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- PublicationInvestigation of halloysite nanotubes reinforcement in self-healing natural rubber composites.(2025-03)Formulating rubbers with both self-healing abilities and excellent mechanical performance poses a significant challenge, as optimizing one property often comes at the expense of the other. The self-healing performance of rubbers relies on a flexible and dynamically reversible cross-linked network. In contrast, mechanical strength is influenced by the rigidity and stability of this network, creating a delicate balance. This research aimed to investigate the reinforcement effect of halloysite nanotubes (HNTs) on the self-healing performance of natural rubber (NR) composites based on metal thiolate ionic networks. The initial work involved characterization of raw halloysite particles which emphasis on particle size, shape, structure and chemical composition, and characterization of self-healing behavior of unfilled natural rubber. The unfilled self-healing NR showed excellent self-healing performance (98.5%) however, the tensile strength was low at ∼1.2 MPa. The halloysite nanotubes reinforced self-healing NR composites were prepared using conventional rubber processing. Materials were compounded using internal mixer and two-roll mill and subsequently vulcanized by compression moulding. The self-healing rubber composites were thoroughly examined and characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), universal testing machine (UTM), dynamic mechanical analysis (DMA), Anton Paar rheometer, and compression set testing. The results revealed that, addition of 8 phr HNTs in the self-healing NR was found to be an optimum reinforcement to the better tensile performance of 4.12 MPa with 89.8% healing efficiency. Furthermore, the surface modification of halloysite nanotubes with acrylic acid (AA) was carried out to improve the compatibility between halloysite and natural rubber, since the major disadvantage of halloysite is poor compatibility with the non-polar rubber. The strategy was to reduce the surface hydroxyl groups on the HNTs and enhanced the interfacial adhesion between filler and rubber. Additionally, AA treatment on HNTs generates massive Zn2+ salt bonding between zinc thiolate grafted NR and carboxylic (COOH) groups of treated HNTs which enable electrostatic interaction and allows additional reversible ionic networks. It was found that the self healing rubber composites filled with 6% (AA) treated HNTs increased tensile strength to 5.56 MPa (increased by 34.9%) with 98.9% healing efficiency (increased by 10.1%) compared to untreated HNTs filled self-healing NR composites. Finally, the thermal stability and flame retardancy of self-healing rubber composites were investigated using thermal constants analyzer, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), flammability test, and limiting oxygen index (LOI) measurements. Altogether, the results revealed that the addition of HNTs into self healing NR nanocomposites enhanced 17.5% of their thermal conductivity, 19% of thermal stability, 27.7% of LOI, and 29% reduction in burn rate compared to the unfilled self-healing NR. A two-phase Lewis–Nielsen model was also utilized to simulate efficiency of the thermal conductivity of self-healing natural rubber containing halloysite nanotubes. The utilization of halloysite nanotubes as a reinforcement in self-healing natural rubber is promising for rubber products that experience high temperature environments especially for seals and gaskets.
- PublicationImage-based texture analysis and machine learning modeling of asphalt pavement anti-skidding performance.(2025-12)The anti-skidding performance of asphalt pavement not only significantly affects road safety, but also affects the comfort and durability of the pavement. During the completion acceptance stage of asphalt pavement, it is necessary to conduct reasonable testing and evaluation of the anti-skidding performance of the pavement. In response to the limitations of traditional asphalt pavement anti-skidding performance evaluation methods, such as reliance on specialized equipment, susceptibility to human factors, and inefficiency in large-scale applications, this study proposes a comprehensive evaluation method that integrates advanced image processing and machine learning algorithms. In this study, a texture image acquisition device was designed to standardize the collection of pavement texture information. Additionally, after cropping, denoising and grayscale processing, statistical features are extracted via Gray-Level Co-occurrence Matrix, while fractal features are derived using fractal theory. Correlation analysis and clustering screening mechanisms are combined to optimize feature combination. Energy, Entropy, Fractal Dimension and Multifractal Spectral Width are selected to form a multi-scale characterization system for asphalt pavement texture features. During the modelling phase, large sample databases from Anhui and Hubei provinces involving different pavement surfacing types (AC20, AC13, and SMA13) were established to compare the optimization strategies and adaptability of four models, namely Multiple Linear Regression, Random Forest, XGBoost, and BP Neural Network models. The results demonstrate demonstrated that the XGBoost model achieved superior accuracy, with an R²of 0.92 and RMSE of 0.04, and its parallel computing capability ensures superior efficiency. Comparison between the developed XGBoost model and the field assessments on pavement anti-skidding performance, both have shown high performance correlation despite different gradation types were adopted, making it the optimal model for evaluating anti-skidding performance based on asphalt pavement texture features. This study has successfully established an evaluation method that integrates machine vision and machine learning technology, breaking through the limitations of single texture feature representation and realizing the application of machine learning in the evaluation of anti-skidding performance of asphalt pavement, providing reliable technical support for road maintenance decision-making.
- PublicationPedestrian detection and trajectory prediction in complex urban environment using deep learning methods for autonomous vehicle system.(2025-08)Pedestrian safety in complex urban environments is a major challenge for intelligent transportation. Many current methods fail to address occlusions, changing scenes, and real-time demands. This study proposes a three-stage deep learning framework for pedestrian detection, tracking, and trajectory prediction. The main novelty is the integration of dynamic scene modeling and probabilistic reasoning with lightweight networks. For detection, a new feature extraction network improves small object detection in crowded scenes. The structure uses EDLAN and VoVGSCPS, which increase accuracy and reduce computation. Experiments show the F1 score and mAP@50 improve by 6.8%, and model size is 67% smaller on the Mix dataset. For tracking, data association is improved with an optimized Kalman filter and camera motion compensation. ByteTrack, enhanced with YOLOv8s, increases Order Tracking Accuracy (HOTA) and Multiple Object Tracking Precision (MOTP) by 5.7% and 7.1% on MOT17. The framework performs well in low-light and high-density scenes. For trajectory prediction, the Dynamic Scene Information Module (DSIM) combines Transformer and Long Short-Term Memory (LSTM) networks. The Mixture Density Network (MDN) provides multimodal prediction. On the DUT dataset, Average Displacement Error (ADE) and Final Displacement Error (FDE) decrease by 60% and 57.7%. The framework supports real-time processing at 133 FPS on Towncentre, which offers practical value for smart city planning and autonomous driving.
- PublicationIntegrated framework development of building information modelling (BIM) for an effective information integration (EII) in construction industry’s productivity.(2025-08)Building Information Modeling (BIM) has emerged as a critical tool for improving efficiency in the construction industry. However, challenges in information integration hinder its full adoption, particularly in developing countries. This study aims to analyze key productivity factors influencing information integration in BIM adoption and establish their significance within the Malaysian construction industry. A Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was employed to evaluate five independent variables (Effective Information Integration, Money/Finance, Methods/Design, Machines, People/Manpower) and their impact on information integration. Data was collected through structured surveys from industry professionals, and results indicated that all identified factors significantly influenced the BIM adoption process. Findings of this study reveal a strong interdependence among productivity factors, emphasizing the need for a structured framework to enhance the BIM adoption process. This study developed an Effective Information Integration (EII) Framework designed to facilitate seamless data exchange in BIM enabled projects. Key findings indicate that the framework enhances collaboration, improves data management, and mitigates interoperability issues. Experts affirmed its clarity, practicality, and alignment with industry standards, highlighting its potential for widespread implementation. This research contributes to the construction industry by providing actionable insights for policymakers, industry leaders, and academics, ultimately facilitating a more seamless BIM integration process.
- PublicationIntegrated artificial intelligence driven methods for evaluating earthquake-induced landslide hazards and road network vulnerability.(2025-10)Earthquake-induced landslides (EQIL) pose critical threats to human safety, infrastructure, and economic stability in seismically active regions such as Sichuan Province, China. Conventional hazard assessment methods often struggle with complex environmental variables and multi-source data, limiting their scalability and predictive accuracy. This study introduces an integrated AI-GIS framework aimed at enhancing EQIL risk assessment and infrastructure vulnerability analysis. The approach combines a Python-based seismic slope stability tool, deep learning models (Temporal Convolutional Networks, Gated Recurrent Units, Random Forests), and a CNN-GIS method for evaluating road network exposure. Results indicate that slope height and soil cohesion significantly influence safety factors, with improved Sarma and Bishop methods outperforming pseudo-static models. Temporal Convolutional Networks achieved the highest predictive performance (ACC = 0.781, AUC = 0.851), while Random Forests identified slope angle, seismic intensity, and soil type as dominant susceptibility factors. The CNN-GIS model demonstrated over 90% accuracy across 8,700 road segments, enabling effective spatial risk visualization. The proposed framework offers a scalable solution for EQIL hazard analysis and supports disaster resilience planning in vulnerable regions.
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- PublicationEvaluating Delinquent Juveniles’ Rehabilitation Programs In The Juvenile Welfare Department In Abu Dhabi(2025-05)This research focused on this important point, aiming to evaluate the juvenile rehabilitation programs in the juvenile welfare department in abu dhabi, united arab emirates. These programs include psychological evaluation and care, social engagement and community-building activities, spiritual guidance and religious education, discipline training and cultural heritage education. The primary objectives include examining the effectiveness of juvenile rehabilitation programs on the behaviour of juvenile delinquents in abu dhabi's juvenile welfare department, assessing the impact of staff competence (service quality) on their behavior, investigating the relationship between the environmental setting of rehabilitation facilities and their behaviour, and exploring the moderating effect of program benefits on the relationship between rehabilitation programs and juvenile delinquents' behaviour in abu dhabi's juvenile welfare department. The research used a quantitative design with surveys to collect data from juveniles in rehabilitation programs at the abu dhabi juvenile welfare department
- PublicationCharacterization of β-tcp coated on stainless steel 316(2022-08-18)Biomaterials are materials designed to interact with the human body. Stainless steels, particularly 316 types, are the most often used metallic biomaterials for biomedical applications due to their superior biocompatibility, low cost, availability and high strength. However, leaching of metallic ions into surrounding tissues is a huge disadvantage which can cause renal and cardiac problems. By coating ß-TCP onto the surface of 316 SS is an alternate way for preventing iron ions from spreading and destroying human tissues. In this project, the main objective are to coat beta tricalcium phosphate on 316 SS via spray coating technique and to evaluate the effect of number of coatings on the properties of the coated 316 SS. The parameter used is different number of coatings which is 1 to 5 layers. The slurry was prepared by mixing PVA into distilled water. After that, let it cool to room temperature and the add β-TCP powder with the right composition. Then, the slurry is sprayed on the 316 SS substrate with different number of layers and sintered at 700°C. Characterization involved are Scanning Electron Microscope (SEM), Atomic Force Microscopy (AFM), Vickers Microhardness Test and Bioactivity test in HBSS. From the result obtained, the coating with five layers had the greatest thickness and hardness while for the surface roughness coating with three layers had the highest value of 6.131 nm. Lastly, for bioactivity test sample with coating of three layer has less gained in weight, while sample with coating of five layer has great weight loss. The amount of weight loss was influenced significantly by the addition layer of coating, particularly at layer five. Increasing number of coatings, increased weight loss.
- PublicationEating habits and menstrual problems among female undergraduate students in school of health science, Universiti Sains Malaysia (USM)(2026-08)Eating habits may influence menstrual health among young women, making it important to understand their relationship among university students. This study aimed to identify eating habits and menstrual problems among female undergraduate students in the School of Health Sciences, Universiti Sains Malaysia (USM) and to examine the relationship between these variables. A cross-sectional study was conducted among female undergraduate students at the School of Health Sciences, Universiti Sains Malaysia (USM). Data were collected from January to March 2026 using a structured self-administered questionnaire. A total of 182 respondents participated in the study. Most respondents demonstrated relatively healthy eating habits, although some reported unhealthy dietary behaviours, including irregular breakfast consumption, inconsistent meal timing, and frequent intake of fast and fried foods. Most participants had regular menstrual cycles and no diagnosed menstrual disorders. Among those with menstrual problems, dysmenorrhea was the most common condition, followed by irregular menstrual cycles and menorrhagia. Overall, no significant relationship was found between eating habits and menstrual problems. Female undergraduate students generally exhibited healthy eating habits and good menstrual health despite the presence of some unhealthy dietary practices and menstrual problems. Continuous health education programmes promoting balanced nutrition and menstrual health awareness are recommended, and future studies involving larger and more diverse populations are needed to further investigate factors associated with menstrual health.
- PublicationRemoval of organic pollutants from humic acid extracted leachate by combined copper sulphate and crosslinked oil palm trunk starch coagulation(2022-04-01)Landfilling is the preferred solution worldwide for overwhelming sold waste management because of operational simplicity and cost-effectiveness. However, the producing leachate is a typical dilemma of this method. Leachate encompasses concentrated refractory organics (humic and fulvic acid) and is toxic. Hence, this study investigated a blended mechanism to treat leachate, i.e., humic acid extracted leachate treatment by combined coagulation-flocculation process with copper sulphate and natural oil palm trunk starch to eliminate the organic pollutants from stabilized leachate. The humic acid was recovered from raw leachate through microfiltrationcentrifugation, and starch (OPTS) was extracted from the oil palm trunk by applying the optimized bisulphite steeping method. The best yield of starch was improved further by applying a crosslinking technique (C-OPTS). The performance of CuSO4, OPTS, C-OPTS, and the combination (CuSO4-OPTS, CuSO4-COPTS), were examined individually using the humic acid extracted leachate. Experimental conditions were optimised by using RSM. The study reported 1380 mg/L of humic acid recovery, and 13.54% yield of OPTS. The combined application of CS-OPTS and CS-COPTS increased the removal efficiencies significantly. CS-COPTS ensured 64.5% COD, 86.79% colour, and 54.79% UV254 removal (at pH=7, CuSO4=4 g/L, COPTS= 1.5 g/L), which eventually reduced 56% of CuSO4 dosage. As a result, this blended mechanism removed 77% COD, 91% colour and 71% UV254 ultimately from the initial concentration of raw leachate. According to the ANOVA for CS-COPTS, the R2 values were 0.91, 0.93, 0.93 and predicted R2 are 0.85, 0.88, 0.89 for colour, COD and UV254, respectively. Therefore, better sludge and floc formation through particle bridging were obtained from CS-COPTS application. Hence the combination of copper sulphate and C-OPTS can be suggested as an effective process for organics removal from humic acid extracted leachate.
- PublicationFostering Resilience, Work-life Balance And Well Being In Promoting A Sustainable Workforce Among Women In Malaysian Public Hospitals(2023-08)As employees in public hospitals institutions around the globe, women play a significant role. They have made significant advances in the public health sector and have made significant contributions to the field of medicine. Women also play an essential role in family management. Women workers particularly in public hospitals, are exposed to a variety of occupational hazards, such as long working hours, infectious diseases, excessive workload, fatigue, stress, and job insecurity, which cause them to quit their jobs or prevent them from achieving the highest level in the industry. To meet the Millennium Goals and Sustainable Development, which emphasise the importance of empowering women, it is important to understand and appreciate the contribution of women to the public health sector and to develop a sustainable workforce in health. This study seeks to examine the significance of nurturing resilience, work life balance, and wellbeing in fostering a sustainable health workforce among women in Malaysian public hospitals. This paper's theory is founded on Maslow's hierarchy of requirements, the job demands-resources model, the job demand-control model, and the spillover theory. In previous research and practise, the theoretical framework is not well linked to practise, so this study examines the combination of these theories in the context of the study of women working in public hospitals in Malaysia. Based on the results, demographic factors are positively related to resilience, suggesting that certain characteristics related to individuals' background contribute to their ability to bounce back from challenges.