Journal of Advanced Research Design

Characterization of Bragg Grating Lengths in Single-Mode Fiber for Strain Sensor Applications

2025-05-27T22:16:21+07:00

This study presents the successful fabrication of Fiber Bragg Gratings (FBGs) using a 248 nm KrF Excimer Laser combined with the optical phase mask technique. The research focuses on investigating the effects of stress on Bragg grating length in single mode fibers (SMFs). A total of five FBGs, each with different grating lengths, were analyzed for strain sensitivity. The findings reveal a linear relationship between strain and Bragg grating length for all tested FBGs. Notably, the FBG with a grating length of 2.50 cm exhibited the highest strain sensitivity, however the strain sensitivity for all FBG grating lengths were found to be consistent. These results suggest the potential for optimizing FBG grating length in multimode fiber to enhance performance in strain-sensitive applications.

Enhance Energy Management in Vehicle to Grid Systems with Blockchain and Machine learning

2025-05-27T22:23:31+07:00

This study explores the integration of blockchain technology and reinforcement learning within Vehicle-to-Grid (V2G) systems to enhance energy management, operational efficiency, and sustainability. With the growing adoption of electric vehicles (EVs) and renewable energy sources, there is a pressing need for decentralized energy solutions that can handle dynamic energy exchanges reliably and securely. To address this, we propose a decentralized V2G framework where blockchain-enabled smart contracts facilitate automated, transparent, and tamper-proof transactions among EVs, grid operators, and distributed energy resources. This approach reduces dependency on centralized control, enhances trust among participants, and improves the scalability of energy trading systems. In parallel, reinforcement learning algorithms are employed to optimize the charging and discharging behaviour of EV batteries based on real-time grid conditions, energy prices, and usage patterns. This adaptive learning mechanism not only maximizes battery longevity and energy efficiency but also contributes to overall grid stability and flexibility. The proposed model is evaluated against key performance metrics, including transaction latency, energy efficiency, demand response capability, and system resilience. Our findings highlight that the synergy between blockchain and reinforcement learning offers a promising pathway for building intelligent, adaptive, and secure V2G systems. By combining the transparency and decentralization of blockchain with the adaptability of machine learning, the framework addresses limitations inherent in traditional centralized energy management. This research contributes a structured approach and a technological roadmap toward developing next-generation V2G infrastructures that are robust, scalable, and aligned with global sustainability goals.

Examining the Adoption Intention of Mobile Marketplace Application among Textile Cyber-Entrepreneurs via Technology Acceptance Model

2025-06-07T10:32:35+07:00

Mobile shopping has become commonplace among customers and retailers worldwide since the evolution in cloud computing and mobile commerce technology. Nonetheless, the adoption of mobile cloud services and applications by entrepreneurs in Malaysia is low and still evolving. It is significant to understand the acceptance of technologies by Malaysian entrepreneurs in particular textile cyber-entrepreneurs for their business sustainability. This study aims to investigate the relevance of Technology Acceptance Model (TAM) in assessing Malaysian textile cyber-entrepreneurs' tendency to use a mobile marketplace application, which was rarely done previously. TAM assesses users' perceived usefulness and perceived ease of use in integrating technology into their business, offering insights into their attitudes and behavioural intentions towards the technology. To achieve this, both printed and online questionnaires were distributed according to the name list provided by Perbadanan Usahawan Nasional Berhad (PUNB) where 348 responses were included for thorough statistical analysis. The data were then analysed using Structural Equation Modelling (SEM) – Analysis of Moments Structures (AMOS) 21 in SPSS version 21. The result shows that there are strong positive influences between all the parameters tested, thus concluding the applicability of TAM in measuring the adoption intention of textile cyber-entrepreneurs towards mobile marketplace application utilisation. Future research directions are also suggested in this study.

Accelerating Insight: A Thorough Systematic Review on Driving Simulators and Their Transformative Effect on Driver Behaviour

2025-05-08T13:43:03+07:00

Driving simulators have emerged as a valuable tool for evaluating driver behaviour and performance in various scenarios, offering a controlled and safe environment for testing and research. In this paper, a systematic review following a Prisma guideline to assess the effectiveness of driving simulators across a range of scenarios and examine their potential applications in enhancing road safety and driver training. The review incorporates studies published between 2019 and 2023 that investigated driving simulator effectiveness. A comprehensive search was conducted across major scientific databases, resulting in a final selection of 39 relevant articles. These studies encompassed diverse scenarios, including urban driving, highway driving, adverse weather conditions, distracted driving and impaired driving, among others. Several studies have reported positive effects on driver skills, knowledge acquisition and hazard perception after simulator-based training interventions. Furthermore, simulator-based assessments and training have the potential to address specific populations, such as older drivers or individuals with certain medical conditions, by tailoring scenarios to their unique needs. However, some limitations were identified within the reviewed studies, including limited sample sizes, variations in simulator fidelity and software and potential discrepancies between simulator and real-world driving experiences.

MRSI: Mobile Road Safety Information Application using Crowdsourcing

2025-04-29T12:06:02+07:00

Road deterioration poses a significant challenge for all. Enhancing road security and minimizing accidents necessitates an effective, dependable tool to assist road users, particularly in UNIMAS and Kota Samarahan. Presently, a gap exists as there is no intuitive application available to offer and report on road traffic information. The development of an app to communicate road conditions and safety is therefore essential. With the growing preference for smartphones over traditional desktops or notebooks, leveraging these devices for rapid dissemination and notification about road status and safety proves to be a more time-efficient and convenient method. Test results indicate that over 70% of users found the application to be user-friendly with stable functionality. Additionally, the app provides real-time traffic condition updates.

An Ultra Wideband CPW-Fed Monopole Antenna Design for Wireless Devices

2025-05-27T22:13:35+07:00

This paper presents a new coplanar waveguide-fed (CPW-fed) Ultra-Wideband antenna design suitable for wireless terminal devices. Specifically, it consists of a modified radiation patch with a circular-ring schematic arranged in a single layer, as well as a half-oval-shaped ground plane. The patch is fed with a coplanar waveguide (CPW) feeder. This configuration is implemented using FR- 4 substrate material, which possesses a tangent loss of 0.02 and a dielectric constant of 4.4, with a thickness of 1.6 mm. This antenna is designed to operate in the frequency range of 2.3 - 18 GHz. The simulated results and relevant measures show a satisfactory degree of agreement. The performance of the proposed structure is compared to similar techniques available in the literature and it is found that it has superior performance regarding the ultra-wideband span as well as the fractional bandwidth. It has a comparable performance regarding gain and efficiency.

Diagnosis of Skin Melanoma Utilizing an Advanced Combination of Improved Meta-GVF Algorithms

2025-05-28T10:11:05+07:00

The rising global prevalence of skin cancer has become a significant public health concern. Although melanoma accounts for only about 1% of all skin cancer cases, it is responsible for the majority of skin cancer-related deaths. Early detection is critical, as it can raise the five-year survival rate to over 90%. However, the prognosis for metastatic melanoma remains poor, with a five-year survival rate of only 10–15%. Computer-aided diagnostic systems, utilizing machine learning and deep learning models, have shown promising results in analyzing skin lesions and detecting melanoma. Despite these advancements, diagnostic accuracy is still limited by challenges such as the lack of distinct color variation in skin lesions and the absence of reliable methods for assessing melanoma thickness—a crucial factor for prognosis and treatment planning. This study introduces an innovative approach to diagnosing skin melanoma using an advanced Meta-GVF algorithm, achieving an accuracy of 85.5%. The main finding is that, through shape modeling and well-chosen initial conditions, the contour can effectively move toward the correct boundary. Additionally, with further development, boundary detection techniques could help automate the initialization process in Meta-GVF, enabling the automated diagnosis of skin abnormalities.

Segmentation of MRI Spine Hemangiomas Tumor: Using FCN Approach to Enhance Tumor Boundary Detection

2025-05-14T20:27:41+07:00

Spinal hemangiomas are the most common benign vascular tumors in the vertebral column, often detected incidentally via magnetic resonance imaging (MRI). Although typically asymptomatic, they can grow in certain cases, compressing the spinal cord and leading to neurological impairments, which require early diagnosis and treatment. Accurate segmentation of these tumors is crucial for clinical decision-making related to treatment and follow-up, but manual segmentation is time-consuming and prone to inter-observer variability. Manual segmentation of spinal hemangiomas in medical images is a labor-intensive process and prone to biases between observers, highlighting the need for reliable automated segmentation methods. This study presents an automated approach for spinal hemangioma segmentation using a modified Fully Convolutional Network (FCN) with a ResNet-50 backbone. This approach represents a significant advancement in deep learning-based segmentation of spinal hemangiomas in MRI images, leveraging advanced techniques to improve segmentation accuracy and reduce errors. The proposed model was trained on a dataset of 2400 annotated MRI volumes across multiple anatomical planes, including sagittal, axial, and coronal views. This diversity in imaging planes enhances the model's ability to adapt to varying MRI patterns. The model utilizes Binary Cross Entropy with Logits Loss and the Adam optimizer to address class imbalance and achieve efficient training. The model's performance was evaluated using metrics such as the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), showing impressive results, with an accuracy of 96.25% on the test set and a Dice score of 0.85. The model also achieved an IoU value of 0.83, reflecting its improved ability to accurately delineate tumor boundaries. Additionally, data augmentation techniques and cross-validation further enhanced the model's generalization, leading to improved segmentation accuracy and reduced false positives. The model demonstrated strong resistance to noise and artifacts in the images, with a 12% reduction in false positive rates compared to traditional methods. Preprocessing techniques, such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and Non-Local Means Denoising, were applied to improve the model's ability to distinguish tumors from surrounding tissues. These techniques proved effective in enhancing the model's accuracy and reducing noise interference

Comparative Analysis of Motorcyclist Visibility using Wearable Reflective Gear: Enhancing Safety and Visibility for Motorcyclists

2025-04-29T14:03:38+07:00

This research paper discusses the problem of motorcycle visibility in low light conditions and at night, as evidenced by fatal accident statistics from various countries. The paper compares the visibility of reflective clothes by taking the driver's reaction time. The paper begins by discussing statistics from Great Britain, Asia, and Brazil, as well as reflective and fluorescent jackets and fabrics used to develop reflective jackets. The paper continues to discuss supplemental lighting systems, electroluminescent panels, and types of reflective clothing. The paper then discusses a survey distributed to 102 respondents to determine the most critical factors for motorcyclists. Following this, the paper discusses the design of three samples of motorcyclist jackets and the experiment conducted to assess the visibilities of the different samples of reflective clothing. The paper concludes that Jacket C (full Retroglo) was the most visible, followed by Jacket B (Fluorescent & Retroglo) jacket and Jacket A (black jacket.

Integrating YOLO Algorithms for Dual-Functionality Surveillance

2025-04-15T09:52:34+07:00

Since the outbreak in December 2019, the COVID-19 pandemic has significantly impacted Malaysia, affecting both the nation and its citizens on a substantial scale. Consequently, the government has put forth several recommended standard operating procedures (SOPs) aimed at mitigating the pandemic's impact. These measures include mask-wearing, frequent handwashing, and adhering to social distancing guidelines. Maintaining social distance is crucial in reducing the transmission of this lethal virus from an individual carrying the infection to a person in good health. The method encompasses the utilization of the You Only Look Once (YOLO) object detection algorithm for the widespread application of identifying and overseeing adherence to social distancing practices within monitoring systems. In addition to its effectiveness in object detection, YOLO has gained extensive global utilization in systems for detecting human presence. Thus, this study provides a review of YOLO-based monitoring systems that have been developed previously. From that, this paper introduces a framework integrating thermal imaging for combined social distancing and body temperature monitoring. From the results, only 14% of the total numbers of visitors at the exhibition complied with the social distancing. The other 12.5% were in danger when they broke the rules while 73% of individuals were observed to be in a situation of moderate risk. However, most of the recorded body temperatures were normal.

Performance Evaluation of Cellulose Tri Acetate-Polyamide Forward Osmosis Membrane: A Mathematical Model Approach

2025-04-15T10:23:24+07:00

Forward Osmosis (FO) membrane is a recent membrane process used in many applications. One of the challenges in FO technology is membrane material, which suits the FO process. The selection of membrane material for FO membrane application is crucial as it determines the membrane efficiency. When characterizing the FO membrane, besides its physical and chemical properties, the intrinsic parameters; A, B, and S, which represent water permeability, solute permeability, and structural parameters, respectively, are also essential. Experiments evaluate these intrinsic parameters, which may require more work and cost. As an alternative, the mathematical model approach is beneficial in determining these intrinsic parameters. This research work applied mathematical models and was solved using Microsoft Excel to evaluate intrinsic parameters for lab-made Cellulose Tri Acetate-Polyamide (CTA-PA) membrane. Besides that, a mathematical model was also applied to predict the CTA-PA membrane performance in terms of water flux and its suitability for power generation. CTA-PA membrane with lower CTA content in the support layer exhibited higher values of A, B, and S. Lower CTA content also contributed to higher flux and higher power density as predicted by the model. The mathematical model was successfully applied in this work to determine the intrinsic parameters and predict the performance.

Implementing Problem Based Learning Strategies to Optimize Science Instruction for Learner Engagement

2025-04-15T10:35:15+07:00

This paper presents the development of the C2HADAM multimedia courseware prototype, employing a Problem-Based Learning (PBL) approach for teaching Science to Form Two students in selected Malaysian secondary schools. Despite the recognized benefits of Problem-Based Learning (PBL) in enhancing student engagement and understanding in the Science course, there is a lack of multimedia courseware that effectively integrates PBL with constructivist, collaborative, inquiry-based, and exploratory learning principles for Form Two students in Malaysian secondary schools. The paper begins by outlining the theoretical and conceptual frameworks that inform the architecture of C2HADAM, emphasizing the application of renowned learning theories such as constructivism and collaborative learning, as well as PBL itself. Additionally, C2HADAM incorporates principles of inquiry-based learning, simulations, exploratory learning, and a student-centred approach. Further, the paper delineates the life cycle of the courseware's development, which includes phases of analysis, design, development, implementation, and evaluation. In conclusion, the paper presents testing results from assessing the courseware's effectiveness.