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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.
Most viewed
- ItemSynthesis Of Zinc Selenide Coated By Zinc Sulfide Nanoparticles Via Modified Polyol Method(Universiti Sains Malaysia, 2011-07)Zinc selenide (ZnSe) nanoparticles and zinc selenide@zinc sulphide (ZnSe@ZnS) core-shell structure were prepared using a surfactant-assisted process. ZnSe nanoparticles were prepared using zinc chloride and selenium powder as the precursor sources. The obtained ZnSe nanoparticles were then coated with ZnS to form the ZnSe@ZnS core-shell structure. Various parameters namely processing time, processing temperature, precursor ratio, concentration of surfactant, molecular weight of surfactant, and type of surfactant have been studied in order to produce finer particle with narrow particle size distribution of the ZnSe and ZnSe@ZnS coreshell nanoparticles. The optimum condition for the synthesis of ZnSe nanoparticles was 195°C for 2 minutes with the Zn to Se ions ratio of 1:1. Furthermore, 0.02g of 10,000 Mw polyethylene glycol (PEG) was found to be the most effective surfactant to form the nanoparticles with the mean diameter of 2.96 nm and particle size distribution within 1.5 – 4.5 nm. TEM and SEM analysis confirmed that the synthesized ZnSe and ZnSe@ZnS core-shell nanoparticles had a small particle size and a quasi-spherical shape with mild agglomeration. The success for the formation of ZnSe and ZnSe@ZnS core-shell nanoparticles was further confirmed by XRD, EDX, and ESI testing. PL and UV-Vis analysis recorded emission and absorption wavelength at 402 nm and 464 nm respectively. The successful of ZnS coating on ZnSe cores were confirmed by the drastic enhancement of PL intensity and red shift of ZnSe@ZnS absorption peak from UV-Vis testing.
- ItemImej insan dalam novel-novel Ahmad Lutfi(Universiti Sains Malaysia, 1981)Kesusasteraan jenis novel telah bermula sejak pertengahan tahun 1920an, yang memperlihatkan tahap-tahap perubahan imej insan. Sesungguhnya terdapat empat zaman yang memperlihatkan perubahan ini. Zaman sebelum Abdullah Munshi, Zaman Abdullah Munshi, Zaman Sebelum Perang dan Zaman Selepas Perang Dunia ke Dua hingga kini.Hikayat Faridah Hanum merupakan novel Melayu pertama.lanya ditulis oleh Syed Sheikh Al- Hadi dalam tahun 1926.Karya tersebut diakui sebaga i novel Melayu pertama kerana didalamnya mengandungi kisah kehidupan insan yang terpijak di alam nyata. Fakta ini sangat bersesuaian dengan pendapat lan Watt : Realisma adalah ciri yang paling nyata bagi membezakan proses cerita moden yang di golongkan sebagai novel dengan cerita - cerita tradisional sebelumnya.
- 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
- ItemStrategi Peperangan Masyarakat Melayu Tradisional Sebelum Abad Ke – 19(Universiti Sains Malaysia, 1989)Seringkali media massa sekarang ini membicarakan tentang peperangan yang berlaku khususnya negeri-negeri luar seperti Afghanistan, Iran, Iraq, Palestin, Vietnam dan lain-lain lagi. Atas dasar peperangan yang berlaku sekarang ini, saya terfikir bahawa sebelum kini khususnya masyarakat dahulu juga mengalami peperangan.
- ItemThe relationship between market orientation and export performance(2007)Manufacturing sector plays a major role in Indonesia’s exports and contributes significantly to its gross domestic product (GDP). Indonesian exporters in the manufacturing sectors are encouraged by Indonesian government to upgrade their implementation of marketing management competency in order to export more. The study investigates the mediation impact of export marketing management competency on the relationship between market orientation and export performance in Indonesian manufacturing firms. Moderating effect of competitive intensity, market turbulence, and technological turbulence on the relationship between export marketing management competency and export performance are also investigated. The study also considers the influence of the control variables (firm size, export experience, export mode, and industrial type) on export performance. Data are collected through mail survey and a total of 109 usable responses, representing 12.43 per cent of the total sample, are analyzed. The results of the study indicate that the constructs of market orientation (customer orientation, competitor orientation, and inter-functional coordination) and export marketing management competency (export product management competency, export pricing management competency, export promotion management competency, and export distribution management competency) are statistically significant predictors of export performance, whereas market orientation is a determinant of export marketing management competency. The results support the mediation effect of export product management competency and export pricing management competency on the relationship between market orientation and export performance. This study does not find any evidence to support the moderating effect of competitive intensity, market turbulence and technological turbulence on the relationship between export marketing management competency and export performance. In addition, the findings revealed that none of the control variables significantly influence export performance.