International Journal of Research in Engineering, Science and Management
Call for Papers Publish in Multidisciplinary Journal Volume 9, Issue 1, January 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.
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
Multimodal Retrieval-Augmented Generation for Financial Report Question-Answering: Architecture and Evaluation
The exponential growth of digital financial reports-rich in text, tables, and graphical data-poses significant challenges for efficient information extraction and analysis. This paper presents a multimodal Large Language Model (LLM)-powered Question Answering (QnA) system for financial documents, focusing on a Retrieval-Augmented Generation (RAG) pipeline. Our system integrates advanced natural language processing, vision-language models, and optimized chunking strategies to retrieve and synthesize information from complex financial filings. Comprehensive evaluation across multiple configurations demonstrates the pipeline's robustness, highlighting the impact of hyperparameter tuning, chunking methods, and embedding strategies on answer faithfulness, relevance, and factual correctness.
Copyright (c) 2025 Pratham Shelke, Omkar Yellaram, Sunil Ghane https://creativecommons.org/licenses/by/4.0
Investigation of Financial Challenges and Opportunities of SMEs in the Context of Oman Vision 2040
This study investigates the financial challenges and strategic opportunities faced by small and medium-sized enterprises (SMEs) in the Sultanate of Oman, particularly within the framework of Oman Vision 2040. SMEs are key drivers of economic growth, job creation, and innovation; however, they encounter significant financial barriers that hinder their performance and sustainability. The research aims to examine the extent and impact of financial constraints such as limited access to capital, high interest rates, and inadequate financial literacy among SME owners. It also explores the strategies SMEs adopt to navigate these challenges, including financial planning, innovation, and leveraging government support programs. A quantitative approach was employed, using structured questionnaires distributed to 150 SMEs based in Muscat Governorate. Data was analyzed using SPSS, incorporating descriptive and inferential statistics to assess relationships among financial challenges, literacy levels, strategic responses, and performance outcomes. The findings reveal a strong correlation between financial literacy, strategic innovation, and improved SME performance. Additionally, the study emphasizes the importance of tailored financial policies and enhanced support mechanisms to strengthen the SME sector and advance the national goals of economic diversification outlined in Oman Vision 2040.
Copyright (c) 2025 P. Venkata Durga Rao, Wahab Juma Al Shihi, Yousuf Khalid Al Bulushi, Mohammed Najeeb Al Zakwani, Ahmed Darwish Al Raiisi, Mohammed Hamed Al Harrasi https://creativecommons.org/licenses/by/4.0
Using Gated Recurrent Unit Recurrent Neural Network for Detection of HTTP Flood Attack
The HTTP flood attacks has been on the rise in disruption of digital services and infrastructure by cybercriminals using readily available DDoS execution tools on the internet. These attacks disrupt online services by bombarding web servers with HTTP Requests to deny legitimate users access to online services. Prevention of these attacks require proactive defense mechanism to differentiate malicious HTTP Requests from legitimate users HTTP Requests in real time, the increased number of online users require more resources for monitoring. Therefore, early detection of the attacks can be utilized to implement reactive mechanism to reduce the effect of attacks by preventing escalation and reduce losses incurred by the online businesses. This research paper presents the use of GRU model to predict Web Server access logs patterns for early detection of the HTTP Flood Attacks. The hidden state in GRU provides memory of past events to enable accurate prediction of attacks based on past trends of HTTP Requests frequency in the Web Server. The model was trained on Web Server Access Logs dataset through multivariate regression analysis and prediction of HTTP Request frequency. This involved analysis of 10,365,093 HTTP Requests from ‘WEB SERVER ACCESS LOG’ dataset and 47,742 HTTP Requests from ‘EPA-HTTP’ dataset. The GRU model prediction was able to achieve the Mean Absolute Error of 0.0188 and 0.0149 respectively indicating high prediction accuracy. Further comparison with LSTM Model using the same hyperparameters and the two datasets, indicated slightly high accuracy of GRU Model of 0.0188 against 0.019 for LSTM Model for the Web Server Access Log dataset and 0.0149 for the GRU model against 0.0158 for the LSTM model using the EPA-HTTP dataset. This research shows that the Gated Recurrent Unit (GRU) simplified modern architecture of LSTM Model can be deployed to detect HTTP Flood Attacks fast by predicting frequency of HTTP Requests Methods received by the Web Server. The highspeed performance and increased accuracy of GRU will enable the analysis of huge the access logs with efficient utilization of resources for real time detection of attacks when they occur.
Copyright (c) 2025 Ezekiel Cheruiyot Ronoh https://creativecommons.org/licenses/by/4.0
Influence of Work Values on the Teaching Performance of Faculty Members in State Universities and Colleges in Sulu
This study aimed primarily to determine the influence of work values on the teaching performance of faculty members in State Universities and Colleges in Sulu. This study has six research problems and three hypotheses. The descriptive- correlational research design with Statistical tools such as T-test, ANOVA, and simple regression were utilized in the analysis and interpretation of data. The findings were majority of the respondents are from Mindanao State University- Sulu, at the age category 36 years above, married, with income 31-40 thousand pesos after taxation, earned doctoral units, rendered service 6-10 years occupy position/rank Instructor I–III. The faculty members have exercised work values to keep them work well in their service. These work values were perceived very important in the job such as: Intellectual, Independence, Achievement, Prestige, Surroundings, Supervisory Relations, and Associates while they perceived important of the work values such as: Economic Returns, Security, and Altruism. The faculty members of State Universities and Colleges (SUCs) in Sulu achieved outstanding performance in terms of knowledge of subject matter, procedures and techniques, classroom management, preparation and utilization of instructional materials, professional growth, community and allied services and personality and human relations while they perceived very satisfactory level of performance in terms of evaluation and remedial procedures and attendance and reports. The hypothesis was accepted. There is no significant influence of the work values of faculty members on their teaching performance. The second hypothesis is also accepted. There is no significant influence of the profile income, educational attainment and length of service on the work values of faculty members of State University and Colleges (SUCs) in Sulu. The third hypothesis was also accepted. Therefore, there is no significant difference of teaching performance of faculty members of SUCs in Sulu when the data are grouped according to profile such as age, gender, and marital status, length of service, monthly income, educational attainment and position/rank. Based on the findings of the study, the conclusions were formulated. Majority of the respondents were from Mindanao State University- Sulu, at the age category 36 years above, married, with the income 31 – 40 thousand pesos after taxation, earned doctoral units, rendered service 6 – 10 years and occupy position/rank Instructor I – III. They exercised very important work values in their job such as: Intellectual, Independence, Achievement, Prestige, Surroundings, Supervisory Relations, and Associates while they perceived important of the work values such as Economic Returns, Security, and Altruism. They achieved outstanding performance in terms of knowledge of subject matter, procedures and techniques, classroom management, preparation and utilization of instructional materials, professional growth, community and allied services and personality and human relations while they achieved very satisfactory level of performance in terms of evaluation and remedial procedures and attendance and reports. The work values have no significant influence on the teaching performance. There is no significant influence of the profile such as income, educational attainment and length of service on the work values of faculty members of SUCs in Sulu. There is no significant difference of the teaching performance of faculty members of the SUCs in Sulu when the data are grouped according to profile such as age, gender, and marital status, length of service, monthly income, educational attainment and position/rank. The significant findings and the conclusion of this study initiated certain recommendations. For research problems the following are forwarded. 1. Strategies on the improvement of work values: re-orientation activities of SUCs in Sulu. 2. Factors affecting the work values of Faculty Members in Tertiary Education. 3. Sourcing more strategies and improving work values and teaching performance. 4. Application of cooperative efforts to promote work values orientation: its effect on teaching performance. 5. Collaborative Efforts of Faculty Members and School Administrators of SUCs to administer work values. For research agenda the following are foresighted. 1. Another study may be conducted to follow up the results of this study. 2. The faculty members may continue to practice work values to enhance outstanding teaching performance. 3. The faculty members may collaboratively use work values to initiate motivation and job satisfaction. 4. The school administrator may include in their School Improvement Plan (SIP) the strategies to improve the work values of faculty members for better teaching performance. 5. The faculty members may develop personally their knowledge and skills work values to improve teaching performance. For research policies the following are advocated. 1. The SUCs in Sulu may understand the problems encountered by the faculty members in terms of work values as it is related to teaching performance. 2. The SUCs in Sulu may monitor the practice work values in relation to teaching performance. 3. The SUCs officials may transmit and find tangible solutions to the problems encountered by the faculty members in practicing work values. 4. The SUCs officials in Sulu may have proper re-orientation of the work values as it relates to teaching performance. 5. The faculty members may freely voice out the encountered problems on the work value.
Copyright (c) 2025 Paradji M. Sayadi https://creativecommons.org/licenses/by/4.0
Stock Growth Forecast Through Sentiment Analysis on Social Media and News with Statistical Factors
The research proposes a comprehensive approach for predicting stock prices over a 30-business-day horizon with an accuracy of 98–99%. By incorporating multiple factors influencing stock prices, the methodology ensures reliable and well-rounded predictions. The base model employs a Univariate Time Series Prediction using a 1D Convolutional Neural Network (CNN-1D), which effectively captures seasonal patterns and general trends. While CNN-1D excels in identifying seasonality, it occasionally struggles with trends. To address this, the research integrates a Kalman Forecaster, which is highly reliable for trend prediction. The combined output of these models provides predictions encompassing both seasonality and trend dynamics, albeit with a slight reduction in accuracy compared to CNN-1D alone. The study further utilizes a Long Short-Term Memory (LSTM) network to predict market indices such as Sensex and Nifty, which, along with CNN-1D and Kalman predictions, inform a Random Forest regressor model trained over 320 days of a 365-day dataset. Predictions for the final 45 days are refined using mean error calculations over the first 15 days, which are then corrected using sentiment scores derived from news analysis powered by a large language model which in this case Gemini Flash Model 1.5. This integrated approach achieves a minimal error margin of 1–3%, successfully combining seasonality, trend, and external market factors for accurate stock price forecasting.
Copyright (c) 2025 Ajeyaraj Upadhyaya, Rahul Shinde, Taha Shaikh, Jyoti Ramteke https://creativecommons.org/licenses/by/4.0
Simulation Driven Decision Framework for Heat Exchanger Retrofits: A Case Study of a Multi Pass Naphtha Cooler
A modular decision framework that integrates process simulation, fouling uncertainty quantification, and lifecycle economics is presented for prioritizing heat exchanger retrofits in petrochemical plants. A multi pass shell and tube cooler used to cool a C₆–C₁₂ naphtha stream was modelled in Aspen HYSYS Exchanger Design and Rating and optimized over tube count, baffle spacing, and cooling water flow to minimize total annual cost subject to hydraulic and mechanical constraints. Fouling was modelled with a combined resistance and a Monte Carlo ±5% sampling quantified economic sensitivity. The optimized retrofit (220 tubes, 0.45 m baffle spacing, 28,000 kg·hr⁻¹ cooling water) increased heat duty from 2,205 kW to 3,150 kW (+42.7%), raised LMTD from 42.3 °C to 58.1 °C (+37.4%), and reduced shell and tube side pressure drops to 0.70 bar and 1.00 bar, respectively. Despite a 6.8% CapEx increase, TAC fell by 10.2% to $115,000/yr, yielding a 2.1 year payback under base economic assumptions. Fouling uncertainty produced a TAC sensitivity of ≈4.8%, shifting payback by ±0.3 years. The framework is transferable across plant exchanger inventories and supports data driven retrofit prioritization.
Copyright (c) 2025 Noble Aggrey Efemena, Peter Odiboroghene Muwarure, Obumneme Okwonna https://creativecommons.org/licenses/by/4.0
Impact of Instagram on the Students of IP University
This research paper examines the impact of Instagram on the academic performance, mental health, social behaviour, and daily lifestyle of students enrolled in five major IP University–affiliated colleges: Tecnia Institute of Advanced Studies (TIAS), Rukmini Devi Institute of Advanced Studies (RDIAS), Geeta Rattan Institute of Business Studies (GIBS), Trinity Institute of Professional Studies (TIPS), and Maharaja Agrasen Institute of Management Studies (MAIMS). The study explores how Instagram, as one of the most popular social media platforms among youth, affects students both positively and negatively. A mixed-method approach was used, involving online surveys and personal discussions with 50 students from each college, making a total sample size of 250 respondents. The findings reveal that Instagram plays an important role in students’ daily communication, academic motivation, and access to educational content. Many students also use it to stay informed about college activities, trends, and social networks. However, the study also highlights several concerns such as excessive screen time, distraction during study hours, sleep disturbances due to late-night scrolling, and increased comparison-based stress. While Instagram offers creative exposure and learning opportunities, its overuse can negatively influence students’ academic discipline and emotional well-being. The research concludes that balanced usage, along with digital well-being awareness, time management, and institutional guidance, is essential for students to benefit from Instagram while avoiding its harmful effects.
Copyright (c) 2025 Amit Sharma, Manya Khurana https://creativecommons.org/licenses/by/4.0
Integrating Chemical Understanding and Continuous Improvement (Kaizen/PDCA) to Eliminate Sulphuric Acid Pipeline Leakage
Failure analysis and reliability improvement in a water treatment plant were studied for leak failures [12] occurring due to corrosion and material selection problems of the supply line carrying 98% sulphuric acid [6]. A Kaizen approach with the support of root cause analysis by the Fishbone Diagram, 5 Whys, and FMEA identified the main contributors: material incompatibility, infrequent inspection, and inadequate corrosion monitoring [21]. Utilizing the Kaizen approach [9], an acid-resistant breather with a silica gel desiccant installed at the storage tank vent reduced leakage incidents by approximately 80% within six months.
Copyright (c) 2025 Ravindra Dubey, Metta Priyanshi https://creativecommons.org/licenses/by/4.0
Documentation of Urban Trees in Chitradurga, Karnataka
Many of the world’s major cities have implemented tree planting programs based on assumed environmental and social benefits of urban forests. Urban trees serve many useful functions such as climate change mitigation by carbon sequestration, air quality improvement by air pollution abatement, biodiversity conservation and source of ecosystem goods to urban inhabitants. They also have aesthetic, socio-religious and recreational value in urban contexts. In spite of the importance, they have not received much scientific attention. In this work, aimed to documentation and identification of trees. A survey was conducted during Dec-2022 to Decmber-2023. This paper investigates the diversity of tree species growing both within the built environment as well as road-side avenues in the seaside town of Chitradurga which is the administrative headquarters of Chitradurga district of Karnataka. The preliminary study of tree flora of Chitradurga comprises of about 89 species in which majority are indigenous species and few others involve exotic and introduced species. The dominant genera are Ficus, Terminalia, Artocarpus and Syzygium. The dominant species are Pongamia pinnata, Alstonia scholaris, Thespesia populnea, Terminalia catappa, Spathodia campanulata, Ficus religiosa and Bauhinia purpurea. The tree diversity represents a good assemblage of different utility categories such as wild and cultivated fruit yielding trees, shade and ornamental trees, sacred and religious trees, etc. Besides the high proportion of older trees of Rain tree and Ficus, presence of wild fruit yielding trees like Artocarpus incisus and Spondias pinnata, large sized sacred trees such as Ficus religiosa and F. benghalensis, rare medicinal species such as Garcinia indica, Saraca asoca, Terminalia bellirica, etc., are some of the notable features of the urban tree flora of Chitradurga.
Copyright (c) 2025 B. H. Ramesh https://creativecommons.org/licenses/by/4.0
Supply Chain Resilience Strategies and Overview
Supply Chain Resilience refers to the ability to address and anticipate the unexpected events, disruptions, even in pandemic situation and come out with the adaptation and recovery to evolve the continuous supply chain and logistics network design. Resilient supply chain maintains the smooth continuity of network by reduction of disruption risks and establishes customer satisfaction. Over the last few years, COVID-19, extreme weather events, Trade war and shifting geopolitical landscape have underscored the vulnerability of supply chains to disruptions. The leadership highlights importance of building resilient supply chains from then and digitalization of process can be leveraged. It certainly requires structured approach that an organization may build supply chain resilience by adopting several techniques like AI, Machine Learning (ML), Implementing supply chain control towers etc.
Copyright (c) 2025 Sumit Ganguly https://creativecommons.org/licenses/by/4.0
Innovative Technologies for Water Conservation in Warming Climate
Climate change is intensifying global water scarcity through rising temperatures, altered precipitation patterns, and increased evapotranspiration. This paper explores innovative technologies that offer sustainable solutions for water conservation in a warming climate. Key advancements include smart irrigation systems, atmospheric water harvesting, solar-powered desalination, precision agriculture, and advanced wastewater recycling. Through a review of recent case studies and performance evaluations, the paper highlights the potential of these technologies to enhance water-use efficiency, diversify supply sources, and build climate resilience. While the technologies show promise, successful implementation depends on factors such as cost-effectiveness, policy support, and local adaptability. An integrated, context-sensitive approach to technology adoption is critical to ensuring long-term water security in an era of climatic uncertainty.
Copyright (c) 2025 Vinayak S. Patil, Rahul N. Rane, Vishal D. Sutar, Sajid R. Shaikh, Vinayak D. Tambekar https://creativecommons.org/licenses/by/4.0
Optimization of Air Velocity and Fan Speed for Improved Outlet Temperature in Water–Air Heat Exchanger Drying Systems
The increasing demand for sustainable thermal energy solutions in food processing. House heating and other domestic applications has intensified interest in solar-assisted drying systems, particularly those integrating heat exchangers to deliver controlled drying air conditions. Efficient dehydration of agricultural products requires precise regulation of air temperature, humidity, and flow rate to preserve quality while minimizing energy consumption. However, determining the optimal combinations of these parameters remains a major challenge, especially for systems relying on variable renewable energy sources such as solar thermal input. This study develops a comprehensive mathematical model for a solar-powered food dryer incorporating a counter-flow water-to-air heat exchanger and a forced-convection drying chamber. The model describes heat and mass transfer dynamics using thermodynamic laws, fluid flow relationships, and heat exchanger effectiveness–NTU formulations. A detailed derivation of heat exchanger performance is presented, linking outlet air temperature to key variables including water inlet temperature, heat transfer area, overall heat transfer coefficient, air velocity, fan sweep area, and volumetric flow rate. By reformulating the number of transfer units (NTU) as a direct function of air velocity, an explicit expression for air-side control of outlet temperature is obtained. Simulations using experimentally realistic parameters show that air velocity, fan speed, and fan diameter exhibit inverse relationships with outlet air temperature; reducing air mass flow increases residence time within the exchanger, thereby improving thermal pickup and raising outlet temperatures. The drying chamber temperature evolution, and air–product interaction is regulated using a feedback-based control strategy to obtain robust drying conditions throughout the dehydration process, ensuring that food products are exposed to temperatures below their thermal degradation thresholds. Pontryagin’s Maximum Principle was used to determine the optimal drying air temperature, with respect to airflow velocity, and fan speed. Results demonstrate that a heat exchanger of area 2.827m2 with inlet water temperature at 000C flowing at Vl=1.128l/s, can transfer heat up to 75.60C of air temperature up from 250C working at efficiency of ε(v)=06826 and fitted with a fan of are Af=0.05m2 spinning at v=20 RPS. These parameters can be adjusted for a different desired output air temperature. The optimization model framework offers a tool for enhancing dryer performance, guiding design decisions, and reducing energy costs in solar-assisted food processing systems.
Copyright (c) 2025 Kenneth Korkoren, Titus Rotich, Samwel Rotich https://creativecommons.org/licenses/by/4.0
IoT-Enabled Autonomous Waste Segregation System with Real-Time Monitoring for Smart Cities
Urban waste management represents a critical challenge for modern smart cities, where traditional collection methods prove increasingly inadequate due to rapid population growth and environmental concerns. This research presents the development and implementation of an innovative IoT-enabled autonomous waste segregation system that integrates real-time monitoring capabilities for enhanced municipal waste management operations. The proposed system utilizes ESP32-based microcontroller architecture combined with multiple sensor technologies including ultrasonic distance sensors, load cells, and optical classification modules to automatically categorize waste into biodegradable, non-biodegradable, and hazardous categories. Through comprehensive experimental validation, the system demonstrates 92.4% classification accuracy while providing continuous waste level monitoring with alert generation capabilities. The implementation incorporates cloud-based data analytics and a centralized dashboard interface that enables municipal authorities to optimize collection routes and make data-driven operational decisions. Field testing across multiple deployment scenarios reveals significant improvements in collection efficiency, reducing unnecessary trips by 38% and operational costs by 24%. The system's modular design ensures scalability for city-wide deployment while maintaining low power consumption through optimized embedded programming techniques. Results indicate substantial potential for transforming conventional waste management practices through intelligent automation and real-time data insights, contributing to sustainable urban development goals.
Copyright (c) 2025 Jatin Kadyan, Neetu Bala, Shubham Tanwar, Deepanshu Khudotia https://creativecommons.org/licenses/by/4.0
Effectiveness of Facilitated Tucking Position on Level Pain During Pentavalent Vaccination Among Infants at Selected Primary Health Centres, Bengaluru, Karnataka State
The pain associated with such injections is a source of distress for children, their parents and those administering the injections. If not addressed, this pain can lead to procedural anxiety in the future, needle fears and health care avoidance behaviors, including nonadherence with vaccination schedules. A true experimental study aimed to assess the effectiveness of facilitated tucking position on level of pain during pentavalent vaccination among infants in selected primary health centers, Bengaluru. Objectives: 1. To assess the level of pain during the pentavalent vaccination among infants in experimental and control group. 2. To assess the effectiveness of facilitated tucking position on level of pain during pentavalent vaccination among infants in experimental group. 3. To find out an association between the level of pain among infants receiving pentavalent vaccination with their selected demographic variables. Methods: A quantitative research approach, true experimental post- test only control group design was used, with non-probability purposive sampling technique. The sample size was 30 infants in experimental group and 30 infants in the control group. Data was collected from 60 infants using structured interview questionnaire which consists of the demographic, infant variables, maternal variables, clinical variables, parenting factors and Neonatal Infant Pain Scale Score checklist was used to assess the level of pain among infants. Facilitated tucking position was given during pentavalent vaccination to the experimental group and control group received vaccination without the intervention, post-test was scheduled during vaccination. Results: The findings of the analysis revealed that Mean pain score among the infants in the experimental group was 5.0 with SD of 1.37 whereas mean pain score of the infants in the control group was 5.7 with SD of 1.22. The mean difference score was 0.7. The calculated independent ‘t’ test value of t- value= 2.2, was found to be statistically significant at p< 0.05 level and the p- value= 0.03. which infers that there was evidence that facilitated tucking position was effective in reducing pain level among infants during pentavalent vaccination. Interpretation and conclusion: In the present study, the researcher analysed the effectiveness of facilitated tucking position among infants during pentavalent vaccination; the results infer that there was a subsequent reduction in the level of pain in experimental group. Hence it is proved that facilitated tucking position is effective in reducing the pain level among infants during pentavalent vaccination.
Copyright (c) 2025 Julice Varughese https://creativecommons.org/licenses/by/4.0
AI-Powered Automated Resume Screening and Job Matching System Using NLP and Machine Learning
Recruiters in today’s job market are frequently presented with the daunting challenge of screening thousands of resumes for one vacancy position, leading to inefficiency, bias, and lost opportunities. Conventional resume screening processes are not scalable and tend to overlook the best candidates, particularly when subtle skill sets and varying levels of experience are factors. To solve these issues, this paper suggests an automated resume screening system powered by AI that uses Natural Language Processing (NLP) and Machine Learning (ML) to simplify and improve the hiring process. The system is meant to scan unstructured resumes, extract key information like education, skills, experience, and certifications, and compare these features against pre-defined job descriptions. Employing sophisticated NLP methods, the system converts text data into semantic vectors and matches them with job requirement profiles based on similarity measures and classification techniques. It ranks candidates according to their relevance and categorizes them into groups like “fit,” “close fit,” or “not fit,” thereby helping recruiters make informed hiring decisions. In addition, the system features a web-based dashboard through which HR staff and recruiters can upload resumes, enter job specifications, and view candidate matching results in real-time. This easy-to-use interface not only minimizes the cognitive burden on recruiters but also shortens the recruitment cycle and enhances overall recruitment effectiveness. The suggested framework has been tested with real world resume datasets and exhibits high accuracy in matching and classification tasks. With potential uses across industries, this system is a major advancement toward intelligent, equitable, and scalable hiring solutions.
Copyright (c) 2025 Vernika Khatri, Vikas Kumar, Kapil Thakur, Vikash Kumar, Tanisha, Vani, Rishav Yadav https://creativecommons.org/licenses/by/4.0
Parenting Styles and Emotional Regulation in Adolescents
Background: Adolescents with poor emotional regulation often experience difficulties managing impulses, handling interpersonal relationships, and maintaining psychological stability. Research suggests that parenting styles play a pivotal role in shaping the emotional and behavioral development of children and adolescents. Both authoritarian and permissive parenting styles have been associated with emotional dysregulation, low self-esteem, and externalizing or internalizing behavioral problems. Objectives: This study aimed to investigate how various parenting styles contribute to the development of poor emotional regulation in adolescents. It further examined therapeutic approaches that focus on restructuring family communication, improving parental attunement, and promoting authoritative parenting practices to enhance emotional regulation in adolescents. Methods: A comprehensive literature review was conducted, synthesizing empirical findings and theoretical frameworks from studies by Baumrind (1967, 1991), Belsky (1984), Buboltz et al. (2007), and others. Additionally, qualitative research themes were analyzed to identify patterns related to family communication, parental modeling, emotional attunement, and therapy as a behavioral modeling tool. Results: Six major themes emerged: (1) characteristics of emotionally dysregulated adolescents, (2) ineffective communication in the family system, (3) parental modeling of poor emotional regulation, (4) lack of parental attunement, (5) therapy as a modeling technique, and (6) individual work with parents. Supplementary themes emphasized the use of specific therapeutic techniques, including communication training, mindfulness, and relaxation strategies, which correlated strongly with improved emotional regulation outcomes. Conclusion: The findings reinforce existing literature linking parenting styles to adolescent emotional regulation and extend current understanding by emphasizing the therapist’s role as a behavioral model. Integrating family-based interventions, mindfulness training, and authoritative parenting strategies can effectively promote emotional regulation and strengthen family relationships.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
The growing demand for efficient agricultural solutions has paved the way for smart plant monitoring systems. This research presents PMS—an IoT-based system that integrates the ESP8266 Wi-Fi module with soil moisture, temperature, and humidity sensors to enable real-time plant health monitoring. Data is stored in Google Firebase, while a mobile application provides farmers with remote access to plant conditions. A key feature of this system is its automated irrigation mechanism, which activates when soil moisture drops below a predefined threshold, thereby reducing water wastage and ensuring timely watering. This not only improves water efficiency but also minimizes human intervention in routine plant care. During implementation, challenges such as unstable connectivity and variations in sensor accuracy were observed. Despite these limitations, the results demonstrate that PMS can effectively automate irrigation, promote healthier plant growth, and enhance resource utilization. Its scalability and cost-effectiveness make it suitable for precision agriculture, while future enhancements may include AI-based predictive analytics and solar-powered operation for greater efficiency and sustainability.
Copyright (c) 2025 Shreeya Dinesh Rajput, Shrawani Mohan Patil https://creativecommons.org/licenses/by/4.0