International Journal of Research in Engineering, Science and Management
Call for Papers Publish in Multidisciplinary Journal Volume 9, Issue 2, February 2026 7000+ Articles | 10,000+ Authors | 30+ Countries Publication Process Submit Manuscript ISSN (Online): 2581-5792 Publication Frequency: Monthly Subject/Scope: Multidisciplinary Areas Publication Format: Online CrossRef DOI is assigned to all published articles Free e-certificates for all authors Fast Publication: Within 3 Days Article Publication Fee (APC): $52 (Africa), $59 (Others), ₹2000 (India) International Journal of Research in Engineering, Science and Management, is an open-access International Journal for Paper Publication. To publish a paper, the authors can submit research papers, case studies, survey papers, academic project works, scholarly articles, academic articles, original or extended versions of previously published papers in conferences, and basic advances from all the areas of academic disciplines. With a broad scope, the journal is focused on multidisciplinary research, from the viewpoints in the advancement of research. The journal welcomes articles from all the academic disciplines. The journal aims to provide a platform for authors to publish research work online. Students at graduate and post-graduate levels are welcome to publish project papers. Academicians, and researchers, who carry out research can submit and publish research work in the IJRESM journal. The authors who wish to publish a paper in IJRESM, are advised to read the publication process and submit a paper for publication.
Impact of Learners’ Psychosocial Attributes and Teachers’ Instructional Characteristics on Students Performance in Mathematics
This study examined the relationship and impact of psychosocial attributes and instructional characteristics on the mathematics performance of Grade 9 students in six public secondary and integrated schools in New Bataan District, Davao de Oro, for the school year 2025–2026. Using a quantitative descriptive-correlational design, 166 respondents completed two adapted questionnaires measuring psychosocial attributes (academic self-efficacy, academic motivation, locus of control, mathematics anxiety, and peer influence) and instructional characteristics (knowledge of mathematics content, communication ability, use of appropriate teaching strategies, and classroom management skills). Data were analyzed using mean, standard deviation, Pearson r, and multiple regression. Results showed that both psychosocial attributes and instructional characteristics were often observed, indicating moderate student engagement and satisfactory teaching practices. Students’ mathematics performance was nearly proficient, suggesting that adequate teaching and classroom conditions alone did not ensure full proficiency. No significant relationship was found between overall psychosocial attributes or instructional characteristics and mathematics performance; however, academic self-efficacy emerged as the only significant predictor. These findings implied that strengthening students’ confidence in their abilities was more critical to improving performance than teaching methods alone, highlighting the need for interventions such as motivational activities, peer tutoring, and confidence-building strategies.
Copyright (c) 2026 Marry Cris Q. Tumimang, Romulo G. Doronio https://creativecommons.org/licenses/by/4.0
The project aims to revolutionize package sorting in local non-metropolitan post offices by introducing an Automatic Package Segregation Machine (APSM) that utilizes image processing technology. Traditional sorting methods are labor-intensive and time-consuming, leading to inefficiencies in package handling. The APSM offers a user-friendly solution by automatically sorting packages based on addresses printed on them, thereby reducing manual effort and time consumption. Unlike bulky machines used in metropolitan cities, the APSM is designed to be space-efficient, making it suitable for regional non-metropolitan post offices. Additionally, a web-based platform will be developed to monitor sorting in real-time and maintain a database of sorted packages, providing enhanced efficiency and transparency in postal operations. Through the implementation of the APSM and accompanying web platform, the project aims to streamline package sorting processes, enhance operational efficiency, and modernize postal services in small towns and cities where alternative sorting methods are limited.
Copyright (c) 2026 Satyam Tiwari, Rudra Talekar, Pranav Raina, Najib Ghatte https://creativecommons.org/licenses/by/4.0
WaveSplit: A Multi-Stage Framework for Audio Enhancement and Audio Denoising – Combining Deep Learning with Psychoacoustic Principles and Adaptive Noise Processing
In the field of audio processing, noise interference poses a significant challenge, affecting speech intelligibility and communication quality across multiple domains. Current audio denoising methods often struggle with the delicate balance be- tween noise removal and speech preservation. This paper presents WaveSplit, a novel multi-stage framework for audio enhancement and denoising that addresses these limitations by combining deep learning techniques with psychoacoustic principles and adaptive noise processing. Building upon the CleanUNet architecture, our approach introduces several innovative components: adaptive SNR-based processing, harmonic enhancement that preserves critical speech components, vocal clarity enhancement, and perceptual processing leveraging human hearing characteristics. Evaluations demonstrate that our framework achieves superior performance compared to baseline models, with significant improvements in SNR (76.36 dB compared to 7.20-8.10 dB in baseline models), PESQ scores (1.05 improvement versus 0.77- 0.91), and STOI metrics (0.15 versus 0.09-0.13) while reducing the “robotic” artifacts common in traditional methods. This research has significant implications for applications including telecommunications, hearing assistive technologies, content production, and speech recognition systems. By addressing both objective quality metrics and perceptual factors, WaveSplit represents an advancement toward more effective, natural-sounding audio enhancement solutions for real-world environments.
Copyright (c) 2026 Parth Gandhi, Kevin Doshi, Anand Godbole https://creativecommons.org/licenses/by/4.0
IoT Driven Battery Health Monitoring Framework for ICE Two Wheelers with WhatsApp Based User Interaction
India’s two-wheeler ecosystem remains heavily dependent on Internal Combustion Engine (ICE) vehicles. Unlike electric vehicles, ICE batteries operate without intelligent monitoring systems, leaving users unaware of battery condition until failure occurs. This research presents a conceptual Internet of Things (IoT) framework designed to introduce real-time battery health visibility to ICE two-wheelers using affordable electronic components and cloud connectivity. The proposed system measures voltage, current, and temperature parameters through sensors connected to an ESP32 microcontroller. Battery data is transmitted to cloud servers and communicated to riders through WhatsApp Business API in the form of alerts and maintenance reminders. In addition to the technical framework, this study introduces an integrated business model that focuses on delivery riders, motorcycle enthusiasts, and garage mechanics. Primary insights were obtained through field interactions with mechanics and battery retailers in West Bengal. The framework demonstrates how low-cost IoT infrastructure can reduce unexpected battery failures while strengthening relationships between riders, service providers, and manufacturers. This study offers a practical approach for modernizing conventional ICE battery systems in emerging mobility markets.
Copyright (c) 2026 Moubani Mukherjee https://creativecommons.org/licenses/by/4.0
Stock Market Prediction and Education Platform Using Machine Learning
The ability to predict stock market trends accurately is highly valuable for investors and traders. This paper presents an integrated machine learning-based system that predicts stock prices using historical data and news sentiment analysis. The platform leverages techniques like sentiment analysis, Named Entity Recognition (NER), and deep learning models such as Bidirectional Long Short-Term Memory (Bi-LSTM). Along with predictions, the platform offers educational resources to enhance financial literacy, making it an ideal tool for novice investors seeking both knowledge and actionable market insights.
Copyright (c) 2026 Kunal Bhatia, Siddhesh Kirdat, Abhimanyu Kapoor, Anuj Tawari https://creativecommons.org/licenses/by/4.0
Disruption of Small World Properties in Functional Brain Networks of Alzheimer’s Disease
Alzheimer’s disease (AD) is a progressive neuro degenerative disorder characterized by cognitive decline and wide spread alterations in brain connectivity. In recent years, graph theory has emerged as a powerful framework for modeling functional brain networks and quantifying their topological properties. In this study we investigate disruptions in small world organizations of functional brain networks in Alzheimer’s disease using graph theoretic measures. Functional connectivity networks are constructed from resting state brain regions and edges denote pairwise functional interactions. Key network metrics including clustering coefficient, characteristic path length and global efficiency are computed and compared between Alzheimer’s patients and health control subjects. The results reveal a significant reduction in local clustering increased path length and decreased global efficiency in AD networks indicating impaired balance between functional segregation and integration. Furthermore, small worldness analysis demonstrates a clear breakdown of optimal network organization in Alzheimer’s disease. These findings provide quantitative evidence of functional brain network disorganization in AD and highlight graph theoretic measures as potential biomarkers for neuro degenerative disorders.
Copyright (c) 2026 Renuka Lakshmi Avvari https://creativecommons.org/licenses/by/4.0
Managing Strategic Uncertainty in Emerging Economies: A 4A Framework for Entrepreneurial Strategy in BRICS SMEs
Small and medium-sized enterprises (SMEs) operating in emerging economies face persistent and multidimensional strategic uncertainty arising from regulatory volatility, institutional heterogeneity, market turbulence, and chronic resource constraints [6], [7], [25]. In BRICS economies - Brazil, Russia, India, China, and South Africa these uncertainties fundamentally shape entrepreneurial decision-making, strategic behavior, and firm survival. While prior research has examined entrepreneurial orientation, innovation capability, dynamic capabilities, organizational agility, and firm performance in emerging markets [1], [2], [4], [15], the literature remains fragmented and conceptually siloed. Most studies examine isolated strategic dimensions without integrating how strategic intent is formed, how actions are executed, how learning and adaptation occur, and how outcomes are evaluated over time. This paper develops a comprehensive 4A framework - Aim, Action, Adaptation, and Accountability to synthesize and systematize the literature on entrepreneurial strategy under uncertainty in BRICS SMEs. Drawing on strategic management, entrepreneurship, dynamic capabilities, organizational learning, and performance measurement research, the study conceptualizes strategy as an iterative, learning-based process rather than a linear planning exercise [1], [2], [10]. The framework emphasizes feedback loops, capability alignment, and strategic renewal as central mechanisms through which SMEs navigate uncertainty in resource-constrained environments. The paper contributes theoretically by integrating dispersed research streams into a coherent process-oriented framework tailored to emerging economies and contributes practically by offering entrepreneurs and policymakers a clear roadmap for managing uncertainty using simple, actionable mechanisms. The study also discusses limitations and outlines a detailed agenda for future empirical research across BRICS contexts.
Copyright (c) 2026 A. Kapit, Neeraj Kumar Kushwaha https://creativecommons.org/licenses/by/4.0
Advanced Camouflaged Target Detection with Quadrapod Drone – A Fusion of Air and Land Technology
In order to identify camouflaged objects in challenging situations, we describe a unique system that combines air and land technologies. The platform, known as a quadrapod drone, combines quadrapod flying with legs to conduct intimate inspection at ground level as well as aerial environment. The drone is about being able to fly and walk; it’s about using both abilities to get best possible view of the environments. This fusion of locomotion modes enables the systems to take position itself for optimal target detection in the environments. Data from the sensors are processed by enhanced YOLOv8 and deep learning framework, which has been gets for the challenges of Camouflaged Object Detection. Our detection process combines a hierarchical sensor-fusion module for air- ground collaboration, multiple imaging, and a deep camouflaged object detection backbone. Using a bespoke dataset, we assess the method in mixed-terrain settings and demonstrate enhanced detection recall. The benefits of using an air-land hybrid to the object detection problem are demonstrated by an experimental evaluation, perceptual algorithms, hardware design, and data-fusion approach. The paper presents complete system architecture, details the camouflaged detection framework and outlines a methodology for its future validation. A robot like this has the potential to make real difference in the world, whether it’s making military missions safer, helping find survivors after a disaster or allowing scientists to study hard to find animals in the wild.
Copyright (c) 2026 Ashish A. Desai, Shravan R. Neje, Shridhar V. Sutar, Alankar U. Patil, Prathamesh B. Tambekar https://creativecommons.org/licenses/by/4.0
Modeling and Virtual Prototyping of a Human Mannequin Equipped with a Multifunctional Solar Backpack
This paper aims to model a 3D mannequin wearing a novel multifunctional electronic-textile backpack. To achieve this goal, a human mannequin is initially created using MakeHuman software involving innovative anthropometric databases. Then, the multi-propose virtual 3D backpack is equipped a mini solar panel, a battery charging circuit, a battery, two microcontrollers (Arduino Uno and ESP32 WROOM-32), DC voltage regulator modules (XL6009 IC), geolocation devices, weight controller sensor, MPU6050 sensor, a testing Smartphone with software application developed via Thunkable. etc. Finally, the overall platform is implemented and well visualized under Blender software, as an innovative virtual 3D studio for multifunctional solar Backpack.
Copyright (c) 2026 Ninon Rosine Nkoulou Nkoulou, Jean Mbihi, Salome Essiane Ndjakomo https://creativecommons.org/licenses/by/4.0
Physiographic Characterization of the Tshopo River and Hydroelectric Dam Using Geomatics and Remote Sensing Tools
The Tshopo River watershed offers significant potential in terms of both energy and navigability. For example, hydroelectric developments have been underway since 1955, and the Kisangani River is supplied with water from this dam. A 2m x 2m spectral band was used to approximate reality, and a 30m x 30m pixel was used to visualize drainage densities and the shape (sinuation) of the watercourses. Maps presenting the study area, the length of the main watercourse, and the boundaries and watercourses were also produced using ArcGIS 10.8 and HEC-HMS 6.4.1 software, which were essential for carrying out this study. We were able to extract 839 sub-watersheds with physiographic characteristics (geometric, topographic/hypsometric, and hydrographic features) with a total area of 19,306–19,317.31 km², a perimeter ranging from 1,030.1 to 1,142.04 km, and a main channel length of 561.7361 km. The Tshopo River has a low drainage density (i.e., less than 1). Our Tshopo River watershed is very elongated, as the gravel coefficient varies from 2.1 to 2.3 depending on the pixel size and spatial resolution; the steepest slopes are concentrated in the south-central part of the Tshopo watershed, with values ranging from 7 to 66 degrees, while the gentler slopes are much more concentrated around the dam (outlet) to the west. The vegetation cover (less dense and dense forest occupies more than 90%. And 10 altitudes classes according to their areas have been identified ranging from 455-506 m and 916-967 m with Time of Concentration (Tc) of 3766.8 minutes.
Copyright (c) 2026 Mokili Enasi Emmanuel, Sansabana Kimvuka Herve, Mokili Enasi Josue, Wabwandi Wabwandi Albert, Isige Getombo Jean Pierre, Lilombi Lokanga Augustin, Misilu Mia Nsokimieno Eric, Kabasele Yenga Yenga Albert https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Machine Learning and Deep Learning Algorithms for Early Detection of Hardware Vulnerabilities in Hardware Security Systems
Early detection of hardware vulnerabilities is critical to ensuring the security and reliability of electronic systems. This study investigates and compares various machine learning and deep learning algorithms for their effectiveness in identifying hardware vulnerabilities during the early stages of the development lifecycle. The analysis focuses on algorithms commonly used in hardware security applications, including anomaly detection, classification, and pattern recognition, to detect issues such as hardware Trojans, side-channel attacks, and other security threats. Key performance metrics such as accuracy, detection speed, computational efficiency, and robustness against adversarial scenarios are evaluated. Furthermore, the study highlights the trade-offs between machine learning and deep learning approaches, considering their scalability and deployment feasibility in resource-constrained environments. The findings aim to provide insights into selecting the most suitable algorithm for early vulnerability detection, contributing to the advancement of secure hardware design and the mitigation of potential risks in critical systems.
Copyright (c) 2026 Milind Paraye, Abhishek Ratnu, Shubham Sinha, Pratik Waghmode https://creativecommons.org/licenses/by/4.0
AI-Driven Employability Prediction: Integrating Machine Learning and Educational Analytics for Student Placements
This paper presents an Artificial Intelligence (AI) and Machine Learning (ML)–based framework for predicting student employability outcomes in higher education. The study aims to bridge the gap between academic learning and industry requirements by analyzing multiple features such as academic performance, technical skills, internships, and leadership experience. Using supervised learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), and XGBoost (XGB), the system forecasts suitable job roles and potential recruiting companies. Among these models, XGBoost achieved the highest performance, with an accuracy of 87.3% and an F1-score of 86.2%. The results demonstrate that combining technical proficiency with behavioral and academic features provides superior predictive capability. The proposed framework contributes to educational data analytics by enabling data-driven career guidance, curriculum design, and employability enhancement. This approach can assist institutions in aligning student competencies with evolving market needs, fostering better preparedness and placement efficiency in business and engineering education.
Copyright (c) 2026 Basuri Bhujade, Anmol Khy, Surekha Dholay https://creativecommons.org/licenses/by/4.0
Comparative Analysis of TF-IDF and LLM-Based Text Corpus Generation for Course Recommendation Systems
Course recommendation systems have become increasingly critical in addressing the complex educational challenges of personalized learning pathways. This research intro- duces a novel approach to text corpus generation that leverages advanced natural language processing techniques to enhance course recommendation accuracy. Traditional keyword extraction methods, particularly TF-IDF, often struggle to capture nuanced semantic relationships within academic content. Our study proposes an innovative methodology that combines traditional statistical methods for large language model (LLM) keyword extraction to generate a comprehensive, multidimensional course corpus. The proposed framework segments college databases across semesters and subjects, generating a three-dimensional keyword representation that captures the intricate relationships between academic content. By comparing traditional TF-IDF keyword extraction with LLM-based semantic keyword generation, we demonstrate significant improvements in recommendation relevance and precision. Experimental results reveal that LLM-based approaches substantially outperform traditional statistical methods in capturing contextual and semantic nuances of academic content.
Copyright (c) 2026 Deepanshu Aggarwal, Neerja Doshi, Sanskar Kamble, Sudhir Dhage https://creativecommons.org/licenses/by/4.0
IoT-Based Smart Egg Incubation System for Improved Hatchability in Small-Scale Poultry Farming
This paper presents the design and experimental evaluation of an Internet of Things (IoT)-based smart egg incubation system developed for small-scale poultry farming applications. Conventional manual incubation practices suffer from inconsistent temperature and humidity regulation, leading to low hatch rates typically limited to 50–60% and requiring intensive manual labor. The proposed system automates the incubation process using an ESP8266 microcontroller integrated with DHT22 temperature and humidity sensors. Critical incubation conditions of 37.5°C ± 0.3°C (after calibration) and relative humidity between 55–70% are maintained throughout the 21-day incubation cycle. The system includes relay-controlled heating elements, automated egg turning using servo motors, and real-time monitoring through an OLED display. Remote access and monitoring are enabled via Wi-Fi using cloud platforms such as Blynk and ThingSpeak. Experimental evaluation conducted over five complete incubation cycles demonstrated stable temperature control within ±0.3°C and humidity regulation within ±3%. The system achieved an average hatch rate of 92%, representing a significant improvement over manual incubation methods. Energy consumption was measured at approximately 0.15 kWh per day, while manual labor requirements were reduced by nearly 80%. With total hardware costs below $60, the proposed system offers a reliable, energy-efficient, and affordable solution for improving hatchability and productivity in resource-constrained poultry farming environments.
Copyright (c) 2026 Aditya Pawar, Malhar Patki, Omkar Shirse, V. S. Jadhav https://creativecommons.org/licenses/by/4.0
Credit Card Fraud Detection: Use of Artificial Intelligence
According to Minchin (2025) fraud is UK’s biggest financial threats, which includes over 2 million cases reported between January and July in 2025 alone. This sums up to over £620 million lost just in the first half of 2025. With regards to its origins, over 70% of fraud incidents starts online, and about 17% over a simple telephone call. However, banks and financial institutions introduce new tactics to reduce fraud incidents daily. Minchin (2025) also explains that UK banks were able to reimburse 98% of victims caught up in fraud incidents where criminals had gained access to their accounts or cards and had stolen money. Whereas, through authorized push payments methods, or APP for short, which ideally means that the victims were on the phone with criminals who had tricked then into sending them money through bank transfers. Banks were able to identify the genuineness of these cases as was able to return money of approximately 62% of such victims. On this paper, fraud and scams pertaining to credit cards are explained and how artificial intelligence and machine learning models, and even generative AI, can be used to identify and prevent such fraudulent activities are discussed.
Copyright (c) 2026 Minul Mindula Subasinghe https://creativecommons.org/licenses/by/4.0
A Risk-Governance and Exposure-Monitoring Framework for Synthetic Food Dyes in Pediatric Diets in the United States: Decision Indicators, Labeling Salience, and Post-Market Surveillance
Purpose: Synthetic food dyes remain pervasive in the U.S. processed-food supply, with exposure concentrated among children and shaped by product formulation, purchasing patterns, and labeling practices. While clinical and toxicological literature increasingly emphasizes heterogeneous susceptibility, regulatory decision-making still faces operational challenges: translating complex evidence into proportionate, implementable risk-management actions. This study proposes a regulatory-science framework that integrates exposure monitoring, decision indicators, and post-market surveillance to strengthen governance for pediatric-relevant dye exposure. Methodology: A conceptual framework was developed using a structured narrative synthesis of (i) clinical evidence on neurobehavioral sensitivity in subgroups, (ii) mechanistic/toxicological plausibility (including oxidative stress and inflammatory signaling), and (iii) regulatory documentation and exposure-context materials from major authorities. The framework was built through an iterative design-science logic: identification of governance gaps; definition of decision indicators; specification of operational levers (benchmark reassessment, labeling as risk communication, surveillance triggers); and articulation of an implementation pathway suitable for routine oversight. Findings: The proposed framework addresses three recurring governance gaps: (1) limited integration of contemporary pediatric exposure patterns into decision thresholds; (2) insufficient operational treatment of heterogeneity and susceptible subgroups; and (3) underutilization of labeling salience and surveillance loops as low-burden risk-management tools. The framework provides a practical approach for tiering exposure contexts, specifying triggers for reassessment, and strengthening post-market monitoring without requiring categorical hazard assumptions. Practical Implications: A proportionate governance model can reduce preventable risk in high-exposure pediatric contexts by improving transparency, enabling caregiver-level exposure management, and supporting periodic reassessment anchored in real-world consumption. The framework is designed to be implementable through existing regulatory infrastructure and adaptable to evolving evidence. Originality: This paper contributes a decision-oriented, implementable governance framework that translates multidisciplinary evidence into operational oversight mechanisms, emphasizing pediatric exposure concentration, susceptibility, and surveillance-driven iteration.
Copyright (c) 2026 Jossineide Manuela da Silva Cordeiro https://creativecommons.org/licenses/by/4.0
Fundamental Properties of Absolute-Type Cesàro Sequence Space on Metric Spaces
Copyright (c) 2026 Adam Fikriansyah Akbar, Manuharawati Akbar https://creativecommons.org/licenses/by/4.0
From Models to Impact: A Systematic Review of Generative Artificial Intelligence in Education
Generative Artificial Intelligence (GenAI) is increasingly influencing educational systems by enabling personalized learning, automated content generation, adaptive assessment, and intelligent tutoring. Despite its rapid advancement and growing adoption, the effective and responsible integration of GenAI in education remains a challenge due to ethical concerns, limited empirical validation, and issues related to user trust. This paper presents a systematic review and analysis of existing literature to examine the pedagogical potential, ethical risks, and trust-related challenges associated with GenAI-enabled educational environments. It further analyzes global and Indian adoption trends to highlight domain-wise and regional variations in GenAI usage. By identifying key research gaps in current studies, the paper emphasizes the need for integrated, trust-aware, and ethically aligned approaches. The study contributes by synthesizing insights from prior research and proposing directions for the sustainable and responsible deployment of Generative AI in education.
Copyright (c) 2026 Prerna Agrawal https://creativecommons.org/licenses/by/4.0
Detection of Alzheimer’s Using Convolutional Neural Network
Alzheimer’s disease is a neurodegenerative disease that usually starts slow and progressively worsens. Early detection of Alzheimer’s can help us prevent deterioration of the tissue damage in the brain. In this paper, a detailed analysis on detection of Alzheimer’s disease using various Convolutional Neural Network (CNN) architectures in Machine Learning (ML) is shown. This disease causes people to suffer from memory loss and unusual brain tissue damages. Several ML models have been used by researchers for detection of Alzheimer’s. Analyzing Magnetic Resonance Imaging (MRI) scans is a very useful and common practice for the diagnosis of Alzheimer’s disease. ML is used to help the computers to train on the large and complicated datasets and detect the presence of Alzheimer’s disease in brain MRI. In today’s world Deep Learning techniques have successfully brought change in numerous fields including medical image analysis. We propose a deep CNN for Alzheimer’s disease using MRI scan data sets. Our model identifies Alzheimer’s disease in its different stages using different CNN architectures to obtain efficiency and accuracy for early-stage diagnosis of Alzheimer’s disease.
Copyright (c) 2026 Kolliboni Tejaswini, Balanagari Neha, Daram Ramya Sri, Indla Geethika Reddy https://creativecommons.org/licenses/by/4.0
Using Tiny-ML Models on Edge Devices to Improve Industrial Control Systems in Real Time
Tiny Machine Learning (Tiny-ML) lets machine learning models run on edge devices with very few resources. This opens up new possibilities for smart Industrial Control Systems (ICS) that don't need to be connected to the cloud all the time. Traditional Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems predominantly depend on static, rule-based logic and face challenges in adapting to dynamic, data-intensive industrial settings that necessitate predictive maintenance, anomaly detection, and real-time process optimization. This paper examines the implementation of highly efficient Tiny-ML models on edge devices, including ARM-based micro-controllers and single-board computers, to attain low-latency inference, bandwidth optimization via local processing, and enhanced energy efficiency and security for industrial Internet of Things (IIoT) sensors. The paper talks about hardware and software limits, how to connect to old PLC/SCADA systems using industrial protocols, and gives an overview of contemporary industrial projects that show how edge AI is useful in modern workplaces.
Copyright (c) 2026 Kadari Achwak https://creativecommons.org/licenses/by/4.0
Emotional Intelligence Across Career Stages: A Study of Interpersonal Skills and Workplace Performance
In modern corporate settings, emotional intelligence (EI) has become a crucial skill affecting interpersonal effectiveness and professional performance. Technical proficiency is still crucial, but employees' capacity to identify, control, and react positively to emotions has a big impact on how they interact with coworkers and perform. This study looks at how emotional intelligence develops and shows up at various levels of professional experience, as well as how it helps people improve their interpersonal abilities. The study uses a mixed-methods approach, gathering primary data from 76 employees via a structured questionnaire. Employees with less than five years of experience (52 respondents) and those with more than five years of experience (24 respondents) were the two groups into which the respondents were divided. Initial emotional reactions, situational emotional intelligence, present emotional intelligence, and self-perceived emotional intelligence were all evaluated in the survey. To facilitate comparison analysis, qualitative replies were methodically transformed into quantitative scores using a predetermined scoring structure. The results show that emotional intelligence does not grow with experience in a linear fashion. While seasoned workers show emotional stability along with less situational flexibility, individuals in the early phases of their careers show more emotional adaptability, stronger situational emotional intelligence, and greater self-perceived emotional confidence. The findings show that learning orientation, exposure to the workplace, job demands, and ongoing reflection—rather than experience alone—all have an impact on the dynamic development of emotional intelligence. In order to maintain interpersonal effectiveness and job success, the study underscores the necessity of experience-specific emotional intelligence development interventions and stresses that emotional intelligence needs constant reinforcement throughout all career stages.
Copyright (c) 2025 Aditi Sharma, Anna Reji, Aayat Rehman https://creativecommons.org/licenses/by/4.0
Between Nationalism and Globalization: A Critical Case Analysis of South Korea’s English Ban in Kindergartens
This paper will critically discuss and analyze the South Korean government's ban on English instruction in kindergartens from management’s perspectives. This case was selected for analysis because of the South Korean government’s dilemma: heritage language for national identity or English for socioeconomic mobility. It will examine the policy's motivations, implementation, and its broader implications for early childhood education and linguistic development within the nation. Additionally, this analysis will delve into the complex interplay between linguistic ideology, educational equity, and social pressures that inform the decisions, and then make a comparison between Southeast Asian countries and South Korea to draw lessons and solutions. The analysis on this dilemma and choice is expected to bring dynamic and new insights into language policy in non-native English-speaking countries.
Copyright (c) 2025 Truong Anh Khoa https://creativecommons.org/licenses/by/4.0
ArthiX – A Language Agnostic System for Real-Time Text, Speech, and Programming Code Translation
The rapid expansion of global communication and collaborative software development has intensified the need for translation systems that can operate across linguistic and technical boundaries. Existing translators primarily focus on human languages and often fail to address challenges related to voice interaction, offline usage, and programming language understanding. This paper presents ArthiX, a language-agnostic translation system designed to support real-time text and speech translation with automatic language detection, local history storage, and offline capabilities. In addition to natural language translation, the system introduces logical translation of programming code across different languages, followed by real-time compilation to validate correctness. ArthiX is implemented as a desktop application using Python-based Natural Language Processing techniques and speech processing libraries. Experimental evaluation demonstrates that the system provides accurate translations, responsive performance, and improved usability compared to conventional translators. The proposed approach highlights the potential of unified translation platforms in education, software development, and multilingual communication.
Copyright (c) 2025 Mahi Paliwal, Kavita Lodhi, Poorvi Sharma, Sandhya Vishwakarma https://creativecommons.org/licenses/by/4.0
Environmental Legal Compliance and its Relationship with Perceived Operational Efficiency in Broiler Breeder Farms
Environmental legal compliance refers to adherence to applicable environmental laws and regulatory requirements. This study examined the relationship between environmental legal compliance and perceived operational efficiency outcomes in broiler breeder farm operations. Using a quantitative correlational design, data were gathered through structured Likert-scale survey instruments administered to key personnel across broiler breeder farms, measuring compliance implementation across major domains (air, water/wastewater, solid waste, hazardous/chemicals, permits/documentation/audits, and training/responsibilities/culture) and perceived efficiency outcomes (e.g., reduced disruptions, improved sanitation/ housekeeping, better coordination, and reduced downtime). Descriptive statistics and Pearson’s correlation were used to determine the strength and direction of association between compliance and perceived operational efficiency outcomes. Findings provide practical insight that integrating environmental compliance into routine farm management is associated with stronger perceived operational control and stability, supporting more disciplined and resilient operations.
Copyright (c) 2025 Jonathan L. Docot, Almira E. Balatico, Renz M. Ancheta, Marvin Mallari, Joeme Carl Demegillo https://creativecommons.org/licenses/by/4.0
Investigating the Correlation Between Dink Mindset, Standard of Living, and Macroeconomic Impact: A Quantitative Study of Bikaner City, Rajasthan
This paper empirically investigates the relationship between the Dual Income, No Kids (DINK) mindset and household standard of living in Bikaner City, Rajasthan. Using primary survey data collected from 500 urban households, the study applies descriptive statistics, correlation analysis, and multiple linear regression techniques. The findings reveal a statistically significant and positive association between DINK orientation and standard of living after controlling for income, education, and age. The results highlight the relevance of changing household structures for urban consumption patterns and regional economic planning in tier-II Indian cities.
Copyright (c) 2025 Cheshta Tuteja https://creativecommons.org/licenses/by/4.0
Monaco Editor – A Web-Based Development Environment with Real-Time Live Preview
The increasing demand for accessible, installation-free software development tools has led to a significant rise in browser-based coding environments. However, many existing platforms separate essential development operations—code editing, documentation writing, and output previewing—forcing users to switch between multiple interfaces. This results in workflow fragmentation, increased cognitive load, and reduced learning efficiency. This research proposes a fully client-side web-based development environment equipped with real-time live preview capabilities for HTML, CSS, and JavaScript. The system integrates a multi-pane code editor, a documentation/notes pane, and an isolated iframe-based rendering engine within a unified interface. Implemented using modern browser technologies such as ES6 JavaScript, LocalStorage, IndexedDB, and iframe sandboxing, the tool operates entirely without a backend server. Extensive testing with students and developers demonstrates improved usability, faster prototyping cycles, and enhanced conceptual understanding through immediate visual feedback. The study confirms the feasibility of browser-native development environments and highlights their potential as lightweight alternatives to traditional IDEs.
Copyright (c) 2025 Rakshit Sharma, Uday Bedi, Manan Gehlot https://creativecommons.org/licenses/by/4.0
Building Work Engagement through Appraisal Satisfaction and Psychological Contract in the Indonesian Public Sector
This study examines how satisfaction with performance appraisal system fosters work engagement through psychological contract among employees in the Financial and Development Supervisory Board in Indonesia. Drawing on social exchange theory (SET), we propose that employees who perceive the appraisal system as fair, unbiased, effective, and useful are more likely to believe that their organization honors implicit promises and mutual obligations, which in turn enhances their engagement at work. Data were collected through a survey from 254 civil servant in Indonesia and analyzed using partial least squares structural equation modeling (PLS-SEM). The study shows that the level of satisfaction that employees feel about the performance appraisal system is closely tied to how they view their relationship with the organization and how engaged they are in their work. When the appraisal process is experienced as fair, clear, and genuinely useful, employees feel that the organization keeps its promises, and this strengthened psychological contract helps transform positive appraisal experiences into higher work engagement. This implies that public sector organizations need not only a technically sound appraisal design, but also careful implementation through good communication, transparent procedures, and consistent follow-up, so that employees perceive the system as a real sign of organizational commitment rather than a mere administrative routine.
Copyright (c) 2025 Izati Choirina, Riani Rachmawati https://creativecommons.org/licenses/by/4.0
Assessment of Solar-Powered Irrigation Systems (SPIS) for CY 2025 in NIA-UPRIIS Division VI
This study assessed the performance, efficiency, and operational effectiveness of Solar-Powered Irrigation Systems (SPIS) implemented in the National Irrigation Administration–Upper Pampanga River Integrated Irrigation Systems (NIA-UPRIIS) Division VI for Calendar Year 2025. Using a descriptive-evaluative research design, data were collected from 33 respondents, including Irrigators Association members, farmers, and NIA personnel, through structured surveys. The assessment focused on technical specifications, water delivery efficiency, system performance, technical issues, maintenance practices, and comparisons with conventional diesel- or electric-powered irrigation systems. Results indicate that SPIS generally meets technical requirements, delivers consistent water supply, and operates reliably under varying conditions, demonstrating higher cost efficiency and reduced maintenance demands compared with traditional systems. Key technical challenges identified include low water output, panel shading, and limited battery capacity, which may affect performance during periods of low sunlight. Respondents recommended improvements such as incorporating backup power units, enhancing site selection, using durable materials, and implementing regular maintenance schedules. Overall, SPIS proves to be a sustainable and effective alternative for irrigation, supporting agricultural productivity while offering opportunities for further optimisation in design and operation.
Copyright (c) 2025 Leonalyn Marquez Irabagon, Michael John M. Villar https://creativecommons.org/licenses/by/4.0
Production and Characterization of Agricultural-Based Composite Biomass Briquettes Using a Novel Heterogeneous Catalyst
Reliance on conventional energy sources, such as wood and coal, has had a detrimental impact on the environment, resulting in air pollution and greenhouse gas emissions. Biomass briquettes offer a sustainable and renewable alternative to traditional energy sources. However, optimizing the quality of briquettes using biowaste materials remains a significant research gap. The study aims to optimize the physicochemical properties of composite briquettes using calcined turtle shell as a heterogeneous catalyst in a blend of 20% palm kernel shell, 30% plantain peel, and 50% cocoa pods biowaste, as per Optimal Custom Design (OCD) DoE 13.0. A maximum yield of heating value 18.14 MJ/kg and a minimum ash content of 6.01% was obtained. The study shows that the heterogeneous catalyst can effectively substitute CaO, advancing the viability of biomass briquettes as a sustainable, cost-effective alternative fuel source.
Copyright (c) 2025 Badmus Sulyman Adekola, Adegbola Adeyinka Ayoade, Ogunsola Akinola David, Faluyi Moses Olufemi, Okoro Dennis Isah, Babatunde Issa Akinol, Sangotayo Emmanuel Olayinka, Aderibigbe Abidemi Adetunji https://creativecommons.org/licenses/by/4.0
Optimization of Physicochemical Properties of Rice Husk, Groundnut, and Coconut Shells Composite Briquettes, Using Cow Bone Catalyst
The rising demand for sustainable and cost-effective energy alternatives has heightened research efforts in biomass briquetting, with a particular focus on optimizing physicochemical properties to enhance fuel quality. This study was carried out to enhance and optimize the physicochemical characteristics of composite briquettes produced from readily available agricultural residues. The main objective was to formulate an optimized composite briquette consisting of rice husk, groundnut shell, and coconut shell, augmented by a novel heterogeneous catalyst obtained from bovine bone. A highly pure calcium (II) oxide (CaO) catalyst was effectively synthesized via calcination of bovine bone at 900 °C, with XRD analysis indicating a prominent peak intensity of 1701.99 μ, thereby corroborating the presence and purity of CaO. Briquette formulation was guided by a Simplex Lattice Mixture Design (SLMD), executed with Design Expert 13.0, facilitating a systematic assessment of biomass component interactions and the identification of optimal mixture proportions. Optimization was conducted using dual criteria: reducing ash content and enhancing calorific value. The model determined an optimal mixture consisting of 20% rice fiber, 30% groundnut shell, and 50% coconut shell. This formulation yielded the most advantageous physicochemical properties, attaining a high calorific value of 19.21 MJ/kg and an exceptionally low ash content of 5.61%, reflecting substantial enhancements compared to non-optimized or catalyst-free briquettes. The improved performance affirms the catalytic efficacy of CaO in facilitating more efficient combustion properties and enhancing briquette quality. This study demonstrates that bovine bone serves as an effective, readily available, and economical precursor for the synthesis of high-purity heterogeneous catalysts, thereby supporting waste-to-wealth initiatives and sustainable biofuel generation. The optimized composite briquette not only complies with but also closely correlates with international standards for solid biofuels, providing a practical alternative for domestic and small-scale industrial energy use. Future research may investigate variations in catalyst dosage, emission profiles, and the feasibility of large-scale production to further promote the adoption of catalyst-enhanced biomass briquettes.
Copyright (c) 2025 Alabi Temitayo, Adegbola Adeyinka Ayoade, Ogunsola Akinola David, Sangotayo, Emmanuel Olayimika https://creativecommons.org/licenses/by/4.0