Volume 14 | Issue 4

 Abstract

This paper examines the historically rooted and socially reproduced traumas experienced by Zainichi Koreans--ethnic Koreans residing in Japan whose presence largely originates from migration during Japan's colonial rule of Korea (1910-1945). Drawing upon historical scholarship, contemporary academic literature, journalistic investigations, and mental-health research, the study argues that Zainichi trauma is embedded in colonial violence and sustained through postwar legal marginalization, structural discrimination, and contemporary hate speech. These traumatic experiences manifest socially and psychologically and are transmitted intergenerationally through narratives of loss, stigma, and survival. The paper analyzes major domains of trauma, including wartime forced labor and atomic bomb survivor exclusion, postwar juridical liminality, discrimination in education and employment, identity erasure, and political scapegoating. It further reviews empirical evidence linking discrimination to adverse mental-health outcomes and highlights community-based coping and resistance strategies. The paper concludes with policy recommendations emphasizing structural redress, historical acknowledgment, and culturally responsive mental-health interventions.

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Trauma, Stigma, Resilience, Zainichi Koreans

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The rapid increase in sophisticated malware poses a significant threat to modern digital systems. Traditional signature-based detection methods are ineffective against zero-day and polymorphic attacks. This paper presents "Shakti Defender AI," an intelligent malware detection system that integrates both static and dynamic analysis using machine learning techniques. The proposed system extracts features from Portable Executable (PE) files, opcode sequences, and real-time behavioral patterns such as API calls, registry activities, and network interactions. These features are processed using an ensemble of machine learning models including XGBoost, LightGBM, Random Forest, and an LSTM-based model for sequence analysis. Experimental results demonstrate high performance with an accuracy of 99.16%, along with strong precision and recall. The system is implemented as a user-friendly desktop application supporting multiple file types with real-time scanning and threat intelligence integration. The results indicate that the hybrid approach significantly improves malware detection capability compared to traditional and single-method approaches, making it suitable for real-world cybersecurity applications.

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Malware Detection, Machine Learning, XGBoost, LightGBM, LSTM, Static Analysis, Dynamic Analysis, Cybersecurity

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INVISIBLE SCARS: EXPLORING GRIEF COUNSELLING FOR CHILDREN AFTER PARENTAL SEPARATION IN INDIA

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INVISIBLE SCARS: EXPLORING GRIEF COUNSELLING FOR CHILDREN AFTER PARENTAL SEPARATION IN INDIA

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Fluoride contamination in groundwater is a major environmental and public health concern in parts of India, especially in Kuttanadu, Kerala. The issue arises from both natural geological sources and the excessive use of phosphate fertilizers. Prolonged exposure to high fluoride levels can lead to serious health problems such as dental and skeletal fluorosis. This study focuses on developing a low-cost and eco-friendly solution for fluoride removal using bio- adsorbents derived from locally available seaweed. Sargassum was selected due to its high availability and the presence of functional groups that enhance adsorption capacity. The raw seaweed was processed through washing, drying, and thermal activation to improve its surface area and porosity. These modifications increase the number of active binding sites, thereby enhancing its effectiveness in removing fluoride from water. Batch adsorption experiments were conducted to evaluate the performance of activated Sargassum in comparison with rice husk ash. Key parameters such as pH, contact time, and initial fluoride concentration were optimized to determine the most effective conditions for fluoride removal. The study is expected to confirm that fluoride adsorption follows pseudo-second-order kinetics and that activated Sargassum shows higher removal efficiency. This research highlights the potential of seaweed-based adsorbents as a sustainable, cost- effective alternative for water treatment, supporting community-level solutions and promoting a circular economy approach.

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 Keywords

Bio-adsorbent, Defluoridation, Sargassum, Groundwater, Kuttanad, Thermal activation, Water treatment, Adsorption kinetics, Sustainable materials, Fluoride removal efficiency

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This study evaluated the effectiveness of an explicit teaching intervention on the basic addition and subtraction performance of grade two students with mathematics learning difficulties in Dekem Primary Schools, Oromia Ethiopia. A quasi-experimental pretest-post-test control group design was employed with 90 students identified through low Basic Numerical and Calculation Ability scores. The 20-week intervention implemented a structured explicit teaching grounded in the Concert-Representation-Abstract instructional sequence. Achievement was assessed through standardized pretest and posttest measures of addition and subtraction. Findings showed significant gains for the intervention group, with mean improvement of 5.6 points in addition and 5.5 points in subtraction compared with control group, indicating a subtraction effect of explicit teaching intervention on basic arithmetic skills. The results support the efficacy of systemic, scaffolded teaching methods for students experiencing mathematics learning difficulties. The study recommends incorporating explicit teaching approaches into routine mathematics teaching and strengthening teacher professional development focused on evidence-based practice. Further investigation of context-responsive interventions that reflect the socio-cultural realities of Ethiopian classrooms also advised. Such efforts have the potential to enhance core mathematical skills and contribute to improved academic outcomes for students with mathematics learning difficulties.

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Concert-Representational-Abstract (CRA) framework, basic arithmetic skills, explicit teaching intervention, mathematics learning difficulties.

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The rapid depletion of global freshwater resources, coupled with the increasing demands of a growing population, necessitates a paradigm shift in agricultural practices. Traditional irrigation methods are notoriously inefficient, often leading to overwatering, soil degradation, and massive water wastage. This paper proposes a comprehensive Smart Irrigation System leveraging the Internet of Things (IoT) and Machine Learning (ML) algorithms to optimize water usage in precision agriculture. The proposed architecture integrates a network of low-cost sensors to monitor real-time environmental parameters, including soil moisture, temperature, humidity, and pH levels. Data collected by the sensor nodes is transmitted via wireless communication modules to a centralized cloud platform for processing. To enhance decision-making beyond simple threshold-based logic, the system employs predictive modeling using algorithms such as Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN). The integration of ML allows the system to forecast irrigation needs based on historical data and weather predictions, achieving predictive accuracies exceeding 95%. Simulated results and real-world deployment scenarios demonstrate that the proposed system can reduce water consumption by up to 40% compared to traditional timer-based systems, while simultaneously improving crop yield. Furthermore, this paper addresses current deployment challenges such as scalability, connectivity, and implementation costs, and explores future trends including 5G integration, Edge Computing, and Artificial Intelligence of Things (AIoT).

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 Keywords

- Internet of Things (IoT), Smart Irrigation, Precision Agriculture, Machine Learning, Wireless Sensor Networks (WSN), Water Conservation, Cloud Computing.

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The Mahabharata, which is the greatest epic of India shows women's complex and layered representation. In this epic woman are not only secondary character, but authority, political influence, family duty and narrative transformation which works as an important agent. But the epic has greatest focus on male heroes and dynastic conflicts, still women's. story events express ethical dilemmas, and played crucial role to guide philosophical role. Mahabharata, written by Vyasa, and further translated in different versions by various translators is one of the ancient epic narratives which offers the theme of complicated gender roles introducing female characters such as Kunti, the mother of the Pandavas; Gandhari, the mother of Kauravas and Draupadi, the wife of the Pandavas. There are other female characters as well that embodies the ideals of devotion and its balance with sacrifice within the structure of patriarchy. Although the text emphasizes on the male heroes and their dominance, this epic also portrays the autonomy of the female characters.

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The role of Women in Mahabharata

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Ayurveda considers Dhatus as the fundamental structural (Dharana) and functional (Poshana) units of the body. Dhatu Sara represents tissue excellence and reflects strength and vitality. Varna means the complexion of the skin, indicates internal health. As per Ayurveda Dhatus are very important which performs the function of Dharana and Poshana. Dhatu sara means excellence of body tissue which is the essence of Dhatu .It also indicates Bala of an individual & Varna means the complexion of the skin that is Twak. The complexion of a person is determined by the dominant of Dhatu sarata .It determines whether the Twak is lustrous or not reddish or not etc. Bala is considered as a complexion promoter. This article aims to give information about interrelationship of Varna & Dhatu sarata in a person as per Ayurveda.

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Dhatu sarata, Varna, Twak, Dhatu, Bala & Varna

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This document focuses on the Supply Chain Dashboard. It is a system that assists small and medium enterprises in managing their supply chains. The traditional method is insufficient. Those methods can lead to errors and misconceptions. So this project named Supply Chain Dashboard is made to address those issues so that the small and medium size enterprises can have transparency in their work ensuring smooth flow of goods and product. The primary element of the supply chain dashboard is AI Analytics Module. The Python Backend utilizing FastAPI manages essential functions and drives the AI Analytics Module. Our whole data is stored in PostgreSQL. Also we have used the Typescript for seamless operations on the supply chain activities.

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 Keywords

Supply Chain Management (SCM), AI-Powered Dashboard, Predictive Analytics, Demand Forecasting, Inventory Optimization, Real-Time Monitoring, IoT, FastAPI, PostgreSQL, React.

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This paper presents the design and implementation of a Smart Bike Safety System that enhances rider protection through automation and real-time monitoring. The system integrates multiple sensors and embedded controllers to verify helmet usage, detect alcohol levels, and control engine ignition based on safety conditions. Two independent modules, placed in the helmet and the bike, communicate wirelessly using the ESP-NOW protocol to ensure reliable and low- latency data exchange. Motion and impact sensors continuously analyze riding conditions to identify potential accidents. Upon detecting a crash, the system initiates an intelligent alert mechanism that seeks rider confirmation and, if necessary, automatically sends emergency notifications through SMS and phone calls using a GSM module. A web-based interface further enables live tracking and system supervision. By combining prevention, detection, and rapid communication features, the proposed system offers an efficient, low-cost, and practical solution to improve two- wheeler safety and emergency response.

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ESP-NOW Communication, Wireless Sensor Network, Accident Detection, Motion and Impact Sensors, Engine Ignition Control, Emergency Alert System, GSM Module

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 Abstract

To define culture is to chuck out a piece of its poly-semic meanings, as not only culture means differently for different people, how and to what extent people participate in this space differs accordingly. But our preoccupation here is to do two things- one, to deal with how this space gets defined in its essentialist domain, that is, how a culture creates and negotiates an identify for itself with which it chooses to define itself, for example the notion of "Englishness" as elucidated by Talal Asad, but the argument can go much deeper. Secondly, how can we study the clairvoyance that is ejected in the process of meaning formation to this identity, through which this identity is given a theoretical and conceptual and ethical validation as well as a pre-history, which is then inserted albeit anachronistically into the modern definitions of this identity. Thirdly, cultural spaces don't always correspond to physical spaces and they have the capability to transcend those spaces, though there are ways in which cultural spaces interact with these physical spaces, in determining how people who are not believed to have belonged to this space historically or culturally, enter and negotiate this space. The meaning formation in this space is layered in relations of power and hierarchy, though not necessarily hegemonic, as the language of the meaning formation in itself provides particular space, particular way to enter that cultural space and their position within the cultural space that they need to respect.

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Anthropology, Cultural Spaces, Language, Negotiation

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Maternal health plays an essential role in the field of medicine because both the mother and the infant are affected. Early identification of any possible risks associated with pregnancy can help minimize the chances of adverse consequences. In this respect, this paper provides a description of pregnancy risk and fetal health care system that can predict potential problems during pregnancy by applying machine learning. This system obtains users' input information, such as their age, blood pressure, sugar levels, temperature, and other data about their health condition. Machine learning enables the system to determine whether there is a low, medium, or high probability of the risks associated with the patient's pregnancy. This project combines various technologies like artificial intelligence, data analytics, and others in order to improve decision-making.

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Maternal Health, Machine Learning, Risk Prediction, AI, Healthcare System, Pregnancy Monitoring, Data Analysis

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of digital commerce has placed product recommendation systems at the center of the modern e-commerce experience. Consumers today navigate catalogs con-taining millions of items, and the ability of a platform to surface the right product at the right moment has become a direct driver of engagement and revenue. Despite their commercial im-portance, most deployed recommendation systems rely on static models trained once on historical data, utilize only numerical ratings while ignoring the qualitative information embedded in textual reviews, and lack any mechanism to adapt as user preferences shift over time.The rapid growth of digital commerce has placed product recommendation systems at the center of the modern e-commerce experience. Consumers today navigate catalogs con-taining millions of items, and the ability of a platform to surface the right product at the right moment has become a direct driver of engagement and revenue. Despite their commercial im-portance, most deployed recommendation systems rely on static models trained once on historical data, utilize only numerical ratings while ignoring the qualitative information embedded in textual reviews, and lack any mechanism to adapt as user preferences shift over time.

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Recommendation System, Collaborative Filter-ing, Sentiment Analysis, TF-IDF, XGBoost, Adaptive Retraining, NLP, Hybrid AI, Machine Learning, Dataset Versioning, MLOps, E-Commerce

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The sustainable management of agro-industrial waste has become a major global concern, necessitating the development of innovative valorization strategies. Dragon fruit peel, a significant by-product of Hylocereus spp., constitutes approximately one-third of the total fruit mass and is an abundant source of bioactive compounds including betalains, phenolics, flavonoids, and polysaccharides. The present review provides an extensive evaluation of the potential of dragon fruit peel as a raw material for the extraction of natural pigments and polymers and their application in the formulation of a probiotic-enriched anti-tan hydrogel. Betalains demonstrate remarkable antioxidant, anti-inflammatory, and photoprotective activities, while pectin serves as a biodegradable and biocompatible polymer capable of forming stable hydrogel networks. The integration of probiotics such as Lactobacillus fermentum and Lactobacillus parabuchneri enhances the functional efficacy of the formulation by modulating the skin microbiome and improving barrier function. The synergistic interaction of these components contributes to effective tyrosinase inhibition, mitigation of oxidative stress, and protection against UV-induced skin damage. This review also explores advanced extraction techniques, nano-encapsulation strategies, rheological characterization, and industrial scalability, highlighting the role of fruit waste valorization in sustainable cosmetic innovation.

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Valorization of Dragon Fruit Peel Waste: A Review on Pigment and Polymer Extraction for Probiotic-Based Anti-Tan Hydrogel Applications

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 Abstract

Distributed Denial of Service (DDoS) attacks continue to pose a serious threat to the availability and reliability of modern networked systems by overwhelming target resources with malicious traffic. With the increasing dependence on cloud services, e-commerce platforms, and online applications, the need for efficient real-time detection and mitigation mechanisms has become critical. This paper presents a real-time DDoS detection and mitigation framework based on packet-level traffic analysis using WinDivert and Python. The proposed system captures live network packets, extracts traffic features such as source IP activity, packet rate, and protocol distribution, and applies threshold-based anomaly detection to identify malicious behavior. Upon detection, the framework performs immediate mitigation by dropping suspicious packets and dynamically blocking malicious IP addresses. A real-time monitoring dashboard is also integrated to visualize traffic flow, incident logs, and mitigation actions for improved situational awareness. Experimental evaluation demonstrates that the proposed system achieves rapid detection, low response latency, and effective mitigation of high-volume DDoS attacks while maintaining minimal impact on legitimate traffic. The framework provides a lightweight, cost-effective, and practical solution for enhancing cybersecurity in real-world environments.

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Distributed Denial of Service (DDoS) attacks continue to pose a serious threat to the availability and reliability of modern networked systems by overwhelming target resources with malicious traffic. With the increasing dependence on cloud services, e-commerce platforms, and online applications, the need for efficient real-time detection and mitigation mechanisms has become critical. This paper presents a real-time DDoS detection and mitigation framework based on packet-level traffic analysis us

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Phishing websites pose a serious threat to online security by deceiving users into revealing sensitive information such as login credentials and financial data. Traditionalblacklist-baseddetectiontechniquesareoftenineffectiveagainstnewly created phishing sites, highlighting the need for intelligent, data-driven solutions. This work presents a phishing website detection system using the XGBoost (Extreme Gradient Boosting) algorithm, a powerful ensemble learning method known for itshigh accuracy and efficiency.The proposed model analyzes multiple website features, including URLcharacteristics, domain information, and content- based attributes, to distinguish phishing websites from legitimate ones.After preprocessing and feature selection, the XGBoost classifier is trained and evaluated on a labeled dataset of phishing and benign websites. Experimental resultsdemonstratethattheproposedapproachachieveshighdetectionaccuracy with low false-positive rates, outperforming several traditional machine learning classifiers.The findings indicate that XGBoost is a robust and effective technique for real-time phishing website detection, contributing to improved cybersecurity and user protection.

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User Management Module URL Submission Module DataPreprocessingModule Feature Extraction Module Feature Selection Module XGBoost Classification Module ResultDisplay/NotificationModule

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Infections such as septic arthritis due to pathogens in the joint or maybe even in the body of the patient would cause an immediately associated emergency condition associated with that effusion. Septic arthritis is inflammation of the joint and severe pain in the joints; it threatens joint survival, requiring treatment. Triggers include infections by bacteria, fungi, or viruses, and among them is Staphylococcus aureus, the most common pathogen. Mainly affected are adults and geriatrics who tend to suffer greatly with comorbidities such as diabetes, immunosuppression, trauma to a joint, and having prosthetic joints. All of these have an age basis in the elderly common group. The main pathogens found to infect the younger individuals under 3 years include Kingella-kingae and Neisseria gonorrhoeae. Signs or symptoms of septic arthritis include pain at the sites of the joints, swelling, fever, and loss of function in joints at the later, knee, hip, and shoulder joints. It involves early detection through tests on synovial fluid, blood cultures, and imaging studies for diagnosis. Then, the treatment entails antimicrobial therapy, joint aspiration, and, on occasions, surgery for drainage of the joint or, if indicated, for removal of the prosthesis. Early commencement of process of antibiotic drug therapy, along with physiotherapy, greatly reduces the incidence of complications like destruction of joints or systemic infection. The advent of new mechanisms for diagnosis and therapy has been a great revolution in both prognosis and outcome in patients.

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Septic arthritis, joint infection, Staphylococcus aureus, synovial fluid, diagnosis, antimicrobial therapy, risk factors, imaging, treatment, joint aspiration.

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This research aims to determine the effect of Trichoderma viride inoculation on the development, cocoon formation, and biodegradation capacity of the epigeic earthworm Eisenia fetida when undergoing vermicomposting of organic waste materials. A total of treatment combinations was The whole procedure was conducted in 150 days with samples being collected at periodic intervals (0, 45, 80, 120 and 150 days). Parameters like physicochemical properties such as pH, OC, TN, available phosphorus (P), K and C:N ratio were considered. Analysis of results revealed that inoculation with T. viride increased the rate of decomposition, decreased organic carbon content up to 75% and enhanced nutrient contents. Biological parameters like biomass production and cocoon formation in E. fetida were also improved in inoculated treatments. It is evident that the use of microbial inoculants with T. viride improves the efficiency of vermicomposting and hence, can be considered as a sustainable solution for organic waste management.formulated using various types of organic materials with or without T. viride inoculation.

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Trichoderma viride, Eisenia fetida, vermicomposting, cocoon production, biodegradation

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This study is based on secondary data. The data for the study have been obtained from the reports of the DAY-NRLM website. Data were compiled from the compiled report of all MPR indicator reports for the year 2024-25. The main objective of this study is to examine the outreach of the Community Investment Fund (CIF) support to Self-Help Groups (SHGs), with a focus on financial inclusion and livelihood promotion. The findings show that a large number of SHGs have achieved operational maturity, with over 3.7 million groups having been operational for more than six months. Out of these groups, 1.23 million SHGs have received CIF of INR618,809.65 lakh. The distribution of CIF has seen greater coverage among socially disadvantaged groups such as SCs and STs. The study further found that SHG interventions have positively impacted over 4.6 million households through livelihood support.

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SHG Federations, VO, CLF, CIF

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This study inspect the impact of store keeping management on consumer satisfaction in the organized retail store. Inventory control, stock management, storage system are important part of effective store keeping practices. All of these play a significant role in improving customer experience. This research emphasizes on how efficiently store operation helps to reduce stock out situations improve service quality and increase consumer loyalty. Primary data were collected from customer of selected retail stores using a structured questionnaire method. Mean, standard deviation, and correlation are used to examine relationship between store keeping practices and consumer satisfaction levels. The finding of the study indicate that proper inventory management, systematic storage of stock positively influence customer satisfaction and purchasing behavior of consumers. This study concludes that proper store keeping management and timely replenishment of stock is an important factor for improving service quality in the retail sector.

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Storekeeping management, Consumer satisfaction, organized retail, Inventory control, Retail management

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Creative Commons Attribution 4.0 and The Open Definition