Your search

Oracle Corporation (hereinafter referred to as Oracle, ORCL, or ‘the Company’) is an American multinational company that provides solutions of products and services that serve the enterprises’ information technology (IT) environment. This thesis is to conduct a business analysis of the Company from a financial perspective, determining the Company’s intrinsic value as of May 31, 2021, and comparing it with the respective market value. Thus, this thesis will study, evaluate, and present an overview of the Company, an analysis of the Company’s market, industry, strategy, financial performance, including external and internal factors, a ten-year pro forma financial statement forecast, and the techniques of using the three discounted cash flow models to estimate the intrinsic value of Oracle. The obtained results from the three valuation models, including the Enterprise Discounted Cash Flow (EDCF) model, the Adjusted Present Value (APV) model, and the Discounted Economic Profit (DEP) model, show that the Company’s intrinsic values were estimated at $74.57, $75.21, and $74.82, respectively. When the results were used to compare with the market price of Oracle’s shares as of May 31, 2021, at $78.74, it reflects that the Company was overvalued

This study explored the effect of communication (i.e., among staff, and between staff and clients) and of cultural diversity on job satisfaction (i.e., intrinsic, extrinsic, and general) and perceived service quality of formal caregivers working in elderly services in Macao. We applied a quantitative methodology, based on a cross-sectional design using a self-response questionnaire to 162 staff in six elderly centres in Macao. Based on an extensive review of the literature, we proposed that: H1) cultural diversity is negatively related to (a) intrinsic job satisfaction, (b) extrinsic job satisfaction, (c) general job satisfaction, and (H5) negatively related to perceived competence and service quality; (H2) communication (a) among staff and (b) between staff and clients is positively related to intrinsic job satisfaction (H3) extrinsic job satisfaction, (H4) general job satisfaction, and (H6) perceived service quality; and finally that (H7) intrinsic, (H8) extrinsic, and (H9) general job satisfaction mediate the relationship between (a) cultural diversity, (b) communication among staff and (c) communication between staff and clients, and perceived service quality. We found that more communication among staff was related to higher intrinsic, extrinsic and general job satisfaction, and perceived competence and service quality. And intrinsic job satisfaction mediated the positive effect of communication among staff on perceived service quality. Opposite to predicted communication between staff and clients was related to lower levels of job satisfaction. And cultural diversity was positively related to satisfaction, as well as perceived competence and service quality. The theoretical and practical implications of findings, as well as limitations and suggestions for future research were discussed

Macau, Macau Business, MAG, MB, MB Featured, Opinion | As Macau strives to revive its post-pandemic economy and to reinject life into its ailing society, calls for investment in human capital resurface, alongside endless mantras of economic diversification which, for years, seem to have fallen on deaf ears, and together with plans for further infrastructure development and construction which have already turned Macau into a concrete jungle.

Macau, Macau Business, MAG, MB, MB Featured, Opinion | Macau’s development of international and tertiary sector industries is the watchword for its long-overdue diversification. Is Macau ready for this?

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.

" Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. While there has been continual improvement in recent years, harmful air pollutant concentration levels are still common, impacting Macau residents' health and creating long-term medical costs to local society. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration and an early alert system instead of only daily real-time reminders. Some scholars have previously carried out studies to develop an air quality forecast for Macau by successfully using statistical models. Therefore, pursuant to the outcomes of previous studies, this dissertation aims to build upon research results and explore further possibilities of building a better ML air quality forecast model based on the time series of air pollutants concentration and meteorological data. Four different state-of-the-art ML algorithms were used to create predictive models to forecast PM2.5, PM10, and carbon monoxide (CO) concentrations for the next 24 and 48-hour. These were Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). In addition, Multiple Linear Regression MLR, a standard ML model, was used for this dissertation as a baseline reference for performance comparison. The daily measurements of air quality data in Macau from 2016 to 2021 were collected for this dissertation. The 2020 and 2021 datasets were used for model testing while the four-year data prior to 2020 and 2021 were used to build and train the ML models. The results showed that SVM, ANN, RF, and XGBoost were able to provide a very good performance in building up a 24-hour forecast with higher R2 and lower RMSE, MAE, and BIAS. Meanwhile, all ML models in 48-hour forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-hour forecast. The 48-hour forecasting model could be further improved by proper feature selection based on the 24-hour dataset, using the SHAP value test, and the adjusted R2 value of the 48-hour forecasting model."

Informal recycling plays a crucial role in municiapl solid waste management in many cities, particularly in the global South. This study examines the practices, challenges, and opportunities of informal recycling in Macau, a small city and Self Autonomous Region (S.A.R.) in China. Using qualitative research methods, including semi-structured interviews, this study explores the motivations and strategies of informal recyclers, the challenges they face, and the potential for collaboration with formal waste management systems. The findings of this study reveal that informal recycling in Macau is a complex and multifaceted reality and practice that involves a range of actors, from waste pickers to small-scale processors to exporters, all with their specific challenges. Informal recyclers are motivated by economic necessity, and they employ a variety of strategies to collect and process recyclable materials. However, they also face significant challenges, including high rental and transportation costs, lack of manpower, China’s waste import policies and ensuing restrictions, fluctuating global price rates of materials and the unstable income as serious consequence, accompanied by limited support from the local Government. This study also identifies opportunities for sustainable development of informal recycling in Macau, supported by the analysis of data collected via questionnaire survey regarding Macau citizens’ waste separation habits and their willingness to pay for resource separation and recovering process. The identified oppurtunities include establishing partnerships between informal and formal waste management actors, improving the infrastructure, and introducing environmental levy system and consistent policies and regulations. Overall, this study contributes to a better understanding of the role of informal recycling sector in waste management in Macau and provides insights into potential strategies for improving the sustainability of resource and waste management practices in the city

Virtual reality, a computer-generated 3D environment, allows one to navigate and possibly interact, resulting in real-time simulation of one or more of the user’s five senses (Gutierrez et al., 2008; Vince, 2004). Virtual tours and places have swiftly become popular in education, professional training, arts, exhibitions, and medication and rehabilitation. The empirical studies derived from the PhD thesis research aim to identify the conditions for Macao’s single-user experience to achieve mindfulness in virtual reality through immersion and interactivity.

The COVID-19 pandemic has posed a significant public health challenge on a global scale. It is imperative that we continue to undertake research in order to identify early markers of disease progression, enhance patient care through prompt diagnosis, identification of high-risk patients, early prevention, and efficient allocation of medical resources. In this particular study, we obtained 100 5-min electrocardiograms (ECGs) from 50 COVID-19 volunteers in two different positions, namely upright and supine, who were categorized as either moderately or critically ill. We used classification algorithms to analyze heart rate variability (HRV) metrics derived from the ECGs of the volunteers with the goal of predicting the severity of illness. Our study choose a configuration pro SVC that achieved 76% of accuracy, and 0.84 on F1 Score in predicting the severity of Covid-19 based on HRV metrics.

The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.

In 2020, the World Health Organization declared the Coronavirus Disease 19 a global pandemic. While detecting COVID-19 is essential in controlling the disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a CT-Scan room. In other cases, the devices are not available. The adoption of ambulatory monitoring examinations, such as Electrocardiography (ECG), can be considered a viable tool to address the patient’s cardiovascular condition and to act as a predictor for future disease outcomes. In this investigation, ten non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) extracted from 2 ECG signals (collected from 2 different patient’s positions). Windows of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested:  (Low vs. Moderate, Low vs. Severe, Moderate vs. Severe) and 1 Multi-class comparison  (All vs. All)). The results indicate that some statistically significant parameter distributions were found for all comparisons.  (Low vs. Moderate—Approximate Entropy p-value = 0.0067 < 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 < 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 < 0.05, All vs. All—Correlation Dimension p-value = 0.0185 < 0.05. The non-linear analysis of the time-frequency representation of the ECG signal can be considered a promising tool for describing and distinguishing the COVID-19 severity activity along its different stages.

Buddhism was founded by Shakyamuni, and this religion has appeared in opposition to Brahmanism's caste system since its inception. Shakyamuni emphasized the important concept of “all beings are equal” from the very beginning. Buddhism spread from India to other regions, and was introduced to China through Central Asia. In China, Buddhism merged the two native cultures of Confucianism and Taoism and finally produced the socalled Chinese Buddhism. Buddhism and its value of equality were also developed and extended in China, and finally spread to Japan. After Buddhism was spread to Japan, it developed rapidly due to official support. Different Buddhist sects have established their own temples in Japan, and among them, Zen Buddhism has the greatest influence among all social classes in this country. Later, after years of development, Zen Buddhism penetrated into many aspects of Japanese culture, such as religion, aesthetics, garden design, samurai spirit and tea ceremony. With the spread of Zen Buddhism, the core value of Buddhism which is “Buddhist concept of equality” was also spread to Japan. The tea ceremony is precisely the place where this value of equality can be detected. This dissertation explains the many processes that reflect this value no matter inside or outside the tea room, and argues that the spirit of the tea ceremony is to pursue the value of equality