Volume 14 | Issue 4 |

 Abstract

Natural fiber reinforced polymer composites have been the subject of much research due to the growing need for environmentally friendly and sustainable products. Elettaria cardamomum leaf midrib (ECLM) fibers were isolated and used as reinforcement in polyester matrix composites in this work. To improve the interfacial adhesion between the fibers and matrix, the fibers were treated with alkali. Compression molding was used after the hand lay-up method to create composites with different fiber weight fractions. The mechanical characteristics, including hardness, tensile strength, flexural strength, and impact strength, were assessed in accordance with ASTM guidelines.

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 Keywords

Elettaria cardamomum fiber, Natural fiber reinforced composites, Polyester resin, Mechanical properties, Morphological analysis

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 Abstract

Predicting student academic performance is a growing area of interest in educational research. Traditional evaluation methods assess students only after examinations, which prevents early identification of at-risk learners. This paper proposes a simple yet effective machine learning-based prediction system that uses five basic student attributes attendance percentage, daily study hours, marks in previous examinations, assignment submission rate, and class participation score to predict whether a student will Pass, perform Averagely, or Fail in their upcoming examination. Three widely-used machine learning algorithms, namely Linear Regression, Decision Tree, and Random Forest, are trained and compared using a dataset of 200 students. Experimental results show that the Random Forest model achieves the highest prediction accuracy of 91%, followed by Decision Tree at 85% and Linear Regression at 78%. The proposed system requires no advanced infrastructure and is practical for adoption in any educational institution to enable timely academic intervention for struggling students.

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 Keywords

Machine Learning, Student Performance Prediction, Decision Tree, Random Forest, Linear Regression, Educational Data Mining, Academic Analytics, Classification.

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 Abstract

Increasing intensity of environmental issues like climate change, degradation of biodiversity, and depletion of resources underscores the necessity of good environmental education systems. Nonetheless, most legacy and the majority of new digital learning tools still use the same traditional models of delivering static content that cannot support user engagement or adapt to a wide variety of learning behaviors. This weakness leads to less retention of knowledge and little effect on behavior in the long term. In this study, a complete EcoQuest platform, EcoQuest, a full-stack, gamified environmental education system, is proposed to overcome these limitations by integrating adaptive learning, real-time interaction, and intelligent assistance. The system is designed to provide individualized learning by dynamically changing the difficulty of questions according to their individual user performance metrics, thus making sure that learners are challenged and motivated throughout their progress. EcoQuest requires a systematic gamification system, comprising experience points (XP), level advancement, achievement badges, streak monitoring, and leaderboards. These aspects will support the regular participation and encourage the further involvement. Moreover, the platform allows real-time multiplayer quizzes with WebSocket-based communication, allowing collaborative and competitive learning experiences, which also increase the engagement of the users. Technically, the system is deployed with a modular design based on a React-powered frontend, a Node.js/Express backend, and a MongoDB database that can store and retrieve data in a scalable way. The use of Socket.io allows real-time communication, and the assistant (EcoBot) is an AI-based one that will use context-dependent answers within environmental-related issues only, which will make it relevant and domain-specific. The design decisions determine performance-oriented, such as caching and indexing of the database, which play a role in efficient functioning of the system and decreased latency. Functional assessment states that the platform has stable real-time synchronization, data processing efficiency, and responsiveness to user interaction in normal usage conditions. The findings prove that the combination of gamification, adaptive learning practices, and AI-guided support can greatly improve engagement and learning outcomes within environmental education platforms. EcoQuest sets a realistic template of building scalable, interactive, and user-focused learning platforms in other related areas.

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 Keywords

Gamified Learning, Adaptive Learning System, Environmental Education, Real-Time Multiplayer Learning, AI-Based Chatbot, Full-Stack Web Application, WebSocket Communication, MongoDB, Role-Based Access Control

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The India-China relation holds a unique position in the world, as cooperation and confrontation are the primary bases of their relationship. The present study focuses on the India-China relationship during the NDA period (2014-2019), under the leadership of Indian Prime Minister Narendra Modi. The objective of the paper is to examine whether the NDA era has been able to bring any changes in India's China policy, or has it merely maintained continuity. Based on the secondary sources, the paper has adopted a qualitative research method and case study approach. The study reveals that this phase portrays a measure adjustment period rather than a total rupture from the past policy. The Indian government has adopted a more assertive approach in its foreign policy during that time. This phase can be marked by a pattern of competitive coexistence.

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 Keywords

NDA Government, Strategic Competition, National interest, economic interdependence, India-China relations, Indian foreign policy, Chinese foreign policy

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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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 Keywords

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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 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition