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This thesis mainly discusses and compares the human rights situation and the problems in China, the USA and the UK. Also, the thesis will give the solution of how to make the world's human rights situation more fairly. Because we sometimes listen to these countries use cruel ways to suppress the opponents from the news. So I think this will be very suitable for understanding the current human rights situation, problems and movements in these countries. This thesis mainly uses the secondary data analysis method to collect and analyse the data. After analysing the data, three main issues affecting human rights were identified. These issues are race, religion, and network privacy. Other important factors still influence human rights, but this dissertation focused on the three issues identified. Finally, I will give two to three recommendations to practice human rights more fairly. Although the step will be very small, we can greatly improve the fair human rights in the future

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

The purpose of the study was to explore the smoking behaviour of the smokers living in Macao. I was part of the 1st group of doctors to have chosen to initiate smoking cessation in Macau. In addition to my diverse academic background of study in laws- a dissertation about the effect of tobacco control program in Macao (submitted in 2011), a master in Public Administration, and five years of working experiences as an Inspector Assistant of tobacco, I had the conviction that I have to write to share my experience and knowledge about smoking cessation to contribute to the field of counselling and psychotherapy as there is little research data concerning smoking prevalence and demographics in Macao. This study consists of two parts – PART I presents the quantitative data collected by a questionnaire-survey over 1378 smokers and PART II qualitative data from semi-structured interviews with 10 adult smokers to explore their experiences of smoking and smoking cessation in Macao. Quantitative data analysis was conducted with the SPSS, while qualitative analysis with coding and theme identification by following the grounded theory procedures. The study found that about 85% smoker surveyed consumed more than half a pack of cigarette per day and about 31% reported various symptoms like irritability, fatigue, loss of appetite and difficulties in concentration. The qualitative study has identified major positive factors related to initiation and maintenance of smoking cessation, namely health concerns, financial concerns and family support. Major negative factors related to relapse of smoking are peer influences, smoking of family members, and impacts of stressful life events. Based on findings of the study, it is argued that preventive anti-smoking education should be implemented among young people. Promotion of health education and preventive anti-smoking strategist and policy in Macao are discussed. The data collected indicate the fact that individuals who have pathologies of the cardiovascular system as a motivating factor for contemplating or taking actions for smoking cessation. Moreover, financial problems, gender (male predominantly), married with family support, higher educational level, without psychological diseases, better economic status, lower nicotine dependent are predictors to success in quit smoking. It also raises the possible need to deepen some evaluation parameters hither to be only superficially addressed. Therefore, and by limitations inherent to the study, this hypothesis needs further investigation. I argue that non-pharmacological treatment methods alone have proven to be effective in the smoking cessation process. However, it is argued that this combined with pharmacological therapy, in particular in specialized consultations, would be more effective and capable in increasing success rates in smoking cessation

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 gold standard to detect SARS-CoV-2 infection consider testing methods based on Polymerase Chain Reaction (PCR). Still, the time necessary to confirm patient infection can be lengthy, and the process is expensive. On the other hand, X-Ray and CT scans play a vital role in the auxiliary diagnosis process. 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 characteristics of pneumonia caused by COVID-19. But before the computerized diagnostic support system can classify a medical image, a segmentation task should usually be performed to identify relevant areas to be analyzed and reduce the risk of noise and misinterpretation caused by other structures eventually present in the images. This chapter presents an AI-based system for lung segmentation in X-ray images using a U-net CNN model. The system’s performance was evaluated using metrics such as cross-entropy, dice coefficient, and Mean IoU on unseen data. Our study divided the data into training and evaluation sets using an 80/20 train-test split method. The training set was used to train the model, and the evaluation test set was used to evaluate the performance of the trained model. The results of the evaluation showed that the model achieved a Dice Similarity Coefficient (DSC) of 95%, Cross entropy of 97%, and Mean IoU of 86%.

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.

"Macau's rapid economic development in recent decades and relatively low usage of public transportation have put considerable pressure on the city's carrying capacity. Improving the transportation system has been a major policy goal of Macau's urban planners. To deepen the understanding of the perspectives of local road users, this dissertation's main research question is: what factors determine the satisfaction of local road users of Macau? After collecting responses using an online questionnaire, quantitative research methods were adopted to analyze travel patterns, satisfaction toward different road usage dimensions, and sociodemographic characteristics of local residents. 145 responses were collected and quota samples were generated to match the distribution of each sociodemographic feature of the population. Most respondents used private vehicles to travel during peak hours on weekdays for work or for school and to travel during the entire afternoon and evening on weekends for shopping necessities and for leisure. The most traveled districts were Baixa de Taipa, Costa & Ouvidor Arriaga, and Baixa de Macau. It was found that the mean overall satisfaction score inclined to the dissatisfaction side (below 3). Only clarity of traffic lights and number of road signs (measuring infrastructure) and temperature and price of fares (measuring public transportation) had mean satisfaction scores that were significantly higher than 3, indicating higher satisfactions. Meaningful hypotheses regarding the differences of different road user groups were set out, then Kruskal-Wallis ANOVA tests and Mann-Whitney U tests were run. The significant findings were such that the elderly aged 65 or above were less satisfied and the unemployed were more satisfied with road usage. The better educated were more satisfied with the environment, and the unemployed were more satisfied with the public transportation. Drivers were less satisfied with transportation costs, and peakhour road users were less satisfied with the infrastructure. The Spearman correlation analyses found that infrastructure had moderately positive correlation with facilities and with travel safety. Based on the findings and their policy implications, policy suggestions could be made. The policies suggested in this study should have favorable short-term and long-term effects on more than one road usage aspects."

Examining consumer perceptions of scent marketing in Macau's retail setting is the goal of this study. The study focuses on the following two primary uses of scent marketing in retail environments: subliminal application and application as a component of branding strategy. Qualitative research methodology is used. A total of ten consumer interviews produced the data. The findings show that consumers are in favor of scent marketing's use in retail settings. The use of scent marketing as a tool for establishing brands is preferred, and consumers find this approach to scent marketing to be more acceptable than its subliminal application. Although consumers believe that other factors, such as price, are typically more significant than scent when making purchases, the use of subliminal scents was not always evaluated negatively. Unless occasionally when making an unplanned purchase, consumers do not think that a subliminal scent can significantly impact their purchasing behavior

Fast and efficient malaria diagnostics are essential in efforts to detect and treat the disease in a proper time. The standard approach to diagnose malaria is a microscope exam, which is submitted to a subjective interpretation. Thus, the automating of the diagnosis process with the use of an intelligent system capable of recognizing malaria parasites could aid in the early treatment of the disease. Usually, laboratories capture a minimum set of images in low quality using a system of microscopes based on mobile devices. Due to the poor quality of such data, conventional algorithms do not process those images properly. This paper presents the application of deep learning techniques to improve the accuracy of malaria plasmodium detection in the presented context. In order to increase the number of training sets, deep convolutional generative adversarial networks (DCGAN) were used to generate reliable training data that were introduced in our deep learning model to improve accuracy. A total of 6 experiments were performed and a synthesized dataset of 2.200 images was generated by the DCGAN for the training phase. For a real image database with 600 blood smears with malaria plasmodium, the proposed Deep Learning architecture obtained the accuracy of 100% for the plasmodium detection. The results are promising and the solution could be employed to support a mass medical diagnosis system.

This thesis explores language teaching and language acquisition by multilingual learners using a Variation Theory approach and multilingual teaching in a university setting in Macao, China. It includes three case studies applied to students of the Spanish language in the introductory level which took place from late August to early December of the year 2017. The first study describes Macao’s multilingual language learners in the University of Macao in 2017. Based on the LEAP-Q questionnaire, a questionnaire was created to inquire all Spanish language students about their languages´ background, their motivations to learn new languages, as well as their learning strategies. The second study shows how the usage of Variation Theory techniques and multilingual teaching techniques boosted the teaching and the learning during the semester. This study employs a case study methodology, by analysing in-class multiple interactions gathering information on how multilinguals´ language background affects the pedagogical process. It analyses a total of 28 classes of 1 hour and 15 minutes. The third study presents the analysis of a questionnaire to 82 students of the initial level of Spanish language in the University of Macao, along with the analysis of interviews from 10 selected multilingual students about their linguistic background and how they experienced the semester. These interviews collected more information about the effectiveness of the Variation Theory in the semester in terms of in-class teaching and learning. From the triangulation of these three studies, some conclusions have been drawn about the advantages of using Variation Theory and multilingual teaching techniques for multilingual students, for the language teacher and ultimately also into the curricular design of foreign language teaching. In sum, that the linguistic background of students plays a major role in how they acquire a new language and, that applying Variation Theory techniques can be an immensely effective technique in a language classroom setting; suggesting that multilingual students will gain from being previously identified and placed in a separate class where these variation techniques were applied. Since this thesis focuses solely on an introductory language course, there is ground to explore this same approach on more advanced multilingual language learners

Traditional text classification models have some drawbacks, such as the inability of the model to focus on important parts of the text contextual information in text processing. To solve this problem, we fuse the long and short-term memory network BiGRU with a convolutional neural network to receive text sequence input to reduce the dimensionality of the input sequence and to reduce the loss of text features based on the length and context dependency of the input text sequence. Considering the extraction of important features of the text, we choose the long and short-term memory network BiLSTM to capture the main features of the text and thus reduce the loss of features. Finally, we propose a BiGRU-CNN-BiLSTM model (DCRC model) based on CNN, GRU and LSTM, which is trained and validated on the THUCNews and Toutiao News datasets. The model outperformed the traditional model in terms of accuracy, recall and F1 score after experimental comparison.

This research paper analyses the marketing efforts and important attributes of Macau tea restaurants that have been open for more than 20 years, especially the digital methods that tea restaurants have used to sustain their business and meet customers' needs in the era of digital transition after the onset of the 2019 Covid 19 pandemic year. Using online marketing channels nowadays become an important tool for communicating with customers today. Despite the old Tea Restaurants in Macau did not put much efforts on digital channels for communication yet, to find out how tea restaurants survive in the rival restaurant industry in Macau, qualitative and quantitative methods were used to see how they meet the needs of their customers. For the quantitative method, an online survey questionnaire in bilingual English and Traditional Chinese was conducted with 280 respondents on customer purchase habits, importance of restaurants’ attributes and social media insights on tea restaurants, and the data was analysis by SPSS for the relationship and significance. For the qualitative method, 7 interviews were conducted with the business owners of the old tea restaurants which that have been opened for more than 20 years, including marketing efforts and their attitudes towards digitalization. To find out the marketing strategy of Macau’s old tea restaurants in the digital transformation era, it was found that “place” and “product” are the most important marketing mix for customers. Tea restaurant owners focus on product quality, and generate “word-of-mouth” which is customer-generated marketing, and is an effective way to influence customers for local tea restaurants. Meanwhile business owners will consider digitalize in the near future as well to match the new young customers’ online habits and the need for “place” such as online delivery platform

This dissertation examines consumers' perceptions of food influencer content on Instagram and the relationship between exposure to food influencer reviews and consumers' intention to visit restaurants in Macau. A mixed-method research design was employed, combining quantitative survey data from 301 responses with qualitative insights from one focus group session involving six participants. The findings suggest that consumers find food influencer content entertaining and inspiring for dining out and exploring new culinary experiences, but they express concerns over its credibility. While food influencer content has a good exposure to consumers, it has a moderate impact on consumers’ restaurant choices, with various factors affecting the relationship. The results highlight the importance of perceived trustworthiness and enjoyability of food influencer content, the visual appeal of food and restaurant environments in photos, and personal relationships in shaping Macau consumers' visit intention to restaurants. The findings can serve as a basis for future research on credibility perceptions of food influencers, the enjoyability of influencer content, and the visual appeal of food and restaurant environments on Instagram. Practically, food influencers should prioritize transparency, authenticity, appealing photos, engaging captions, and leverage the role of personal relationships to increase their impact on consumers' restaurant choices

Food waste has become an increasingly pressing issue worldwide, and Macau is no exception. A substantial portion of municipal solid waste in Macau is comprised of organic waste, with household food waste being a significant contributor. This can be attributed to households purchasing excessive food, preparing more than necessary, or not consuming items before they spoil, leading to detrimental resource and environmental impacts, particularly in the form of greenhouse gas emissions. It is crucial for Macau to develop a sustainable food waste management system that fosters prevention and responsible consumption and to develop food waste recycling habits among its residents. This study employed a two-pronged approach: a literature review of food waste source separation policies and practices in four neighboring regions to identify adaptable good practice examples for Macau, and a questionnaire survey completed by 143 local residents. The survey aimed to comprehend residents' behaviors and awareness regarding food waste prevention and treatment, evaluate their satisfaction with Macau's existing food waste recycling program, examine their resistance and motivation toward participating in the recycling program, and assess their attitudes toward implementing a polluter-pays-principle system in Macau. The survey aimed to provide insights into the current state of household food waste in Macau and inform future waste management policies and strategies of stakeholders. The survey findings highlight the need for enhanced public awareness and education on food waste prevention. Additionally, upgrading recycling facilities may encourage residents to participate more actively in food waste recycling. Ultimately, implementing suitable policies can help prevent food loss and waste and regulate food waste generation and elevate recycling rates. This study offers preliminary recommendations for policymakers or government entities as a orientation for future planning

The world we live in today is constantly changing, with new technologies and innovations being introduced all the time. Through the continuation and innovation of Chinese medicine theories, scientific research methods, and progress, Traditional Chinese Medicine continues to evolve and develop more innovative Modern Chinese Medicine. In this research, Molecular Chinese Medicine is studied as a new form of Chinese medicine, intended to provide a more pleasant and safe experience for consumers. Thus, the objective of this study is to identify both positive and negative perceptions that contribute to the adoption of innovative technologies and their related products. In order to reach this goal, a robust value-based theoretical model is used. To examine behavioural intentions to adopt Molecular Chinese Medicine (MCM) in Macau, this study combines quantitative and qualitative methods. Thus, a Value-based Acceptance Model (VAM) was used to determine Macau citizens' attitudes towards Molecular Chinese Medicine, including their perceptions of its usefulness, enjoyment, technicality, and perceived fee, in relation to the perceived value of the product, which may ultimately determine adoption intentions. Structural Equation Modeling (SEM) was used to process a sample of 194 Macau residents. The data analysis supported our model's explanatory and predictive power and helped describe the characteristics of the local population. Our results provide insight into the acceptance of innovative products that may be useful in designing more accurate strategies and facilitating the introduction of MCM to Macau and similar markets. It was found that low perceived fees demonstrated a relatively strong positive correlation with potential users' perceived value, whereas usefulness and enjoyment showed a medium-strong positive correlation. Also, a strong positive correlation was found between potential consumers' perception of the value of Molecular Chinese Medicine and their intention to adopt it

Human emotions can be meticulously associated with decision-making, and emotion can generate behaviours. Due to the fact that it could be bias and exhaustively complex to examine how human beings make choices, important groups of study in finance are stock traders and non-traders. The objective of this work is to analyze the connection between emotions and the decision-making process of investors and non-investors to understand how emotional arousal might dictate the process of deciding policy. As facial expressions are fleeting, neuroscience tools such as AFFDEX (Real-Time Facial Expression Analysis), Eye-Tracking, and GSR (galvanic skin response) were adopted to facilitate the experiment and its accompanying analysis process. Thirty-seven participants attended the study, ranging from 18 to 72 years old; the distribution of investors and non-investors was twenty-four and thirteen, respectively. The experiment initially disclosed a thought-provoking result between the two groups under the certainty and risk-seeking prospect theory; there were more risk-takers among non-investors at 75%, while investors were inclined toward certainty at 79.17%. The implication could be that the non-investing individuals were less complex in thought and therefore pursued higher returns besides a high probability of losing the game. In addition, the automatic emotion classification system indicates that when non-investors confronted a stock trending chart beyond their acquaintance or knowledge, they were psychologically exposed to fear, anger, sadness, and surprise. Investors, on the contrary, were detected with disgust, joy, contempt, engagement, sadness, and surprise, where sadness and surprise overlapped in both parties. Under time pressure conditions, 54.05% of investors or non-investors tend to make decisions after the peak(s) of emotional arousal. Variations were found in the deciding points of the slopes: 2.70% were decided right after the peak(s), 37.84% waited until the emotions turned stable, and 13.51% were determined as the emotional indicators started to slide downwards. Several combinations of emotional responses were associated with decisions. For example, negative emotions could induce passive decision-making, in this case, to sell the stock; nevertheless, it was also examined that as the slope slipped downwards to a particular horizontal point, the individuals became more optimistic and selected the "BUY" option. The support of physiological monitoring tools makes it possible to capture the individuals' responses and discover the science of decision-making. Future works may consider expanding the study to more significant demographic populations for further discoveries