Volume 13 | Issue 8 |

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

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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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

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

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The spontaneous combustion of coal presents serious dangers in mining operations, resulting in loss of life, financial setbacks, and harm to the environment. This systematic review assesses the efficacy of different technologies designed to prevent and manage spontaneous combustion, concentrating on research published in Fuel, Energy, and Applied Energy from 2000 to 2025. A comprehensive analysis was conducted on 29 articles, focusing on various technologies including gels, gel foams, inhibitors, inert gas injection, and aqueous solutions. Notable findings reveal that gels, especially those enhanced with polymers such as konjac glucomannan (KGM) and expandable graphite (EG), demonstrate enhanced thermal stability and effective oxygen barrier properties. Gel foams, such as gel-stabilized and three-phase varieties, exhibit superior fire suppression capabilities due to their enhanced film-forming properties and water retention. However, challenges with stability persist. Methods involving inhibitors and gas injection demonstrate potential, particularly as the depth of nitrogen injection plays a crucial role in regulating temperature within coal gobs. The review emphasizes progress in material science, showcasing polymer-modified gels and high-water-retaining foams as the leading solutions. Nonetheless, the practical use and enduring reliability necessitate additional exploration. This research brings together current insights, highlights areas needing further exploration, and suggests directions for upcoming studies aimed at enhancing technologies for preventing spontaneous combustion in coal mining

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 Keywords

Spontaneous combustion, coal mining, fire prevention, gel technology, foam suppression, meta-analysis

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Coal continues to be the most essential fossil fuel resource globally, representing 94% of reserves, while oil and natural gas make up just 6%, guaranteeing its steady industrial demand and utilization. While coal plays a significant role in the global energy landscape, it is also a key factor in the occurrence of spontaneous combustion within the mining sector, which can result in the ignition of fires in coal mines. Fires in coal mines present significant dangers to those working underground, and these dangers have a direct influence on both the global economy and the environment. The combustion processes emit harmful gases, including carbon monoxide, hydrogen sulfide, methane, carbon dioxide, sulfur dioxide, nitrogen oxides, and fine particulate matter, along with various by-products. This results in significant health risks, such as pneumoconiosis, alongside detrimental effects on the environment and human distress caused by heat, land subsidence, and pollution. Spontaneous coal combustion (SCC) serves as the main trigger for coalmine fires, and tackling this issue can greatly diminish the occurrence of underground fires. Prioritizing prevention is crucial when addressing coalmine fires. Utilizing methods such as index gas monitoring, gravity heat pipes, oxygen inhibitors, gel suppressants, grouting, and foaming gels can significantly reduce the impact of SCC and coalmine fires. Moreover, significant attention must be directed towards the mechanical, electrical, natural, and human-induced factors contributing to mine fires in the effort to combat these blazes. Creating affordable techniques like foam, liquid nitrogen, and innovative fire-fighting technologies (such as the three-phase foam initiative) is essential for the prevention, detection, and extinguishing of difficult-to-find underground fires. Enhancing safety laws and creating an autonomous monitoring system for coalmine safety will bolster enforcement efforts. Effective implementation of ongoing safety training, thorough evaluations, and heightened awareness can significantly decrease the occurrence of coalmine fires when managed correctly from an administrative standpoint.

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 Keywords

Coalmine fires; Spontaneous coal combustion; Fire prevention; Methane explosion; Particulate matter

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This study explores the effectiveness of Personal Protective Equipment (PPE) in enhancing workplace safety and minimizing occupational hazards in the industrial sector. Despite the critical importance of PPE, data from sand and gravel mining sites reveal inconsistent utilization among workers, leading to a higher incidence of workplace accidents. This study employed a systematic review approach, where data were searched through various databases, including Scopus, PubMed, ScienceDirect, Web of Science, and Google Scholar. The methodological quality assessment of the studies was analysed for quality appraisal. Out of 900 found references, only 9 studies met all the criteria according to the STROBE checklist. The discussion highlights the diversity of PPE types, including advanced innovations such as smart helmets and respirators, which incorporate cutting-edge technologies to monitor environmental conditions, enhance communication, and ensure user safety. For instance, smart helmets, equipped with sensors, au

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PPEs, workplace safety, smart helmets, respiratory protection, protective clothing, occupational hazards

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