8 research outputs found
The Behavioral Intention of Young Travelers to Use Virtual Reality Technology in Cultural Tourism Destinations: An Application of Technology Acceptance Model
No full textTechnology plays a crucial role in safeguarding cultural and heritage assets for tourism destinations. Despite youths\u27 apparent technological proficiency, there has been limited research on their intention to use virtual reality (VR) in such settings. This study stands as one of the pioneering efforts to examine young individuals\u27 behavioral intention to utilize VR technology in cultural heritage tourism destinations within the Borneo region, specifically Sarawak. Drawing from the concept of the technology acceptance model, this study investigates how various factors of perceived usefulness (such as accessibility to information, information quality, and media richness) and perceived ease of use (such as interactivity) influence the behavioral intention to use VR technology in cultural tourism settings. Statistical Package for Social Sciences (SPSS) and WarpPLS were used for data analysis. This study gathered data from 250 valid responses from young visitors at cultural tourism sites in Sarawak, Malaysia. Employing a quantitative methodology, the interrelationships among the study variables were examined through partial least squares - structural equation modelling. The current research reveals that young individuals prioritize factors such as information quality, media richness, and interactivity when considering their intention to use VR technology in cultural tourism destinations. However, the accessibility of information was not found to be a significant concern. This study lies in its focus on the Borneo region, offering new insights into the adoption of VR technology in cultural heritage tourism among youths
Transformational Leadership, Motivation, and Organizational Commitment towards Corporate Social Responsibility in Banking Service Industry, Sarawak
No full textThe behavioral intention of young travelers to use virtual reality technology in cultural tourism destinations: An application of technology acceptance model
No full textTechnology plays a crucial role in safeguarding cultural and heritage assets for tourism destinations. Despite youths' apparent technological proficiency, there has been limited research on their intention to use virtual reality (VR) in such settings. This study stands as one of the pioneering efforts to examine young individuals' behavioral intention to utilize VR technology in cultural heritage tourism destinations within the Borneo region, specifically Sarawak. Drawing from the concept of the technology acceptance model, this study investigates how various factors of perceived usefulness (such as accessibility to information, information quality, and media richness) and perceived ease of use (such as interactivity) influence the behavioral intention to use VR technology in cultural tourism settings. Statistical Package for Social Sciences (SPSS) and WarpPLS were used for data analysis. This study gathered data from 250 valid responses from young visitors at cultural tourism sites in Sarawak, Malaysia. Employing a quantitative methodology, the interrelationships among the study variables were examined through partial least squares - structural equation modelling. The current research reveals that young individuals prioritize factors such as information quality, media richness, and interactivity when considering their intention to use VR technology in cultural tourism destinations. However, the accessibility of information was not found to be a significant concern. This study lies in its focus on the Borneo region, offering new insights into the adoption of VR technology in cultural heritage tourism among youths
Survival and quality of life in incident systemic sclerosis-related pulmonary arterial hypertension
No full textBackground: Pulmonary arterial hypertension (PAH) is a leading cause of mortality in systemic sclerosis (SSc). We sought to determine survival, predictors of mortality, and health-related quality of life (HRQoL) related to PAH in a large SSc cohort with PAH. Methods: We studied consecutive SSc patients with newly diagnosed (incident) World Health Organization (WHO) Group 1 PAH enrolled in a prospective cohort between 2009 and 2015. Survival methods were used to determine age and sex-adjusted standardised mortality ratio (SMR) and years of life lost (YLL), and to identify predictors of mortality. HRQoL was measured using the Short form 36 (SF-36) instrument. Results: Among 132 SSc-PAH patients (112 female (85%); mean age 62 ± 11 years), 60 (45.5%) died, with a median (±IQR) survival time from PAH diagnosis of 4.0 (2.2-6.2) years. Median (±IQR) follow up from study enrolment was 3.8 (1.6-5.8) years. The SMR for patients with SSc-PAH was 5.8 (95% CI 4.3-7.8), with YLL of 15.2 years (95% CI 12.3-18.1). Combination PAH therapy had a survival advantage (p < 0.001) compared with monotherapy, as did anticoagulation compared with no anticoagulation (p < 0.003). Furthermore, combination PAH therapy together with anticoagulation had a survival benefit compared with monotherapy with or without anticoagulation and combination therapy without anticoagulation (hazard ratio 0.28, 95% CI 0.1-0.7). Older age at PAH diagnosis (p = 0.03), mild co-existent interstitial lung disease (ILD) (p = 0.01), worse WHO functional class (p = 0.03) and higher mean pulmonary arterial pressure at PAH diagnosis (p = 0.001), and digital ulcers (p = 0.01) were independent predictors of mortality. Conclusions: Despite the significant benefits conferred by advanced PAH therapies suggested in this study, the median survival in SSc PAH remains short at only 4 years.Kathleen Morrisroe, Wendy Stevens, Molla Huq, David Prior, Jo Sahhar, Gene-Siew Ngian, David Celermajer, Jane Zochling, Susanna Proudman, Mandana NikpourEmail author and the Australian Scleroderma Interest Group (ASIG
Economic aspects of interlocking hollow brick system designed for industrialized building system
No full textPhotoluminescence and photoluminescence excitation study of semiconductor materials
No full textSemiconductor materials are very important in modern optoelectronics and microelectronics applications. These applications demand a better understanding of the fundamental properties of semiconductors and the optimization of growth and post-growth process. Consequently a variety of characterization techniques which are sensitive to various physical properties are needed. Photoluminescence (PL) and photoluminescence excitation (PLE) is one of those techniques.
Through the study of PL and PLE spectra, the quality of material, and important material
parameters, such as, the band gap for bulk material and the effective band gap for
semiconductor quantum well can be extracted. In this final year project, the author will be able to learn the working principle of PL and PLE, gain hands-on experience on how to
operate the PL and PLE system, measure and analyze the PL and PLE spectra of GaAs and
GaInNAs semiconductor quantum wells.Bachelor of Engineerin
Early assessment of tumor response to photodynamic therapy using combined diffuse optical and diffuse correlation spectroscopy to predict treatment outcome
No full textPhotodynamic therapy (PDT) of cancer involves the use of a photosensitizer that can be light-activated to eradicate tumors via direct cytotoxicity, damage to tumor vasculature and stimulating the body's immune system. Treatment outcome may vary between individuals even under the same regime; therefore a non-invasive tumor response monitoring system will be useful for personalization of the treatment protocol. We present the combined use of diffuse optical spectroscopy (DOS) and diffuse correlation spectroscopy (DCS) to provide early assessment of tumor response. The relative tissue oxygen saturation (rStO2) and relative blood flow (rBF) in tumors were measured using DOS and DCS respectively before and after PDT with reference to baseline values in a mouse model. In complete responders, PDT-induced decreases in both rStO2 and rBF levels were observed at 3 h post-PDT and the rBF remained low until 48 h post-PDT. Recovery of these parameters to baseline values was observed around 2 weeks after PDT. In partial responders, the rStO2 and rBF levels also decreased at 3 h post PDT, however the rBF values returned toward baseline values earlier at 24 h post-PDT. In contrast, the rStO2 and rBF readings in control tumors showed fluctuations above the baseline values within the first 48 h. Therefore tumor response can be predicted at 3 to 48 h post-PDT. Recovery or sustained decreases in the rBF at 48 h post-PDT corresponded to long-term tumor control. Diffuse optical measurements can thus facilitate early assessment of tumor response. This approach can enable physicians to personalize PDT treatment regimens for best outcomes.Version of Recor
Advancing tunnel equipment maintenance through data-driven predictive strategies in underground infrastructure
No full textUrban tunnel infrastructure, crucial for societal well-being, depends on reliable Tunnel Electromechanical Equipment (TEE), including ventilation, drainage, and lighting systems. A key challenge is these systems’ proactive and efficient maintenance, particularly under limited resources. This study introduces a novel deep learning-based multi-output prediction model developed to enhance the understanding and predictive accuracy Tunnel Boring Machine (TBM) performance, with a specific focus on machine wear and tear (y1) and adapting to ground conditions and geotechnical data (y2) in complex underground environments. The model employs an advanced deep learning approach, att-GCN, which innovatively integrates Graph Convolutional Networks (GCN) with a scaled dot-product attention mechanism. This combination notably improves model performance and interpretability. Experimental results indicate that att-GCN model achieves a Mean Absolute Percentage Error (MAPE) of 17.1% for y1 and 16.8% for y2, outperforming other established algorithms, including the Deep Neural Network (DNN)-Genetic algorithm hybrid. Furthermore, an online learning variant of att-GCN was developed that integrates real-time data during tunneling operations. This version demonstrated enhanced predictive accuracy, with a MAPE of 8.7% for y1 and 8.1% for y2. Applying att-GCN for real-time TBM performance estimation based on dynamic monitoring data offers significant insights for intelligent TBM control, improving construction efficiency and reliability
