Journals of Universiti Tun Hussein Onn Malaysia (UTHM)
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Modelling Learning in Technical Vocational Education through Power Law: A Study of Textile Garment Machinery Operation
Get PDFThe development of practical competencies in technical and vocational education and training (TVET) has garnered significant interest in recent years, particularly in applying tools for measuring and understanding the learning process. This study aims to model and analyze the learning process of three high-performing students in operating straight stitch sewing machines using Wright\u27s Learning Curve model. A non-experimental quantitative design with a descriptive scope was employed. Data were collected from three high-performing students during their learning process in operating straight stitch sewing machines. Wright\u27s Learning Curve model (1936) was used for analysis, applying a logarithmic transformation to estimate parameters through linear regression. Model validation was performed using the coefficient of determination (R²) and mean absolute percentage error (MAE%). Additionally, standard and total production times for 200 cycles were calculated using integral formulas. The model showed a good fit with determination coefficients between 0.69 and 0.74, and mean percentage errors between 12.97% and 19.74%. The standard times achieved for 200 cycles ranged from 23.23 to 62.13 seconds, with total production times between 79.13 and 211.88 minutes. These indicators provide measurable targets for planning vocational training in garment manufacturing, specifically concerning the operation and use of the sewing machine
Exploring Technological Gaps in the Implementation of Auto-Electricity/Electronics: Aligning Motor Vehicle Mechanic Programs in Nigeria with Modern Automobile Industry Needs
Get PDFThis study explores the technological gaps in the implementation of the Motor Vehicle Mechanic (MVM) program, with a particular focus on auto-electricity and electronics in Nigerian technical colleges. Using a qualitative research design, data was collected through semi-structured interviews with 20 stakeholders, including MVM administrators, teachers across nine technical colleges in Delta State and master craftsmen, and managers from the automotive industries in Nigeria. Reflexive thematic analysis, supported by NVivo 14 software was utilized to identified key themes that illustrate critical gaps impeding the program’s ability to meet industry standards. Findings revealed gaps in curriculum alignment with evolving industry practices, outdated teaching and learning resources, insufficient cutting-edge equipment and tools, and the limited technological expertise of teachers. Furthermore, the study highlighted a lack of industrial partnerships necessary for experiential learning and exposure to modern automotive technologies, such as electric and hybrid vehicles. These deficiencies hinder the development of practical skills required for maintaining contemporary vehicles. The study emphasizes the urgent need for curriculum reform, enhanced teacher training, and strengthened collaboration with industry stakeholders to bridge these gaps. Addressing these challenges is essential for equipping MVM graduates with relevant competencies, ultimately aligning technical training programs with the demands of the modern automotive sector and improving graduates’ employability in Nigeria’s evolving labour market
Exploring Technical Vocational Teacher Education: Inputs To Curriculum Model Development
No full textThe global demand for skilled workers, particularly in digital sectors, is projected to increase significantly, with an estimated 92 million digital jobs expected by 2030, according to the World Economic Forum. This trend presents a unique opportunity for countries like the Philippines to enhance their Technical and Vocational Education and Training (TVET) systems to meet evolving global labor market needs. Hence, this study endeavored the development of a comprehensive, research-based curriculum model for the Bachelor of Technical-Vocational Teacher Education (BTVTEd) program in the Philippines. Using an exploratory sequential mixed-methods approach, the research examined current challenges, and the extent of knowledge, experiences, and practices within the BTVTEd curriculum by gathering insights from experts in universities across the country. The findings reveal that the main challenges identified by respondents are related to curriculum and program design, particularly in adapting to industry demands. However, issues related to resources and facilities were also highlighted as central concerns. The study further underscores the need for the inclusion of emerging key courses, such as research, innovation, sustainability, and regulatory frameworks, alongside strengthening core areas like pedagogy, instructional design, and personal and professional development. Additionally, the study identified prominent factors, practices and strategies used to improve the BTVTEd program. Based on these findings, a new curriculum model is proposed, aimed at strengthening the current technical education program. The study suggests further exploration on how universities balance the use of quality assurance practices in curriculum delivery. The results provide valuable insights for the continued development of technical vocational teacher education in the Philippines through comprehensive quality assurance
A Geographical Information System-based Mapping Model for Aligning Vocational High Schools with Industrial Needs
No full textVocational High Schools (SMKs) are essential in linking education with regional industry needs. However, mismatches between SMK programs and industry demands often hinder graduate employability. This study develops a GIS-based mapping model to analyze the spatial alignment between SMKs and industries in Greater Bandung, covering Bandung City, West Bandung, Bandung Regency, and Cimahi City. A descriptive quantitative method was used, employing ArcGIS 10.2 and satellite imagery. Data included 39 public SMKs and 2,535 industries, categorized by vocational sectors based on Ministry Regulation No. 024/H/KR/2022. Mapping and spatial overlay analysis were conducted to assess alignment. Findings show limited and uneven alignment. While some SMKs align with the manufacturing sector, key sectors such as tourism, health, and creative economy lack nearby or relevant SMK support. Many schools are not strategically located near related industries, reducing collaboration and practical training opportunities. The study recommends integrated spatial planning that aligns vocational education with local economic strengths. Improved coordination between education and industry stakeholders is essential to enhance relevance and job outcomes for graduates
Cold Extraction of Phyllanthus Niruri, Chromolaena Oodorata, Melastoma Malabathricum & Azadirachta Indica Via High-Pressure Processing (HPP): Evaluation of Physiochemical Properties and Antioxidant Activity
No full textPhyllanthus niruri, Chromolaena odorata, Melastoma malabathricum, and Azadirachta indica are among the many medicinal plants found worldwide. These plants have been widely used for medicinal purposes due to their rich bioactive compounds. Traditional extraction methods, such as boiling at high temperatures, can lead to the degradation of certain bioactive compounds, including phenolic compounds, which contribute to antioxidant activity. This degradation occurs due to heat-induced denaturation. As an alternative, High-Pressure Processing (HPP) was employed to minimize quality deterioration during extraction. HPP was conducted at two different pressures (200 MPa and 600 MPa) with three holding times (5, 10, and 15 minutes). For comparison, a traditional extraction method was also performed by boiling the samples at 100°C for 30 minutes. The extracts were analyzed for their physicochemical properties (pH and color), antioxidant activity, and total phenolic content (TPC). Antioxidant activity was assessed using the DPPH radical scavenging method, while TPC was determined using the Folin-Ciocalteu reagent. Among the tested samples, HPP-treated A. indica extract exhibited the highest antioxidant activity at 600 MPa for 10 minutes, achieving 88.55 ± 0.04% scavenging activity. The highest TPC was observed in HPP-treated A. indica at 200 MPa for 5 minutes. Based on the results, the optimal HPP parameters for maximizing antioxidant activity were found to be 600 MPa with a 10-minute holding time. In conclusion, HPP demonstrates significant potential as an effective extraction method for medicinal plant materials, offering a promising alternative to traditional heat-based extraction techniques. This study is novel in its comparative evaluation of multiple medicinal plant species under varying HPP conditions, providing new insights into parameter optimization for maximizing antioxidant yield
Experimental Study on Flexural Performance of Reinforced Concrete Beams with Lap Splices and Threaded Coupler-type Mechanical Splice
No full textThis study aimed to assess the feasibility of mechanical splicing as an alternative to traditional lap splicing in reinforced concrete (RC) beams. Six RC beams, including a control beam, a lap-spliced beam, and a mechanically spliced beam using threaded couplers, were subjected to two-point load tests. While the lap-spliced beam exhibited the highest load-carrying capacity, the control beam demonstrated superior ductility, as indicated by higher deflection. The mechanically spliced beams, on the other hand, displayed inferior flexural performance compared to both the control and lap-spliced beams. This reduced performance was attributed to the decreased cross-sectional area of the rebars due to threading and the limited strain distribution caused by the short coupler sleeve. Consequently, this study concludes that lap splicing remains a more effective method for achieving desired levels of flexural strength and ductility in RC beams, and threaded coupler splicing, in its current form, is not a suitable replacement
Exploring Time-Domain OFDM Signal Generation: Performance Analysis in Communication Systems
Get PDFOrthogonal Frequency Division Multiplexing (OFDM) is a method used to send multiple signals over a communications link where it can transmit subchannel frequencies closely spaced without overlapping. Furthermore, OFDM is a multicarrier modulation commonly used in telecommunication, especially in wireless communication systems such as the 4th Generation (4G) and Long-Term Evolution (LTE) due to its capability of providing some advantages in terms of data transmission. In a conventional OFDM system, the OFDM signal generation is made using complex input data represented in the frequency domain before being transformed into the time domain for transmission. This paper intends to explore the viability of generating the OFDM signal using a time-domain approach. This study then further assesses the processing performance of OFDM signal simulated as a communication system, in the frequency domain and the time domain by analyzing the Bit-Error-Rate (BER). In this study, Quadrature Amplitude Modulation (QAM) and Quadrature Phase Shift Keying (QPSK) were applied. The findings indicate that, the generated OFDM signal using the time domain method shows similar characteristics in terms of amplitude to those generated conventionally, specifically at , and for the time and frequency domain, respectively, when QAM is utilized. Meanwhile, using QPSK, at , the amplitude for the frequency and time domain are and . The error obtained between frequency and time domain are relatively small, and for QAM and QPSK, respectively. Besides, the BER value using QAM, at , is about for both time domain and frequency domain, which closely mirrors the theoretical BER. Similarly, for QPSK, at , BER is 0.0023, 0.0030, 0.0035 for the theoretical, time domain, and frequency domain, respectively. Based on the findings, the time-domain approach can be used in generating the OFDM signals and less complexity rather than the conventional, and the choice of domain does not introduce significant variances in the performance of the OFDM signal in communication system
Adaptive Production Capacity Planning Under Variable Electricity Cost Using Deep Reinforcement Learning
Get PDFReinforcement learning is gaining traction for its ability to solve complex tasks that are intractable or impossible for other machine learning techniques. This paper proposes a novel approximation technique for production capacity and inventory planning using deep reinforcement learning (DRL). To address practical implementation challenges, we incorporate demand uncertainty and time-of-use electricity price-driven demand response patterns (PDDR) into the model. We compare the performance of two DRL techniques, A3C and PPO, in learning to optimize production planning over time to minimize total cost. The Discrete-Time MILP with new changeover constraint equations was formulated to take the model\u27s optimal solution as an upper benchmark. Our results show that the PPO outperforms the A3C and expert heuristics with an optimality gap of 4.03% compared to MILP, and its simulation time is 2,502 times faster than that of MILP. Furthermore, our findings suggest that PPO is more robust regarding demand fluctuations than A3C due to its objective clipping mechanism stabilizing policy updates. This makes our PPO-based production planning model a promising candidate for real-world applications where demand fluctuations are common
Distance Estimation using Deep Learning Approaches for Rear-end Collision Avoidance Alerts
Get PDFAutonomous Emergency Braking (AEB) and Autonomous Emergency Steering (AES) are part of the advanced driver assistance system (ADAS) equipped in intelligent vehicles. AEB is a system that warns drivers of potential collisions and assists them in utilizing the vehicle\u27s maximum capabilities. AES is an active safety system that aids in evasive steering. If it detects a potential collision, unlike AEB, the AES system will autonomously adjust the steering to prevent it. The challenges for AEB and AES include determining how much space is required to avoid an accident while turning or braking and how much distance is required to avoid an impact when braking and turning simultaneously. Considering such inquiries, it is necessary to devise a system to estimate the distance between the vehicles. Therefore, this study proposes a Monocular Vision Distance Estimation (MVDE) method employing deep learning techniques for accurately calculating the distance between vehicles, particularly for use in AEB and AES systems. The MVDE technique uses monocular vision, emphasizing object detection and distance estimation. In contrast to complex depth estimation techniques, the proposed method employs a Single Shot Detector (SSD) with MobileNet architecture for object recognition and Deep Artificial Neural Networks (Deep ANN) for accurate distance estimation. Using a real-world dataset collected in Cyberjaya, Malaysia, this study rigorously assesses the performance of this method. Results indicate that the MVDE method with four hidden layers in Deep ANN outperforms earlier techniques, with a maximum measured error of 4m to actual distances. In addition, it is competitive with RADAR-based systems and offers a cost-effective alternative for widespread adoption. These findings support the potential of MVDE for augmenting vehicle safety, shaping future automotive standards, and facilitating the widespread implementation of AEB and AES systems
CFD Investigation on the Influence of Roof Box Cargo Carrier Designs on Automobile Aerodynamics
No full textIn recent years, roof carrier boxes have become increasingly popular among travelers and larger families for their added luggage capacity. While these boxes address insufficient boot space, they also increase the vehicle\u27s frontal area, adversely affecting aerodynamics and increasing drag. Given that aerodynamics significantly impacts vehicle efficiency, the design of the roof box is critical in determining drag force. This study aims to minimize drag to enhance fuel economy by utilizing ANSYS, a commercial Computational Fluid Dynamics (CFD) software, to analyze the coefficient of drag (Cd) for a numerical car model equipped with three different roof box designs in three locations, as well as in the absence of a roof box. The simulation employs Reynolds Averaged Navier-Stokes (RANS) equations in combining with the k-ε turbulence model. Concerning the stability of the vehicle influenced by the addition of the roof box, force coefficients, including drag and lift coefficients, were assessed. Results indicated that the drag and lift coefficients were highest at a speed of 25.5 m/s for all roof box configurations. The maximum Cd (0.4423) occurred with the XL model in the far backward position, while the highest Cl (0.4169) was observed with the Alpine model centrally positioned. Flow structure analysis highlighted vortex formation and wake turbulence at the vehicle\u27s rear. Among the designs, the XL model in the central position was the most aerodynamically efficient, closely matching the base car\u27s Cd and exhibiting the lowest Cl