Volume 14 | Issue 2 |

 Abstract

Classical dominance-based and indiscernibility relations are often too rigid to analyse real data affected by noise, perturbation, or instability. This paper introduces a strong hybrid equivalence framework that integrates exact algebraic dominance with a ?-restricted similarity induced by a sensitivity distance. The sensitivity distance measures behavioural deviation between objects, allowing both metric analysis and tolerance-based interpretation. We study the algebraic properties of sensitivity relations, derive necessary and sufficient conditions for transitivity via ultrametric inequalities, and construct ?-neighbourhoods that generate covering-based approximations. Lower and upper rough approximations, lattice ordering under ?-variation, and the interaction between hybrid equivalence and dominance are established. A healthcare risk-classification case study demonstrates how the hybrid model yields robust and interpretable groupings in the presence of uncertainty. The proposed framework provides a mathematically consistent foundation for sensitivity-aware reasoning, stability-based decision analysis and approximate classification.

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 Keywords

Dominance relation; Sensitivity distance; Rough sets; Tolerance relation; Hybrid equivalence; ?-neighbourhood; Approximate reasoning; Robust decision analysis.

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In the contemporary and highly competitive manufacturing environment, exemplary corporate leadership and a strong organizational culture have emerged as critical drivers of organizational development. The present study aims to examine the impact of exemplary corporate leadership and organizational culture on organizational development in selected manufacturing companies. The research focuses on identifying key leadership practices and cultural dimensions that influence organizational growth, employee engagement, operational efficiency, and long-term sustainability. A descriptive and analytical research design is adopted, using primary data collected from managerial and supervisory employees through structured questionnaires, supported by secondary data from journals, reports, and published literature. Appropriate statistical tools such as percentage analysis, correlation, and chi-square tests are employed to analyse the data. The findings of the study reveal a significant relationship between exemplary corporate leadership, organizational culture, and organizational development, indicating that effective leadership practices coupled with a supportive and adaptive culture positively influence organizational performance. The study provides valuable insights for manufacturing organizations, policymakers, and academicians by emphasizing the need to strengthen leadership practices and cultivate a positive organizational culture to achieve sustainable organizational development.

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 Keywords

Exemplary Leadership, Organisation Culture, Manufacturing industries etc.

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 Abstract

Digital banking has revolutionized financial services by offering convenience, accessibility, and efficiency. However, its rapid adoption has introduced significant cybersecurity challenges. This paper explores the key threats, vulnerabilities, and mitigation strategies associated with digital banking adoption, focusing on phishing, malware, social engineering, regulatory compliance, and emerging technologies. The study highlights the importance of robust cybersecurity frameworks to ensure trust and resilience in digital banking ecosystems.

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 Keywords

Digital Banking, Cybersecurity, Phishing, Malware, Social Engineering, Regulatory Compliance, Multi-Factor Authentication, AI Threat Detection, Mobile Banking Security, Financial Technology, Data Breaches, Ransomware, Insider Threats, Blockchain Security

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Pandit Deendayal Upadhyay ke Samajik Darshan ki Samkaleen Sarkari Nitiyon ke sath Tulnatmak Vishleshan.

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Pandit Deendayal Upadhyay ke Samajik Darshan ki Samkaleen Sarkari Nitiyon ke sath Tulnatmak Vishleshan.

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract Plant-derived exosome-mimetic nanocarriers (PDENMs) represent a rapidly advancing class of bioinspired drug delivery systems that bridge the gap between natural extracellular vesicles and synthetic nanocarriers. These vesicles, derived from edible and medicinal plants or fabricated using plant lipid extracts, exhibit unique advantages such as excellent biocompatibility, low immunogenicity, biodegradability, scalability, and intrinsic affinity for biological membranes. Oral and vaginal routes of drug administration are particularly attractive due to their non-invasive nature; however, both routes present formidable physiological barriers that limit drug stability, absorption, and therapeutic efficacy. PDENMs have demonstrated the ability to protect encapsulated drugs from harsh biological environments, enhance mucosal penetration, and enable targeted or sustained drug release. This comprehensive review critically examines the current state of research on PDENMs, covering their sources, isolation and fabrication techniques, physicochemical characterization, drug loading and functionalization strategies, and mechanisms of cellular uptake. Special emphasis is placed on oral and vaginal drug delivery applications, supported by preclinical evidence. Safety, toxicity, regulatory considerations, and future research directions are discussed to provide a translational perspective on the clinical potential of PDENMs.

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 Keywords

Plant-derived nanocarriers; Exosome-mimetic vesicles; Oral drug delivery; Vaginal drug delivery; Nanomedicine; Mucosal delivery

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Creative Commons Attribution 4.0 and The Open Definition

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This study investigates the aetiology of opioid dependence among the female demographic in the Tezu Administrative Centre. Moving beyond individual pathology, the research identifies a confluence of socio-cultural, environmental, and biomedical determinants that precipitate addiction. Key findings isolate seven primary variables: (1) seasonal exposure and labour during the opium lancing and latex extraction period, (2) analgesic substitution for chronic morbidity, (3) recreational peer reinforcement, (4) ritualistic cultural sanctioning, (5) dyadic influence within domestic partnerships, (6) geographical location within a high-yield cultivation belt encompassing Lohit and Anjaw, and (7) high social acceptability. The study argues that addiction in this region is a structural outcome of these intersecting factors.

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 Keywords

Opioid Dependence, Women in Arunachal, Mishmi Tribe, Tezu, Agrarian Labour, Social Aetiology, Kaanang.

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Creative Commons Attribution 4.0 and The Open Definition

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: Facial Emotion Recognition (FER) is a vital part of affective computing as well as human-computer interaction since it allows intelligent systems to recognize human emotional states based on their facial expression. Although convolutional neural networks (CNNs) with deep learning have been used to successfully perform spatial models in images of faces, these still have a shortcoming in their ability to capture the temporal changes in emotions that are in the real world. On the other hand, time models enhance dynamic expression but have low spatial discrimination. In response to these shortcomings, this paper will suggest a Hybrid Spatial-Temporal Deep Learning Framework that learns to model both the appearance and emotional state of faces of both still images and video frames. The framework proposed combines the CNN-based spatial feature extraction with the Long Short-Term Memory (LSTM) based temporal modeling in a unified end-to-end implementation. The comprehensive experiments done on FER-2013 and CK+ data sets prove that the hybrid CNN-LSTM model greatly outperforms the spatial-only baselines, having a higher accuracy, stability, and generalization. Besides, statistical significance testing establishes that the performance gains realized are not deceptive and are not as a result of random variation. The findings confirm such a problem as the efficacy of hybrid spatial-temporal learning of strong facial emotion recognition in unconstrained settings.

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 Keywords

Facial Emotion Recognition; Hybrid Spatial-Temporal Learning; Convolutional Neural Networks; Long Short-Term Memory; Deep Learning; Affective Computing; Human-Computer Interaction.

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 Abstract

A study with quasi-experimental one group pre-test-post-test design was used to assess the effectiveness of structured teaching programme on knowledge regarding ill effect of plastic on health among the community people of Andharua, Jagganathprasad, Bhubaneswar. In this study community people were selected by using simple random sampling technique and data were collected by using self-structured questionnaire method. Demographic variables reveals that 23 community people were male whereas 7 community people were female. From the pre-test and post-test knowledge scores, it was found that in pre-test 20% of people had poor level of knowledge whereas none of them in post-test.70% of people had average level of knowledge in pre-test while 6.7% in post-test.10% of people had good level of knowledge in pre-test whereas 33.3% in post-test.In pre-test none of them had excellent level of knowledge whereas almost i.e. 60% in post-test. At the end, it is concluded that maximum people had poor and average knowledge in pre-test whereas maximum people had good and excellent knowledge in post-test. Therefore, it is interpreted that post-test scores were astonishingly much more than the pre-test scores after implementing the structured teaching programme. The difference in paired pre-test and post-test is (-485). This indicates an increase in knowledge scores after implementing structured teaching programme and calculated value of 't' (29): 15.88 was found highly significant at 0.05 level of significance. Association between the level of knowledge among the community people was highly significant with their age and significant with their education and not significant with gender, family income, occupation, types of family, method of waste disposal and previous source of knowledge.

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 Keywords

STRUCTURE TEACHING PROGRAM ,ILL EFFECT, PLASTIC, COMMUNITY

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Serverless computing (Function-as-a-Service, FaaS) is severely limited by the COLD start problem, a latency penalty incurred during container initialization. Current mitigation approaches, relying primarily on static keep-alive policies or single-source time-series prediction, fail to balance performance consistency with resource cost, often leading to a 40-60% waste in resource provisioning. We introduce COLDSTART (Cost- and Latency-Optimized Serverless Orchestration via Multi-Modal Prediction and Confidence-Based Warming), a novel, intelligent orchestration framework designed to minimize COLD start occurrences and resource overhead simultaneously. Our core contribution is a Multi-Modal Prediction (MMP) architecture that fuses six heterogeneous data streams--spanning user behavior, API metrics, and system telemetry--into a unified feature space. This space is analyzed by a tri-model Ensemble Learning Engine (XGBoost, LSTM, and Transformer) to generate highly accurate invocation forecasts. Crucially, we propose a Confidence-Based Warming (CBW) mechanism that gates resource allocation based on a calculated prediction confidence score (C), thereby minimizing expensive false positives. Through extensive experimentation on simulated production workloads, COLDSTART achieved a 68% reduction in COLD starts and improved 95th percentile latency to under 200 ms, concurrently delivering a 45% reduction in operational waste compared to single-source ML baselines. This work demonstrates a robust, context-aware solution essential for sustainable and high-performance serverless deployments. Index Terms--Serverless Computing, Function-as-a-Service (FaaS), COLD Start Mitigation, Multi-Modal Prediction, Ensemble Learning, Cost Optimization, Proactive Warming.

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 Keywords

Serverless Computing, Cold Start Mitigation, Function-as-a-Service (FaaS), Multi-Modal Prediction, Ensemble Learning, Confidence-Based Warming, Cost Optimization, Latency Optimization

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Creative Commons Attribution 4.0 and The Open Definition

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In this modern digital era, interface design has transformed from static to dynamic interactions, where motion plays an important role in guiding user experience. Motion and animation has no longer restricted to decorative functions; Rather than, they act as narrative medium that leads users, visualize meanings, and enhance emotional involvement. This paper observes motion as narrative in interface design, concentrating on how storytelling is crafted through animation, transitions, feedback and micro-interactions Anchored in Narrative Theory, the study examines user interactions as narrative sequence in which motion organize the initiation, development, and resolution of tasks. Guided by qualitative and descriptive case study methodology, the study focus on digital platforms such as Duolingo, Airbnb, and Google's Material Design to explain how narrative motion shapes onboaring, navigation, feedback, and brand experience. These findings highlights that well-designed motion amplifies clarity, stability, usability and emotional engagement, while overused or poorly implemented motion may lead to inclusive design and performance challenges. The study highlights that narrative motion is an effective design strategy that connects functionality and experience, with growing conceptual importance in AI-driven personalization and immersive AR and VR environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Interface Design, Human-Computer Interaction (HCI), Digital Storytelling, Motion Design, Narrative Theory, Animation, User Experience, Immersive Interfaces

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Creative Commons Attribution 4.0 and The Open Definition