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The scientific literature indicates that pregnant women with COVID-19 are at an increased risk for developing more severe illness conditions when compared with non-pregnant women. The risk of admission to an ICU (Intensive Care Unit) and the need for mechanical ventilator support is three times higher. More significantly, statistics indicate that these patients are also at 70% increased risk of evolving to severe states or even death. In addition, other previous illnesses and age greater than 35 years old increase the risk for the mother and the fetus, including a higher number of cesarean sections, higher systolic and diastolic maternal blood pressure, increasing the risk of eclampsia, and, in some cases, preterm birth. Additionally, pregnant women have more Emotional lability/fluctuations (between positive and negative feelings) during the entire pregnancy. The emotional instability and brain fog that takes place during gestation may open vulnerability for neuropsychiatric symptoms of long COVID, which this population was not studied in depth. The present Chapter characterizes the database presented in this work with clinical and survey data collected about emotions and feelings using the Coronavirus Perinatal Experiences—Impact Survey (COPE-IS). Pregnant women with or without COVID-19 symptoms who gave birth at the Assis Chateaubriand Maternity Hospital (MEAC), a public maternity of the Federal University of Ceara, Brazil, were recruited. In total, 72 mother-infant dyads were included in the study and are considered in this exploratory analysis. The participants have undergone serological tests for SARS-CoV-2 antibody detection and a nasopharyngeal swab test for COVID-19 diagnoses by RT-PCR. A comprehensive Exploratory Data Analysis (EDA) is performed using frequency distribution analysis of multiple types of variables generated from numerical data, multiple-choice, categorized, and Likert-scale questions.
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Starbucks Corporation (hereinafter “Starbucks” or “the Company”) is a worldwide coffee retailer, which operates over 33,000 stores located in over 83 countries nowadays. The purpose of the study is to estimate Starbucks’ intrinsic value as of December 31, 2021 and identify whether the Company was overvalued or undervalued. Several analyses give investors and shareholders an insight into how the Company may develop or identify the ability to generate positive returns from investing in Starbucks. This study is mainly separated into two aspects. The first part specifically discusses the Company overview, industry analysis, and economic outlook, which includes SWOT analysis, PESTEL analysis, Porter's five forces analysis, and value chain analysis to identify external and internal factors that may influence the Company. The second part focuses on financial analyzes, including both historical and forecasted financial statement. Three valuation models and a sensitive analysis are applied to understand the Company’s financial conditions and performance. Starbucks’ intrinsic value is derived from the three discounted cash flow models, indicating the market overvalued the Company’s stock price as of December 31, 2021. Finally, investors and shareholders can understand more about Starbucks’ capital structure, financial highlights, and intrinsic value, because this set of information is critical for existing investors and potential investors to make investment decisions
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Road transportation is one of the main sources of air pollution in Macao. This study mainly explores four major roadside locations with high traffic flow in Macao from March to May 2022 and measures their pollutant concentrations (PM10 and PM2.5), traffic flow and their fuel type, as well as considering the meteorological parameters and pollutant concentration of SMG Macao to analyze the relationship between traffic flow and pollutants on roadside locations. Under the measuring distance between 3 and 6 meters, showing that the four locations had a good correlation with the roadside station data provided by SMG on both weekdays and weekends/holidays (PM2.5: R2 is 0.59 to 0.81 on weekdays and 0.79 to 0.88 on weekends/holidays, p<0.01; PM10: R2 is 0.33 to 0.82 on weekdays and 0.30 to 0.58 on weekends/holidays, p<0.05), the overall PM2.5 is 41 to 86% higher than that of the same period of Macao roadside station (SMG), and 68 to 186% higher than that of Taipa Ambient (SMG), indicating that it is more harmful to daily pedestrians. The overall relationship between PM concentration and traffic flow is small on the long-term scale (PM2.5: R2 is 0.01 to 0.13; PM10: R2 is 0.00 to 0.02). This study also analyzed air quality on EBL, the overall PM2.5 and PM10 decreased by 12.3% to 24.8% compared with non-EBL during the period, so that is indeed beneficial to the reduction of pollutant concentrations. In addition, narrower roads were overall higher when road widths added for comparison. Lastly, meteorological data added for comparison, except for relative humidity, it can be found that there is a significant correlation with long-term pollutants (p<0.05). While previous studies have found that single-day traffic flow is related to the increase in PM concentration, this paper is more inclined to their two-way effect when exploring their long-term relationship
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Nowadays marketers and practitioners recognize the importance of social marketing as a strategy to acquire customers. Accordingly, the enterprises and brands are embracing the use of social network in communicating with potential consumers ad actual consumers, in order its performance so as to increase its sales. Among all those social media platforms, Instagram is one of the platforms which is image orientated and can easily connect with users and provide information with photos and images. As Macau is small‐sized city where there are abundant small to medium‐sized enterprises, the local small firms are switching their marketing strategies by utilizing the Instagram to promote their product online. In order to get a better grasp of how Instagram affects the promotion of the food and beverage product in Macau, the research question has been formulated: “To what extend can the local food sectors in Macau use Instagram as a marketing tool to engage purchasing intention and strengthen their brand equity? From the perspective of customers.” Semi‐structured interviews were conducted with 13 participants who are the Instagram users, with diverse situations of engagement on Instagram. According to the interviews, participants expressed that how Instagram can associate effects with arousal and brand equity (brand awareness, brand image, perceived quality and brand loyalty) and formation of purchase intention eventually through the Instagram marketing activities. The results showed that Instagram marketing activities strongly correlated the brand equity (brand awareness, brand image, and perceived quality while which in turn led to purchasing intention towards the brand. This research comprehensively illustrates the influences of Instagram marketing activities on customer‐based brand equity. The findings of this study will enable local food brands to more accurately forecast the future purchasing behaviors of their customers through Instagram marketing activities and provide a guide in managing brand equity as well
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The phenomenon of burnout has been recognised as a worldwide occupational health issue after being vastly studied for decades. Trait Emotional Intelligence (trait EI) and resilience have been identified as personal protective factors (Gutierrez & Mullen, 2016; Listopad et al., 2021), while organisational socialisation is suggested to be an organisational factor in helping people in preventing burnout (Taormina & Law, 2000). With the purpose of 1) investigating the phenomenon in the counselling profession, as well as 2) exploring how trait EI and resilience are related to burnout and whether organisational socialisation might impose moderating effects in between, the present study examined 115 counselling professionals currently employed and working in organisational settings in Macau by snowball sampling, using a quantitative and cross-sectional approach through self-reported online questionnaires. From the data obtained, different burnout patterns were observed according to job titles and work settings, indicating that counselling professionals with different specialties and work in different settings have unique sources of stress, which resulting in differences in their burnout patterns. No between-group differences were observed in age and work experience, while male participants have a higher burnout perception than female participants in the current study. On the other hand, current results suggested trait EI and four components of resilience (determination, endurance, adaptability and recuperability) are negatively correlated to counselling professionals’ burnout perception, providing supportive evidence that trait EI and resilience are protective factors against burnout. Moderation analysis results revealed that organisational socialisation has some moderating effects on the relationship between trait EI, resilience and burnout. However, differences in direction and intensity indicated that the moderating effects of organisational socialisation might be influenced by individual differences. Further studies are needed to better the understanding of the moderating effect of organisational socialisation. Limitations of the current research and implications for counselling professionals and organisations were also discussed in the study
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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
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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
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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.
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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?
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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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" 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."
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