Volume 14 | Issue 4 |

 Abstract

The rapid integration of digital media into everyday life has significantly reshaped childhood experiences, particularly in urban environments where access to smartphones, tablets, and online platforms is widespread. While digital technologies provide valuable opportunities for learning, communication, and entertainment, concerns are increasingly being raised about their impact on children's psychological development and family relationships. One area of growing importance is the influence of screen exposure on parent-child emotional bonding, especially within the framework of attachment relationships. Grounded in Attachment Theory, which emphasizes the importance of early emotional bonds between caregivers and children for healthy socio-emotional development, this study investigates how digital media use affects mother-child attachment among school-aged children. Secure attachment, characterized by trust, emotional closeness, and effective communication, plays a critical role in shaping a child's emotional regulation, resilience, and interpersonal functioning. However, the increasing presence of digital devices in daily routines may interfere with opportunities for meaningful interaction and responsive caregiving, thereby influencing attachment quality. The present study focuses on children aged 6-12 years in Bengaluru, an urban setting marked by rapid technological adoption and changing family dynamics. This developmental stage is particularly significant as children gradually gain independence while still depending on parental emotional support. The research adopts a quantitative correlational design to examine the relationship between screen time and mother-child attachment, while also exploring the role of technoference--defined as interruptions in interpersonal interactions caused by digital device use--as a contributing factor. A sample of 120 mother-child dyads was selected using stratified sampling from schools and residential communities. Data were collected using standardized instruments measuring children's screen time (in terms of duration, frequency, and type of use), mother-child attachment (across dimensions such as emotional closeness, communication, trust, and responsiveness), and technoference in daily interactions. In addition to quantitative data, qualitative responses were gathered to better understand family experiences and behavioral patterns related to digital media use. Statistical analysis revealed a significant negative relationship between screen time and attachment quality, indicating that increased digital media use is associated with reduced emotional bonding between mothers and children. Screen time was also found to be a significant predictor of attachment outcomes, explaining a notable proportion of variation in attachment levels. These findings suggest that excessive engagement with digital devices may limit opportunities for face-to-face interaction, emotional exchange, and shared experiences that are essential for building secure attachment relationships. Importantly, technoference emerged as a strong influencing factor, further intensifying the negative impact of screen use. Frequent interruptions caused by mobile devices during conversations and routine family activities were found to reduce parental responsiveness and attentiveness. This disruption in real-time interaction weakens the emotional connection between mother and child, highlighting that not only the amount of screen time but also the context and timing of device use play a crucial role in shaping relational outcomes. The qualitative findings supported these results by providing insight into everyday family dynamics. Many mothers reported that children with higher screen engagement tended to show reduced interest in communication, family participation, and shared activities. Patterns such as emotional distancing, decreased verbal interaction, and a growing preference for solitary digital engagement were commonly observed. Traditional bonding activities, including storytelling, play, and shared meals, were often replaced or interrupted by screen use, contributing to a decline in emotional closeness. At the same time, the study identified important moderating factors that can help mitigate these negative effects. Parental involvement in children's digital activities--such as co-viewing, guided usage, and setting structured screen time limits--was associated with improved communication and stronger emotional connection. When digital media was used interactively and under supervision, its adverse impact on attachment appeared to be significantly reduced. This highlights the role of mindful parenting in promoting healthy digital habits while preserving relational quality. The findings also align with the displacement hypothesis, which suggests that time spent on digital devices often replaces time that could otherwise be devoted to direct social interaction. In the context of parent-child relationships, this displacement reduces opportunities for emotional engagement and shared experiences, both of which are fundamental to secure attachment formation. Furthermore, the study underscores that the quality of screen use matters; passive and unregulated consumption tends to have more negative effects compared to purposeful and interactive engagement. Despite its contributions, the study acknowledges certain limitations. The use of self-reported measures may introduce bias, and the cross-sectional design does not allow for causal conclusions. Additionally, the focus on an urban population limits the generalizability of findings to other settings. Future research could benefit from longitudinal approaches and inclusion of multiple caregivers to provide a more comprehensive understanding of family interactions in the digital age. In conclusion, this study highlights that excessive and unregulated screen time can negatively influence mother-child attachment by reducing emotional closeness, communication, and responsiveness. Technoference further exacerbates these challenges by interrupting meaningful interpersonal interactions. However, the findings also offer a hopeful perspective, demonstrating that active parental involvement and balanced digital practices can significantly buffer these effects. The study emphasizes the need for greater awareness among parents, educators, and mental health professionals regarding the impact of digital media on children's emotional development. Encouraging mindful technology use, promoting family interaction, and fostering consistent emotional engagement are essential steps toward maintaining strong parent-child relationships in an increasingly digital world. Ultimately, achieving a balance between technological benefits and human connection is key to supporting healthy developmental outcomes for children.

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 Keywords

- Screen time, attachment theory, mother-child relationship, techno Ference, digital media, child development, parental mediation, urban families.

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Gratitude has been increasingly recognized as a significant construct within positive psychology, contributing to various aspects of mental health and well-being. However, limited research has examined how its influence varies across different stages of life. The present study explores the relationship between daily gratitude and key mental health outcomes--emotional well-being, life satisfaction, and perceived stress--among adolescents, adults, and older adults. A quantitative cross-sectional design was employed with a sample of 120 participants equally distributed across three age groups. Standardized instruments, including the Daily Gratitude-Mental Health Lifespan Scale (DG-MHLS), PANAS, Satisfaction With Life Scale (SWLS), and Perceived Stress Scale (PSS-10), were used. Data were analyzed using descriptive statistics, one-way ANOVA, Pearson correlation, and regression analysis. Findings revealed that gratitude is significantly associated with emotional well-being (r = .50, p < .01) and life satisfaction (r = .40, p < .01), but not significantly related to perceived stress (r = -.06, p > .05). Significant differences across age groups were observed for gratitude and positive mental health variables, while stress remained consistent. Regression analysis indicated that gratitude significantly predicts emotional well-being and life satisfaction but not stress. The findings highlight gratitude as a resource that enhances positive mental health outcomes, while its role in reducing stress appears limited. The study contributes to a lifespan understanding of gratitude and its psychological relevance.

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 Keywords

Gratitude, Mental Health, Emotional Well-being, Life Satisfaction, Perceived Stress, Lifespan Development

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

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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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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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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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Creative Commons Attribution 4.0 and The Open Definition