Volume 13 | Issue 11 |

 Abstract

Self-ordering kiosks (SOKs) are rapidly reshaping service interaction models in quick-service restaurants (QSRs), offering customers independence, convenience and reduced waiting times. However, their effectiveness depends heavily on intuitive interaction design and the ability to accommodate diverse user needs in real-world scenario. This study evaluates the user experience (UX) and interaction design efficiency of self-ordering kiosks in QSRs across Delhi NCR, an urban region characterized by high digital adoption and demographic diversity. Using a mixed-methods approach, data was collected through an online survey (n=115) and in-store observations with short interviews (n=8). Quantitative results indicate that perceived ease of use (PEOU) has a stronger influence on user satisfaction and intention to use than perceived usefulness (PU), suggesting that frictionless interaction is the primary driver of kiosk acceptance. Qualitative findings reveal three dominant behavioral patterns: kiosks enable younger users to bypass social interaction, group ordering creates cognitive load due to linear interface flows, and older users experience exclusion due to low digital comfort. The study confirms both hypotheses, PU predicts intention to use and PEOU predicts satisfaction, while also highlighting a digital divide shaped by age and literacy. These insights emphasize the need for hybrid service models and more inclusive kiosk designs that support varied demographic and contextual use cases. The results contribute to interaction design research by linking behavioral acceptance with real-time interaction outcomes in an emerging market context.

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Self-ordering kiosks; User experience; Interaction design; Quick-service restaurants; Human-computer interaction

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The Information Technology (IT) industry has moved beyond relying solely on technical proficiency and now places substantial importance on soft skills such as communication, analytical thinking, teamwork, adaptability, and professional conduct. However, numerous employer surveys and industry reports continue to highlight a noticeable gap between the soft-skill requirements of organizations and the abilities demonstrated by newly graduated IT professionals. This study investigates how colleges and universities currently teach and assess these essential competencies and examines the extent to which academic practices align with industry expectations. Using a mixed-method design that incorporates curriculum analysis, structured surveys, and interviews with stakeholders, the research identifies both the strengths and shortcomings of existing evaluation approaches. The results show that while soft skills are introduced in many academic programs, their assessment often lacks consistency, practical relevance, and behavioural depth. The study therefore underscores the need for clearer assessment frameworks, stronger collaboration between academia and industry, and wider use of experiential learning strategies to enhance the overall employability of IT graduates.

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Soft Skills, Employability, IT Graduates, Academic Evaluation, Industry Requirements, Communication Skills, Problem-Solving, Skill Gap, Higher Education, Professional Competencies.

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Phishing threats in Unified Payments Interface (UPI) dealings have developed into an increasing concern for cybersecurity, sometimes resulting in financial loss or data theft. The goal of this project is to propose a machine learning-based method to detect phishing messages in communications concerning UPI transactions. A total of 200 transaction messages were gathered and denoted as appropriate phishing and legitimate examples. These messages were cleaned, and relevant features, such as message length, number of links, digits, and keywords related to phishing content, were extracted from them. A Gaussian Naive Bayes (GNB) classifier is then applied, and the messages are classified as normal-like or phishing-like based on the noted features of each example. The classifier model shows acceptable accuracy in distinguishing fraudulent messages. The study demonstrates the usefulness of relatively simple statistical features and the feasibility of an approach to detect phishing in electronic messages used in digital payments. The work addressed a dimension of online financial transaction security.

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 Keywords

Phishing Detection, UPI Transactions, Cybersecurity, Machine Learning, Gaussian Naive Bayes, Financial Fraud, Feature Extraction, Digital Payments, Message Classification, Data Security.

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Creative Commons Attribution 4.0 and The Open Definition

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South Korea is globally admired for its cultural influence and economic strength, but few recognize the paradox at the heart of its rise. This research explores what can be termed THE KOREAN PARADOX: the nation's ability to balance two seemingly opposite forces namely, glamour and grind, which has made it both a cultural trendsetter and an economic powerhouse. On the one hand, the global appeal of Korean soft power: K-pop, K-drama, K-beauty and cutting-edge design. On the other hand, the country's relentless work culture, competitive education system, and corporate discipline, have helped its rise from a war-torn nation to a global leader. Through the lens of business and culture, this study examines how South Korea has worked to develop a dual brand identity. It looks at the role of major conglomerates such as Samsung, Hyundai, LG and SK in building Korea's global credibility, and how pop culture reinforced national branding on an emotional level. Special focus is given to the Hyundai and BTS partnership, which represents the perfect fusion of Korea's industrial might and cultural resonance. From the perspective of business and management, the study treats this concept not merely as a cultural exploration but a strategic case study. It analyses how contrast, when intentionally managed can function as a national asset. The Korea model offers valuable insights for leaders and policymakers navigating the balance between performance and perception, tradition and innovation. This study does not seek to glorify or criticize South Korea. Rather, it presents a nuanced analysis of how a nation transforms its internal contradictions into a competitive advantage, offering a distinctive contribution to debates on national branding, soft power, and sustainable development.

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 Keywords

chaebol, cultural diplomacy, dual branding, hard power, industrial development, Korean wave (Hallyu), South Korea, soft power

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The contemporary higher education landscape is characterized by unprecedented volatility, uncertainty, complexity, and ambiguity (VUCA), demanding a shift from traditional, rigid academic structures toward more agile and responsive institutions. At the heart of this transformation are academicians, whose ability to adapt, learn, and proactively respond to change--termed Workforce Agility (WA)--is critical. While antecedents of agility, such as leadership and human resource practices, have been explored, the intrinsic psychological mechanisms that enable agility are less understood. This conceptual paper posits that Work Engagement (WE), a positive, fulfilling, work-related state of mind characterized by vigor, dedication, and absorption, plays a pivotal mediating role in cultivating workforce agility. Through a comprehensive review and synthesis of recent literature, this article develops a model proposing that key psychosocial factors--specifically, perceived organizational support (POS), transformational leadership (TL), and job resources (JR)--foster work engagement. This heightened state of engagement, in turn, serves as the primary psychological engine driving the cognitive, behavioral, and motivational dimensions of workforce agility. The study delineates the theoretical foundations of work engagement and workforce agility, explores their interrelationship, and argues for work engagement as a critical mediator. The paper concludes with practical and managerial implications for higher education institutions (HEIs), suggesting that targeted interventions to boost work engagement are not merely beneficial for well-being but are strategic imperatives for building an agile, resilient, and high-performing academic workforce capable of thriving in the 21st-century knowledge economy.

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Workforce agility, Work engagement, Academicians, higher education, organizational support, transformational leadership and job resources.

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The present study aims to construct and analyze optimal investment portfolios using the Markowitz Mean-Variance Model with reference to the BSE Sensex 30 companies during the period (April, 2022-October 2025) 4.5 years. From the Sensex list, five companies exhibiting high return and high risk -- Eternal, Trent, Mahindra & Mahindra, Bharti Airtel and Bharat electronics -- were selected for the risk-taker (aggressive) portfolio, while five companies with moderate return and low risk -- ICICI, Bharti Airtel, ITC, Kotak Mahindra and Tech Mahindra -- formed the risk-averse (conservative) portfolio. Monthly closing prices were used to compute individual stock returns, standard deviations, and inter-stock covariances, leading to the formation of a comprehensive covariance matrix that quantified diversification benefits. The average expected return of the risk-taker portfolio was 3.39% with a standard deviation of 6.46%, while the risk-averse portfolio yielded 1.27% and 3.84%, respectively. The efficient frontier was constructed to demonstrate the trade-off between risk and return, showing all possible optimal portfolio combinations. The findings highlight that the Markowitz Model effectively identifies efficient portfolios and provides strategic insights for both risk-taking and risk-averse investors in the Indian equity market.

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Markowitz Model, Portfolio Construction, Risk and Return, Efficient Frontier, BSE Sensex, Covariance Matrix

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Creative Commons Attribution 4.0 and The Open Definition

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Sustainability is mostly perceived as the combination of environmental, social and economic performance; while still remain difficulties in defining sustainable economic development. Today, the human needs of many people are not met, and by the same time the ability of future generations to meet their needs is being compromised. It is because this that sustainable development has raised as a concept, affected by the need to offer people a kind of development which can meet the needs of the present without compromising the capacity of future generations to meet theirs. Economic growth may result in an improvement in the standard of living related to a small proportion of the population whilst the majority of the population remains poor. It is how the economic growth is distributed amongst the population that determines the level of development. The concept of sustainable development has become a widely recognized goal for human society of the 21st century. In this research it is analyzed the why and how has emerged the concept of sustainable development; how should it really be conceptualized and what should be done in order to achieve it.

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Creative Commons Attribution 4.0 and The Open Definition

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Economics, Sustainable Development, Social Issues, Human Welfare, Distribution

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

??? ??? (1632??1704) ?? ???????? ??????? ????????, ???????? ?????? ?? ?????? ??????? ?? ??? ???? ???? ???? ???? ?????? ?????? ???????? ????????, ??????? ?????? ?? ???? ???????? ?? ????????? ?? ????? ??? ??? ??? 17??? ??????? ?? ???????? ??? ????? ??? ??? ?? ?? ??? ??? ??? ???? ????? ???????? ??? ?? ??????? ????? ??????? ?? ?? ??????? ???? ?? ???????? ?? ??? ????? ?????? ???? ??? ???????? ???? ???? ?? ?????? ?? ?? ?????? ???? ?? ????? ?? ????? ???? ?? ????? ?? ?? ??? ?? ???? ??, ?? ???????? ?? ??? ?????? ??? ??? ??????? ??? ?? ????? ???? ???? ??? ????? ??? ??? ?? ???? ?????????? ???? "???? ?????????? ? ???????????" ???? ???? ??, ?????? ???????? ????????? ???????? ?? ??????? ?????? ?? ????????? ?? ????? ?? ??????? ??? ?? ?? ????? ?? ?? ?????? ???? ?? ?? ??? ????? ???????? ?? ??? ??? ?? ?? ???? ????, ?????????? ?? ??????? ?? ??????? ?? ?????? ?????? ?? ????? ?????? ?????? ??? ?? ??? ?? ????? ?? ??????????? ??? ?????????? ???? ??? ?? ????, ????? ?? ?????? ?? ???????? ?? ? ?? ???? ???? ?? ?? ? ?? ??? ???? ??? ??? ?? ???? ?? ?? ????? ?? ????? ?? ???????? ??? ????????? ???? ?? ?? ?? ?? ????????? ???????? ?? ????? ?? ??? ???? ???? ??? ??? ???????? ?? ?? ??? ?? ??? ??? ????? ?? ???????? ?? ??????? ???? ?? ?? ?????? ???????? ???? ??? ???? ???? ??, ?? ???????? ?? ?? ????? ?? ??????? ??????? ???? ?? ????? ?????? ??? ?? ????? ?? ??? ?? ???????? ???????? ?? ??? ?????? ??????????? ??, ?????? ???? ?? ????? ?? ????????? ????? ???? ????? ?? ????????? ???? ???? ??? ????????? ???????? ??? ???? ???? ?????? "???? ?? ??????" ??? ??? ?? ????? ?? ?? ?? ??????? ?? ???? ???? ?? ????? ?? ?????? ???? ?? ??????? ??? ???? ?? ?? ???? ?? ???? ????? ?? ??? ???? ??? ????? ?? ?????? ???? ??, ???? ?? ??????? ?? ?? ?????? ???? ????? "?????????? ?? ??????" ?? ???? ?? ?????????? ??? ???? ???? ?? ?? ?? ??????? ?? ???? ???, ??????????, ??????? ?? ????? ???? ?? ?????????? ???? ????? ?? ?????? ?? ?? ?????? ?? ???????? ?? ??????? ? ???? ?? ?????????? ???? ????? ?? ?????? ???? ??? ????? ?????? "??????? ?? ??????" ??, ???? ??? ?? ???? ????? ????? ???? ????? ?? ?? ??????? ???? ???? ?? ?????? ?? ?????? ?? ??? ?????? ???? ??, ?? ???? ??????? ??, ?? ??? ???? ???? ????? ?? ??? ?? ???? ?? ????? ???? ??????, ??????? ??? ? ???? ????? ??????? ????? ???, ????? ??????? ?? ???, ???? ?? ??? ?? ???? ??????? ???? ?? ????? ???????? ?? ????, ?????????? ?? ??????? ?? ?? ????????? ????? ??? ??? ??????? ?? ???? ????? ?????? ??? ?? ???? ???, ????? ?????? ?? ?????? ?? ???????? ?? ???? ??? ???? ??? ?? ??????, ?? ???????? ?? ????? ?? ???? ?? ????? ?? ???????? ???????? ???? ?????? ??? ??? ?? ?? ??? ??? ???? ??????? ?????????? ?? ????? (1776) ?? ????????? ??????? (1789) ???? ???????? ???????? ?? ??????? ???? ???? ?? ??, ??????????? ?????????? ??? ????? ???????? ?? ?? ??? ???? ??, ???? ???? ???? ? ???? ??? ??? ?? ????????? ?????? ???????? ??? ?? ????? ???? ????????? ??????, ??????? ??????, ????? ?? ????? ??????, ?? ??-????? ?? ???????? ???? ???? ??????? ???????, ??????????? ?? ????? ????? ?? ?????????? ???? ???? ?? ????????? ?? ????? 17??? ?? 18??? ??? ??? ???, ?? ????? ??? ???????? ???????? ??????????? ?? ???? ????? ????? ??? ???, ???? ?? ???? ????? ???? ???????? ?? ?????????? ?????? ??? ??? ??? ?? ?????? ?? ??????? ?? ???????? ?????

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John Locke, Natural Rights

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Creative Commons Attribution 4.0 and The Open Definition

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Juvenile Delinquency in India: A Critical Examination of Child Rights and Legal Frameworks Abstract Juvenile delinquency remains a significant concern in India, where an increasing number of children are becoming involved in criminal activities. This article critically examines juvenile delinquency in the Indian context, focusing on the intersection of child rights and the legal frameworks designed to address this issue. Despite various legal reforms, particularly the Juvenile Justice (Care and Protection of Children) Act, 2015, the challenges of aligning child rights with legal responses to juvenile delinquency persist. The study explores the root causes of juvenile crime, including socio-economic factors, lack of access to education, and the breakdown of family structures. It also assesses how India's legal system both upholds and undermines the rights of children in conflict with the law, with particular attention to the adequacy of rehabilitative and restorative approaches. Through a critical evaluation of existing policies, the paper calls for an integrated child rights approach that balances the need for justice with the imperative of protecting vulnerable children. The article emphasizes the need for reforms that not only focus on punitive measures but also prioritize the rehabilitation and reintegration of juveniles into society, ensuring that their fundamental rights are upheld in every step of the legal process.

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Key words: Juvenile Delinquency, Child Rights, Legal Framework, Juvenile Justice Act, Rehabilitation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Hybrid deep learning architectures have emerged as a powerful solution for addressing the complexity of MRI-based brain tumor diagnosis, a domain characterized by heterogeneous tumor morphology, multimodal imaging protocols, and the need for accurate, early detection. Traditional convolutional neural networks (CNNs), although highly effective at learning spatial features, often struggle to capture long-range dependencies, multi-sequence relationships, and contextual variations inherent in brain tumor MRI data. By integrating complementary modules such as recurrent neural networks, transformers, generative adversarial networks, and classical machine-learning classifiers, hybrid models leverage the strengths of each component to enhance feature representation, robustness, and generalization across scanners and institutions. Advances in multimodal MRI--including T1, T2, FLAIR, and contrast-enhanced sequences--further motivate hybrid architectures by providing diverse inputs that benefit from sophisticated fusion strategies. Preprocessing pipelines involving skull stripping, normalization, and denoising underpin the reliability of these systems, while hybrid CNN-GAN and CNN-Transformer approaches demonstrate improvements in tumor detection, classification, and segmentation, particularly for complex tasks such as delineating glioma subregions or distinguishing tumor grades. Benchmarking on datasets such as BraTS, TCGA, and REMBRANDT reveals consistent performance gains for hybrid models, although issues of domain shift, annotation scarcity, and limited cross-dataset validation remain significant barriers. Despite impressive accuracy metrics--including enhanced Dice scores, F1-scores, and reduced false positives--clinical integration is hindered by computational cost, interpretability challenges, and regulatory constraints. Overall, hybrid deep learning architectures represent a promising path toward more reliable and clinically useful MRI-based brain tumor diagnosis, while highlighting the need for improved datasets, standardized evaluation protocols, and greater emphasis on robustness and transparency.

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Deep Learning, MRI, Brain Tumor, Convolutional Neural Networks, T1, T2, FLAIR and GAN

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Creative Commons Attribution 4.0 and The Open Definition