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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.

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.

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.

This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity.

COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94.

The Covid-19 pandemic evidenced the need Computer Aided Diagnostic Systems to analyze medical images, such as CT and MRI scans and X-rays, to assist specialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the spread of the virus. There are some types of CAD systems, such as Machine and Deep Learning-based and Transfer learning-based. This chapter proposes a pipeline for feature extraction and classification of Covid-19 in X-ray images using transfer learning for feature extraction with VGG-16 CNN and machine learning classifiers. Five classifiers were evaluated: Accuracy, Specificity, Sensitivity, Geometric mean, and Area under the curve. The SVM Classifier presented the best performance metrics for Covid-19 classification, achieving 90% accuracy, 97.5% of Specificity, 82.5% of Sensitivity, 89.6% of Geometric mean, and 90% for the AUC metric. On the other hand, the Nearest Centroid (NC) classifier presented poor sensitivity and geometric mean results, achieving 33.9% and 54.07%, respectively.

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.

The use of computational tools for medical image processing are promising tools to effectively detect COVID-19 as an alternative to expensive and time-consuming RT-PCR tests. For this specific task, CXR (Chest X-Ray) and CCT (Chest CT Scans) are the most common examinations to support diagnosis through radiology analysis. With these images, it is possible to support diagnosis and determine the disease’s severity stage. Computerized COVID-19 quantification and evaluation require an efficient segmentation process. Essential tasks for automatic segmentation tools are precisely identifying the lungs, lobes, bronchopulmonary segments, and infected regions or lesions. Segmented areas can provide handcrafted or self-learned diagnostic criteria for various applications. This Chapter presents different techniques applied for Chest CT Scans segmentation, considering the state of the art of UNet networks to segment COVID-19 CT scans and a segmentation experiment for network evaluation. Along 200 epochs, a dice coefficient of 0.83 was obtained.

The global pandemic triggered by the Corona Virus Disease firstly detected in 2019 (COVID-19), entered the fourth year with many unknown aspects that need to be continuously studied by the medical and academic communities. According to the World Health Organization (WHO), until January 2023, more than 650 million cases were officially accounted (with probably much more non tested cases) with 6,656,601 deaths officially linked to the COVID-19 as plausible root cause. In this Chapter, an overview of some relevant technical aspects related to the COVID-19 pandemic is presented, divided in three parts. First, the advances are highlighted, including the development of new technologies in different areas such as medical devices, vaccines, and computerized system for medical support. Second, the focus is on relevant challenges, including the discussion on how computerized diagnostic supporting systems based on Artificial Intelligence are in fact ready to effectively help on clinical processes, from the perspective of the model proposed by NASA, Technology Readiness Levels (TRL). Finally, two trends are presented with increased necessity of computerized systems to deal with the Long Covid and the interest on Precision Medicine digital tools. Analyzing these three aspects (advances, challenges, and trends) may provide a broader understanding of the impact of the COVID-19 pandemic on the development of Computerized Diagnostic Support Systems.

This chapter presents a systematic review of research on human resources management (HRM) and employee relations (ER) in Angola to identify the main challenges and opportunities presented. To achieve that goal, this chapter characterises research conducted in the country, investigates its main findings, and proposes some directions for the future. Based on a bibliographic search in the EBSCO Discovery database of empirical articles about HRM and ER in Angola, we collected a sample of 28 studies published between 2009 and 2022. Most studies have focused on the development and retention of human resources. Other topics included diversity management, workplace attitudes and behaviours, scale validations, leadership and decision-making, performance appraisal, quality assessment, corporate social responsibility, and expatriates. We identified three main challenges and opportunities in HRM and ER in Angola. First, the policies and the planning, implementing, and evaluating processes of human resources development and retention strategies should be improved. Second, effective leadership and participation should be promoted while navigating the tensions between autocratic and participative leadership styles. Finally, positive ER and employee well-being should be promoted. Understanding these challenges and opportunities may contribute to the development of human capital in Angola and, ultimately, the country’s socioeconomic development.

Substitute foods are increasingly popular to reduce our environmental footprint and promote food security. As the world population is expected to grow and food resources become scarce, insects as food have recently gained attention as a viable alternative. In the present study, a model grounded on the Theory of Planned Behavior (TPB) is proposed and analyzed through structural equation modeling software (SmartPLS) to assess consumers intentions toward insects as food. Except for subjective norm, both attitude and perceived behavioral control were key determinants of intention and, in turn, of actual use behaviour. Despite insects being consumed in nearly 1/4 of the sample (for instance in Chinese medicine), the study found that respondents were on average relatively unwilling to use them as a dietary habit. Also, it appeared that men were more likely to consume insects as food than women. The insights of our study have important implications for practitioners and policymakers seeking to promote sustainable nutritional practices among consumers. This study is particularly relevant for Macau, as the city positions itself as a "UNESCO Creative City of Gastronomy" with the aim to develop internationally a unique and sustainable food image.

With the rapid urban development, Macao SAR has become one of regions with the with highest population density in the world, characterized by high traffic flow and dense building aggregations. Noise has become one of the major environmental problems in Macao. Besides having an impact on human health and wellbeing, noise pollution is known to impact ecological systems and the image of a place. Before proposing a plan to reduce noise pollution, it is necessary to have a general understanding of the current noise levels in Macao, how they have changed over time and the main noise pollution sources and environmental concerns. This dissertation relies on the publicly available data from DSPA (Macao Environmental Protection Bureau) monitoring stations concerning noise levels over the past decade. The main research goals were: 1) Characterize changes in noise levels from 2010 to 2021 during daytime, nighttime, and full-day from multiple noise stations located in Macao, Taipa, and Coloane Peninsula; and 2) associate changes in noise levels with potential factors such as location, number of residents/tourists, number of vehicles, among others. This work provides an important framework for future studies concerning noise monitoring and mitigation strategies

Online shopping in Macau has developed rapidly in recent years. And the success of Taobao is significantly hard to not notice. Its’ sales are breaking the record every year. However, there are a lot of negative comments towards Taobao. Various researches and data have shown that live-streaming is one of the biggest contributions towards Taobao’s sales and record breaking. This research aims to investigate deeply to understand how Taobao counters those issues and the role of live-streaming in relation to it. Based on a review of the literature in the relevant areas , qualitative methodology is adopted after thorough considerations. A small sample size of 10 were selected to conduct semi-structured in-depth interviews and the participants agreed to respond to answer the original interview questions and the follow-up questions. Analysis of the responses demonstrated e-customer service is the most influential variable towards repurchase intention. Live-streaming strategy can effectively and directly reduce constomer’s uncertainty of products and increase the efficiency of responsiveness. And product uncertainty and responsiveness speed are variables that impact purchase intention. The result demonstrated live-streaming's effectiveness in combating multiple negative aspects of Taobao and strengthening the positive aspects. On this basis, live-streaming is an impactful method to combat Taobao. In addition, e-service in terms of sufficiency of the staff’s communication skill have been found important towards customer’s satisfaction. A gap related to such an issue has been recommended in the further research recommendations along with other factors or sample groups, which are needed to explore deeply in the future

This dissertation focused on the consumers’ consumption behavior in Macau flower retailers. It revealed what factors affect consumers’ choice of flower shops during purchase. Also, what contributes to the customers’ loyalty towards a flower retailer by having interviews with the female target groups aged between 20 to 50 who work in the office. This study is important to the flower business owner because the result can allow them to understand the thoughts and feelings of their consumer. By interviewing 11 heavy users of floral products with 16 questions based on the five consumption values, we can communicate with the consumers directly and learn the reasons behind their purchasing behavior. Questions have been divided into 5 consumption values according to the previous research by (Sheth, Newman, & Gros, 1991), including functional, conditional, social, emotional, and epistemic values. The research has collected data on how and at what level these values may affect consumer consumption choices individually. The finding part analyzed the reason why those values affect consumers’ choices. Most of the interviewees stated that service is the most important compared to quality because the owner should be enthusiastic to provide product information and understand the consumer very well to impress and retain them with good personal service

This study presents the intrinsic value of Moody’s Corporation, a leading credit rating agency in the U.S. The results of the valuation were compared to the market value of Moody’s Corporation of the same date. The aim of the research is to provide a perspective to the investors on whether the actual value of the Company was overvalued or undervalued in the market, and how much the volatility of the stock price by the change of some factors. Both qualitative and quantitative analyses were applied in the research. The historical data, economic outlook, and the Company’s strategies were collected to be the metrics to determine the intrinsic value and provide an analysis of the prospects of Moody’s Corporation. Three valuation models were applied in the research to estimate the intrinsic value of the Company’s common stock. The cost of debt, cost of equity, the weighted average cost of capital, and the market risk premium were introduced and calculated in the research as they were the critical components in the valuation process. Since the valuation was based on assumptions and historical data to determine future growth, which indicates that the results could be changed due to uncertain factors. This study demonstrates that there was some discrepancy between the stock’s market price and the intrinsic value per share of Moody’s Corporation as of December 31, 2021

This study analyzed Tencent Holdings Limited’s internal and external factors and future strategies, forecasted the financial statements of the Company for the next ten years, and measured the intrinsic value of the Company on December 31, 2021 and compared it with the stock market price on the same date. The share price’s sensitivity analysis was also conducted to understand the Company's capital structure and profitability, and the sensitivity analysis can be used to reflect the impact of these factors and strategies on the future share price, which can have a high impact on investors' future investment decisions. This study also used the SWOT Analysis and Michael Porter's Five Forces Analysis to analyze the Company's strengths, weaknesses, opportunities and threats and the Five Competitive Forces that shape the technology industry to evaluate the Company's external and internal environment and enrich the management with a more intuitive analysis leading to recommendations

Seagrasses play a critical role in coastal ecosystems worldwide, providing various ecosystem services based on their region and genus. In Southeast Asia, where seagrass biodiversity and extents are at their highest, the livelihoods and food security of many coastal communities depend on these plants. Despite their ecological and economic importance, seagrasses face global threats from human activities such as pollution and land use changes. Enhalus acoroides, a widely distributed seagrass species in the tropical Indo-Pacific region, is particularly valuable for coastal management and conservation efforts due to its size and provision of various ecosystem services. Although previous research has indicated that it is less sensitive to environmental changes than other tropical seagrass species, recent reports highlight its vulnerability to siltation and eutrophication. This dissertation aimed to examine how Enhalus responds and adapts to changes in light availability, taking into account both morphological adaptation and phenotypic plasticity. Field surveys, reciprocal transplantation field experiments, and investigations of sexual reproductive effort were conducted in the Bolinao-Anda Reef system (NW Philippines) to evaluate the impact of long-term environmental changes on Enhalus populations. The findings of this study revealed that Enhalus has the capacity to adapt its traits and survive changes in depth, light gradients, and different habitat types. This is evidenced by larger shoots in low-light environments, which is apparently a response to the reduction in light availability, as evidenced in both in situ and experimental setups. Larger leaf surface area in light-reduced setups also had higher concentration of chlorophylls a and b pigments. Transplants from light-reduced environments, although morphologically large, appeared more vulnerable (with low survival values) to environmental changes associated with translocation. Being morphologically large is therefore likely a stress response to light reduction, allocating more energy on light harvesting than sexual reproduction. Reciprocal transplantation experiments indicated a high survival rate, suggesting the potential of Enhalus for use in rehabilitation. However, despite having wider plasticity to adapt to light-limitation, they can be wiped out when threshold is reached. This thesis underscores the need for further research on Enhalus' response to stressors, genetic variation, and adaptive capacity to address conservation and management challenges