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The key challenge of Unsupervised Domain Adaptation (UDA) for analyzing time series data is to learn domain-invariant representations by capturing complex temporal dependencies. In addition, existing unsupervised domain adaptation methods for time series data are designed to align marginal distribution between source and target domains. However, existing UDA methods (e.g. R-DANN Purushotham et al. (2017), VRADA Purushotham et al. (2017), CoDATS Wilson et al. (2020)) neglect the conditional distribution discrepancy between two domains, leading to misclassification of the target domain. Therefore, to learn domain-invariant representations by capturing the temporal dependencies and to reduce the conditional distribution discrepancy between two domains, a novel Attentive Recurrent Adversarial Domain Adaptation with Top-k time series pseudo-labeling method called ARADA-TK is proposed in this paper. In the experiments, our proposed method was compared with the state-of-the-art UDA methods (R-DANN, VRADA and CoDATS). Experimental results on four benchmark datasets revealed that ARADA-TK achieves superior classification accuracy when it is compared to the competing methods.

This dissertation aims to research the need and viability of creating a digital platform that assists the creative processes naturally linked to Visual Merchandising. Through the proposal of a digital platform that works as a co-creative tool for display archives and a co-creative tool for display set-ups through mood boards, we will aim at improving the teamwork in retail and provide a unique, fast-forward platform for information sharing and input under the direction of Visual Departments. Building on rich source materials such as bibliography, scientific papers, news, and articles, and interviewing Visual Merchandisers actively working in the field, we will show the importance of creativity in Luxury Retail and what are the most common challenges in the field that we will propose a solution to. We will focus on the study of concepts, reviewing digital application tools being used by professionals and their best features and improvement opportunities. By gathering this information, we hope to provide accurate insights and information that proves the viability of this proposal and understand what features could serve best the target audience. Finally, we will present a conceptual idea in the form of sketches with functions of this digital application tool to be fully developed in the future and hopefully build a consistent well, designed commercial web-based application

Adverse Childhood Experiences (ACEs) are common in life trajectories, and continued exposure to childhood adversities has long-term consequences that can lead to lifelong physical, mental, and emotional deficiencies. This study aims to address the research gap in the ACEs context in Macao by enhancing our understanding of the cultural sensitivity, applicability, and acceptability associated with assessing ACEs among Chinese childhood and adolescents in Macao. The study utilized the Chinese version of The International Trauma Exposure Measure – Children and Adolescents (ITEM-CA) to facilitate the exploration of cultural adaptation needs related to ACEs measurement tools in Macao. Employing a qualitative methodology with an explanatory and descriptive design, the study collected opinions and suggestions through an online questionnaire from professionals working with children aged 7-17 years old, as well as parents or caregivers of children within the same age group. Several key findings emerged from the study. Firstly, it underscored the adequacy and pertinence of the Chinese ITEM-CA in covering the topics of traumatic events and deeming it more suitable for children aged 12 or above. Secondly, it emphasized the importance of maintaining anonymity and the involvement of dedicated and trained personnel throughout the ACEs assessment process. Lastly, it highlighted the need to increase public awareness regarding traumatic events and ACEs in Macao society. These findings have significant implications for researchers studying the prevalence of ACEs in Macao and for policymakers in Macao implementing ACEs surveillance. Additionally, there is a recommendation for organization, especially schools, to respond to ACEs using trauma-informed approaches, supporting the well-being of children, fostering resilience, and minimizing the risk of re-traumatization

The extent of citizens' trust in government determines the success or failure of e-government initiatives. Nevertheless, the idiosyncrasies of the concept and the broad spectrum of its approach still present relevant challenges. This work presents a systematic literature review on e-government trust while elaborating and summarizing a conceptual analysis of trust, introducing evaluation methods for government trust, and compiling relevant research on e-government trust and intentional behavior. A total of 26 key factors that constitute trust have been identified and classified into six categories: Government trust, Trust in Internet and technology (TiIT), Trust in e-government (TiEG), Personal Beliefs, Trustworthiness, and Trust of intermediary (ToI). The value added of this work consists of developing a conceptual framework of TiEG to provide a significant reference for future in-depth studies and research on e-government trust.

The adoption of computer-aided diagnosis and treatment systems based on different types of artificial neural networks (ANNs) is already a reality in several hospital and ambulatory premises. This chapter aims to present a discussion focused on the challenges and trends of adopting these computerized systems, highlighting solutions based on different types and approaches of ANN, more specifically, feed-forward, recurrent, and deep convolutional architectures. One section is focused on the application of AI/ANN solutions to support cardiology in different applications, such as the classification of the heart structure and functional behavior based on echocardiography images; the automatic analysis of the heart electric activity based on ECG signals; and the diagnosis support of angiogram images during surgical interventions. Finally, a case study is presented based on the application of a deep learning convolutional network together with a recent technique called transfer learning to detect brain tumors using an MRI images data set. According to the findings, the model has a high degree of specificity (precision of 0.93 and recall of 0.94 for images with no brain tumor) and can be used as a screening tool for images that do not contain a brain tumor. The f1-score for images with brain tumor was 0.93. The results achieved are very promising and the proposed solution may be considered to be used as a computer-aided diagnosis tool based on deep learning convolutional neural networks. Future works will consider other techniques and compare them with the one presented here. With the comprehensive approach and overview of multiple applications, it is valid to conclude that computer-aided diagnosis and treatment systems are important tools to be considered today and will be an essential part of the trend of personalized medicine over the coming years.

Technological advancements have enabled machines to offer more efficient assistance to people across several domains. Art and creativity are what distinguish people or societies the most. The arrival of AI has caused a fresh wave of concern among many of us; it is crucial that we evaluate the weight of the positives and the drawbacks of how people employing these technologies will influence societies. Legislation is frequently delayed because of the various revolutions througout history. Along with the difficulties raised in the continuing debate, it is critical to identify a route or alternative to assist existing challenges for artists. This dissertation includes our case studies, analysis, and conclusions, which explain the effects on imagination, creativity, communication, and culture when individuals use AI technologies to create art or/and images. According to our findings, AI technology should be considered as one way to solve problems. The vast majority of created outputs are unable to accomplish the desired reach. Furthermore, even if the ethical issues cannot be resolved, individuals should not ignore it and keep their moral compass. Importantly, we must realise that individuals should always be involved in and influence the intricacies and components of the creative process. However, it is commonly understood that the deployment of artificial intelligence has an influence on people’s communication and societies. There are still no standards, frameworks, or norms on this front of Ai-generated images. This might contribute to an increase in industrial imbalance and discrimination. As a result, to safeguard our individuality, we must take the initiative to uncover any answers or directions that would aid in this scenario. As with any experience, whether internal or external, we should use it to our advantage to apply our creative process, something the computer cannot accomplish. Still, it gives many benefits and is effective in assisting people in completing challenging tasks, so it is smart not to avoid teamwork. Furthermore, because social media and the internet provide powerful expression and communication tools, their advantages should help build universal rules to safeguard artistic work

This study examined 206 casino dealers in hospitality at Macau to investigate the extent of their subjective career success and work engagement. Casino dealers were work engaged, but their subjective career success was fairly low, with significant difference between them, which indicates they have cognitive dissonance about their jobs. Several personality variables (emotional suppression and work ethic), organizational variables, i.e., organizational socialization (training, understanding, coworker support, future prospects), and distributive justice, were assessed in relation to subjective career success and work engagement. Organizational socialization, work ethic, and distributive justice were positively correlated with and predictors of subjective career success and work engagement; while emotion suppression was negatively correlated with and predictor of work engagement. This study provides evidence of extending the theories of subjective career success and work engagement in Chinese society and hospitality. Also, it identifies factors that could resolve the employees’ cognitive dissonance, and implementations for management were discussed.

The following Arbitration practice note provides comprehensive and up to date legal information on Arbitration as a dispute resolution method in Macau

It has been proven in numerous research that mindfulness can be helpful to reduce stress and chronic pain (Hall, 2014; Lindström, n.d.; Tong et al., 2015). While interactive mindfulness has been one of the focuses in the recent mobile applications market, usually tackling three essential human senses: audio, visual, and touch, each mobile application has quite some different approaches in terms of interactivity. Some focus on the touch and visual, and some on audio (environmental sounds or instructing meditation). Immersing oneself in virtual reality (VR) creates a constant stream of interactivity. Nonetheless, what are the conditions for an (in)tangible virtual reality to be more effective? Under the COVID-19 pandemic and lockdown since the end of 2019, Macao has been facing a social concern that we cannot travel easily to visit our decedents’ graves abroad, let alone the existing concerns of expensive burial services, lack of space, and alternative burial options. Also, taking into consideration that standard funeral service in Macao is often too brief, and getting briefer, thus lacking the opportunity to properly farewell the decedent, this research is proposing a virtual reality 3D model construction of the Chapel of St. Michael, located in St. Michael the Archangel Cemetery in Macao, to be streamed on a 360 virtual tour platform, Kuula. co. By immersing in this virtual reality, the participant is to have a single user experience for mindfulness with the decedent. To ensure valid and reliable results that address the research aims and objectives, a single-user experiment is going to be set up with multiple electronic devices, namely, the smartphone iPhone X with cardboard VR, the tablet iPad Pro, and the Oculus Quest 2. The methodology to collect the data will be using observation and simulation. The experiment will be started with an introduction to the project and conducted with no instruction, allowing users to explore and examine all features in this immersive experience. Along with a post-experience survey (interview + questionnaire), we seek its conditions and impacts on Macao residents in terms of interactive mindfulness and participants’ expectation of testing, for the first time in Macao, a virtual reality grave mourning experience.

Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution episodes to warn the public ahead of time. Five different state-of-the-art machine learning (ML) algorithms were applied to create predictive models to forecast PM2.5, PM10, and CO concentrations for the next 24 and 48 h, which included artificial neural networks (ANN), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR), to determine the best ML algorithms for the respective pollutants and time scale. The diurnal measurements of air quality data in Macau from 2016 to 2021 were obtained for this work. The 2020 and 2021 datasets were used for model testing, while the four-year data before 2020 and 2021 were used to build and train the ML models. Results show that the ANN, RF, XGBoost, SVM, and MLR models were able to provide good performance in building up a 24-h forecast with a higher coefficient of determination (R2) and lower root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). Meanwhile, all the ML models in the 48-h forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-h forecast. The 48-h forecasting model could be further improved by proper feature selection based on the 24-h dataset, using the Shapley Additive Explanations (SHAP) value test and the adjusted R2 value of the 48-h forecasting model. In conclusion, the above five ML algorithms were able to successfully forecast the 24 and 48 h of pollutant concentration in Macau, with the RF and SVM models performing the best in the prediction of PM2.5 and PM10, and CO in both 24 and 48-h forecasts.

In February 2020, Macau became one of the first regions where the pandemic of coronavirus or Covid-19 affected the totality of social and economic life leading to increased anxieties over movement and distance. Although Macau has had very few actual cases of the virus – 46 in total –and no deaths from it, the Macau government rapidly instituted a lock­ down. The aim of this article is to reflect on how the social experience of being in lockdown can provide insights into understanding the type of experience or condition that we provisionally term ‘anxious immobility.’ Such a condition is characterized by a total disruption of everyday rhythms and specifically anxious waiting for the normalization of activity while being the subject of biosocial narratives of quarantine and socially responsible. The paper is based upon 3 months of ethnographic research conducted by two researchers based in Macau. We also reflect upon some aspects of the politics of mobilities in the light of disruptions and friction points between Hong Kong, Macau, mainland China, and the rest of the world.

Approximately 50 million people are suffering from epilepsy worldwide. Corals have been used for treating epilepsy in traditional Chinese medicine, but the mechanism of this treatment is unknown. In this study, we analyzed the transcriptome of the branching coral Acropora digitifera and obtained its Kyoto Encyclopedia of Genes and Genomes (KEGG), EuKaryotic Orthologous Groups (KOG) and Gene Ontology (GO) annotation. Combined with multiple sequence alignment and phylogenetic analysis, we discovered three polypeptides, we named them AdKuz1, AdKuz2 and AdKuz3, from A. digitifera that showed a close relationship to Kunitz-type peptides. Molecular docking and molecular dynamics simulation indicated that AdKuz1 to 3 could interact with GABAA receptor but AdKuz2–GABAA remained more stable than others. The biological experiments showed that AdKuz1 and AdKuz2 exhibited an anti-inflammatory effect by decreasing the aberrant level of nitric oxide (NO), IL-6, TNF-α and IL-1β induced by LPS in BV-2 cells. In addition, the pentylenetetrazol (PTZ)-induced epileptic effect on zebrafish was remarkably suppressed by AdKuz1 and AdKuz2. AdKuz2 particularly showed superior anti-epileptic effects compared to the other two peptides. Furthermore, AdKuz2 significantly decreased the expression of c-fos and npas4a, which were up-regulated by PTZ treatment. In addition, AdKuz2 reduced the synthesis of glutamate and enhanced the biosynthesis of gamma-aminobutyric acid (GABA). In conclusion, the results indicated that AdKuz2 may affect the synthesis of glutamate and GABA and enhance the activity of the GABAA receptor to inhibit the symptoms of epilepsy. We believe, AdKuz2 could be a promising anti-epileptic agent and its mechanism of action should be further investigated.