Volume 13 | Issue 8 |

 Abstract

The field of portfolio construction has evolved significantly, driven by the increasing complexity of financial markets, investor behavior, and emerging risks. Traditional models such as the Markowitz Mean-Variance framework and the Sharpe Single Index Model, while foundational, often fall short in volatile and illiquid environments, particularly in emerging markets. This study explores optimum portfolio construction by integrating both classical and modern approaches across multiple industries and market contexts. Using Sharpe ratio optimization, cut-off rate methods, and statistical tools such as t-tests, correlation matrices, and risk-return analysis, the research identifies the most efficient portfolio combinations from 20 companies spanning four sectors. The study is supported by extensive literature, ranging from early beta-based models to modern advancements incorporating machine learning, Bayesian inference, ESG filters, and risk-aware rebalancing strategies. Empirical findings confirm that diversified, data-driven portfolios significantly outperform traditional allocation methods in terms of Sharpe ratio, total risk reduction, and sectoral balance. The research contributes to a deeper understanding of how modern tools can adapt classical theories for more robust and resilient portfolio performance. It provides a strategic roadmap for investors navigating uncertain, high-risk markets with the goal of achieving superior risk-adjusted returns through scientifically grounded portfolio construction.

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 Keywords

Optimum Portfolio, Mean-Variance Optimization, Sharpe Ratio, CVaR (Conditional Value at Risk), Asset Allocation, Portfolio Risk Management, Indian Stock Market.

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Driver fatigue significantly contributes to road accidents, but current detection methods often rely on intrusive sensors or computationally heavy models. We propose a vision-based driver alertness monitoring system using a standard webcam. The system analyzes facial cues by calculating the Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and head pose from Dlib facial landmarks. These metrics indicate signs of drowsiness. We compare our approach with recent deep learning solutions (YOLOv8-Face, MobileNetV3-based keypoint regression, and ResNet50-LSTM classifiers). On a standard CPU, our lightweight method achieves ~92.6% detection accuracy and ~94.1% sensitivity with an average latency of 1.1 seconds, while being 30% faster and 45% smaller than the MobileNetV3 model. These results suggest our system is well-suited for low-power, real-time deployment in smart vehicles for enhanced driver safety.

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 Keywords

Driver drowsiness detection, Eye Aspect Ratio, Mouth Aspect Ratio, Head-pose estimation, Real-time monitoring, Facial landmark tracking, Computer vision.

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This study examines the impact of Unified Payments Interface (UPI) and digital payment systems on the financial behaviour of small retailers across five districts in Karnataka. As digital payment adoption accelerates in India, understanding the behavioural patterns and satisfaction levels of small business users is critical for driving financial inclusion and digital transformation. The study aims to identify the key factors influencing behavioural intention, actual usage, and user satisfaction with UPI services. A descriptive research design was adopted, utilizing primary data collected from 400 small retailers through structured questionnaires, supported by secondary data from RBI, NPCI, and economic reports. Statistical tools such as descriptive statistics, one-sample t-tests, and ANOVA were used for analysis. Results show that ease of use, smartphone comfort, cashback incentives, and peer recommendations significantly impact adoption. User satisfaction is influenced by app usability, transaction speed, customer support, and trust in service providers. Gender and location emerged as major demographic influencers, while age had limited effects. The study concludes that strengthening digital infrastructure and enhancing service quality are vital to encourage consistent UPI usage among small retailers. Future research can explore UPI usage among other business segments, assess the long-term impact of digital incentives, and compare adoption patterns between rural and urban markets to guide policy and fintech innovations.

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 Keywords

UPI, Digital Payments, Small Retailers, Behavioral Intention, User Satisfaction, Karnataka

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This research paper highlights how the Chamba Princely State changed after British colonial rule. Through this paper, an attempt has been made to find out how agriculture changed, how the kings of Chamba adopted pro-British policies. New canals and kuhls were built for irrigation. The changes made in this by the British government were studied systematically. Through this research paper, an attempt has been made to know that land and revenue reforms were done during the British era. Through this study, an attempt will also be made to understand the revenue historical background of Chamba.

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 Keywords

Agriculture, Horticulture, Chamba, Princely State, and British Period

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Creative Commons Attribution 4.0 and The Open Definition

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Accurately predicting flight delays is a key challenge in aviation analytics, with significant implications for airline operations and passenger satisfaction. This project presents a machine learning-based system for predicting flight delays using historical U.S. domestic flight data. The dataset is extensively preprocessed, including the imputation of missing values, encoding of categorical variables, and generation of time-based features such as scheduled departure hour and total delay duration. A binary classification approach is adopted, labeling flights as delayed when the arrival delay exceeds 15 minutes. Three models--Random Forest, Decision Tree, and Logistic Regression--are developed and evaluated. Both the Random Forest and Decision Tree classifiers achieved 100% accuracy on the test set, while Logistic Regression reached 98.4%, indicating the strong predictive capability of the selected features. However, such perfect accuracy suggests potential data leakage or overfitting, as the delay-related columns used in training directly influence the target variable. A user-friendly graphical interface, built with Tkinter, allows users to input flight number, tail number, origin, and destination to receive real-time delay predictions and reasons. Additionally, visual analytics generated using Seaborn highlight delay trends by departure hour and weekday. The system demonstrates how machine learning, combined with rich flight data, can support operational decisions in aviation, although care must be taken to ensure model generalization and reliability

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 Keywords

Flight delay prediction, machine learning, Random Forest, Decision Tree, Logistic Regression, data preprocessing, binary classification, aviation analytics, graphical user interface, Tkinter, overfitting, data leakage, visual analytics, Seaborn

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Forests and wildlife are vital resources that have played a crucial role in human beings' social, economic, and religious life since the beginning. Forests were essential for the livelihoods of forest dwellers who depended on them for food, shelter, fodder, fuelwood, and other daily necessities. Humans hunted animals in forests and obtained meat for food from wild animals; however, over time, they cleared forests for agriculture, which put pressure on soil and water resources. They were unfamiliar with agriculture and relied on animal flesh, but some wild fruits and vegetables grew in abundance in the forests, as well as leaves and fur for clothing. They didn't have fixed houses; they might have made huts from tree leaves. Forests and wildlife play a crucial role in maintaining environmental balance. Every species plays a vital role in maintaining an ecological balance among the Earth's living systems. This research paper highlights the hunting practices in the hills of Himachal during the British period. This paper also highlights the relation between wildlife and Human activities.

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 Keywords

Hunting, Wildlife, Forest, Hilly State, and British Period.

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid integration of digital devices into children's daily lives has raised serious concerns about the adverse effects of screen time on pediatric health. From sleep disturbances and obesity to behavioral and developmental delays, the implications are multifaceted and growing. Artificial Intelligence (AI) offers a promising solution for early detection, personalized monitoring, and evidence-based interventions. This paper explores the role of AI in identifying screen time-related health risks in children and how AI-powered tools can assist pediatric nurses, parents, and healthcare systems in managing these challenges effectively. The study highlights recent advancements, ethical considerations, and practical applications of AI in pediatric care. A multidisciplinary approach, combining AI technology with pediatric nursing, can potentially mitigate the "digital danger" and promote healthier screen habits among children

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Digital danger, AI, health issue

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Creative Commons Attribution 4.0 and The Open Definition

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The study was conducted to assess the quality of life of a hemodialysis patient in Tamralipto Govt. Medical College & Hospital. The study was conducted by adopting descriptive research design. 60 hemodialysis patient are taken who fulfilled the inclusion and exclusion criteria were selected by non probability sampling technique. The patients were explained about the assessment tool. To find out the quality of life of a hemodialysis patient the score should be obtained from WHO recommended WHOQOL-BREF questionnaire. It has total 26 questions, and which was given the score from 26-130. The following score indicates the quality of life of hemodialysis patient. The analysis of the study revealed that in initial stage of ESRD patient remain asymptomatic and they may not perceive the significant effect of their quality of life by hemodialysis. The patient who receiving hemodialysis had adequate self efficacy and had a good quality of life. It is important to identify the hemodialysis patient who lack the self care efficacy required self care and focus on educational interventions to build confidence.

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o Quality of Life, o Assessment

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Creative Commons Attribution 4.0 and The Open Definition

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In the 2nd chapter of the Bhagavadgītā introduces the idea of 'Sthitaprajña' as a person who is self-realized, possessing a stable intellect, and who remains unaffected by attachment-dissatisfaction, joy-pain, aversion, and anger. This research article offers a comparative analysis of the interpretations of verses 2.55 to 2.57 of the Gītā by Śaṃkarācārya and Rāmānujācārya. Śaṃkarācārya focuses on knowledge, detachment, and illusionism rooted in Advaita Vedānta , whereas Rāmānujācārya views devotion, dedication, and divine grace as foundational for self-realization in Viśiṣṭādvaita. Although the perspectives of both Acharyas may vary, their objective remains focused on self-realization and salvation. This article demonstrates that both knowledge and devotion play significant roles in the process of becoming a Sthitaprajña. This research enhances Vedantic discussions and motivates contemporary individuals to attain self-discipline and inner tranquility through the combined path of knowledge and devotion.

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Sthitaprajña, Bhagavad Gītā, Śaṃkarācārya, Rāmānujācārya, Advaita Vedānta, Bhaktiyoga, Self-realization

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 Abstract

Systematic Review of Literature of Community Based Tourism

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 Keywords

8591

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Creative Commons Attribution 4.0 and The Open Definition