Volume 14 | Issue 8

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Prachin Bharat Mein Jal Sansadhan Prabandhan Aur Paramparik Jal Pranaliyan: Ek Avalokan

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Prachin Bharat Mein Jal Sansadhan Prabandhan Aur Paramparik Jal Pranaliyan: Ek Avalokan

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The digital transformation of higher education has expanded students' access to search engines, learning management systems, online courses, video resources, virtual meeting platforms, electronic scholarly resources and artificial intelligence applications. This study examines the pattern of digital tool usage and the major challenges faced by higher education students in using digital technologies for learning. The analysis is based on 564 responses collected from Higher education pursuing students from Coimbatore District. The Analysis shows that Google Search has the highest mean usage score, followed by SWAYAM/NPTEL) and Google Classroom. YouTube and AI Chat Bots share a mean of, while zoom/Google Meet and online libraries/e-journals record lower values respectively. The study also examines barriers measured on a five-point scale. The results reveal a strong preference for accessible and user-friendly digital resources, while scholarly e-resources and synchronous platforms are comparatively underused. The findings highlight the need to improve digital infrastructure, affordability, training, academic resource awareness and responsible technology use.

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Digital learning tools, higher education, SWAYAM, NPTEL, Google Classroom, AI chatbots, e-resources, digital barriers

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Hospitalized children; Anxiety; Pain perception; Structured play; Therapeutic communication; Pediatric nursing; Randomized controlled trial.

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Real estate plays a crucial role in the economy through its significant contribution to national GDP growth. Housing is the principal collateral and consumer commodity, serving as the largest fixed asset for families, which in turn influences their savings and consumption decisions. In the present paper, an attempt is made to offer conceptual order, investigate key topics, and recommend future research trajectories. The study analyses a sample of 805 journal articles from the Scopus database from 2016 to 2026 using a bibliometric analysis and a literature review. The article compiles a list of the top papers, authors, nations, organizations, and journals related to digital-based financial inclusion based on the Scopus database. The analysis was prepared using advanced Excel tools, RStudio, and VOSviewer. Networks of scientific publications, journals, researchers, institutions, nations, and phrases are constructed using VOSviewer. The study discovered that publication on this topic is continuously increasing. The writers with the most documents in this field include Gurran Nicole, Rowley Steven, Biswas Sujoy, Mukerji Arjun. Moreover, with 1780 papers, USA is the top-ranking nation in the field of digitally based financial inclusion. Sustainability International Journal of Housing Markets and Analy has published 55 papers in journals, making it the most sources in this field. Gurran, nicole and Rowley, steven receive the most co-citations. keyword analysis demonstrates the eight clusters that, combined with bibliographical coupling, aid in recognising the different streams of affordable housing market.

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Housing Market, Bibliometric Analysis, Scopus, Scientific Mapping, Research Trends.

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Ian McEwan is a British author, dramatist, and one of the most well-known contemporary writers. He is best known for his award-winning novels, which have left a lasting influence on contemporary literature through their distinctive narrative style. His works have received six Booker Prize nominations and are widely admired for their elegance, precision, and exploration of complex human relationships and moral dilemmas. McEwan's ability to seamlessly combine psychological depth with social criticism distinguishes his fiction and makes his novels intellectually engaging through their exploration of the complexities of human experience. This paper focuses on literary techniques such as intertextuality, open endings, flashbacks, and metafiction by examining Ian McEwan's novels Atonement and Saturday.

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Ian McEwan, literary techniques, intertextuality, open ending, flashback, metafiction.

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The behaviour of investors has an important role in determining the movement of stock prices. Traditional financial theories assume that investors make rational decisions and that market prices quickly reflect available information. However, actual market behaviour often shows that investors do not always respond to information in a rational manner. Two important behavioural patterns associated with this issue are overreaction and underreaction. Overreaction occurs when investors respond too strongly to new information, while underreaction refers to a delayed or insufficient response. Overreaction occurs when investors respond too strongly to new information. This can push stock prices away from their actual value and may lead to a later fall in prices. Underreaction is different. It occurs when investors respond slowly to new information, which may allow the price movement to continue for some time. This study examines evidence of overreaction and underreaction in selected Indian stocks of Sensex using a winner-loser portfolio approach. Weekly closing prices of 15 selected companies are used for the analysis. The five companies with the highest cumulative abnormal returns (CAR) are classified as winners, while the five companies with the lowest CAR are classified as losers. The BSE Sensex is used as the market benchmark. Abnormal returns are calculated using the market-adjusted return method. Average abnormal return (AAR) and cumulative average abnormal return (CAAR) are then used to examine the performance of the winner and loser portfolios. Five formation and testing periods are considered. These periods cover short, medium and long-term horizons. One-sample t-tests, paired-sample t-tests and Wilcoxon signed-rank tests are used to examine the significance of the results. The findings show mixed evidence. Negative Winner-Loser CAARs are found in three of the five testing periods. This shows that past winners performed worse than past losers in these periods and provides some evidence of overreaction. The strongest reversal is found in the medium-term period, with a Winner-Loser CAAR of -5.6658%. However, positive CAARs are found in the long-term period and one short-term period. These results suggest continuation and are consistent with possible underreaction. The findings therefore suggest that investor behaviour varies across investment horizons. The evidence of reversal and continuation indicates that both overreaction and underreaction may occur in the Indian stock market, but their presence is not consistent across all periods.

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Investor Behaviour; Overreaction; Underreaction; Behavioural Finance; Indian Stock Market; Investment Decision-Making, Price Reversal, Winner-Loser Effect.

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Trauma and empowerment continue to be themes of focus in modern women's writing, given the challenges of oppression, survival and transformation. Chika Unigwe's On Black Sisters' Street and Alice Walker's The Color Purple offer strong and interesting stories of women who suffer physically, emotionally and socially, who are trying to find themselves and their dignity. The novels, despite their distinct settings and time periods, show striking similarities in their depiction of gender discrimination, sexual violence, exploitation and the domination of men. This comparative reading explores how both writers portray trauma not as a victimization experience but as a transformative process that helps grant access to resilience, self-discovery and empowerment. The analysis also examines how the protagonists challenge oppressive structures, how they begin to construct themselves in a new way, and how they challenge society's norms and expectations that aim to silence them. The comparison of these two texts emphasizes the common experiences of women in different cultures and the timeless power of books to depict human strength, hope, and the struggle for freedom.

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Trauma, Empowerment, Women's Resilience, Identity, Patriarchy, Gender Oppression, Female Agency.

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The non-intrusive nature of face recognition and its broad range of applications in security, surveillance, access control, and identity verification systems have made it one of the most popular biometric technologies. The need for precise and effective real-time face recognition systems has grown dramatically with the quick development of digital imaging equipment, artificial intelligence, and embedded computing platforms. However, differences in lighting, position, facial expression, occlusion, and computing restrictions make it difficult to achieve high identification accuracy while meeting real-time requirements. A thorough analysis of real-time face recognition systems is given in this study, which covers both conventional and modern methods, publicly accessible datasets, performance assessment measures, and deployment issues. In addition to local feature-based approaches like LBP, HOG, SIFT, and their variations, the study methodically examines traditional appearance-based and statistical techniques like PCA, LDA, and ICA. The contributions and limits of key-point descriptors and correlation filter-based techniques in real-time settings are also examined. The study also addresses the shift to deep-learning-based facial recognition models, with a focus on resource-constrained and real-time systems that are light weight. The size, annotation kinds, and appropriateness for real-time assessment of publicly accessible datasets that are often used for benchmarking face recognition systems are summarized. Along with the trade-offs between accuracy, speed, memory usage, and energy efficiency, this study also looks at the role of hardware acceleration employing CPUs, GPUs, and FPGAs. There is also discussion of important issues including scalability, privacy, security, and ethical issues. This study seeks to provide academics and practitioners a comprehensive knowledge of the present status of real-time face recognition and to suggest attractive avenues for future research by compiling recent developments and highlighting outstanding research concerns.

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Real-Time Face Recognition; Face Detection; Computer Vision; Feature Extraction; Face Recognition Datasets; Performance Evaluation; Hardware Acceleration; Security and Surveillance

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Hospitalized children; Play-based intervention; Therapeutic communication; Anxiety; Pain perception; Recovery outcomes; Pediatric nursing; Randomized controlled trial.

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Movie production includes huge amount of financial risk, and investment decisions are frequently based on previous experience or market intuition rather than statistics. We propose a machine learning based methodology for predicting a movie's Return on Investment (ROI) based in information available before its release. Our proposed model considers budget, runtime, genre, director, production company, release timing and movie overview to estimate the expected ROI. Multiple machine learning models were compared, and Random Forest showed the best performance and it was selected as final prediction model. Then, we used SHAP (Shapley Additive exPlanations) to find and explain the factors that significantly impact each prediction. A Decision Support System (DSS) is developed to help producers estimate different production scenarios before making investment decisions. This allows the user to change important factors such as budget, runtime, franchise status and release timing and see how those changes affect the predicted ROI. Our system suggests an investment based on the estimated ROI and helps producers in making better investment decisions.

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Movie ROI Prediction, Machine Learning, Random Forest, SHAP, Explainable Artificial Intelligence (XAI), Decision Support System (DSS), Return on Investment (ROI), Film Industry.

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Emotional displacement, migration, trauma, family separation, Borderlands, divided identity, memory, belonging.

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Gharelu Shram Ka Ling Aadharit Vibhajan Ev Kamkaji Mahilao Ke Dohre Boj Ka Samajshastriya Adhyan

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Gharelu Shram Ka Ling Aadharit Vibhajan Ev Kamkaji Mahilao Ke Dohre Boj Ka Samajshastriya Adhyan

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Reliable electricity supply remains a major requirement for rural and geographically dispersed communities, where conventional grid extension can be technically difficult and economically burdensome. This paper presents a MATLAB/Simulink-based modeling, simulation, and performance assessment of a hybrid solar-wind energy system developed in the uploaded thesis for Guru Nanak Dev Engineering College (GNDEC), Ludhiana, Punjab. The study combines photovoltaic generation, wind energy conversion, maximum power point tracking, power electronic converters, battery energy storage, and an inverter within an integrated renewable-energy architecture. Meteorological inputs include solar irradiation, wind speed, and ambient temperature, while an appliance-based institutional load profile represents academic, laboratory, hostel, administrative, and auxiliary demand. The reported results show that the hybrid configuration produces 4.5 kWh/day and 1,642 kWh/year, compared with 2.8 kWh/day and 1,022 kWh/year for solar PV alone and 1.9 kWh/day and 694 kWh/year for wind alone. The hybrid system also reduces the reported loss of power supply probability to 3.2%, compared with 12.5% for solar and 16.8% for wind. Its reported LCOE is INR4.9/kWh, while solar and wind values are INR5.8/kWh and INR6.4/kWh, respectively. These results demonstrate the value of resource complementarity and integrated control for reliable decentralized electricity supply.

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Hybrid renewable energy; Solar PV; Wind energy; MATLAB/Simulink; Rural electrification

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representation of women, gender stereotypes, women empowerment, Turkish TV series

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The main objective of this project is to formulate and evaluate polyherbal anti-diabetic tablet with enhanced disintegration time.The polyherbal tablet consist of five traditional herbs Gurmar (Gymnema sylvestre), Karela (Momordica charantia), Tulsi (Ocimum sanctum), Ginger (Gingiber officinate), Cinnamon (Cinnamum zelacicum). In the formulation the Gurmar (leaf powder dry) , Karela (dry fruit powder) , Tulsi (dry leaf powder), Ginger (dry root powder) , and Cinnamon (bark powder) were used. The polyherbal tablets were prepared by wet granulation method with excipients: Gum Acasia, Polyvinylpyrrolidine, Aerosil, Talc, and Magnesium Stearate.. The preliminary phytochemical analysis of powder mixture was done using the methanolic extract of powder mixture. The preformulation studies of tablets were performed including Angle of repose, tapped density, bulk density, carr's index, hausner's ratio. The dried polyherbal remove was streamlined for its quality measures and its cluster consistency by making 5 diverse preliminary clumps(Trial 1,2,3,4,5). The preliminaries were exposed to preformulation boundaries to confirm the consistency and quality. The outcome assumes that the initial five formulations demonstrated across all parameters, and were therefore utilized to create a detailed polyherbal tablet formulation. The developed polyherbal phytochemical study showed the presence of flavonoids, alkaloids, tannin, phenolic compound in the formulation by using phytochemical analysis. The laboratory scale preparation of polyherbal tablet can lead to new powerful and oral dosage formulation for diabetes mellitus and lighten by synergic area of action herbs.

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POLYHERBAL TABLET, MEDICINAL PLANT, GYMNEMA SYLVESTRE, MOMORDICA CHARANTIA, OCIMUM SANCTUM, GINGIBER OFFICINATE, CINNAMUM ZELACICUM

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In patriarchal societies, women have historically occupied marginalized positions across various spheres of life--including the political, social, economic, and cultural. Consequently, seat reservation and participation are crucial for ensuring and safeguarding equal status for women within the framework of gender rights, equality, and social justice. This article discusses the issue of seat reservation for women in India, specifically focusing on quota systems in the realms of political representation, education, and employment. Additionally, this article discusses the objectives of women's empowerment, gender equality, the impact of women's empowerment, and the challenges facing this process.

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women reservation, gender equality, women entrepreneurship, women participation.

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This paper examines the role of the Department of Humanities in strengthening elementary teacher education through the functioning of District Institutes of Education and Training (DIETs) in Karnataka. The study is descriptive in nature and follows the normative survey method. A total of 18 faculty members working in the Department of Humanities across DIETs representing the Bangalore, Mysore, Belgaum and Gulbarga educational divisions were selected by purposive sampling for the study. Data were analyzed using the independent 't'-test with respect to sex, educational qualification, locality of the institute, and teaching-cum-research experience of the faculty. The results indicate that the role of the Department of Humanities did not differ significantly by locality or experience, but differed significantly with respect to sex and educational qualification, with female faculty and faculty holding qualifications above the prescribed minimum reporting more favorable perceptions of the department's functioning. Implications for faculty development in DIET humanities departments are discussed.

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Elementary Teacher Education, DIET, Department of Humanities, DIET Functioning.

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Background Substance Use Disorder (SUD) is a chronic and relapsing disorder that adversely affects the physical, psychological, social, and occupational well-being of affected individuals. Despite the availability of de-addiction services, many patients continue to experience poor Quality of Life and unsatisfactory Treatment Outcomes due to the complex nature of addiction and its psychosocial consequences. Assessing these outcomes is essential for planning comprehensive, patient-centered rehabilitation services and improving the effectiveness of addiction treatment programs. Objectives 1. To assess the Quality of Life among patients with substance use disorder attending selected de-addiction centers. 2. To assess the Treatment Outcomes among patients with substance use disorder attending selected de-addiction centers. 3. To determine the association between Quality of Life and selected demographic and clinical variables. 4. To determine the association between Treatment Outcomes and selected demographic and clinical variables. Methods A quantitative descriptive cross-sectional study was conducted among 200 patients with substance use disorder attending selected de-addiction centers in Bhopal, Madhya Pradesh. Participants were selected using a purposive sampling technique. Data were collected using a structured demographic and clinical profile, the World Health Organization Quality of Life- BREF (WHOQOL-BREF) questionnaire, and a standardized Treatment Outcome Assessment Scale. Descriptive and inferential statistics were used to analyze the data. Results A total of 200 patients participated in the study. The findings revealed that 58.5% of participants had a moderate level of Quality of Life, 27.5% had a low level, and 14.0% had a high level of Quality of Life. Similarly, 61.0% demonstrated satisfactory Treatment Outcomes, while 39.0% had poor Treatment Outcomes. Significant associations were observed between Quality of Life and educational status, employment status, family support, and history of relapse (p < 0.05). Treatment Outcomes were also significantly associated with duration of substance use, family support, and treatment adherence (p < 0.05). Conclusion The study revealed that a considerable proportion of patients with substance use disorder experienced moderate Quality of Life and satisfactory Treatment Outcomes. The findings emphasize the need for comprehensive recovery-oriented interventions and continuous nursing support to support long-term rehabilitation and overall patient well-being.

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Substance Use Disorder; Quality of Life; Treatment Outcomes; WHOQOL-BREF; De- Addiction Centers; Descriptive Study; Mental Health Nursing.

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Microservices architecture has become the dominant paradigm for building scalable, independently deployable cloud-native applications, and orchestration platforms such as Kubernetes have emerged as the de facto standard for managing the lifecycle of these distributed services. As the number of microservices grows, an API gateway becomes essential for consolidating cross-cutting concerns such as routing, authentication, rate limiting, load balancing, and observability at the edge of the system. However, the proliferation of API gateway solutions -- including Kong, NGINX, Traefik, Spring Cloud Gateway, and the Istio Ingress Gateway -- makes the selection of an appropriate gateway for a given orchestrated deployment a non-trivial engineering decision. This paper presents an empirical evaluation of five widely used API gateways deployed on a Kubernetes-orchestrated microservices testbed. We measure and compare latency, throughput, error rate, CPU utilization, and memory footprint under varying levels of concurrent load, and we qualitatively compare the gateways on feature dimensions including service discovery integration, protocol support, extensibility, and operational complexity. Results indicate that NGINX-based gateways deliver the lowest latency and highest raw throughput for simple routing, Kong offers the strongest balance of performance and plugin-based extensibility, Traefik provides the most seamless Kubernetes-native service discovery, while Spring Cloud Gateway and the Istio Ingress Gateway trade raw performance for deeper integration with application-level and service-mesh-level concerns respectively. The paper concludes with guidance for architects choosing a gateway based on workload characteristics, team expertise, and operational priorities.

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API Gateway; Microservices; Kubernetes; Orchestrated Deployment; Kong; NGINX; Traefik; Spring Cloud Gateway; Istio; Service Mesh; Load Balancing; Cloud-Native Architecture; Performance Evaluation

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Education in the 21st century is fundamentally concerned with preparing students with the knowledge and skills they need to succeed in rapidly changing world, making curricula learner-centered, socially relevant and leveraging the potential of emerging technologies to provide meaningful, real-world, problem-solving-based learning experiences (Kum Yoke et al., 2020). From personalized learning pathways to data-driven decision-making, AI is helping schools move beyond one-size-fits-all education toward more inclusive, adaptive, and future-ready learning environments. Classroom technologies powered by AI allowed educators to create adaptive and dynamic learning paths that cater to diverse student abilities, comprehension levels, and learning styles (Bean & Melzer, 2021). To adequately equip students to navigate competencies, curricula must adapt to seismic shifts that necessitate reevaluating curricular priorities. By blending technical proficiency with essential human qualities, educational systems can produce graduates capable of thriving in a rapidly evolving technological landscape. By designing and implementing such curricula, significant gaps in teaching and learning can be addressed and students will be well-equipped to navigate the complexities of future job markets, where both technical and human-centric skills are highly valued. Therefore, in light of the accelerating rate of technological change, curricula that are more student-driven, interconnected across disciplines, and connected to real-world problem-solving should be given priorities. Educators and researchers must collaboratively navigate to ensure that AI technologies are not only integrated effectively but also aligned with pedagogical principles that foster the growth of responsible and socially conscious individuals (Shepherd & Majchrzak, 2022).

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Artificial intelligence, AI ethics, Curriculum development, Personalized learning, pedagogy; Educational technology.

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