-regulation. A mixed-methods approach was used, incorporating surveys, academic records, and interviews with undergraduate and graduate students across multiple disciplines. Quantitative results indicate that students exposed to constructivist learning environments exhibit significantly higher academic achievement, engagement, and self-regulation compared to those in traditional settings. Qualitative findings support these results, highlighting increased motivation, deeper conceptual understanding, and improved collaboration. The findings suggest that constructivist learning fosters more meaningful educational experiences, advocating for the adoption of active learning strategies in higher education. The study provides valuable insights for educators, curriculum designers, and policymakers on the benefits of constructivist pedagogy and its potential to improve student learning outcomes.

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Keywords: Constructivist learning, higher education, academic achievement, student engagement, self-regulation, educational outcomes

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Creative Commons Attribution 4.0 and The Open Definition

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Influencer marketing has emerged as a dominant force in digital commerce, particularly in shaping how Generation Z (born 1997-2012) discovers, evaluates, and purchases products. This paper investigates the extent to which social media influencers alter the buying behaviour of Gen Z consumers, drawing on a primary Google Form survey (n = 16) conducted in April 2026 alongside a comprehensive secondary review of marketing literature and established consumer behaviour theories. Survey results show that 62.5% of respondents purchased a product on an influencer's recommendation, while 81.25% rated influencer marketing as superior to conventional advertising. Instagram dominated platform preference (75%), with trust and product reviews consistently cited as the foremost purchase triggers.

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 Keywords

Trust, Buying behaviour, social media, Digital marketing , Consumer behaviour, Generation Z, Influencer Marketing

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 Abstract

Safeguarding justice and promoting equality among its people have always been a primary core objective of the State. By virtue of human's birth, they are inherently entitled to fundamental rights and freedoms, which cannot be altered or removed. Everyone in our country has the freedom to exercise all rights without any gender discrimination. Throughout history, we can see transgender individuals are also integral part of our society. They are also humans, entitled to acquire and enjoy rights without any barriers. Judiciary recognized them as third genders in NALSA v. Union of India. A legislation was also enacted, the Transgender Persons (Protection of Rights) Act, 2019 for the protection of their rights. But they are always excluded from the society and face problems in several spheres. The existence of social stigma makes this community isolated. This societal exclusion led to face inequality in the field of education, employment, and in attaining medical facilities. Hence, this paper by using doctrinal research methodology, study focuses on the role played by the trans community in our society by tracing the historical aspects, their battle to achieve third gender recognition, pros and cons of the legislation. Gaining acceptance from the society is an in tremendous task, only through the elimination of economic and social disparities it can be erased. A supportive space and an open-minded society combined with initiatives from the State will uplift the trans community by fostering inclusivity, ensuring equal rights and providing necessary welfare measures for their empowerment.

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 Keywords

Transgenders, Social Exclusion, Constitutional Rights, Recognition, Measures

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 Abstract

The amalgamation of Artificial Intelligence (AI) into music has transformed the processes of composition, analysis, and pedagogy within the guru-shishya Parampara globally. However, the application of AI within Carnatic classical music has unique challenges due to the tradition's emphasis on the bhava (emotional expression) and the sadhana (disciplined practice). This paper examines whether AI can meaningfully understand or replicate bhava in Carnatic music and also explores whether AI-driven tools reduce the necessity of a sustained sadhana. Based on Carnatic musicology, aesthetic theory and AI based researches, this study contends that although AI can identify and reproduce the structural patterns associated with the raga and tala, it lacks the experiential, cultural, and embodied dimensions of bhava. In addition, AI tools are better understood as augmenting rather than replacing sadhana, as they enhance technical training while leaving an emotional connection and artistic maturity depending on the human experience. This paper concludes that the essence of Carnatic music remains fundamentally human, despite any technological advancements.

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Artificial Intelligence, Bh?va, S?dhana, Carnatic music, Gamaka, R?ga, Manodharma.

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 Abstract

"A COMPARATIVE STUDY TO ASSESS THE KNOWLEDGE REGARDING NURSING CARE OF THE PATIENTS UNDERGOING CARDIAC CATHETERIZATION AMONG STAFF NURSES WORKING IN SELECTED PRIVATE AND GOVERNMENT HOSPITALS OF DISTRICT PATIALA, PUNJAB WITH A VIEW TO DEVELOP AN INFORMATIONAL BOOKLET."

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Creative Commons Attribution 4.0 and The Open Definition

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"A COMPARATIVE STUDY TO ASSESS THE KNOWLEDGE REGARDING NURSING CARE OF THE PATIENTS UNDERGOING CARDIAC CATHETERIZATION AMONG STAFF NURSES WORKING IN SELECTED PRIVATE AND GOVERNMENT HOSPITALS OF DISTRICT PATIALA, PUNJAB WITH A VIEW TO DEVELOP AN INFORMATIONAL BOOKLET."

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Patient satisfaction is considered one of the key performance indices in evaluating quality of health services is patient satisfaction, more so in OPD, because it is the first contact point between patients and hospitals. This study aims to identify and analyse the key factors associated with patient satisfaction in OPD services. The study emphasises factors like waiting time, doctor-patient communication, personnel behaviour, cleanliness and availability of amenities. A well-structured questionnaire was used to gather primary data from patients receiving OPD care. Simple statistical techniques, such as percentage analysis and graphical depiction, were used to analyse the data. The study's conclusions show that although most patients are usually happy with the services they receive, a few elements have a big impact on their entire experience. Among these, waiting time and communication emerged as the most critical factors affecting satisfaction levels. Language barriers were also found to impact patient understanding and interaction with healthcare providers. The study highlights the importance of improving operational efficiency, enhancing communication skills, and providing patient-centred services. Lastly, Healthcare organisations can also enhance the quality of OPD services and guarantee a better patient experience by implementing the suggestions made in this research.

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Patient Satisfaction, OPD Services, Healthcare Quality, Waiting Time, Communication

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The E-Waste Management System is a web-based portal that was created to facilitate the process of collecting, segregating, and recycling e-waste. It is very easy to register on this platform and create an account in order to book an appointment with an authorized recycler in your vicinity. The system allows the user to interact with the recycling center or the authorized recycler without much hassle, and it ensures eco-friendly and sustainable recycling processes for all its users. The major objective of this platform is to prevent environmental pollution by minimizing the environmental hazards due to incorrect e-waste disposal. The system also encourages people to recycle their e-waste and use sustainable methods for managing their electronic waste. The platform categorizes the different types of e-waste into various sections, such as household appliances, mobiles, computers, other industrial equipment, and batteries. One of the best features of this portal is that it maps e-waste collection vendors in specific areas.

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E-waste Classification: Should you classify various categories of electronics.

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The fast pace of electric cars (EVs) adoption in India is an important move towards sustainability. The increasing popularity of EVs in Uttar Pradesh is linked to incentives by both the state and central government (e.g., road tax exemption, waived registration fees, etc.) as part of schemes like Faster Adoption and Manufacturing of Electric Vehicles (FAME) India. As the proportion of EVs and government subsidies in the form of incentives and tax exemptions increases with a goal towards ensuring sustainable mobility, it has an impact on the fiscal state of the state and EVs acceptance among various categories of vehicles. In this paper, we will look into two areas of the EV transition in Uttar Pradesh: how much fiscal spending can be estimated based on the amount of EV-related subsidies and tax exemptions, and which of the segments of EVs are being most impacted by the incentives provided. The study estimates the direct costs incurred by the state in terms of financial incentives by using secondary data on the FAME India dashboard, VAHAN vehicle registration portal, and the UP Budget documents. Fiscal spending to encourage EV adoption is calculated with the help of a formula that is determined by the policy guidelines. The findings suggest that even with continued provisioning of EV subsidies and exemption from road tax, state governments still have a lower impact on fiscal revenue due to factors such as lower adoption of EVs, the amount of subsidy allotted to different segments of EVs, and road tax exemption provisioned as per policy. The results highlight the significance of the balance between environmental aspirations and financial stability and indicate that a gradual and data-driven method should be used to encourage the electrification of mobility in Uttar Pradesh.

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E-Vehicle, FAME, VAHAN, Government Policy

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Creative Commons Attribution 4.0 and The Open Definition

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This study examines the effectiveness of virtual hiring practices in post-COVID India. It evaluates recruitment efficiency, cost-effectiveness, quality of hire, and candidate experience using a mixed-method approach. Findings suggest that virtual hiring reduces time-to-hire and cost-per-hire while maintaining acceptable hiring quality. However, challenges such as technological barriers and limited interpersonal assessment persist. The study concludes that hybrid hiring models are most effective.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Virtual Hiring, Recruitment Efficiency, Post-COVID HR Practices, Digital Recruitment, Artificial Intelligence in HR, Candidate Experience, Cost-per-Hire, Time-to-Hire, Hybrid Recruitment Model, Talent Acquisition in India

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Creative Commons Attribution 4.0 and The Open Definition

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This field study investigates digital learning platforms (e.g., Coursera, Udemy, LinkedIn Learning) and AI tools (e.g., ChatGPT, Grammarly) in enhancing employability skills among students and early-career professionals. Using a descriptive-analytical mixed-methods approach, surveys of undergraduates aged 20-25 revealed high adoption, with most frequently using platforms and reporting gains in independent learning, job knowledge, tech adaptability, problem-solving and productivity. Nearly all view these tools as boosting employability, though challenges like AI inaccuracies, biases and ethics persist, urging personalized training and safeguards.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digital Learning Platform, AI Tools, Employability Skills, Workforce Readiness

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Creative Commons Attribution 4.0 and The Open Definition