UTAR Institutional Repository (Universiti Tunku Abdul Rahman)
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The effects of green finance on employment in developing countries: The role of human capital development
Get PDFDeveloping countries face climate pressures alongside persistent socioeconomic challenges, making green adoption strategies such as green finance essential for sustainable employment creation. Yet, high unemployment and underemployment remain unresolved, job displacement due to rapid digitalization and artificial intelligence, while the transition from fossil fuels to low-carbon industries raises questions about net job outcomes due to limited resources, skills gaps, and uneven growth and inclusivity. This study investigates how green finance influences employment in 88 developing countries from 2008 to 2022, emphasizing the moderating role of human capital development (HCD). Using a dynamic panel approach with two-step System GMM, the results reveal that green finance generally enhances employment, and its effects are amplified when combined with higher levels of HCD. However, contrasts emerge across HDI groups: in low-HDI countries, green finance alone exerts negative employment effects but becomes strongly positive when interacted with HCD; however, in high-HDI countries, green finance shows no significant impact, reflecting structural maturity and capital-intensive transitions. These findings underscore that without sufficient skills and education development; green finance cannot deliver inclusive job growth. Policy implications highlight the need for targeted vocational training, skills-upgrading programs, and complementary institutional reforms to maximize the socioeconomic and environmental benefits of green investments in resource-constrained contexts
Fear of uncertainty, academic self-efficacy, and perceived social support as predictors of life satisfaction among Malaysian undergraduate students
Get PDFUniversity life entails multiple challenges including academic and social adjustments, and future-related uncertainties, that heighten undergraduate students’ vulnerability to mental health issues and ultimately affect their life satisfaction. Therefore, the present study aimed to investigate the predictive effects of fear of uncertainty, academic self-efficacy, and perceived social support on life satisfaction among Malaysian undergraduate students. A cross-sectional study was conducted using purposive sampling method. A total of 397 Malaysian undergraduate students between the age range of 18 to 24 were recruited via online platforms. The instruments used in the present study were Dark Future Scale, Academic Self-Efficacy Scale, Multidimensional Scale of Perceived Social Support, and Satisfaction with Life Scale. A linear regression model showed that academic self-efficacy and perceived social support positively predicted life satisfaction. Meanwhile, fear of uncertainty was not indicated as a significant predictor of life satisfaction. Self-Determination Theory was used as the theoretical framework in this study. The current study supports Self-Determination Theory by showing that academic self-efficacy (competence) and perceived social support (relatedness) significantly contribute to life satisfaction among Malaysian undergraduates, whereas future-oriented fear (future autonomy) does not influence life satisfaction. It extends the theory by situating these needs in an academic context, suggesting the potential role of meaningfulness, and the timing of needs fulfilment. This study contributes to the current literature by identifying potential predictors of life satisfaction in the context of Malaysian undergraduate students. Interventions aimed at improving life satisfaction among Malaysian undergraduate students should be implemented through fostering their academic self-efficacy and promoting their perceived social support
Impact of urea and molasses supplementation and ensiling duration on fermentation quality and nutritional composition of silage: Monitoring pH and lactic acid concentration
Get PDFSilage is fermented forage produced by ensiling foliage crops with additives under anaerobic conditions, which improves nutrient content for livestock feeding. This study investigated the effects of urea and molasses supplementation on the fermentation quality and nutritional composition of Common Napier grass (Cenchrus purpureus [syn. Pennisetum purpureum]) silage at different ensiling durations. The four treatments were: Control (Napier grass only), 0.5% urea, 5% molasses, and a combination of 0.5% urea + 5% molasses. Each treatment was ensiled for 7, 14, or 30 days. Fermentation quality
was assessed based on dry matter (DM), crude protein (CP), neutral detergent fibre (NDF), and acid detergent fibre (ADF), while nutritional composition was evaluated through pH and lactic acid concentration (LAC). Significant differences (P 0.05) among treatments. Regarding ensiling duration, significant differences (P < 0.05) were found for ADF, NDF, DM, CP, and LAC, after 30
days, silage had the lowest ADF (37.08 ± 1.04%), NDF (47.67 ± 1.25%) and DM (16.42 ± 1.11%) contents, along with the highest CP (13.98 ± 0.36%) and LAC (35.20 ± 6.58 g/L) values. Although pH decreased with longer fermentation duration, the changes was not statistically significant (P > 0.05). Overall, the results indicate that adding 5% molasses enhances fibre degradation, lactic acid production, and overall fermentation quality of Napier grass silage, while a 30-day ensiling period increases protein content and lactic acid production, and reduces fibre content, indicating improved preservation and potential feed value
Apply and optimize machine learning algorithms for estimating battery health
Get PDFWith the growing demand for energy-efficient and reliable battery-powered systems, accurate estimation of battery State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL) must be monitored to ensure safety, performance, and longevity. Traditional estimation techniques such as Coulomb counting and model-based approaches often suffer from error accumulation, calibration complexity, and poor adaptability to dynamic conditions.
This project investigates machine learning (ML) techniques for estimating the SOC and RUL based on Electrochemical Impedance Spectroscopy (EIS) data. A range of regression and classification models including Random Forest (RF), Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN) were evaluated on both full-frequency and single-frequency EIS inputs. Results show that full-spectrum EIS features provide superior predictive performance, with Random Forest excelling in regression tasks and ANN achieving the highest classification accuracy. For RUL estimation, ANN and CNN-SAM models demonstrated competitive accuracy compared to baseline Gaussian Process Regression, effectively capturing degradation patterns across different operating temperatures.
To enable deployment on resource-constrained embedded systems, pruning and quantization techniques were employed to compress model size while preserving predictive accuracy. Optimization reduced ANN size from 260 kB to 26 kB and CNN-SAM from 1679 kB to 158 kB, confirming that lightweight yet robust models can be achieved without significant performance loss.
The findings confirm the potential of integrating EIS data with optimized ML models for real-time battery state estimation. This work provides a pathway toward practical, efficient, and intelligent BMS capable of supporting the growing adoption of lithium-ion batteries in diverse applications
Development of a multi-agent chatbot for user query resolution for UTAR
Get PDFUniversities generate vast amount of information daily, including programme details, course
structures, schedules, policies, and procedures. This information is often distributed across
multiple sources such as university websites, portals, and PDF documents, making it difficult
for students, staff, prospective applicants, and parents to quickly access accurate and up-to
date details. At Universiti Tunku Abdul Rahman (UTAR), this challenge highlights the need
for a unified and intelligent information access system. To address this, the project proposes
and develops a multi-agent Retrieval-Augmented Generation (RAG) chatbot designed
specifically for UTAR. The chatbot architecture employs specialized agents namely
Admissions Agent, Finance Agent, and Examinations Agent each connected to its own vector
database containing structured knowledge extracted from official university sources. Data
ingestion is automated through a web scraping and PDF download module that handles
inconsistencies such as broken SSL certificates on UTAR domains, ensuring reliable and up
to-date knowledge collection. The system integrates OpenAI API service as the base large
language model (LLM), with LangChain for orchestration, Chroma as the vector database,
Flask for backend development, and React for the frontend user interface deployed on Firebase.
The backend is deployed on Render to support scalability, concurrency, and real-time
availability. Evaluation was carried out using technical performance testing alongside user
experience testing through a structured Google Form survey. The results show that the chatbot
delivers accurate and contextually relevant answers within an acceptable response time, while
user feedback indicates strong satisfaction with ease of use, usefulness, and willingness to reuse
the system. Open-ended responses also highlighted areas for improvement, such as expanding
departmental coverage and tighter integration into UTAR’s official website. By enabling 24/7
access to official university knowledge sources, the chatbot improves information accessibility
and user satisfaction, demonstrating the feasibility of applying multi-agent RAG architectures
in the higher education context. Future enhancements will focus on integration with UTAR’s
official platforms, and extending the system to additional departments and more
The influence of income level, home ownership, attitude, perceptions and experience on public preparedness for disasters across different age group in Penang
Get PDFThis study explores how income level, homeownership, attitude, perceptions, and experiences affect disaster preparedness across age groups in Penang. With rising risks of floods, landslides, and earthquakes, it is vital to understand the socioeconomic and psychological factors shaping readiness. A total of 384 respondents from Generations X, Y, and Z were surveyed using a structured questionnaire within a quantitative framework. Factor analysis, reliability testing, correlation, and multiple regression were employed to assess the relationships between variables and preparedness. Results indicate that attitude and perception significantly enhance preparedness behaviors across all generations, highlighting the central role of psychological factors. Income level shows a small but significant positive effect, while homeownership exerts a positive yet non-significant influence. Conversely, prior disaster experience has a significant negative effect, suggesting that previous encounters may reduce rather than strengthen preparedness. These findings underscore the importance of integrating socioeconomic and psychological perspectives in disaster risk management. While financial and institutional factors contribute to readiness, attitudes and perceptions remain the strongest drivers. The study recommends age-specific preparedness programs, community training, and targeted awareness campaigns to address generational differences. Such strategies can strengthen resilience and promote a culture of preparedness in Penang. Keywords: Disaster Preparedness, Income Level, Home Ownership, Attitude, Perception, Past Experience, Age Groups, Penang Subject Area: HV551.2-639 Emergency managemen
Techtutor: AR-integrated learning application for computer and technology use
Get PDFIn today’s digital era, the ability to operate basic computer technologies has become
increasingly important. However, many beginners and older adults continue to face
difficulties in learning digital tools such as email, word processors, and calculators,
mainly due to the lack of accessible and interactive resources. This project addresses
this issue by developing TechTutor, an Augmented Reality (AR)-integrated and
Artificial Intelligence (AI)-assisted mobile learning application designed to provide a
simple, intuitive, and engaging platform for digital literacy. TechTutor was developed
using Unity 2022.3.57f1, AR Foundation (ARCore and Vuforia), and OpenAI
integration, following the Agile Software Development methodology. The system
consists of 3 main components: AR Learn, which enables users to identify computer
components, interact with 3D models, and attempt AR-based quizzes; AR Guide,
which provides step-by-step AR tutorials for tasks such as sending an email, using
Microsoft Word, the Snipping Tool and a calculator; AI Tutor, which offers chatbotbased assistance with both text and image inputs, enhanced by EasyOCR for keyword
detection and contextual guidance. Speech-to-Text (STT) and Text-to-Speech (TTS)
technologies were integrated across the modules to enable hands-free navigation,
interactive feedback, and accessibility for diverse user needs. The novelty of TechTutor
lies in combining AR-driven experiential tutorials and AI-powered assistance with
keyword highlighting, creating an adaptive learning environment tailored for users with
minimal prior exposure to computers and technology. By delivering immersive,
interactive, and personalized guidance, TechTutor aims to reduce the digital divide and
promote inclusive technology use among underserved groups, particularly the
beginners and older adults
Profmate: AI-powered lecturer assistant
Get PDFProfMate is a cutting-edge AI-powered lecturer assistant aimed at revolutionizing the way
academic tasks are managed and executed. The project aims to address the inefficient and timeconsuming aspects of manual grading and administrative duties through the integration of
machine learning and automation technologies. ProfMate comprises several core modules: (1)
an AI-driven image recognition system designed to assess and validate the accuracy of
manually marked papers, identifying missed questions and verifying correct score
computation; (2) an automated grade entry tool that streamlines the process of recording and
updating student grades; (3) a scheduling interface that facilitates seamless appointment
booking between students and lecturers via a shared calendar; (4) a comprehensive login
system that distinguishes between student and lecturer access, enabling efficient management
of grades, meetings, and review tools; and (5) a results portal that aggregates and presents
outcomes from paper reviews and assessments. By leveraging these technologies, ProfMate
aims to significantly enhance the efficiency, accuracy, and overall effectiveness of academic
administration, thus allowing educators to devote more time to teaching and student
engagement. The proposed solution promises to transform educational management, offering
a more organised and responsive approach to handling academic tasks
The interrelationship between crude oil price, exchange rate and stock index: an analysis on 5 oil-producing countries
Get PDFThis chapter describes the study's scope using the problem statement and research background. The presentation of the study purpose, research questions, and hypothesis serves as a guide for the inquiry. Furthermore, this report has recognized the study's relevance. Finally, this chapter will conclude with the study's outline.
This study focuses to investigate the interrelationship between crude oil prices, exchange rates and stock indices on the top 5 oil-producing countries: the United States, Saudi Arabia, Russia, Canada, and China. By exploring these relationships, the study seeks to identify long-run trends as well as potential causal and significant relationships that can guide financial and investing decisions in oil-dependent economies
Studymate: a smart mobile task manager for peak student productivity
Get PDFThis project aims to address critical challenges in academic task management through the development of an innovative task management system tailored specifically for any level of students who need a personalized assistant with their studies. Many existing productivity tools fall short in key areas such as task prioritization, real-time collaboration, and progress tracking, leading to confusion, miscommunication, and increased stress. This system introduces several advanced features, including a dynamic task prioritization algorithm, real-time collaboration capabilities, and comprehensive project tracking and visualization tools. The system seeks to enhance students' organizational efficiency, improve collaboration, and provide clear visibility into task progress. The project employs a prototyping approach and utilizes modern technologies to develop a robust platform that addresses the unique needs of students in managing their academic workloads