Journals of Universiti Tun Hussein Onn Malaysia (UTHM)
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A Novel Hybrid Deep Learning-based Approach for Sensor Data Recovery in Structural Health Monitoring
No full textStructural health monitoring (SHM) systems contribute significantly to ensuring the safety of construction works. However, in reality, data loss often occurs due to many different reasons. A unique hybrid deep learning-based method for recovering sensor data in structural health monitoring (SHM) is presented in this research. The suggested technique accurately reconstructs missing or corrupted sensor data by utilizing the advantages of both Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). While the RNN models time dependencies to recover the missing sequences, the CNN pulls important patterns from the data. The method\u27s great accuracy in recovering sensor data, even under complex circumstances, is proven using case study real-world bridge monitoring data. The steps taken and the analysis of the results are clearly stated in the study. According to the results, the CNN-RNN combination performs better than conventional techniques and provides notable reliability gains for SHM applications. Future studies will try to improve the model even further and investigate how it may be used to a variety of sensor data and structural types.
The Relationship Between Perception of Environmental Knowledge and Practices Among Upper Secondary Vocational Program Students in Batu Pahat District
No full textEnvironmental education plays a crucial role in raising awareness and fostering environmental responsibility, particularly among students. However, the level of environmental knowledge and practices among Malaysian students remains a concern. This study aims to examine the relationship between students\u27 perceptions of environmental knowledge and their environmental practices. A quantitative survey design was employed, involving 92 Upper Secondary Vocational Program (PVMA) students from four schools in Batu Pahat District. Data were analyzed using descriptive statistics, including mean and standard deviation, as well as Pearson correlation analysis. The findings indicate that while students exhibit a high level of perceived environmental knowledge, their environmental practices remain at a moderate level. Additionally, no significant relationship was found between students\u27 perceptions of environmental knowledge and their actual environmental practices. These results suggest that possessing environmental knowledge does not necessarily translate into proactive environmental behaviour. In conclusion, strengthening environmental education through practical engagement is essential to bridging this gap. The findings of this study can serve as a valuable reference for future research aimed at enhancing environmental practices among students
Bi- Level Optimization of Multi-Microgrids: A Review Considering Demand Response Aggregators
No full textThe increased penetration of renewable energy sources (RES) and electric vehicles (EVs) in addition to the non- renewable energy sources such as microturbine (MT), combined heat and power (CHP) and diesel generator (DEG) must have a coordinated operation for achieving optimal energy management system (EMS) and operation of microgrids. The demand side management (DSM) can be utilized using demand response program (DRP) becomes an essential work to deal with distributed generation (DG) that are integrated with distribution grid to minimize the cost of operation and maximizing the profit of microgrids owners (MGO), this can be achieved by exchanging data between MGO and distribution system operator (DSO). The optimal operation of microgrid is subject to various constraints that must not be violated. The complication of the energy management is due to the uncertainties of RES, electricity prices and EVs state of charge (SoC) and arrival/departure time, have to be solved with a stochastic based modeling. The demand response aggregator (DRA), electric vehicles aggregator (EVA) and energy storage system (ESS) are an important player in microgrid optimization that have to be studied thoroughly as they participate in DSM to modify the load demand pattern. The review assists the authors to find the latest achievements in microgrids management
Using Ansys Fluent to Study Flow Characteristics and Heat Transfer Enhancement by Inserting Different Sizes of Dimple in Three-Dimensional Horizontal Single Pipe Heat Exchanger
No full textIn many engineering applications, the relevance of heat transfer improvement has grown, and a lot of work has gone into using various ways to enhance the hydraulic thermal performance of fluids running through pipes. This study uses numerical analysis to examine the thermal performance of turbulent flow properties and heat transfer under a uniform 30000 W/m2 heat flux in dimpled pipes. For several designs of dimpled pipes, the friction factor, heat transfer rate, and performance assessment criterion were calculated and compared with smooth pipes. The examples under consideration are within the range of 8000 to 14,000 Reynolds numbers. For this, the ANSYS Fluent 2023 R2 is employed. The governing flow equations are modeled using the Reynolds-averaged Navier-Stokes equations (RANS). To simulate turbulent flow next to the inner wall surface, the realizable k-ε turbulence model is applied with increased wall conditions. The results of the current study showed the presence of dimples on the surface of the pipe significantly enhances the rate of heat transfer represented by the Nusselt number compared to the normal smooth pipe. Also, the analyzed models indicated by the results of the numerical investigation have high average Nusselt counts, low pressure, overall performance criterion, and low average thermal resistance due to the increase in the Reynolds number. The dimpled pipe with different radii (2,3, and 4 mm) has an increased percentage of enhanced heat transfer (79.91, 86.77, and 94.47%) compared to the smooth pipe
Taguchi-Grey Optimisation of TIG Welding Parameters and Its Impact on the Mechanical Properties of Austenitic Steel
No full textWelding, albeit tungsten inert gas welding; is a unique and widely adopted joining process especially for austenitic stainless steel arising from its advantages over other welding process. However very often, mechanical properties and weld quality could be deteriorated if the weld parameters are not controlled during a welding operation. Selecting the appropriate process parameters for welding operations is crucial for forming weld microstructures with excellent mechanical properties, thereby enhancing weldment performance during service. Thus in this study, the integrated Taguchi-Grey optimisation technique has been adopted in the optimisation of welding parameters. The Taguchi L27 orthogonal array design of experiment consisting of three levels of four factors of current, voltage, speed and gas flowrate was deployed for the determination of multi performance index for the output parameters of tensile strength and microhardness. The results obtained, after a confirmatory test to validate the predictive model indicates that there was an improvement from 0.0409 to 0.495 in MRPI, for the optimal parameters setting of current at 95 A, speed at 0.7 mm/s, voltage at 25 V and gas flow rate at 20 L/min respectively. This study could influence industrial practices by improving weld quality, reducing material wastage, and enhancing process efficiency. For example, optimizing welding parameters can minimise defects in aerospace applications or lead to stronger, lighter components in automotive manufacturing, improving both safety and performance. Potential cost savings could also be achieved from optimised parameter selection and reduced trial and-error approaches in industrial applications. This methodology can also be adapted to other materials or welding techniques like friction stir welding or laser welding
Gait Analysis for Walking on Flat Footwear and High Heels
No full textGait analysis has transformed traditional methods of studying human locomotion by enabling precise monitoring and real-time analysis of critical biomechanical parameters. The purpose of this project is to design a portable gait analysis system as a prototype to evaluate the effects of different footwear on walking dynamics. The acceleration sensor was implemented into a wearable device to collect data and analyze gait variations between walking in flat footwear and high heels. The system monitors angular velocity, acceleration, and an-gles across three axes (x, y, and z). Key features of this system in-clude its ability to record motion data in real time using the WitMotion mobile application. The application visualizes gait parameters, enabling users to identify and understand how different footwear impacts balance, posture, and muscle activation. This portable and user-friendly device offers an accessible alternative to traditional gait analysis tools, making it suitable for individuals, clinicians, and researchers. The resulting system is a practical solution and a cost-effective innovation that bridges the gap between advanced gait analysis and real-world applications
Advanced LWAVF Framework Based on Neural Network Security in IoMT for Managing Patient Data Authentication and Integrity Validation with Consensus Mapping
Get PDFThe Internet of Medical Things (IoMT) plays an essential role in health care systems to provide ubiquitous patient monitoring, health record management, and disease diagnosis support. The sensitive data requires robust security measures to protect sensitive health information. As IoMT data sharing is pursued through the decentralized cloud, authentication is vital to prevent anonymous access/ modification of data. However, the existing systems face difficulties such as integrity, authentication, and privacy issues while managing sensitive data. The research difficulties are overcome by applying the Light-weight Authentication and Validation Framework (LWAVF), which uses the encryption to ensure data security. This framework performs patient-monitored data authentication and integrity validation at the sender and receiver ends. A sender-receiver utilizes the AES-256 bit authentication to format the patient data to meet the sharing security requirements. The data authentication signature uses the sender and receiver conjugation time and agreement to verify the integrity at the receiver end. During this process, a consensus mechanism is deployed to monitor the mapping of time, agreement status, sender’s data count, and receiver’s data count that supports integrity. Therefore, the validation is performed by disjoining the agreement after matching with the consensus data. The matching, verification, and agreement status validation are eased using one-track neural learning. The mapping parameter validation and their existence are verified by this learning model recurrent to the conjugation time in which the proposed system ensures 416.35ms time for data sharing time, 3.22ms for authentication, 5.98ms for integrity verification, 455.55ms for latency and 20.14ms complexity
A Systematic Review Of Multi-Agent Systems And Mobile Edge Computing In Intelligent Transportation Systems
Get PDFThe Internet of Vehicles (IoV) can be identified as the most progressive evolutionary stage in the development of Intelligent Transportation Systems (ITS), being, in fact, the result of a merging of modern communication technologies with the traditional vehicle network. This review provides an in-depth survey of the current state of knowledge regarding Multi-Agent Systems (MAS) and Mobile Edge Computing (MEC) applications in Intelligent Transportation Systems. The review further explored the MEC and MAS mechanisms as technological solutions of modern times, as they enable real-time data processing and intelligent decision-making at the edge of the network. This analysis ascertains the gaps in the literature. Further, it explores the subject within the broader scholarship, thereby defining the study objectives in the arena of improving ITS through Internet of Vehicles (IoV) technologies
Assessing TVET Leadership Development Programmes: Implications to TVET Leaders
Get PDFLeadership is a pivotal element in advancing the objectives of Technical and Vocational Education and Training (TVET), especially in the context of higher education institutions tasked with driving workforce readiness and institutional transformation. Despite increased investments in leadership development, many training programmes lack comprehensive evaluation mechanisms beyond initial participant satisfaction. This study assesses the effectiveness of leadership development programmes at Universiti Tun Hussein Onn Malaysia (UTHM), guided by the Kirkpatrick Four-Level Training Evaluation Model. The evaluation focused on Levels 1 (Reaction), 2 (Learning), and 3 (Behavior), encompassing 12 leadership programmes under three clusters: ULead, Hi-Lead, and Hi-Per. A descriptive quantitative approach was used, involving post-training surveys, pre- and post-tests, and supervisor feedback three months post-programme. Findings reveal high participant satisfaction (mean scores between 4.30–4.90), statistically significant learning gains across all tested modules (p < 0.05), and varying degrees of behavioral application, with reported workplace transfer rates ranging from 7.14% to 53.33%. However, inconsistent data collection at Level 3 and in some coaching-based programmes indicates the need for improved follow-up mechanisms. The study underscores the importance of integrating comprehensive evaluation practices within TVET leadership initiatives to ensure programme impact and sustainability
Molecular Phylogenetics of Dominant Endophytic Fungi in Leaves of Vitellaria paradoxa using Internal Transcribed Spacer gene region
Get PDFOn a global scale, diverse fungal species live inside plant tissues in mutualistic association either as highly specific to single hosts or widespread in many species. Despite the huge importance of Vitellaria paradoxa (Shea Butter Tree) in Africa, the fungal endophytes associated with this plant have not been documented. In this study, we characterized and determined the phylogenetic relationship of the endophytic fungi in V. paradoxa. Fungal isolation was done from V. paradoxa between July and December, 2019 by cutting, sterilizing and inoculating the healthy leaves on Potato Dextrose Agar (PDA) and Water Agar (WA) for 7 and 21 days respectively. Result of the isolation showed a dominant fungal endophyte repeatedly isolated from the leaves of V. paradoxa. Morphological features of the isolates were described and the genomic DNA was extracted for molecular analyses. Accurate molecular phylogenetic analyses of the sequences of the Internal Transcribed Spacer (ITS) gene regions using maximum likelihood analysis revealed the isolate as Lasiodiplodia theobromae. This study therefore succinctly represents the first report of L. theobromae as an endophyte in leaves of V. paradoxa using a robust analysis of its phylogeny. The implication of the occurrence of this endophytic fungus from shea butter fruit is also discussed