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Consumer neuroscience analyzes individuals’ preferences through the assessment of physiological data monitoring, considering brain activity or other bioinformation to assess purchase decisions. Traditional marketing tactics include customer surveys, product evaluations, and comments. For product or brand marketing and mass production, it is important to understand consumer neurological responses when seeing an ad or testing a product. In this work, we use the bi-clustering method to reduce EEG noise and automatic machine learning to classify brain responses. We analyze a neuromarketing EEG dataset that contains EEG data from product evaluations from 25 participants, collected with a 14 channel Emotiv Epoch + device, while examining consumer items. Four components comprised the research methodology. Initially, the Welch Transform was used to filter the EEG raw data. Second, the best converted signal biclusterings are used to train different classification models. Each biclustering is evaluated with a separate classifier, considering F1-Score. After that, the H2O.ai AutoML library is used to select the optimal biclustering and models. Instead of traditional procedures, two thresholds are used. First-threshold values indicate customer satisfaction. Low values of the second threshold reflect consumer dissatisfaction. Values between the first and second criteria are classified as uncertain values. We outperform the state of the art with a 0.95 F1-Score value.

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

Introduction: SARS-CoV-2, a virus responsible for the emergence of the life-threatening disease known as COVID-19, exhibits a diverse range of clinical manifestations. The spectrum of symptoms varies widely, encompassing mild to severe presentations, while a considerable portion of the population remains asymptomatic. COVID-19, primarily a respiratory virus, has been linked to cardiovascular complications in some patients. Notably, cardiac issues can also arise after recovery, contributing to post-acute COVID-19 syndrome, a significant concern for patient health. The present study intends to evaluate the post-acute COVID-19 syndrome cardiovascular effect through ECG by comparing patients affected with cardiac diseases without COVID-19 diagnosis report (class 1) and patients with cardiac pathologies who present post-acute COVID-19 syndrome (class 2). Methods: From 2 body positions, a total of 10 non-linear features, extracted every 1 second under a multi-band analysis performed by Discrete Wavelet Transform (DWT), have been compressed by 6 statistical metrics to serve as inputs for an individual feature analysis by the means of Mann-Whitney U-test and XROC classification. Results and Discussion: 480 Mann-Whitney U-test statistical analyses and XROC discrimination approaches have been done. The percentage of statistical analysis with significant differences (p<0.05) was 30.42% (146 out of 480). The best overall results were obtained by approximating the feature Energy, with the data compressor Kurtosis in the body position Down. Those results were 83.33% of Accuracy, 83.33% of Sensitivity, 83.33% of Specificity and 87.50% of AUC. Conclusions: The results show that the applied methodology can be a way to show changes in cardiac behaviour provoked by post-acute COVID-19 syndrome.

Critical thinking disposition (CTD) is increasingly recognized as an important trait in education, reflecting the inclination and habits necessary for addressing complex challenges in today's world. This study assessed the CTD of students enrolled in a tourism and gaming management programme, focusing on two key dimensions: Analyticity and Open-Mindedness. This study was conducted at a university in Macao and involved 65 participants. The students were presented with an article relevant to their major, written in Traditional Chinese, and were asked to provide their opinions on each statement in the article. A rubric was designed to analyze their responses and assess their Analyticity and Open-Mindedness within the CTD framework. The results demonstrated high reliability (Cronbach's α = 0.91) and revealed an association between Analyticity and Open-Mindedness. Using Python programming, the study analyzed the frequency of parts of speech (POS) in students' responses, introducing a novel approach for evaluating CTD in Traditional Chinese. Regression modeling showed that parallel and adversative conjunctions significantly predicted Analyticity, while the frequency of conjunction use varied across Open-Mindedness classifications. These findings highlighted an innovative and objective method for assessing CTD through text analysis, offering promising applications for educational research in Traditional Chinese-speaking contexts.

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.

Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.

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