Journals of Universiti Tun Hussein Onn Malaysia (UTHM)
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Environmental Impacts of Commercial Haulage Operations on Air Quality in Nigeria
Get PDFThe operation of large haulage vehicle parks in Nigeria significantly contributes to ambient air pollution, posing serious environmental and health risks. This study investigates the adverse environmental consequences of air pollutants released by haulage vehicles in a major highway park connecting Osun state to other parts of Nigeria. Focusing on Oke Ese, Ilesha, Nigeria, the research identifies primary pollutants, measures concentrations, and analyzes air quality impacts. Total Suspended Particulates (TSP) and X-ray Fluorescence (XRF) analyses were conducted across various sample sites, including the truck park and surrounding residential areas. Findings reveal that Total Suspended Particulate (TSP) concentrations range from 83.14 μg/m3 to 720.59 μg/m3, exceeding Nigerian air quality standards and posing health risks to nearby residents. Average ambient sampling values were 404.86 μg/m3, significantly higher than the FEPA standard of 250 μg/m3. Source Sampling and residential sampling averages were 990.2μg/m3 and 235.29 μg/m3, respectively. XRF analysis detected high levels of magnesium in truck exhaust, with a maximum value of 189.25106 ×106 (µg/m³), far exceeding the US National Ambient Air Quality Standard (NAAQS) of 100 µg/m³. The study concludes that stricter emission regulations and alternative energy solutions are necessary to mitigate environmental and public health impacts. Achieving recommended air quality guidelines could save millions of lives globally, emphasizing the importance of addressing air pollution. The research underscores the need for urgent action to protect the health and well-being of communities surrounding haulage vehicle parks in Nigeria
Comparison of Myocardial Mechanical Metrics in Electromechanical Model vs. Fluid-Electromechanical Model
Get PDFThe development of multiphysics heart model has grown tremendously over the past decade. In this paper, we compare two multiphysics approaches: electromechanics and fluid-electromechanics, in simulating left ventricular mechanics and the output of mechanical metrics. Cardiac electromechanical (EM) model refers to the approach of simulating heart mechanical deformation, triggered by cardiac action potential, while the generated ventricular pressure is determined by a penalty function and applied uniformly across the endocardium. Fluid-electromechanics (Fluid-EM) approach relies on similar action potential wave to trigger mechanical deformation but the ventricular pressure is determined by solving the Navier-Stokes equations within the ventricular cavity. Thus, Fluid-EM is more accurate as it models the blood-ventricular interaction, producing more realistic loading on the endocardium and enabling analysis of blood flow dynamics. Due to its complexity, the Fluid-EM approach is more computationally demanding than the EM approach. We assessed several mechanical metrics within the ventricle namely stresses and strains to assess regional differences in the heart by implementing both approach in a heart geometry extracted from a healthy patient. Differences in the mechanical metrics were noted indicating both models were loaded differently due to differences in modelling the blood. This suggests that in greater differences can be expected should there be more regional differences in the myocardium such as in infarct cases
Long-term Continuous Monitoring of Dissolved Oxygen Concentration Based on Multi-Spectral Sensors and Machine Learning
Get PDFDissolved oxygen (DO) is a crucial indicator of water quality and requires continuous monitoring across various applications. Although optical sensors are widely used for DO measurement, their large-scale deployment for long-term monitoring remains challenging due to high costs and the need for periodic replacement of sensing probes. This study presents a novel non-contact DO monitoring system that integrates a low-cost multispectral sensor with an optimized machine learning framework, offering a practical solution for long-term, continuous monitoring. A compact spectroscopic sensing unit was developed to continuously acquire absorbance data from water samples across 18 wavebands ranging from 410 to 940 nm. Multiple machine learning models were trained under different configurations, and several waveband selection algorithms were applied to identify the optimal predictive model. The neural network regression model utilizing four wavebands (460, 585, 680, and 760 nm) achieved the best result with a coefficient of determination and a root mean square error of 0.99 and 0.22 mg/L, respectively. These findings demonstrate the high accuracy and practical potential of the proposed system for long-term DO monitoring in aquaculture and environmental applications
Solar Power Forecasting of 8 MWp Solar Farm Malacca using LSTM-based Model with Weather Forecast Data: A Case Study of Malaysia
Get PDFMalaysia\u27s tropical climate offers significant potential for photovoltaic (PV) installations due to abundant solar irradiance. However, the variability in solar energy generation due to weather fluctuations presents challenges for grid integration and energy reliability. The study evaluates the performance Univariate LSTM, Multivariate LSTM (with weather sensor data), Multivariate LSTM (with weather sensor and meteorological data), and Bidirectional LSTM (Bi-LSTM) models using input data from 8 MWp solar farm in Ayer Keroh, Malacca, along with weather sensor and meteorological data. Results show that the Univariate LSTM model consistently outperformed others across all forecasting horizons, achieving the lowest error metrics (MAE: 0.0275, MSE: 0.0037, RMSE: 0.0611) and the highest R² value (0.94), making it the most reliable choice for both short- and long-term forecasts. However, weather uncertainty remains a significant challenge, directly impacting solar power production. Thus, Multivariate LSTM models is more practical. From the result, Multivariate LSTM with weather sensor and MET input data demonstrated some advantages as its gives lowest error metrics (MAE: 0.0375, MSE: 0.0044, RMSE: 0.0664) and the highest R² value (0.92), compared to Multivariate LSTM with weather sensor input and Bi-LSTM model. However, at intermediate horizons the accuracy is decreased which might be caused by the increased complexity of meteorological inputs. In contrast, the Bi-LSTM model performed the weakest, with the highest error metrics, suggesting potential overfitting or limited generalization capabilities. This research provides valuable insights into the trade-offs between model simplicity and performance including across different forecasting horizons in renewable energy forecasting
Investigating The Effect of Piezoelectric Material Type on Biomechanical Energy Harvesting Efficiency
Get PDFThis paper investigates the influence of piezoelectric material type on the performance of biomechanical energy harvesters, with a particular focus on footstep and finger-mouse-click energy-harvesting applications. A combination of design modelling and finite element analysis was employed to assess the energy conversion efficiency of various piezoelectric materials and their adaptability to dynamic forces generated by human motion. The findings indicate that PZT5H is the most efficient material for power generation in footstep harvesters, producing the highest power output of . Moreover, increasing the tip mass in the footstep model was found to enhance the voltage output further, reaching up to 3 . In contrast, PZT4 exhibited the highest energy harvesting efficiency from mouse-click motion, generating a power output of . These findings offer in-depth insight into the capabilities of piezoelectric materials for integration into self-powered biomedical and wearable energy-harvesting applications
Dual-Layer Self-Healing Coatings for Carbon Steel: A Sustainable Approach to Enhanced Performance and Durability
Get PDFThe industrial sectors are continually searching for durable materials with high resistance to wear. While traditional coatings are commonly used, they tend to deteriorate over time, resulting in costly repairs and negative environmental impact. Therefore, this study investigates the development of single and double-layer smart coating systems designed to enhance both durability and sustainability for carbon steel, particularly in harsh environments. The double-layer smart coating (DL-SC) incorporates benzotriazole and boiled linseed oil within an epoxy matrix. The DL-SC is applied in two layers, while the single-layer smart coating (SL-SC) with the same composition is used for comparison. Characterization techniques, including Fourier transform infrared (FTIR) spectroscopy and field emission scanning electron microscopy (FE-SEM), confirm the successful incorporation of the self-healing agents and corrosion inhibitors into the coatings. Additionally, the adhesion strength of DL-SC retained 68.93% of its adhesion strength after immersion in a 3.5 wt% NaCl solution in contrast to 61.05% for SL-SC and 47.92% for traditional epoxy coatings, which highlights the enhanced durability and long-term sustainability of the double-layer system. The enhanced performance of DL-SC is due to the efficient release of BTA and BLO in response to external stimuli, providing extended protection. In conclusion, the double-layer smart coating provides superior durability and long-term protection for carbon steel compared to single-layer
Enhancing Spare Parts Inventory Control in Automotive SMEs: A Digital Approach with Google Tools Integration
Get PDFSmooth operation of manufacturing processes is critical for maintaining production efficiency, and machine downtime can significantly disrupt operations, leading to substantial financial losses. One of the primary contributors to extended downtime is the unavailability of replacement parts due to inadequate spare parts management. Traditional manual inventory control often results in unclear spare part statuses, leading to stockouts and further production delays. This study aims to digitalize and optimize spare parts inventory management for small and medium-sized enterprises (SMEs) in the automotive sector by developing an integrated system using Google-based tools. The system leverages Google Sheets as the central database, AppSheet for mobile-based data input and transaction management, and Looker Studio for real-time visualization of inventory status through interactive dashboards. Key features of the system include stock level alerts, barcode scanning for faster data entry, and predictive analytics for demand forecasting. The integration of these tools creates a cohesive system that eliminates manual record-keeping, enhances the accuracy of spare parts tracking, and prevents issues such as stockouts and overstocking. Additionally, the system allows easy access to critical information, including supplier details, pricing, and specifications. Through effective spare parts management, the system not only reduces machine downtime but also optimizes operational costs and improves decision-making processes in inventory management, contributing to more efficient and cost-effective manufacturing operations
Faktor Peribadi dan Persekitaran yang Mempengaruhi Pelajar Kolej Komuniti Meneruskan Kerjaya dalam Bidang Pertanian: Personal and Environmental Factors that Influenced College Community Students to Pursue Career in Agriculture Sector
Get PDFMalay:
Kekurangan tenaga pekerja mahir dalam sektor pertanian akan menjadi masalah sekiranya jumlah kemasukan pelajar baharu ke kolej pertanian yang semakin menurun tidak dibendung. Pelbagai faktor seperti persepsi kurang baik terhadap sektor pertanian menyebabkan pelajar tidak berminat untuk menceburi bidang ini. Maka, kajian ini bertujuan untuk mengkaji faktor-faktor yang mempengaruhi penyertaan pelajar kolej komuniti dalam meneruskan kerjaya dalam bidang pertanian. 80 orang mahasiswa daripada Kolej Komuniti Jerantut, Kolej Komuniti Bera, Kolej Komuniti Tambunan dan Kolej Komuniti Rembau telah menjadi responden dalam kajian ini. Borang soal selidik diedarkan secara dalam talian (Google Forms) melalui wakil institusi. Data telah dianalisis secara deskriptif, skewness, korelasi dan regrasi dengan menggunakan SPSS versi 27.0. Secara keseluruhan, hasil kajian mendapati faktor peribadi dan faktor persekitaran mempunyai hubung kait yang signifikan dengan tahap penyertaan pelajar dalam meneruskan kerjaya dalam bidang pertanian dan elemen minat merupakan faktor yang paling mempengaruhi pelajar dalam menyertai bidang pertanian.
English:
The shortage of skilled labour in the agricultural sector will become a problem if the decline in new student enrollment in colleges is not curbed. Various factors, such as negative perceptions of the agricultural sector, cause students to lose interest in pursuing this field. Therefore, this study aims to examine the factors that influence the participation of community college students in continuing their careers in agriculture. Eighty students from Jerantut Community College, Bera Community College, Tambunan Community College and Rembau Community College were respondents in this study. Questionnaires were distributed online (Google Forms) through institutional representatives. Data were analyzed descriptively, using skewness tests, correlations and regressions using SPSS version 27.0. Overall, the results of the study found that personal factors and environmental factors have a significant relationship with the level of student participation in continuing their careers in agriculture, and the element of interest is the factor that most influence students in participating in the agricultural field
Assessing Poverty and Unemployment in Sabah: The Role of Mobile Job Matching Platforms in Reducing Economic Disparities
Get PDFSabah, also called the Negeri di Bawah Bayu, is the second largest state in Malaysia, covering 72,500 square kilometres. Sabah is the world\u27s third-largest island in northern Borneo (Press Release Re-Discovering Sabah, 2023). The Sulu Sea also encloses the state in the northeast, the Celebes Sea in the east, and the South China Sea in the west (Sabah Government Official Website, n.d.). Despite its vast population, the state has a high poverty rate. In order to achieve the research aim, which is to identify the reasons for the existence of a population with poverty in Sabah. Quantitative research design has been employed as the primary method to achieve the research objective. A set of online surveys has been conducted with 383 respondents from Kota Marudu to identify the poverty population and the reason for poverty in Sabah. Kota Marudu was chosen as the sampling of the research because it was recorded as the poorest city in Sabah (Chan, 2022; Azzeri et al., 2020). Therefore, the researcher also used Google Forms to analyse the data collected from the online survey. As the results revealed, Sabah has a high poverty rate, which is caused by several reasons: foreign immigration, inadequate infrastructure, and rural neglect. Undoubtedly, the unemployment rate has reached its peak figure, which has led users to use the job matching platform on mobile applications to reduce unemployment in Sabah. This study significantly helps to reduce poverty, offers a better standard of living, and influences Sabah\u27s socioeconomic status
Exploring Data-Driven Culture in The Construction Industry: Insights from Industry Practitioners
Get PDFIn previous studies, attributes, sub-attributes and indicators of data driven culture has been studied within the organizations specifically for the construction industry. Nevertheless, there has been no further study conducted on the perspective of construction industry personnels about their viewpoint on the indicators associated with a data-driven culture. Thus, the aim of this research is to cover the gap of data by identifying “perspectives” of industry practitioners on data driven culture in the construction industry. The study started by using Systematic Literature Review (SLR) as a method of literature study, then obtaining validation from 18 practitioners from construction industry analysed by using thematic analysis, and Relative Importance Index (RII). The highest RII value recorded based on each sub-attributes listed are: D1 – knowledge and expertise, ID3 – availability of fund, C4 – competency, U7 – building model exploration, P1 – register based statistics, P3 – information extraction and P8 – control strategy. The finding of this study benefit construction industry stakeholders to become more skilled, cooperative and dynamic through the documented acknowledgment of their perspective as practitioners in digital construction as emphasized in Malaysian Digital Construction and Industry 4.0 Roadmap 2020-2025