Journals of Universiti Tun Hussein Onn Malaysia (UTHM)
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An Analysis of Industry-Specific Variations and Sectoral Differences in Sustainability Disclosure among Indian Companies
No full textThe importance of understanding industry-specific variations and sectoral differences in sustainability disclosures is critical in today\u27s corporate landscape. Rapid urbanisation and industrialisation in India have created significant challenges for manufacturing companies, including environmental degradation, income inequality, and greenwashing. Balancing sustainability with economic growth remains a persistent issue, particularly in a developing economy like India. This study assesses the degree of industry-specific variations and sectoral differences in sustainability disclosures within the annual reports of 22 Indian manufacturing companies across various sectors. The study adopts a quantitative method and ex post facto research design, utilising data from the annual reports of sampled manufacturing companies for the fiscal years 2021–2022. The total enumeration sampling technique was employed, and data were validated through cross-examination of the annual reports by auditors. Content analysis was used to address key questions regarding document selection, environmental disclosure criteria, data processing, and scoring. Despite the increasing adoption of frameworks like the Global Reporting Initiative (GRI), International Integrated Reporting Council (IIRC), and Business Responsibility and Sustainability Reporting (BRSR-SEBI), greater knowledge and integration of these standards remain necessary. The findings highlight industry-specific trends, with some sectors demonstrating stronger commitments to sustainability reporting. The study concludes that Indian companies show a growing dedication to transparency and the adoption of sustainability frameworks, though sectoral differences persist. These findings underscore the need for standardized reporting practices and greater alignment with global sustainability goals to enhance corporate accountability and environmental stewardship
Examining the Mediating Effect of ICT Adoption on Personnel Training and Technological Infrastructure in Local Government Revenue Generation
No full textThis study provides valuable insights into the dynamics of Information and Communication Technology (ICT) adoption concerning revenue generation within the Nigerian local government system. By analysing the mediating role of ICT adoption in relation to personnel training, technological infrastructure, and revenue collection, this study advances the understanding of how technology serves as a catalyst for converting human and technological resources into tangible organizational outcomes. This study investigates beyond surface-level interactions to reveal the complex interplay among these variables through a quantitative method of data collection and analysis. Data were collected through a structured questionnaire from the revenue staff of Ilorin West Local Government of Kwara State, Nigeria. The findings reveal that well-trained personnel and robust technological infrastructure synergistically contribute to the adoption of ICT, resulting in optimized revenue collection mechanisms. Thus, this study underscores the transformative potential of technology, positioning it as a cornerstone for organizational growth and efficiency. The findings provide a framework for comprehending the pathways through which technology permeates various dimensions of local government organizations, leading to enhanced revenue management and performance
Pengetahuan, Sikap dan Amalan Pelajar dalam Peralihan Malaysia ke Negara Industri Rendah Karbon: Students\u27 Knowledge, Attitudes, and Practices in Malaysia\u27s Transition to a Low-Carbon Industrial Nation
No full textMalay:
Peralihan ke arah negara perindustrian rendah karbon merupakan langkah penting dalam menangani perubahan iklim dan mempromosikan pembangunan mampan. Malaysia telah berkomitmen untuk mencapai pelepasan karbon sifar bersih, yang memerlukan penglibatan aktif daripada pelbagai sektor, termasuk pendidikan. Kajian ini menilai tahap pengetahuan, sikap, dan amalan (KAP) berkaitan kelestarian rendah karbon dalam kalangan pelajar Ijazah Sarjana Muda di Universiti Tun Hussein Onn Malaysia (UTHM) sebagai persediaan terhadap peralihan Malaysia ke arah industri rendah karbon. Menggunakan pendekatan kuantitatif, soal selidik dalam talian telah diedarkan kepada 358 responden bagi menilai kesediaan mereka terhadap amalan rendah karbon. Dapatan kajian menunjukkan tahap pengetahuan, sikap dan amalan berada pada tahap tinggi, mencerminkan kesedaran yang kukuh terhadap kelestarian alam sekitar. Walau bagaimanapun, analisis ujian-t bebas mendapati tiada perbezaan yang signifikan antara jantina dalam aspek KAP rendah karbon. Kajian ini menekankan keperluan untuk memperkukuh inisiatif pendidikan kelestarian, khususnya dalam mempertingkatkan amalan rendah karbon dalam kalangan pelajar bagi menyokong matlamat pembangunan mampan negara.
English:
The transition to a low-carbon industrial nation is a crucial step in addressing climate change and promoting sustainable development. Malaysia has committed to achieving net-zero carbon emissions, requiring active participation from various sectors, including education. This study assesses the level of knowledge, attitudes, and practices (KAP) regarding low-carbon sustainability among undergraduate students at Universiti Tun Hussein Onn Malaysia (UTHM) in preparation for Malaysia’s transition towards a low-carbon industrial nation. Using a quantitative approach, an online survey was conducted among 358 respondents to evaluate their readiness for low-carbon practices. The findings indicate high levels of knowledge, attitudes and practices demonstrating strong awareness of environmental sustainability. However, an independent t-test analysis revealed no significant gender differences in KAP levels. This study highlights the need to enhance sustainability education initiatives, particularly in reinforcing low-carbon practices among students to support the country’s sustainable development goals
Transforming TVET with AI: Economic Benefits of Technological Innovation in Education
No full textTVET is indispensable for building up the skilled human capital which is vital for the competitiveness of the economy and enhancing social cohesion. This systematic review focuses on the adoption of AI in TVET to assess its potential to revolutionize the sector and contribute to economic development. The research includes identification of the key academic databases containing articles and papers relevant to the AI in TVET, and emphasis on the recent publications. This is in line with the key findings highlighted above in sections on; Personalisation in learning. Administrative automation in institutions, AI and Industry needs at TAFE Queensland and Nanyang Polytechnic institutions. The review finds that AI in TVET positively impacts employment prospects, alleviates costs, and fosters innovations, implying that coordinated partnerships and policies should be pursued to optimise the AI benefits and contribute to sustainable economic development
The Effect of Hope on The Psychological Well-Being of Police Officers in Perak
No full textThe psychological well-being of police officers is a critical area of research, particularly given the unique challenges and stressors associated with law enforcement work. Police officers are prone to witnessing shocking incidence and exposure to traumatic events which can therefore put them at risk of stress disorders; such include PTSD and other psychological conditions. This will impact police officers individually as well as the organization in its unit interactions with the community. In this regard, it is important to understand the mechanisms through which hope promotes psychological wellbeing. Research shows that encouraging hope can benefit police officers\u27 resilience, mental health, and job satisfaction—all of which are critical for preserving their efficacy and wellbeing in high-stress situations. This study intends to investigate the effect of hope on psychological well-being. 137 police officers from Perak participated in this study. The approach used is quantitative by using online survey questionnaire by utilizing random sampling technique. Statistical Package for the Social Science (SPSS) software was used to analyze the collected data. The findings show that police officer in Perak has high level of psychological wellbeing. Hope showed significant relationship with psychological well-being. Future studies should investigate how police forces may optimize hope for varying demographic groups in order to improve psychological well-being in general
Design and Fabrication of an Innovative Rattan Splitting Machine for Efficient and Sustainable Production of High-Quality Rattan Material
No full textThis research was conducted to address the limitations of the traditional rattan splitting process, which had been time-consuming, labour-intensive, and heavily reliant on manual labour to produce thin strips for furniture and handicrafts. To improve productivity and alleviate physical strain on workers, a fully mechanical rattan splitting machine was developed and tested, purposely designed without modern technology that could be unfamiliar to the labour force. The machine was built with a durable blade system that precisely split rattan into thin, uniform strips, ensuring consistent, and high-quality results. Its compact and adaptable design included adjustable settings to accommodate different rattan sizes and thicknesses, powered by electricity for straightforward operation and maintenance. Research involved meticulous engineering design, material selection, prototype construction, and performance testing to measure efficiency, precision, and consistency in output quality. Key findings revealed significant reductions in manual labour requirements, increased production speed, and uniform quality in processed rattan strips. These results indicated that the machine had greatly enhanced productivity within the rattan industry, enabling larger-scale production with consistent quality. In conclusion, the mechanical rattan splitting machine provided a practical and efficient solution for the industry, transforming a labour-intensive process into a streamlined operation that increased productivity, lowered labour costs, and maintained product standards. This development represents a significant advancement for the rattan industry, offering a sustainable solution that meets the current capabilities of the workforce while supporting industry growth and modernization
Ultrasonication-Assisted Preparation of Virgin Coconut Oil-Kelulut Honey-Vitamin E Emulsion and Its Bioactivity for Lip Balm Application
No full textThe growing demand for natural and eco-friendly skincare products has spurred innovation in cosmeceutical formulations, including lip balms. This study developed an emulsion-based lip balm incorporating Virgin Coconut Oil, Kelulut honey, and vitamin E, utilizing ultrasonication to enhance stability and bioactivity. The moisturizing, antibacterial, and antioxidant properties of these ingredients were combined with surfactants (Tween 20, soy lecithin, or a 50:50 blend) and processed through ultrasonication and/or homogenization. Bioactivity was assessed using pH, Total Phenolic Content (TPC), Total Flavonoid Content (TFC), 2,2-Diphenyl-1-Picrylhydrazyl (DPPH), and Ferric Reducing Antioxidant Power (FRAP) assays. Sample F (50:50 blend with ultrasonication) demonstrated the highest TPC (38.95 µg GAE/mL), TFC (5.59 µg QE/mL), DPPH inhibition (17.14%), and FRAP activity (2.68 µg GAE/mL), with a skin-compatible pH (4.80–5.50). The storage stability test conducted at 4°C for one week revealed visible phase separation in all samples, with samples B, D, and F (with ultrasonication) demonstrating comparatively less separation than their respective counterparts A, C, and E, (homogenization only) indicating enhanced emulsion stability under chilled conditions. Sensory evaluation identified Sample F as the most preferred, excelling in texture, spreadability, and absorption. This study underscores the significance of ultrasonication, surfactant synergy, and ingredient selection in developing sustainable, high-performance cosmeceutical formulations
CFD Analysis on the Effects of Various Train Lengths on Aerodynamic Loads and Flow Structure for Train Travelling Through Various Crosswind Conditions
No full textTo ensure that railroad vehicles operate safely in crosswind conditions, it is essential to take into account the aerodynamic contribution of train length. As a result, this study aims to investigate the effect of different lengths of the Next-Generation High-Speed Trains (NG-HST) model when traveling under various crosswind conditions in terms of aerodynamic loads and flow structure formation with a Computational Fluid Dynamics (CFD) technique known as Reynold-Averaged Navier Stokes (RANS) combined with the k-epsilon (k−ε) turbulence model. Based on the train model\u27s height and speed, the Reynolds number used is 1.3 x 106. The two aerodynamic performance characteristics, aerodynamic loads and flow structure formation, were analyzed using different train lengths: Case 1 (1 middle coach), Case 2 (3 middle coaches), and Case 3 (5 middle coaches) and varied crosswind yaw angles: 0°, 15°, 30°, 45°, and 60°. The findings indicate that as the crosswind yaw angle and train model’s length increase, more flow comes into contact with the train model\u27s surface on the windward side, resulting in a huge area of the high-pressure region and low-pressure region on the windward and leeward sides, respectively. In addition, the side force coefficient increases for the 60° crosswind yaw angle by about 12% for Case 3 (train with 5 middle coaches) compared to Case 1 (1 middle coach). Therefore, it can be concluded that the longer the train, the more pronounced the aerodynamic forces under high crosswind conditions, which may negatively impact stability and operational safety
Classifying Nutrient Deficiencies in Palm Oil Leaves Using Convolutional Neural Network with Class Weights and Early Stopping Techniques
Get PDFMonitoring the health level of palm oil plants guarantees optimal production and quality oil yield. Experts have historically assessed plant health visually, but this method is limited by the number of samples that can be evaluated. Through automatic feature extraction, this study uses Convolutional Neural Networks (CNN) to classify nutrient deficiencies in oil palm leaves through leaf image analysis. Nevertheless, class imbalances in the dataset can lead to biased predictions. A CNN model with a class weight and an early stopping technique was developed to address this. A CNN model with three layers and a SoftMax activation function was trained over 200 epochs using Adam\u27s optimizer and a categorical cross-entropy loss function to overcome this problem. According to the study, class weighting improves the classification accuracy of oil palm leaf photos. The classification accuracy of boron and potassium increased from 0.6119 to 0.7015 and 0.7500, respectively. However, magnesium classification still presents a challenge as accuracy drops to 0.4615, indicating the need for additional strategies to improve model performance across all nutrient classes
An IoT-Based Active Learning Convolutional Neural Networks Model for Predicting River Water Quality
Get PDFThe conventional procedures used to check river water quality involve slow processes that require significant expenses and fail to deliver immediate results. Without automated predictive systems, there are no capabilities to detect pollution trends ahead of time. The goal of this investigation is to establish an Internet of Things (IoT)-driven deep learning model that conducts real-time river water quality predictions through Deep Neural Network (DNN) algorithms with enhanced precision and effectiveness. This study uses an IoT system to acquire pH parameters and a DNN to predict water quality in the Tigris River of Iraq. Subsequently, an IoT-based Active Learning Convolutional Neural Network (IoT-ALCNN) model is developed to predict river water quality. The model is trained by a backpropagation algorithm that has an active learning mechanism. This mechanism provides an interface to enable the user to update the training data to improve the learning procedure. The performance of the IoT-ALCNN model is assessed through the use of classification assessment metrics. The performance of the model fit to the data is evaluated through the relationship between the residuals and updated values. The test results of the IoT-ALCNN model show that it can be used as a method for predicting and monitoring river water quality, as it achieves the best accuracy of 98.72% and outperforms other baseline models of both generalization and efficienc