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This review article is among the first to examine the new junket regulations in the Macau gaming industry. Particular emphasis is on the legal and regulatory framework governing the junket activity of gaming promoters and their associates. The recent changes to Macau gaming laws have resulted in stronger licensing requirements for local junket participants and precipitated the collapse of the VIP room system in casinos. Furthermore, this article highlights the policy and managerial implications of the current junket environment for the gaming industry in Macau and possibly other regional gaming jurisdictions. The effects of the new legal environment for Macau junkets could also provide insights into the implementation of similar legislation in other jurisdictions.
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Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.
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Crowdsensing exploits the sensing abilities offered by smart phones and users' mobility. Users can mutually help each other as a community with the aid of crowdsensing. The potential of crowdsensing has yet to be fully realized for improving public health. A protocol based on gamification to encoura...
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Objective: As a world tourist destination, Macao is inevitably under the impact of the COVID-19 pandemic. However, the market of integrated resorts in Macao are shared by only a few casino concessionaries, together forming an oligopoly. While the firms attempted to adjust price, quantity and quality of their hotel services in response to the pandemic, they could not overlook the strategic interactions with other players in the market. Hence, this paper aims to investigate the possible impact of the pandemic on the oligopolistic strategies in the integrated resort market in Macao. Methodology: Application of a theoretical model of differentiated oligopoly to this six-firm case shows that price differences across firms depend on their quality differentiation. In order to analyze these price differences empirically, this paper collects data of hotel room rates of the integrated resorts from November, 2019 to mid-August, 2020, covering the periods before and after the outbreak of COVID-19. Originality: In the existing literature, there is a lack of studies of the oligopoly in the hospitality industry of Macao. Furthermore, the effect of COVID-19 is still ongoing, so this present paper is one of the first to perform such analysis. Results: The regression of each of the hotel price differentials on the COVID-19 dummy variable shows that COVID-19 has statistically significant impacts on almost all the price differentials. Intuitively, MGM and Wynn were in the high-price segment before and after the outbreak, while other firms switched positions in the low-price segment during the pandemic. One obvious downstream movement was by Conrad. According to the proposition derived from the theory, these imply that COVID-19 should have significant impact on the quality differentiation of the firms. Practical implications: The results are in line with the observations that the integrated resorts have rolled out staycation packages according to preferences of local residents. These quality adjustments observed in Macao’s hospitality industry currently only involved variable inputs rather than fixed inputs of production; therefore, the impact of COVID-19 should be seen as short-term effects. Keywords: Covid-19; Differentiated oligopoly; Hospitality industry; Hotel room rate; Oligopolistic market structure; Pricing strategy.
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Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease.
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The gold standard to detect SARS-CoV-2 infection considers testing methods based on Polymerase Chain Reaction (PCR). Still, the time necessary to confirm patient infection can be lengthy, and the process is expensive. In parallel, X-Ray and CT scans play an important role in the diagnosis and treatment processes. Hence, a trusted automated technique for identifying and quantifying the infected lung regions would be advantageous. Chest X-rays are two-dimensional images of the patient’s chest and provide lung morphological information and other characteristics, like ground-glass opacities (GGO), horizontal linear opacities, or consolidations, which are typical characteristics of pneumonia caused by COVID-19. This chapter presents an AI-based system using multiple Transfer Learning models for COVID-19 classification using Chest X-Rays. In our experimental design, all the classifiers demonstrated satisfactory accuracy, precision, recall, and specificity performance. On the one hand, the Mobilenet architecture outperformed the other CNNs, achieving excellent results for the evaluated metrics. On the other hand, Squeezenet presented a regular result in terms of recall. In medical diagnosis, false negatives can be particularly harmful because a false negative can lead to patients being incorrectly diagnosed as healthy. These results suggest that our Deep Learning classifiers can accurately classify X-ray exams as normal or indicative of COVID-19 with high confidence.
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Even with more than 12 billion vaccine doses administered globally, the Covid-19 pandemic has caused several global economic, social, environmental, and healthcare impacts. Computer Aided Diagnostic (CAD) systems can serve as a complementary method to aid doctors in identifying regions of interest in images and help detect diseases. In addition, these systems can help doctors analyze the status of the disease and check for their progress or regression. To analyze the viability of using CNNs for differentiating Covid-19 CT positive images from Covid-19 CT negative images, we used a dataset collected by Union Hospital (HUST-UH) and Liyuan Hospital (HUST-LH) and made available at the Kaggle platform. The main objective of this chapter is to present results from applying two state-of-the-art CNNs on a Covid-19 CT Scan images database to evaluate the possibility of differentiating images with imaging features associated with Covid-19 pneumonia from images with imaging features irrelevant to Covid-19 pneumonia. Two pre-trained neural networks, ResNet50 and MobileNet, were fine-tuned for the datasets under analysis. Both CNNs obtained promising results, with the ResNet50 network achieving a Precision of 0.97, a Recall of 0.96, an F1-score of 0.96, and 39 false negatives. The MobileNet classifier obtained a Precision of 0.94, a Recall of 0.94, an F1-score of 0.94, and a total of 20 false negatives.
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The global living standard improved significantly in the last decades and China moved 800 million people out of poverty since 1980. However, production and consumption in their current forms are unsustainable in matters of resource and energy use and involved emissions with their significant ecological impact. The global human community committed itself in the COP21 Agreement of Paris 2015 to reduce Green House Gas (GHG) emissions based on National Determined Contributions (NDCs) in order to limit the increase of global average temperature below 2°C or better 1.5°C above pre industrial levels. This commitment entails a comprehensive transformation of the current social and economic system in view of decoupling economic growth from both resource extraction and GHG emissions, in view of fostering a resource sensitive and CO2 neutral Circular Economy (CE) based on Sustainable Development (SD). China submitted its first Intended Nationally Determined Contributions (INDCs) in 2015 with policies and measures affecting 15 major areas. In 2020, President Xi Jinping announced the commitment to peak China’s carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060. Proper and Integrated Resource and Waste Management is central on the way to achieve the transformation into a CE. The State Council of the People’s Republic of China (PRC) released its plan “生活垃圾分类制度实施方案” to promote source separation of household waste on March 30, 2017 aiming at the recycling rate for household waste to reach 35% by 2020 for the selected cities. The first China’s Mandatory Waste Source Separation Law “廣州 市生活垃圾分類管理條例” was enforced by the city of Guangzhou on July 1st, 2018. One of the key strategic plans in China is the development of the Greater Bay Area (GBA) with its comprehensive development plan released on February 18, 2019. Following the INDCs by China, parts of the GBA Development Plan emphasize that the development of the CE systems and the implementation of extended producer responsibility (EPR), are effective principles to provide financial incentives in view of reducing embedded emissions in material and processes. The present research studied, analysed, and compared the MSW treatment strategies, rules, regulations, and retrievable data, which lead to MSW source separation and the reverse logistic of separated waste among 4 selected cities Guangzhou, Zhuhai, and the 2 S.A.R.s of Hong Kong and Macao. The experience and comparison from Guangzhou and Hong Kong revealed that a Top-down approach in environmental policy decision making is more efficient and is able to implement necessary policies faster. However, the experiences from Guangzhou and Zhuhai indicate, that also a more participatory implementation process is crucial, as it enables the involved stakeholders to express their experiences and opinions properly, which can lead to a higher level of policy feasibility and acceptance and a smoother operation accompanied with a higher effectiveness. For the two SARs, to achieve the objective to increase the recycling rate, the local Government must seek approval from China’s Central Government to allow locally generated recyclable material, in their original form, to enter mainland China for further treatment and to be turned into secondary raw material. Without such a proper support by a reverse resource logistic from the mainland, the CE schemes, such as the Mandatory Waste Source Separation, Producer Responsibility Scheme (PRS), Waste Charging Scheme, are not able to be implemented effectively. The current approach of the Local Government purchasing of recycling and exportation services of recyclable materials from private companies, and the sole reliance on existing market forces to handle, process, and export recyclable material out of the S.A.R.s cannot ensure a reliable and continuous operation in view of mitigating involved emissions. By way of a comparative analyses, the present investigation works out and distils suggestions for best practices of implementing the CE to comply with targets of emission reductions
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