Volume 14 | Issue 4 |

 Abstract

Nomophobia is the irrational fear of being without your mobile phone or being unable to use your phone for some reason, such as the absence of a single or running out of minutes or battery power. In the case of nomophobia, the events that the user fears are not terribly unlikely, so that part of it is not irrational.

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 Keywords

Nomophobia, Students

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 Abstract

Dynamic Input-Correlated Layer Execution (DICLE) establishes a novel theoretical paradigm that transitions from binary "keep-or-skip" execution models to a continuum of input-correlated dynamic scaling. This framework addresses the inherent computational inefficiency of static inference by integrating a formal Input-Correlation Scoring mechanism (Scorr) with low-rank Dynamic Adapters to counteract representation drift. By modelling inference as a stochastic path-selection process within a discretised search-space, DICLE enables fractional execution where layer depth and bitwidth precision are modulated by representational maturity. Analytical findings indicate that DICLE can prune transformer-based architectures by up to 40% while maintaining 98% of baseline accuracy. Furthermore, by identifying semantic redundancy within hidden states, the framework achieves substantial reductions in both Floating Point Operations (FLOPs) and Memory Access Cost (MAC), particularly by bypassing redundant key-value (KV) cache updates in correlated layers. Index Terms - Large Language Models, Adaptive Inference, Dynamic Neural Networks, Fractional Skipping, Representation-Aware Execution, Hardware Optimisation.

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 Keywords

Transformers, LLMs, Self Attention, Multi-Head-Attention, Inference, Compute, Cosine Similarity, KV Cache

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 Abstract

Traffic offenses like signal jumping, unhelmeted riders, lane indiscipline, etc., occur frequently in urban areas and cannot be corrected manually as it is time consuming and prone to inconsistency and inaccuracy. This paper proposes an AI-based Traffic Violation Detection System with a conceptual design that is capable of detecting vehicles and riders, helmets and traffic lights using the YOLOv8-small deep learning model. The whole system is based on object detection, Tracking and Frame-by-Frame analysis of the vehicles, identifying the violations and also calculating the speed of the vehicle from the video input, without the need of any external hardware (radar, sensors, etc.). Combining different types of detections into a single pipeline also provides a more holistic traffic monitoring approach. The primary requirements for this pipeline from an implementation viewpoint are reliable real-time detection, violation automation, and a speed that can support practical use. The proposed approach is extensible for centralized monitoring dashboard for traffic authorities. The framework provides a proof of concept on the role of AI helped solutions in traffic law enforcement, reducing human intervention and making roads safer and more efficient in urban settings.

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 Keywords

Traffic violation, YOLOv8, real-time object detection, smart transportation systems, computer vision

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 Abstract

Abstract Artificial Intelligence is everywhere in 2026. It is used in self-driving cars and computers that can make things.. With all the good things it can do there are also big problems. For example computers can be. Make unfair decisions. They can also be. Hurt people.. Training these computers uses a lot of energy which is bad for the Earth. We looked at what happened in 2025 and what might happen next. We found some areas that need research like making Artificial Intelligence more transparent and fair. We also need to find ways to keep peoples information private and to make sure Artificial Intelligence is used in a way that's good for everyone. By looking at all the information and working together we can make sure Artificial Intelligence is used in a way that helps people and does not hurt them. We can make a future where Artificial Intelligence's a good thing for everyone.

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 Keywords

Artificial Intelligence, AI Challenges, Cyber security Threats, Explainable AI, Federated Learning, Ethical AI, AI Regulation, Sustainable AI, Human-AI Collaboration

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This study explores the determinants of employee engagement and their influence on organizational commitment among employees. Data were collected from 230 respondents using a structured questionnaire through convenience sampling. Key variables examined include career growth opportunities, employee involvement, interpersonal relationships, role clarity, job crafting, and work environment. The study employs statistical tools such as ANOVA, multiple regression, spearman's correlation coefficient and Structural Equation Modelling (SEM) using AMOS to analyse the data. The findings indicate that employee engagement determinants significantly and positively influence organizational commitment. Among the variables, career advancement opportunities, role clarity, and participation in performance-enhancing activities emerged as major contributors to employee motivation and engagement. SEM results further validate the strong relationship between employee engagement and organizational commitment. The study also reveals high levels of job satisfaction and employee retention intention. Overall, the findings emphasize the importance of strengthening engagement practices to enhance employee performance and ensure long-term organizational success.

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 Keywords

Employee Engagement, Organizational Commitment, Career Growth, Role Clarity, SEM, Employee Motivation, Work Environment

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 Abstract

Regenerative medicine is a novel multidisciplinary approach that is quickly evolving and aims to repair, replace, and regenerate damaged organs and tissues. It has progressed over the last few decades from simple tissue engineering principles to complicated therapeutic approaches that mix gene and cell-based products. This advancement is critical to the function of gene and cell treatments, which enable fine-tuning of cellular activity, improved tissue re-modeling, and the correction of genetic abnormalities that underlie many illnesses. New improvements in gene delivery technologies have significantly improved therapeutic efficacy and safety. Adenoviral, adeno-associated viral, and lentiviral systems are thought to be particularly effective in transfection, although non-viral carriers, such as lipid nanoparticles and polymers, are safer and more flexible. Embryonic stem cell-based, induced pluripotent stem cell-based, and mesenchymal stem cell-based stem cell technologies have all demonstrated exceptional regeneration capabilities. Furthermore, the introduction of genome editing devices such as CRISPR-Cas9 has resulted in great precision in gene editing, allowing for tailored and focused treatment. The study focuses on key advances in gene and cell therapy in the field of regenerative medicine, including clinical applications and technological achievements. Despite the constraints of delivery efficiency, safety, and cost, continuing research continues to push the development of increasingly efficient, scalable, and accessible regenerative treatments.

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 Keywords

Cell therapy,Gene therapy,Regenerative medicine,Non-viral delivery systems,Tissue engineering.

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 Abstract

Virtual Reality (VR) is transforming modern education by providing immersive and interactive learning experiences. However, the integration of Big Data Analytics enhances VR systems by enabling personalized learning, performance tracking, and intelligent decision-making. This research paper explores the use of Big Data Analytics in VR-based educational systems. The proposed system uses data collected from student interactions in VR environments to improve learning outcomes. Technologies such as Hadoop and Apache Spark are used for large-scale data processing, while analytics techniques help in understanding student behavior and optimizing content delivery. The results show improved engagement, learning efficiency, and adaptive education systems.

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 Keywords

Big Data Data Analytics Data Science Data Mining Predictive Analytics Descriptive Analytics Prescriptive Analytics Hadoop HDFS (Hadoop Distributed File System) Apache Spark MapReduce Apache Hive Apache Pig NoSQL Databases (MongoDB, Cassandra)

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Women's self-silencing remains an underexplored psychological phenomenon within urban Indian contexts despite strong global evidence linking voice suppression with mental health risks. The present study examined the lived psychological experience of self-silencing among women in Mumbai using a qualitative-dominant mixed-methods design (N = 12). Narrative interviews were combined with standardized psychometric instruments including the Silencing the Self Scale (STSS), Rosenberg Self-Esteem Scale (RSES), and General Health Questionnaire-28 (GHQ-28). Results indicated elevated self-silencing across participants (M = 83.7) and high psychological distress, with 83% scoring above the GHQ-28 threshold. Strong correlations were observed between self-silencing and distress (r = .73) and between self-silencing and self-esteem (r = -.68). Reflexive thematic analysis identified six experiential themes: early socialization into silence, gendered double binds, erasure of women's contributions, hidden voice-preservation strategies, the psychological impact of being heard, and intergenerational aspirations for change. Findings suggest that women's silencing operates as a structural psychological stressor rather than an individual trait. Implications are discussed for counselling practice, educational reform, and gender-responsive policy frameworks in urban India.

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 Keywords

Women's self-silencing; psychological distress; feminist psychology; Mumbai women; intersectionality; narrative research; voice suppression; mental health

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The present research critically examines how Amitav Ghosh narrativizes neocolonialism by weaving socio-political, economic, psychological, and cultural dimensions into his literary works. The study explores how Ghosh critiques global capitalist hegemony, loss of indigenous cultural memory, and reproduction of colonial hierarchies by native elites, presenting them as ongoing extensions of imperialism. Ultimately, the research aims to establish that Ghosh's narrative technique offers multidirectional resistance to neocolonial structures by re-inscribing suppressed histories, challenging western epistemic authority, and restoring subaltern agency. The study concludes that Ghosh's works constitute a form of literary decolonization, provoking the need for critical reflection on how neocolonial forces persist in shaping global postcolonial realities.

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 Keywords

Neocolonialism, Postcolonial Trauma, Cultural Alienation, Bourgeois Complicity, Epistemic Resistance, Narrativizing Neocoloniality

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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 Keywords

Keywords: Chronic bronchitis, structured teaching programme, knowledge, risk factors, home care management, adults

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Creative Commons Attribution 4.0 and The Open Definition