Volume 13 | Issue 8 |

 Abstract

Children and adolescents of parents with mental illness (CAPMI) face significant psychological, social, and developmental challenges. Despite growing awareness, interventions often fail to adequately target those most at risk or effectively deliver support tailored to their needs. This study aims to identify which children are most vulnerable (the 'Who') and how interventions can be optimally designed (the 'How') to mitigate the adverse effects of parental mental illness. Using a mixed- methods approach, this research will integrate quantitative analysis of large-scale datasets with qualitative interviews to understand CAPMI experiences and intervention effectiveness. The findings will contribute to developing evidence-based strategies for mental health practitioners, policymakers, and educators', ensuring that support is both accessible and impactful. This research is critical to breaking the cycle of mental illness transmission across generations and fostering resilience in children at risk.

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 Keywords

Parental mental illness, children and adolescents with parents with mental illness (CAPMI), intergenerational mental illness, evidence-based interventions, mixed-methods study design, qualitative interviews, quantitative analysis, scalability, mental health support, resilience, psychological development.

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This paper offers a comparative analysis of the short stories of Ruskin Bond and R.K. Narayan, two major Anglophone writers from India whose work--while equally accessible--reveals markedly different literary sensibilities. Using close reading of representative stories (Bond's "The Eyes Are Not Here"/"The Eyes Have It," "The Night Train at Deoli," "The Blue Umbrella," and "Panther's Moon"; Narayan's "An Astrologer's Day," "A Horse and Two Goats," "Engine Trouble," and "Lawley Road"), the paper contrasts their themes, narrative strategies, characterization, humor and irony, treatment of place, and language. It argues that Bond's lyric humanism and nature-centered intimacy create moments of tender melancholy, while Narayan's comic realism and gentle satire construct a moral universe grounded in the rhythms of small-town life. Both develop a distinct Indian idiom in English and celebrate ordinary people, but they diverge in the textures of memory, landscape, and social critique they bring to the short form.

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 Keywords

Ruskin Bond; R.K. Narayan; Indian short story; Malgudi; Himalaya; realism; nostalgia; satire; nature writing; postcolonial literature

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The kinetics of L-threonine oxidation in an acidic medium using tetramethylguanidinium chlorochromate (TMGCC) as an oxidant was systematically investigated. The study explores the impact of various reaction parameters, including substrate concentration, oxidant concentration, acidity, and temperature on the rate of oxidation. A first-order dependence on both L-threonine and TMGCC was observed, while the reaction showed a fractional-order dependence on hydrogen ion concentration. Activation parameters such as the enthalpy, entropy, and the energy of activation were calculated from temperature-dependent data using the Arrhenius equation. A plausible reaction mechanism has been proposed based on experimental results, supported by the formation of intermediate complexes. This study provides comprehensive insights into the oxidation process of L-threonine, which could contribute to a deeper understanding of amino acid oxidation reactions in biological systems.

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 Keywords

L-Threonine, Kinetics, Oxidation, Mechanism

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Feminism, as both a socio-political movement and philosophical framework, has undergone profound transformation since its emergence in the late 19th century. What began as a concentrated demand for women's suffrage has expanded into a broad, multidimensional struggle for gender justice and inclusive human rights. This paper traces the trajectory of feminist thought through its four major waves--each uniquely shaped by historical, cultural, and political conditions. From the early battles for political agency to the digital activism of the fourth wave, feminism has grown more inclusive and intersectional, incorporating diverse voices and identities. This study aims to offer a critical yet accessible overview of each wave's core issues, achievements, and limitations, ultimately emphasizing the need for continued evolution as feminism navigates contemporary challenges such as systemic inequality, digital misogyny, and the politics of identity.

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 Keywords

Feminism, gender equality, first wave, second wave, third wave, fourth wave, intersectionality, digital feminism, feminist movements, feminist theory, gender justice, identity politics.

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Deep neural networks (DNNs) have revolutionized social media by personalizing content feeds, yet their pervasive influence raises significant concerns regarding user behavior and mental well-being. This paper examines how these advanced recommendation systems, including those leveraging Reinforcement Learning (RL) and Large Language Models (LLMs), create feedback loops that can narrow users' informational exposure and foster addictive behaviors, contributing to phenomena like "Brain-rot." Through a comprehensive literature review and the development of a semantically enriched recommendation model, we analyze the intricate mechanisms of DNNs, their psychological and cognitive impacts, and propose design strategies for more balanced algorithms. Findings indicate that while DNNs enhance engagement, their optimization for metrics like watch time can inadvertently lead to increased anxiety, reduced attention spans, and diminished critical thinking. The study highlights the urgent need for ethical algorithm design, advocating for the integration of diversity, transparency, and privacy to create systems that not only engage but also empower and protect users. This research underscores that the future of AI-driven personalization must prioritize human values and well-being over mere engagement maximization.

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 Keywords

Deep Neural Networks(DNNs), Recommendation System, Reinforcement Learning(RL), Large Language Model, Algorithmic Personalization, Brain-rot, Ethical AI, Social Media Addiction.

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 Abstract

In some situations, it is needed to give some time to items in inventory before allowing it for sell and it spends time in the inventory besides the starting time of depleting it from the inventory up to the zero level of inventory. This service time is cost of holding on all received items in the inventory. If we can reduce the service time by increasing service persons or facilities and the total cost goes near to cost of classical EOQ model.

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Inventory system, Demand rate, Service rate, Replenishment rate, Lead time, Queuing system Service time, re-order point.

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The rapid expansion of connected and autonomous vehicles necessitates real-time anomaly detection methods that uphold both data privacy and computational efficiency. Conventional centralized learning systems often require the transfer of raw sensor streams to external cloud infrastructures, introducing latency challenges and potential data exposure. To mitigate these issues, this paper presents a software-defined, federated learning architecture tailored for vehicular edge networks. Our framework leverages a lightweight Convolution Neural Network (CNN) design, optimized for temporal sensor data such as accelerometer and gyroscope readings, enabling each vehicle to conduct localized model training. Instead of sharing raw inputs, nodes transmit securely encrypted model updates, which are aggregated at a central coordinator using a privacy- aware averaging scheme. To further protect individual data characteristics, differential privacy is implemented by injecting calibrated noise into gradient updates, ensuring obfuscation of user- specific behaviour. Experiments conducted on a synthetic vehicular dataset demonstrate a detection accuracy exceeding 96%, alongside a notable 40% reduction in communication overhead. The model also maintains efficient performance under resource-constrained conditions, confirming its suitability for real-time deployment in smart vehicular ecosystems. These results support the framework's potential as a decentralized, privacy-conscious solution for anomaly detection in next-generation transportation systems.

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 Keywords

Federated Learning, Vehicular Edge Networks, Anomaly Detection, Differential Privacy

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 Abstract

Executive function is a psychological term used to analyze a person's cognitive function. This executive function can be identified from a person's behavior and the way they react to a situation. The application of executive function in the right way can improve a person's cognitive skills which can indirectly improve their academic performance. Students in Malaysia are more inclined towards the development of technology where they depend one hundred percent on technology. This makes their cognition untrained and weak. The main objective of this study is to determine the relationship between executive function and cognitive performance of students. This is to identify whether a student's academic achievement is affected by executive function. This study uses a quantitative research design. A survey study was conducted to identify student behavior based on their executive function and academic achievement. The results of the study found prove that, students who have behavior based on executive function, have a high level of academic achievement, this can lead to the conclusion that, the application of behavior based on executive function can provide higher cognitive skills that allow students to achieve high performance in their education. Improvements that can be made in this study by using the action research method where it is more effective and the results that will be obtained will be more accurate. In order to do this kind of research, a longer time allocation needs to be given.

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 Keywords

executive function, cognitive skills, academic performance, academic achievement.

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 Abstract

Safe and secure operations of robotic systems are of paramount importance. Aiming for achieving the trusted operations of a military robotic vehicle under contested environments, we introduce a new cyber-physical system based on the concepts of deeplearning Convolutional Neural Network(CNN).The proposed algorithm is specifically designed to reduce the cyber vulnerability of the Robot Operating System (ROS), a well-known middleware platform, widely used in both civilian and military domains. To demonstrate the efficacy of the proposed algorithm, we conduct penetration testing (real-time man-in-the-middle cyberattacks) on the GVR-BOT ground vehicle, a replicate of a military ground robot, developed by the United States Army Combat Capabilities Development Command(CCDC),Ground Vehicle Systems Center. The cyberattacks also exploit the vulnerability of the Robot Operating System employed on its onboard computer. We collect experimental data and train our CNN based on two different operating conditions, namely, legitimate and malicious. We normalize and convert the network traffic data in the form of RGB or grayscale images. We introduce two different types of windowing techniques, namely, the independent and overlapping sliding epochs to efficiently feed the network traffic data to our CNN system. Our research indicates the efficacy of the proposed algorithm. In addition, we also compare and demonstrate the relative merits of our proposed algorithm with respect to the performance of some well-known techniques, namely, 'bag-of-features' (BoFs) and Support Vector Machine (SVM) algorithms.

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The existing literature on cyber-physical systems, such as unmanned aerial vehicles (UAVs) and ground robots, high- lights several vulnerabilities and limitations in the attainment of strong operational efficiency. Homomorphic encryption research highlights controller-level security at the cost of adaptive, deep-learning-based intrusion detection systems for facilitating dynamic interactions in real-time environments. Likewise, UAV flight control system and mobile robot dy- namics resear

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 Abstract

In contemporary society, where individuals predominantly seek happiness through material wealth, power, and social status, nonetheless, in this constantly changing world, the conditions are subject to the law of impermanence, constantly changing; everything is illusionary because it is subject to the law of existence of Anatta, so no one could promise you a fixture of fulfilment. So, we can affirm that happiness based on matter, which is happiness in the interim in the moments, is just an illusion or a fulfilment that will soon fade away like the early morning mist disappearing at daybreak. So, the ancient Buddhist teachings offer an alternative pathway to genuine well-being. This paper investigates the Mah?satipa??h?na Sutta, one of the most important Suttas in Therav?da Buddhism, which contains many methods for application in modern psychological therapy. Through an investigation of the Four Foundations of Mindfulness, this study demonstrates how these ancient principles can be effectively joined into contemplation-based therapies like a MBSR and MBCT , contributing to enhanced mental health and life's quality. The study shows that this system of mindfulness practice through body awareness, emotional regulation, mental clarity, and the contemplation of mental objects integrates a total approach to address and alleviate problems with modern psychopathology, thus contributing developmentally to holistic health.

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 Keywords

Anatta, Mah?satipa??h?na, mindfulness, psychopathology, well-being, awareness, happiness, mental-health

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Creative Commons Attribution 4.0 and The Open Definition