Malaysian Journal of Science and Advanced Technology

Evaluation of Assessment of Flexible Pavement Damage Conditions on Urban Roads in Indonesia

2025-09-13T12:14:11+00:00

Road infrastructure plays a vital role in supporting social and economic activities. However, deterioration and damage to urban roads remain a persistent issue in Indonesia, including in West Java Province. In Cirebon City, the Dr. Wahidin Sudirohusodo – Dr. Cipto Mangunkusumo road segment serves as a key arterial route, facilitating the mobility of people and goods. Due to its high traffic volume and strategic importance, maintaining this road section is crucial for urban functionality and development. This study aims to establish a systematic and objective priority scale for urban road maintenance by integrating the Pavement Condition Index (PCI), Surface Distress Index (SDI), and Analytical Hierarchy Process (AHP) methods. While PCI and SDI are used to assess the technical condition of the pavement, AHP allows for decision-making based on multiple criteria, including traffic volume and land use. The combination of these three methods provides a more comprehensive and balanced framework for prioritizing repair efforts, aligning technical evaluations with functional and strategic considerations. The analysis revealed varying levels of pavement damage across the study area. Based on the AHP results, the STA 2+300 to 2+400 road segment was identified as the highest priority for intervention due to its critical condition and functional importance. This integrated approach ensures that limited resources can be allocated more effectively to road sections requiring immediate attention, supporting sustainable and data-driven infrastructure management in urban environments.

Analysis of Blackspot on National Roads in West Java Province

2025-09-13T12:14:09+00:00

Road accidents are one of the biggest challenges facing the transportation system and have a significant impact on the safety of road users, which is a problem that requires serious attention, especially in Indonesia, especially in West Java Province with high population density and vehicle volume. Identification of Accident Prone Locations is done based on the number of accidents and the severity of victims, so that it can be used as a basis for planning more effective transportation policies. This research analyzes the distribution of accident-prone locations on the national roads of Java Province using a quantitative approach with secondary data from the West Java Police Traffic Directorate for the period 2022 - 2024. The accident data was identified in accordance with the Guidelines for Handling Traffic Accident Prone Locations (Pd T-09-2004-B), with 133 accident-prone locations. Then it was analyzed using the Accident Equivalent Number (AEK) method to determine the weighting value of the accident location and determine the severity of the accident at each location. To calculate the accident rate limit, calculations are carried out using the Upper Control Limit (UCL) method. Locations with AEK values > UCL values amounted to 48 LRKs spread across national roads, West Java Province. The highest LRK is on Cipularang Toll Road, from KM 97B to KM 88A, located in Sukajaya, Sukatani Sub-district, Indonesia. The analysis identified the 10 highest blackspot locations, which were then presented in the form of a map showing the distribution of accident-prone locations on national roads in West Java Province. Recommendations were also made for handling the highest accident-prone locations according to the most common types of accidents, optimizing the improvement of road markings and traffic signs, and educating and enforcing traffic violations.

A Smart Approach to Aquatic Ecosystem Protection: IoT-Based Water Quality Monitoring System (WQMAS)

2025-09-13T12:14:01+00:00

In an era increasingly threatened by pollution and climate change, water quality is under serious risk. Spatial water quality monitoring and traditional laboratory testing are inadequate to protect aquatic ecosystems. An innovative solution emerges from the Internet of Things (IoT), which enables the Water Quality Monitoring System (WQMAS) to transform traditional water resource monitoring practices. Through advanced sensor technologies, this system obtains real-time critical measurements including ambient temperature (25°C–30°C), water temperature (25°C–30°C), turbidity, pH values, electrical conductivity (500–1000 µS/cm), and dissolved oxygen levels. The system automatically pushes data into an easy-to-use IoT dashboard that allows users to immediately understand and assess data for smart and knowledgeable choices. The system utilizes Polyethylene Terephthalate Glycol-modified (PETG) material for durable 3D-printed enclosures that make it affordable and suitable for different fields due to its scalable characteristics and environmental resiliency. The automated system for water quality checks removes manual errors and performs fast and precise checks that manual operations cannot match. The technical capabilities of this solution become part of a sustainability showcase which enables communities to protect aquatic ecosystems while enhancing water resource optimization for creating a more environmentally friendly world. Within the water management industry, the Water Quality Monitoring System operates beyond its status as a tool to serve as a fundamental step towards sustainable development.

Agrilink: Design and Evaluation of an IoT-Based Smart Agriculture System for Plant Watering and Humidification

2025-09-13T12:14:04+00:00

The Smart Water Management for PlantPal: IoT Plant Watering and Monitoring System is an innovative automated solution that helps to maintain an optimal growing environment through real-time monitoring. The work implements IoT technology which integrates ESP32 and ESP8266 microcontrollers with multiple sensors including ultrasonic sensor, soil moisture sensor and DHT22 temperature/humidity sensors. These sensors continuously retrieve important data to assess plant needs. The work consists of two major components, which is the smart water control device assist in control water usage and the mist maker contributes to create an optimum humidity for the plant. Local LCD display will be used to display critical data while web dashboards ensure reliability and user convenience. The testing phase demonstrated the effectiveness of the work, reducing water waste while promoting healthier plant growth. This work offers practical applications particularly for home gardening, small-scale agriculture and urban farming where automation of plant care and efficient water usage are needed.

The Effect of Bosu Ball Strengthening Exercise on Dynamic Balance Among UniKL RCMP Athletes

2025-09-13T12:14:07+00:00

Acute ankle sprains are prevalent among athletes and often result in long-term impairment, including chronic ankle instability. Strengthening interventions like Bosu ball exercises may enhance dynamic balance and reduce injury recurrence. This randomized control trial compared the effects of Bosu ball exercises versus general ankle stretching on dynamic balance in 50 UniKL RCMP athletes aged 18–25. Participants were assigned to either an intervention group (Bosu ball exercises) or a control group (general stretching). Both groups trained once weekly for four weeks. The Y Balance Test assessed dynamic balance pre- and post-intervention. The intervention group showed significant improvements (p < 0.05), whereas the control group showed minimal change. In conclusion, Bosu ball exercises are more effective than general stretching in enhancing dynamic balance and should be integrated into athletic training programs.

Effect of Different Number of Coating for Anode Functional Layer on the Performance of Proton Ceramic Fuel Cell

2025-03-06T23:53:27+00:00

The effect of different numbers of anode functional layers (AFL) on the performance of an anode-supported proton ceramic fuel cell (PCFC) was investigated. Button cells of NiO-BCZY (50:50) | BCZY | LSCF (BCZY = BaCe₀.₅₄Zr₀.₃₆Y₀.₁O₂.₉₅) were fabricated with 3, 6, and 9 layers of AFL consisting of NiO-BCZY (10:90). Microstructural images clearly show that the button cell with 3 AFL layers exhibits better contact between the anode and electrolyte layers. Both the polarization resistance (RP) and ohmic resistance (Ro) of cell A (3 layers) were lower than those of cell B (6 layers) and cell C (9 layers). At 800°C, the RP decreased from 79.6 Ωcm² for cell C (9 layers) to 19.3 Ωcm² for cell A (3 layers). Similarly, the RP for cell B (6 layers) decreased from 11.2 Ωcm² to 9.2 Ωcm² for cell A. These results suggest that increasing the number of AFLs may reduce cell performance by hindering hydrogen ion diffusion, as the ions must travel a longer pathway, leading to higher polarization resistance.

Application of Non Parametric and Semi-Parametric Models on Survival Time of Patients with Cardiovascular Disease: Case Study of Barau Dikko Teaching Hospital, Kaduna, Nigeria

2025-01-01T03:07:52+00:00

This study examines the survival times of cardiovascular patients using Kaplan-Meier (KM) survival curves and the Cox Proportional Hazards model. Analysis of data from Barau Dikko Teaching Hospital, Nigeria, revealed shorter survival times among male patients and those consuming alcohol or smoking. KM curves and Log-rank tests identified significant risk factors such as high blood pressure and irregular pulse rates. Cox model hazard ratios highlighted alcohol consumption as the highest risk factor. These findings demonstrate the utility of non-parametric and semi-parametric models in identifying survival determinants among cardiovascular patients.

Preliminary Investigation of Unsaturated Polyester/Jute Fibre (UPE/JT) Composites: Effects of Fabrication Methods and Fibre Orientations

2025-04-16T09:22:20+00:00

This study focuses on the development of unsaturated polyester (UPE)/ jute fibre (JT) composites using two fabrication methods such as vacuum resin infusion and compression moulding. The effects of different JT fibre orientations (randomly oriented, 0°, and 90°) on UPE composite properties were investigated. The results indicate that UPE/JT composites fabricated via vacuum resin infusion exhibit higher tensile strength, elongation, and Young’s modulus compared to those produced by compression moulding. In terms of fibre orientation, 0° oriented UPE/JT composites produced via vacuum resin infusion showed the highest tensile strength (92.4 MPa), elongation at break (12.3%), and Young’s modulus (3654 MPa) when compared to both randomly oriented and 90° oriented composites, as well as all specimens fabricated through compression moulding. This improvement is attributed to better fibre impregnation achieved through vacuum resin infusion and the load bearing capacity of 0° oriented JT fibres, which effectively transfer stress between the fibre and matrix, enabling joint load-bearing by JT fibre and UPE matrix during the tensile test.

Quantum Support Vector Machines for Early Detection of Neurodegenerative Disorders Using Multimodal Brain Imaging

2025-04-27T09:39:25+00:00

The prompt detection of neurodegenerative diseases (NDDs) like Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Parkinson's Disease (PD) is essential yet difficult. This research proposes a hybrid quantum-classical machine learning methodology to categorize NDD using multimodal brain imaging data.ADNI and PPMI datasets' MRI, PET, and fMRI images were preprocessing by Python-based tools such as Scikit-image, SimpleITK, and FreeSurfer via Nipype. Major steps were denoising, registration, normalization, segmentation, and extraction of features from structural and functional imaging. Training was done with a Quantum Support Vector Machine (QSVM) using Qiskit's ZZFeatureMap and QuantumKernel and compared with a conventional SVM. Performance measures-precision, recall, F1-score, class accuracy, AUC, and 10-fold cross-validation-were calculated. QSVM performed better than the standard SVM on all metrics, with 91.25% overall accuracy and macro F1-score of 0.913 as opposed to 87.75% and 0.879 for the SVM. The results validate QSVM's advantage in handling complex, high-dimensional neuroimaging data and its relevance to aiding clinical diagnosis.

Keywords: Quantum Machine Learning, Neuroimaging, QSVM, Alzheimer’s Disease, Parkinson’s Disease, Multimodal Classification