Volume 14 | Issue 4 |

 Abstract

Phobias are among the most common anxiety disorders, characterized by persistent and excessive fear of specific objects, situations, or environments. These fears often lead to avoidance behaviors that significantly disrupt an individual's mental health and daily functioning. The present study examines the impact of phobias on mental health and Activities of Daily Living (ADL), including selfcare, social participation, and occupational functioning. A descriptive quantitative research design was employed, involving 50 participants selected through simple random sampling. Data were collected using standardized tools such as the Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), and ADL assessment scales. Findings indicate that individuals with severe phobic symptoms experience higher levels of anxiety, emotional distress, and functional impairment. Approximately 50% of participants reported a high impact on mental health, while 40% experienced severe difficulty in daily activities. The study concludes that phobias significantly impair both psychological well-being and everyday functioning. Early identification, psychological interventions, and supportive strategies are essential to improve quality of life.

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 Keywords

Phobia, Mental Health, Anxiety Disorders, Activities of Daily Living, Psychological Wellbeing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The evolution from fifth-generation (5G) to sixth-generation (6G) wireless networks is expected to revolutionize communication by enabling ultra-low latency, high data rates, and intelligent network automation. However, the integration of emerging technologies such as artificial intelligence (AI), quantum communication, terahertz (THz) frequencies, and distributed architectures introduces significant security challenges. This paper presents a comprehensive analysis of security threats in 6G networks, focusing on vulnerabilities arising from AI-driven architectures, network softwarization, edge computing, and heterogeneous connectivity. Unlike previous generations, 6G networks are expected to be highly decentralized and AI-native, which expands the attack surface and introduces new adversarial threats, including AI poisoning, data breaches, and quantum attacks. Additionally, traditional issues such as Distributed Denial of Service (DDoS), man-in-the-middle attacks, and virtualization vulnerabilities persist and become more complex in 6G environments. This study also explores potential mitigation strategies, including zero-trust architecture, blockchain-based security, and quantum-safe cryptography. Finally, the paper highlights open research challenges and future directions to develop resilient, adaptive, and secure 6G communication systems.

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 Keywords

6g networks, security challenges, artificial intelligence, quantum communication, terahertz frequencies, network automation, vulnerabilities, ai-driven architectures, edge computing, decentralized, adversarial threats, ddos attacks, zero-trust architecture, blockchain security

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract: Continuous Professional Development (CPD) is globally recognized as a critical determinant of teaching quality and student achievement [1]. In India, national digital platforms such as DIKSHA, NISHTHA, and SWAYAM have expanded access to teacher upskilling at an unprecedented scale, particularly after the COVID-19 pandemic [3][5][7]. The National Education Policy (NEP) 2020 further institutionalizes lifelong professional learning by mandating a minimum of 50 hours of annual CPD for teachers through blended modes [4]. This study critically examines the effectiveness, challenges, and institutional implications of online teacher upskilling in India using secondary data analysis and systematic literature review. Data were drawn from national policy documents, Ministry of Education reports, platform analytics, UNESCO and OECD publications, and peer-reviewed studies published between 2018 and 2024 [3][16][17]. Descriptive trend analysis was applied to participation statistics, while thematic synthesis was used to examine learning outcomes and implementation barriers [15]. Findings indicate that online professional development has improved teachers' digital competencies, pedagogical awareness, and access to professional learning communities [11][14]. However, persistent issues such as the digital divide, low completion rates, limited mentoring, and weak classroom transfer constrain long-term impact [12][7][15]. Comparative evidence suggests that countries integrating online learning with school-embedded mentoring systems achieve stronger instructional improvement [18][19]. The study concludes that online upskilling must be embedded within institutional ecosystems to achieve sustainable professional growth aligned with NEP 2020 objectives [4][21].

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 Keywords

Keywords: Teacher professional development; online upskilling; DIKSHA; SWAYAM; digital pedagogy; NEP 2020; educational technology

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Creative Commons Attribution 4.0 and The Open Definition

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The design of autonomous vehicle systems is necessary as efficient simulation environments can mimic the complexities of the real world. The traditional approach of simulating AV systems is not effective enough because it cannot capture the unpredictability and dynamic nature of real-world traffic, especially in countries like India. This study proposes a CARLA-based autonomous vehicle simulator with real-time traffic flow, pedestrian dynamics, and object detection using YOLOv5. The simulation platform presented in this research serves as an effective solution to develop an autonomous driving model in a highly realistic environment. The use of dynamic agents and deep learning in the designed simulator will improve the decision-making ability of the developed autonomous vehicle. The cost-effectiveness of the suggested system makes it possible to conduct experiments in different situations.

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 Keywords

Autonomous Vehicles, CARLA Simulator, YOLOv5, Object Detection, Real-Time Traffic, Pedestrian Simulation, Deep Learning, Virtual Testing, Smart Mobility, Simulation Environment

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

High-strength steel is susceptible to fatigue damage during service, which alters its internal microstructure and subsequently affects its mechanical performance. This study explains the related damage mechanisms in high-strength steel, presents a methodical comparison of conventional fatigue and pre-fatigue, and describes how to assess pre-fatigue situations in order to better comprehend these impacts. Additionally, it looks at how pre-fatigue affects hysteresis behavior, static tensile characteristics, and overall fatigue performance. A technique to evaluate the mechanical performance of high-strength steel that has had pre fatigue damage is suggested based on these observations. The findings show that pre-fatigue causes microstructural changes that hasten the development of early-stage damage. This reduces yield strength, ultimate tensile strength, and ductility when loaded. It also promotes earlier fracture initiation and faster crack propagation, which ultimately shortens fatigue life. Pre-fatigue also reduces the material's ability to dissipate hysteretic energy in steel structures that already exist. In order to support deeper understanding and future developments in this field, the study finally expands its analysis to the post-pre-fatigue mechanical response of high strength steel, taking into account the combined influence of environmental conditions, machine learning-based damage evaluation approaches, and multi-scale, interdisciplinary analysis.

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 Keywords

Fatigue fracture, High-strength steel, Mechanical properties, Crack initiation, Microstructure

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In the rapidly evolving landscape of artificial intelligence (AI) and edge computing, the deployment of AI models to edge devices presents significant security challenges that traditional security models fail to address adequately. This research paper explores the application of Zero-Trust Architecture (ZTA) as a comprehensive security framework for AI model deployment in edge environments. The primary objective of this study is to identify and analyze the unique security challenges associated with edge-based AI systems and to develop a robust ZTA framework that ensures the integrity, confidentiality, and availability of AI models and their data in distributed edge environments. The paper addresses the multifaceted security concerns in edge AI, including model tampering, data poisoning, inference attacks, and unauthorized access to sensitive AI assets. These challenges are exacerbated by the resource constraints of edge devices, the heterogeneity of edge environments, and the dynamic nature of AI model updates. We propose a novel ZTA framework specifically designed for edge AI deployments that incorporates continuous authentication, fine-grained access controls, encrypted model storage and communication, and runtime integrity verification. This research introduces an innovative approach that combines hardware-based security mechanisms, such as Trusted Execution Environments (TEEs) and secure enclaves, with software-based security measures, including model partitioning, differential privacy, and federated learning techniques. This hybrid approach provides robust protection for AI models throughout their lifecycle on edge devices, from deployment to inference and updates. Furthermore, we investigate the use of blockchain technology for creating immutable audit trails and establishing trust in distributed edge AI environments. To validate the effectiveness of this proposed framework, we conduct extensive experiments on various edge platforms, including IoT devices, mobile phones, and edge servers, deploying different types of AI models such as convolutional neural networks, transformers, and reinforcement learning agents. The results demonstrate significant improvements in security posture with minimal impact on model performance and inference latency, addressing the critical balance between security and efficiency in resource-constrained edge environments.

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 Keywords

Zero-Trust Architecture, Edge Computing, AI Model Security, Federated Learning, Edge AI, Blockchain AI.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The plant disease detection system is an intelligent, ai-powered platform designed to simplify the process of identifying and diagnosing plant diseases from leaf photographs using deep learning and computer vision techniques. in today's agricultural era, farmers and agronomists frequently deal with crop diseases that cause 20-40% annual yield loss globally, making early and accurate diagnosis critical for food security and sustainable agriculture. the proposed system addresses this challenge by integrating convolutional neural networks (CNN), transfer learning, and gradient-weighted class activation mapping (grad-cam) into a unified framework.the system allows users to upload a leaf photograph through a web interface, which is then processed through multiple stages including image preprocessing, normalization, data augmentation, and feature extraction using the mobilenetv2 deep learning architecture pre-trained on the imagenet dataset containing 1.2 million images. the extracted features enable accurate visual understanding of disease patterns, allowing efficient classification of plant diseases based on leaf texture, color, spot characteristics, and lesion patterns. the classified result is then passed to a disease information module which generates accurate, concise, and context-aware responses including disease name, severity level, treatment recommendations, and prevention guidelines.the system is implemented using a modern full-stack architecture, with a flask-based backend for rest api processing, a drag-and-drop web interface for user interaction, and tensorflow with keras for deep learning model training and inference. the training pipeline follows a two-phase strategy -- transfer learning with frozen backbone layers followed by fine-tuning of the top 30 layers -- achieving 97.8% top-1 accuracy and 99.5% top-3 accuracy across 38 plant disease categories on a curated dataset containing 15,000 leaf images collected from 14 plant species including tomato, potato, apple, corn, grape, pepper, strawberry, peach, and cherry. additionally, grad-cam visualization ensures model explainability by highlighting the exact leaf regions that triggered the disease prediction. the plant disease detection system provides real-time inference under 10 milliseconds, a lightweight 14mb deployable model, and a user-friendly interface, making it suitable for agricultural, research, and field applications.

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 Keywords

Plant disease detection, convolutional neural network, transfer learning, mobilenetv2, grad-cam, deep learning, computer vision, tensorflow, flask, image classification, 15000 leaf images, data augmentation, precision agriculture, ai-powered diagnosis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the contemporary digital era, the extensive use of technology has significantly transformed daily life, particularly among adults who rely heavily on digital devices for work, communication, and entertainment. While digital platforms offer convenience and efficiency, excessive and prolonged usage has led to emerging concerns related to digital fatigue, sleep disturbances, and emotional well-being. This study aims to examine the interrelationship among digital fatigue, sleep quality, and emotional regulation, providing a comprehensive understanding of how these factors interact in modern lifestyles. Digital fatigue, characterized by mental exhaustion, reduced concentration, and emotional strain, arises from continuous exposure to screens and information overload. This condition not only affects cognitive functioning but also contributes to unhealthy digital habits, especially during late hours. Such patterns negatively influence sleep quality by increasing sleep latency, reducing sleep duration, and causing frequent disturbances. Poor sleep, in turn, impairs emotional regulation, making individuals more susceptible to stress, irritability, and emotional instability. The study adopts an integrated approach to analyze these three constructs collectively rather than in isolation. It highlights the cyclical nature of their relationship, where digital fatigue leads to poor sleep, which subsequently affects emotional regulation, further reinforcing excessive digital engagement. The findings emphasize the need for balanced digital usage, improved sleep hygiene, and strategies to enhance emotional regulation among adults. By addressing these interconnected factors, individuals can achieve better mental health and overall well-being. The study contributes to the growing body of literature by presenting a holistic perspective on the impact of digital lifestyles, offering practical insights for healthier behavioral patterns in an increasingly technology-driven world.

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 Keywords

Digital Fatigue, Sleep Quality, Emotional Regulation, Screen Time, Mental Health, Digital Lifestyle, Sleep Disturbance, Well-being

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 Abstract

Adolescence represents a critical neurodevelopmental phase marked by rapid cognitive, emotional, and social transformations. This study investigates the interaction between socio-emotional learning (SEL) competencies and neurodevelopmental conflicts among students aged 12-18 years in residential school environments. Such institutions impose structured discipline, peer immersion, and separation from familial support, creating a distinctive psychosocial ecosystem. A mixed-method design was employed involving 120 participants from residential schools. Quantitative data were obtained through standardized socio-emotional competency scales, while qualitative insights were derived from semi-structured interviews and behavioral observations.Findings indicate that early adolescents exhibit heightened emotional dysregulation, internal conflict, and dependency needs, attributed to the developmental asynchrony between limbic reactivity and prefrontal regulatory systems. However, students demonstrating stronger SEL competencies showed significantly improved adaptive behaviors, including emotional regulation, peer bonding, and academic persistence. The study underscores the mediating role of SEL in mitigating developmental conflicts within institutional settings. It advocates for the systematic integration of SEL-based interventions into residential schooling frameworks to support holistic adolescent development. Implications for educational policy, counselling practice, and future research are discussed.

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 Keywords

Socio-emotional learning, adolescence, residential schools, brain development, emotional regulation, counselling psychology

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Neuroplasticity refers to the brain's ability to change, adapt, and reorganize itself by forming new neural connections throughout life. This process allows the brain to adjust in response to learning, experience, and injury. Neuroplasticity plays a vital role in memory, skill development, and recovery from brain damage such as stroke or trauma. It involves both structural and functional changes in the brain, enabling neurons to strengthen or weaken their connections over time. Understanding neuroplasticity has important applications in education, rehabilitation, and mental health, as it shows that the brain is flexible and capable of continuous growth and improvement.

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 Keywords

Neuroplasticity, Brain adaptability, Neural connections ,Learning ,Memory ,Brain development ,Cognitive function ,Synaptic plasticity, Brain reorganization Rehabilitation ,Neural pathways ,Brain recovery.

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Creative Commons Attribution 4.0 and The Open Definition