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"This study investigates the perspectives, challenges, and strategies of STEM teachers in Macau as they integrate STEM education into their curricula. STEM teachers in Macau generally consider the integration of STEM education essential for enhancing students' creativity and problem-solving abilities. However, during the implementation process, they face challenges such as insufficient resources, limited opportunities for professional development, and difficulties in interdisciplinary collaboration, which restrict the effectiveness of STEM teaching. This study employed a qualitative research approach, conducting in-depth, semi-structured interviews with STEM teachers in primary and secondary schools in Macau to understand their experiences and needs. Purposeful sampling was used to ensure diversity and representativeness of the sample. The findings indicate that schools lack sufficient STEM teaching resources (such as equipment and materials), and limited professional development opportunities prevent teachers from effectively mastering new technologies and teaching methods. Furthermore, interdisciplinary teaching is constrained by challenges in collaboration and curriculum design. Low student engagement and insufficient parental support also affect the effectiveness of STEM education. The results suggest that schools should enhance resource allocation, promote professional development for STEM teachers in Macau, and encourage interdisciplinary teaching collaboration to improve the quality of STEM education, thus strengthening students' creativity and problem-solving skills. These findings offer valuable insights for STEM educators and policymakers to foster the comprehensive development of STEM education.

This dissertation explores the concept of a topographic shopping mall in Macau inspired by the region's mountainous landscapes and its potential to integrate nature within urban environments. As consumer preferences increasingly shift towards sustainable and health- oriented experiences, the incorporation of nature-inspired design principles emerges as a critical response to these demands. This study investigates how blending natural elements— such as local flora, organic forms, and water features—can enhance the shopping experience, fostering a deeper connection between individuals and their environment. The research addresses key questions regarding the effective integration of nature-inspired design in commercial architecture, the impact of these elements on user experience and well-being, and their influence on economic performance. By examining existing design strategies and their effects on visitor satisfaction, this dissertation aims to contribute to a deeper understanding of the benefits associated with nature-inspired retail environments. Ultimately, the findings highlight the importance of creating biophilic spaces that not only fulfill practical needs but also promote health and a sense of community, positioning the topographic shopping mall as a potential model for future commercial developments.

In the everchanging environment of the digital era, incorporating new technologies has become a critical engine for company development and progress Mobile applications, for example, have emerged as important tools for reaching and engaging a diverse client base These apps° user interface ¥UI¦ design is essential to this engagement since it directly influences the ease of use, user experience ¥UX¦, and, ultimately, the organisation°s overall reputation Macau has experienced a significant shift in food delivery service in recent years, with a quick transition to an online platform Three significant companies have emerged in this fastpaced atmosphere, each vying for a piece of the market These platforms act as meal order brokers, linking customers and restaurants, with most eateries available on all three apps In this situation, it is vital to identify the factors that influence clients to pick one application over another Simultaneously, pinpointing the pain points inside competitors° applications that generate unhappiness among certain customers becomes a crucial goal This research aims to uncover the primary factors influencing user app preferences in the Macau food delivery business, providing insight into the strengths and weaknesses that impact customer choices and, as a result, the trajectory of these organisations

Environmental concerns drive corporate and consumer focus on sustainable packaging. Research explores key factors influencing consumer intent, emphasizing the importance of strategic integration for enhanced purchase intentions and environmental goals. A comprehensive literature review identifies factors such as perceived value, willingness to pay, environmental concern, and attitude toward sustainable packaging. Empirical validation using survey data demonstrates the statistical significance of these factors on consumer purchase intentions, with the willingness to pay to emerge as the most influential determinant. Stakeholders are urged to incorporate these findings into strategies for sustainable packaging, fostering positive environmental impact, and informing academic and managerial discussions.

The current quantitative study aims to understand/investigate the perception of Macao society towards couple therapy and therapists and the perception of Macao's professionals working with couples about couple therapy services in Macao. Given the small sample of this study of professionals who work as therapists for couples in Macao, this indicates that there is not a large number of them. The result of the research indicates also a general positive attitude of professionals towards clients but also indicates that couple therapy needs improvement. The sample used to measure the society shows that the society manifested moderate positive attitudes toward couple therapy. However, the findings may reveal that there are still weak points of societal knowledge and contact for couples therapy services. The outcome manifested that Macao has limited literature about Family and Couple Therapy which may be relevant to understand the poor knowledge of the society related to this field. The current study suggests the elaboration of future studies about Couple Therapy services and about the concept of couple therapy to promote therapeutic service to couples and academic researchers that promote governmental support to offer professionals validation.

We demonstrate that the flavor oscillation when a neutrino travels through spacetime, is equivalent to permanent changes on the vacuum state condition perceived by the same particle. This can be visualized via the Quantum Yang Baxter equations (QYBE). From this perspective, the neutrino never breaks the symmetry of the ground state because it never selects an specific vacuum condition. Then naturally the Higgs mechanism cannot be the generator of the neutrino masses. The constraints emerging from this model predict a normal mass hierarchy and some specific values for the mass eigenvalues once we fix the mixing angles. Interestingly, the model suggests that the sum of the mix angles is equal to $\pi/2$.

The origins of neutrino masses is one of the biggest mysteries in modern physics since they are beyond the realm of the Standard Model. As massive particles, neutrinos undergo flavor oscillations throughout their propagation. In this paper we show that when a neutrino oscillates from a flavor state {\alpha} to a flavor state \b{eta}, it follows three possible paths consistent with the Quantum Yang- Baxter Equations. These trajectories define the transition probabilities of the oscillations. Moreover, we define a probability matrix for flavor transitions consistent with the Quantum Yang-Baxter Equations, and estimate the values of the three neutrino mass eigenvalues within the framework of the triangular formulation.

Battery Electric Aircraft (BEA) technology is gaining attention due to the potential to reduce carbon emissions and noise pollution, contributing to global environmental sustainability. Grounded in the Theory of Planned Behavior (TPB), this paper explores the determinants of attitude, social norm, behavioural control, and perceived risks related to the intention of Macau residents to use electric aeroplanes within the Greater Bay Area (GBA). This research uses a quantitative approach. Data is collected through structured surveys distributed to potential adopters. To assess the relationships between the determinants in our model, Structural Equation Modeling (SEM) is employed. The findings reveal that a favourable attitude and perceived behavioural control positively influence individuals’ intentions to adopt electric aeroplanes. However, perceived risks strongly impact adoption intentions, suggesting that addressing safety and reliability concerns is essential for promoting the technology within the region. The implications of this research extend beyond academic interests, as Macau’s unique position within the GBA offers the opportunity for electric aeroplanes’ adoption. Further, the reduced carbon emissions and noise pollution align with the city’s objectives and create a harmonious balance between economic prosperity and environmental preservation for future generations. This study offers important insights for integrating advanced computing technologies into BEA systems to enhance electric aeroplanes’ operational efficiency and safety to support their adoption. It also provides a path for policymakers and industry stakeholders toward sustainable economic development and integration of Macau within the GBA.

Construction projects are complex endeavours, with potential obstacles that can cause delays which can have particularly profound implications potentially impacting on company's financial health, business continuity and reputation. It is becoming increasingly recognised that delays are context-specific and multifaceted, requiring more industry-oriented perceptions. This work proposes the exploratory use of Machine Learning based on Classification and Regression Trees (CART) Decision Trees (DT) to assess the predictive analysis of these approaches, considering surveys (primary data) collected from 100 specialists with different backgrounds and experiences in the construction industry. Survey responses are discussed, followed by the CART DTs, which are used as predictor for clarifying underneath relationship among different variables in a project environment. The major issue presented is related to Project Design, with "The firm is not allowed to apply for an extension of contract period", with two possible predictors, firstly, as the main factor it is found "Mistakes, inconsistencies, and ambiguities in specification and drawing", while other aspect highlights "Poor site supervision and management by the contractor". The results indicate that the correct use of Artificial Intelligence techniques with relevant data are potential tools to support the analysis of scenarios and avoidance of project delays in Project Management.

In the wave of digital transformation, Chinese banks have prioritized digital banking services as key strategic goals, aiming to revolutionize the mobile banking experience. This study aims to assess the factors influencing the willingness to use the various financial and contextual services offered through digital banking. Specifically, it is proposed a model based on users' perceptions of mobile banking scenarios and examines how the development of digital banking services influences users' willingness to use them. The study involved qualitative in-depth interviews with 12 mobile banking users, with the interview content analyzed using Nvivo qualitative analysis software. The data analysis identified 9 core coding categories: Financial Professionalism, Security, Marketing Stimulation, Innovative Products, Use Experience, Strong Relationship, Trust, Perceived Usefulness, and Willingness to Use. These categories were further refined to construct a theoretical model of user willingness in digital banking services, drawing from the optimized Technology Acceptance Model (TAM). The findings provide valuable insights for the banking industry in Macau, aiding in understanding customer needs and supporting the positive development of mobile finance and contextual digital banking services in the region.

There are many systematic reviews on predicting stock. However, each reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review the existing systematic reviews on Artificial Intelligence (AI) models applied to stock market prediction to provide valuable inputs for the development of strategies in stock market investments. Keywords that would fall under the broad headings of AI and stock prediction were looked up in Scopus and Web of Science databases. We screened 69 titles and read 43 systematic reviews, including more than 379 studies, before retaining 10 for the final dataset. This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction. In addition, the time series of historical closing stock prices are the most commonly used data source, and accuracy is the most employed performance metric of the predictive models. We also identified several research gaps and directions for future studies. Specifically, we indicate that future research could benefit from exploring different data sources and combinations, while we also suggest comparing different AI methods and techniques, as each may have specific advantages and applicable scenarios. Lastly, we recommend better evaluating different prediction indicators and standards to reflect prediction models’ actual value and impact.

<jats:title>Abstract</jats:title><jats:p>This research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k‐Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1‐score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists.</jats:p>

This work provides a comprehensive systematic review of optimization techniques using artificial intelligence (AI) for energy storage systems within renewable energy setups. The primary goals are to evaluate the latest technologies employed in forecasting models for renewable energy generation, load forecasting, and energy storage systems, alongside their construction parameters and optimization methods. The review highlights the progress achieved, identifies current challenges, and explores future research directions. Despite the extensive application of machine learning (ML) and deep learning (DL) in renewable energy generation, consumption patterns, and storage optimization, few studies integrate these three aspects simultaneously, underscoring the significance of this work. The review encompasses studies from Web of Science, Scopus, and Science Direct up to December 2023, including works scheduled for publication in 2024. Each study related to renewable energy storage was individually analyzed to assess its objectives, methodology, and results. The findings reveal useful insights for developing AI models aimed at optimizing storage systems. However, critical areas need further exploration, such as real-time forecasting, long-term storage predictions, hybrid neural networks for demand-based generation forecasting, and the evaluation of various storage scales and battery technologies. The review also notes a significant gap in research on large-scale storage systems in Brazil and Latin America. In conclusion, the study emphasizes the need for continued research and the development of new algorithms to address existing limitations in the field.

Government service mini-programs (GSMPs) in mobile payment have become integral to the eGovernment in China’s Greater Bay Area (GBA). The ubiquitous nature of WeChat and Alipay provides excellent flexibility for accessing public e-services. Yet, the determinants and mechanisms of adoption have not been identified. A convenience sample was collected from GBA core cities for statistical and SEM analysis. The findings suggest that service quality, trust in eGovernment, ubiquity, and social influence constitute the determinants. A structural model grounded on Self-Determination and Motivation theory is verified, where perceived value and intention contribute a high explanatory power. Benevolence, integrity, and competence are crucial indicators of trust, while social influence amplifies risk perception. Surprisingly, government support negatively moderates the impact of determinants on intention, indicating that over-intervention leads to inhibition. The mechanism illustrates the beneficial impact of GSMPs as the smart government channel and provides insights into addressing service homogeneity and policy applicability. Relevant theoretical and managerial implications are instructive to policymakers and practitioners of smart city innovation and in-depth integration in GBA.

The potential of blockchain technology extends beyond cryptocurrencies and has the power to transform various sectors, including accounting and auditing. Its integration into auditing practices presents opportunities and challenges, and auditors must navigate new standards and engage with clients effectively. Blockchain technology provides tamper-proof record-keeping and fraud prevention, enhancing efficiency, transparency, and security in domains such as finance, insurance, healthcare, education, e-voting, and supply chain management. This paper conducts a bibliometric analysis of blockchain technology literature to gain insights into the current state and future directions of blockchain technology in auditing. The study identifies significant research themes and trends using keyword and citation analysis. The Vosviewer software was used to analyze the data and visualize the results. Findings reveal significant growth in blockchain research, particularly from 2021 onwards, with China emerging as a leading contributor, followed by the USA, India, and the UK. This study provides valuable insights into current trends, key contributors, and global patterns in blockchain technology research within auditing practices, and future research may explore thematic areas in greater depth.