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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.

<jats:p>Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals’ nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal’s structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of 0.0070±0.0028 and an average MAE of 0.0522±0.0098 across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model’s accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring.</jats:p>

Under Macau Arbitration Law (MAL, art 64.1), an award shall be made in writing and shall be signed by the arbitrator or arbitrators. Furthermore, the law provides that in case of arbitral proceedings with more than one arbitrator, the signatures of the majority of all members of the arbitral tribunal shall suffice, provided that the reason for any omitted signature is stated (MAL, art 64.2).

Integrating financial technologies with green initiatives is critical to the sustainable development agenda. This is particularly true for newly developed smart cities like Tongzhou, the sub-city center of Beijing. To assess the adoption of green fintech in Tongzhou, this paper extends the EnergyAugmented Technology Acceptance Model (EA-TAM) to incorporate two green factors – environmental awareness and green knowledge. This paper applies structural equation modeling techniques to analyze data from 403 respondents who live, work, or study in Tongzhou and finds allhypothesized constructs significant. Since green knowledge is significant to the adoption of green fintech, this paper further divides the sample into a high-education group (162 respondents with university-or-above degrees) and a low-education group (251 respondents with post-secondary-orlower degrees) to evaluate the impact of education. All the hypothesized factors are significant to the high-education group,but environmental awareness and perceived usefulness are insignificant to the low- education group. Hence, the results provide evidence that people in the newly developed smart city adopt green fintech due to their environmental sensitivity. The adoption of green fintech is more environmentally sensitive for people with high education levels.

Battery electric vehicles (BEVs) are living up to their claims as consumers choose them more frequently. The increasing demand for sustainable vehicles translates into the global need for specific components, materials, and infrastructures and drives the regulatory frameworks in each country. While BEVs offer environmental benefits and global business opportunities, the technology has not yet gained mainstream acceptance. Thus, this work aims to investigate the characteristics of BEV users and their role in the diffusion of products to larger segments, as this may vary from country to country. For this purpose, a survey based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) (Venkatesh et al., 2012) framework and structural equation modeling (SmartPLS) was adopted. The results indicated that, except for the constructs of effort expectancy (EE) and social influence (SI), the predictors in the model performed well in this context. Current users are satisfied with their vehicles and are supportive of BEVs in the future. The analysis also revealed that in addition to the availability of financial resources, early adopters are attracted by new technologies in a way that leads them to make decisions outside of the traditional influence of the other members of society. It is suggested to leverage the perceived benefits of status, differentiation, or uniqueness motives, to appeal to those seeking to appear trendy and tech-savvy in society. Companies and policymakers should acknowledge the peculiarities of early customers in their communication strategies to reach a wider audience around the globe and encourage the adoption of BEV technology.

Objective: This study highlights the potential of an Electrocardiogram (ECG) as a powerful tool for early diagnosis of COVID-19 in critically ill patients with limited access to CT–Scan rooms. Methods: In this investigation, 3 categories of patient status were considered: Low, Moderate, and Severe. For each patient, 2 different body positions have been used to collect 2 ECG signals. Then, from each collected signal, 10 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) were extracted every 1s ECG time-series length to serve as entries for 19 Machine learning classifiers within a leave-one-out cross-validation procedure. Four different classification scenarios were tested: Low vs. Moderate, Low vs. Severe, Moderate vs. Severe and one Multi-class comparison (All vs. All). Results: The classification report results were: (1) Low vs. Moderate - 100% of Accuracy and 100% of F1–Score; (2) Low vs. Severe - Accuracy of 91.67% and an F1–Score of 94.92%; (3) Moderate vs. Severe - Accuracy of 94.12% and an F1–Score of 96.43%; and (4) All vs All - 78.57% of Accuracy and 84.75% of F1–Score. Conclusion: The results indicate that the applied methodology could be considered a good tool for distinguishing COVID-19’s different severity stages using ECG signals. Significance: The findings highlight the potential of ECG as a fast and effective tool for COVID-19 examination. In comparison to previous studies using the same database, this study shows a 7.57% improvement in diagnostic accuracy for the All vs All comparison.

Critical thinking (CT), as a form of higher-order thinking, is intended to help individuals form reasonable reflection and judgment to deal with increasingly severe employment situations. As the primary workforce in the labor market, undergraduates must possess a strong critical thinking disposition (CTD) to make better use of CT. Despite extensive research on components of CTD from the perspective of educational practices, there is limited emphasis on investigating the components and their relationships of CTD in the labor market and the impact of gender differences. Therefore, this study presented an analysis of 1535 Chinese undergraduates (Mage = 20.89; SD = 1.43) using the Employer-Employee-Supported Critical Thinking Disposition Inventory (2ES-CTDI), aiming to explore the CTD that undergraduates should possess before entering the labor market. The relationships among the components were examined using SmartPLS4.0 in conjunction with Partial Least Squares Structural Equation Modeling (PLS-SEM). Additionally, a multigroup analysis (PLS-MGA) with a measurement invariance (MI) test was conducted to validate the moderating effects of gender. The findings indicate that (a) self-efficacy has a significant negative effect on habitual truth-digging, and boys are more affected than girls, instant judgment plays a competitive partial mediating role in this relationship; (b) self-efficacy has a significant positive effect on instant judgment, and boys are more likely to make instant judgments than girls; (c) instant judgment significantly positively affects habitual truth-digging. These findings highlight the dynamic equilibrium among the internal components of CTD in the labor market and call for increased attention from educators to the importance of gender differences in the cultivation process.

The Revenue Management (RM) problem in airlines for a fixed capacity, single resource and two classes has been solved before by using a standard formalism. In this paper we propose a model for RM by using the semi-classical approach of the Quantum Harmonic Oscillator. We then extend the model to include external factors affecting the people’s decisions, particularly those where collective decisions emerge.

The emergence of blockchain technology in 2008 marked a significant milestone in the evolution of digital currencies, paving the way for the emergence of cryptocurrencies such as Bitcoin. Since then, blockchain has undergone four generations of development, expanding its applications across various sectors. In particular, the integration of blockchain into accounting and auditing practices has garnered significant attention due to its potential to transform traditional methods. However, there's a lack of clear understanding of how blockchain impacts traditional auditing practices and finance recordkeeping and the implications for audit quality. Significant challenges and uncertainties hinder its widespread adoption, including technical hurdles, regulatory complexities, and practical barriers. This dissertation aims to determine the transformative impact of blockchain technology on auditing practices and finance recordkeeping. In order to fully understand the impact of blockchain on auditing practices and finance recordkeeping, the dissertation utilizes a mixed sequential research approach that is divided into three phases. The first approach involves gathering qualitative data through interviews with blockchain experts. The second approach involves collecting secondary qualitative data through a systematic literature review to determine the changes that blockchain has brought to traditional auditing practices and finance recordkeeping. This is followed by a bibliometric analysis to identify current trends in blockchain research related to auditing practices and finance recordkeeping. The third approach involves gathering data through an online-focused survey distributed to finance and other industry professionals to determine the challenges organizations face in implementing blockchain technology in auditing practices and finance recordkeeping. Additionally, in phase three, case studies will be conducted based on the survey responses to examine the hindrances and challenges faced by organizations in implementing blockchain and its impact on auditing practices in different regions and among different demographic groups. As the findings indicate, Integrating blockchain technology into accounting and auditing practices can bring about significant improvements in transparency, efficiency, and fraud prevention. However, there are several challenges that must be overcome for successful implementation, such as technical difficulties, regulatory uncertainties, and privacy concerns. To overcome these hurdles, it is necessary to establish clear regulatory frameworks and innovative solutions. Although smart contracts offer automation, they also pose security risks that need to be addressed. Despite these challenges, blockchain has the potential to revolutionize auditing by enabling real-time auditing and enhancing integrity verification. To ensure audit quality, auditors must adapt to new responsibilities and stay up-to-date with emerging trends. Collaboration among stakeholders and continuous education and training programs are key to driving the successful adoption of blockchain technology

In the 21st century, complex problem-solving (CPS) serves as a key indicator of educational achievement. However, the elements of successful CPS have not yet been fully explored. This study investigates the role of strategic exploration and different problem-solving and test-taking behaviors in CPS success, using logfile data to visualize and quantify students’ problemsolving behavior on 10 CPS problems with different characteristics and levels of difficulty. Additionally, in the present study, we go beyond the limits of most studies that focus on students’ problem-solving behavior pattern analyses in European cultures and education systems to examine Arabic students’ CPS behavior. The results show that computer-based assessments of CPS are feasible and valid in Jordanian higher education. The findings also confirm the structural validity of CPS, indicating that the processes of knowledge acquisition (KAC) and knowledge application (KAP) can be distinguished and separated in the problem-solving process. Large differences were identified in students’ test-taking behavior in terms of the efficacy of their exploration strategy. We identified four latent classes based on the students’ exploration strategy behavior. The study thus leads to a better understanding of how students solve problems and behave during the problem-solving process in uncertain situations.

To evaluate 3M's intrinsic value and compare the calculated results with the stock market price as of 31 December 2022. To study 3M's future operating strategies and the internal and external factors that may impact the Company's operations

This dissertation investigates the relationship between nature exposure in the workplace, job satisfaction, and creativity among full-time employees in Macao. The study aims to examine the impact of exposure to greenery and sunlight on job creativity and job satisfaction, as well as the mediating role of job satisfaction in the relationship between workplace nature exposure and creativity. Drawing on existing literature, the study proposes the following hypotheses: (H1) Workplace nature exposure, including greenery and sunlight, positively influences job creativity. (H2) Workplace nature exposure, including greenery and sunlight, positively affects job satisfaction. (H3) Job satisfaction mediates the positive relationship between workplace nature exposure (specifically, greenery and sunlight) and job creativity. A quantitative methodology utilizing cross-sectional self-response questionnaires was employed for data collection. The measures used were pre-validated and translated into Chinese through a rigorous process of translation and back-translation. The final sample consisted of 450 full-time workers in Macao. The results highlight the significance of integrating natural elements, such as greenery and sunlight, into the work environment to promote a more favourable setting for innovation and the overall well-being of employees. The implications of these findings, both in theoretical and practical terms, are thoroughly discussed. By examining the relationships, this study contributes to the understanding of how the physical environment influences employee well-being and performance. The findings offer valuable insights for organisations and policymakers in designing workspaces that foster creativity and job satisfaction among employees

It has been claimed in \cite1, that the idea proposed in \cite2 has certain mistakes based on arguments of energy conditions and others. Additionally, some of the key arguments of the paper are criticized. Here we demonstrate that the results obtained in \cite2 are correct and that there is no violation of any energy condition. The statements claimed in \cite1 are based on three things: 1). Misinterpretation of the metric solution. 2). Language issues related to the physical quantities obtained in \cite1, where the authors make wrong interpretations about certain results over the geometry proposed in \cite2. 3). Non-rigorous evaluations of the vacuum condition defined via the result over the Ricci tensor R_\mu\nu=0.

This dissertation examines the current implementation of specific Sustainable Development Goals (SDGs) within the Macau legal system, despite the non-mandatory nature of these goals and how they were implemented. The SDGs, established by the United Nations, provide a comprehensive framework for addressing global social, economic, and environmental challenges. While Macau is not obliged to adopt the SDGs, this study aims to explore the extent to which the principles and objectives of specific SDGs have been integrated into the legal system of Macau. Through an analysis of relevant laws, and policies, this research seeks to identify alignment and visible gaps between the SDGs and the Macau legal framework. The findings of this study may contribute to a deeper understanding of the voluntary implementation of the SDGs in Macau and provide insights for other jurisdictions facing similar circumstances

Public transportation is an important component of urban infrastructure and has a significant impact on the convenience and well-being of citizens. In Macau, with rapid economic development and urbanization, an efficient land transport system is essential to support sustainable development. Previous research has primarily focused on the sustainable development of the transportation sector in Macau, with little attention given to studies concerning employees. Therefore, the purpose of this study is to examine the impact of transformational leadership on employee work performance in the land transportation sector in Macau, with a particular focus on the mediating role of employee work engagement. Through a quantitative research methodology, this study utilized an online questionnaire to collect data from 194 employees from the bus and LRT sectors in order to achieve the research objectives. Convenience sampling and snowball sampling methods were used in this study to analyze the data through SPSS software to assess the relationship between transformational leadership, employee work engagement and employee performance. The findings reveal that there is a significant positive relationship between transformational leadership and employee performance, as well as a significant positive relationship between transformational leadership and employee work engagement. In addition, employee work engagement played a partial mediating role between transformational leadership and employee performance. The findings suggest that transformational leadership not only directly enhances employee performance, but also indirectly enhances employee performance by increasing employee work engagement. These insights have important practical implications, indicating that adopting transformational leadership behaviors can effectively enhance employee engagement and performance, thereby improving organizational effectiveness. Specifically, managers in Macau's transportation sector should pay attention to and cultivate transformational leadership behaviors in order to enhance employee work engagement and performance, and thus achieve higher organizational effectiveness and building a strong team

Abstract The Jesuit missionary Matteo Ricci's teaching on the goodness of human nature in The True Meaning of the Lord of Heaven represents the fruit of the first encounter between Catholicism and Confucianism. This article will consider the Thomistic and neo‐Confucian sources in Ricci's enunciation of the Catholic doctrine on the goodness of human nature in this Chinese catechism. It will illustrate that Ricci developed his teaching, which is fundamentally Thomistic, with the help of terminology borrowed from the Chinese philosophical tradition. His distinction between the good of nature and the good of virtue leads to prioritising the cultivation of human nature. Ricci's teaching reflects the early modern Jesuits’ appreciation of human freedom. It also displays a Catholic reaction to the sixteenth‐century neo‐Confucian intellectual trend that ignored the importance of moral cultivation.

The design thinking methodology is a problem-solving approach that involves empathising with end-users, (re)defining problems, brainstorming solutions creatively, and experimenting with prototypes and testing. It has been widely adopted in education to help students develop critical thinking, creativity, and problem-solving skills in design. On the other hand, text-to-image artificial intelligence is a method used to generate images from natural language descriptors (usually referred to as prompts). Design thinking methodology can teach students to think creatively and critically about real-world problems when applied in the classroom. In the context of design teaching at the University of Saint Joseph, Macao, students use the design thinking methodology to develop innovative proposals for furniture design solutions. Combining design thinking methodologies with text-to-image artificial intelligence can further enhance the learning experience by allowing students to generate visual representations of their ideas during the ideation phase. The authors developed a systematic approach to generate images for ideation on furniture design based on prompting text-to-image (PTI). The analysis related students’ results who applied the design thinking methodology without using AI tools and the results generated using a standard text-to-image programme. By combining both methods, teachers can help students develop critical thinking, creativity, and problem-solving skills, while also allowing them to generate visual representations in a different paradigm and, by so, being able to communicate their ideas with the most appropriate support for them.

As the world becomes more globalized, it is now more important than ever for brands and advertisers to find effective ways to engage with consumers of different cultural backgrounds. Developing marketing that targets people of different cultural backgrounds, or multicultural marketing, carries specific nuances and complexities that may make traditional methods fall short. With this being said, there is still a lack of studies that explore the correlation between consumer's cultural background and their overall brand perception. Neuromarketing has proven to be an effective tool to understanding consumer behavior, by utilizing neuroscience tools. To employ a more sophisticated and in-depth understanding of consumer perception, the current research study makes use of neuroscience tools and aims to study the influence of cultural background in brand perception, while in a controlled environment. Using physiological neuroscience tools, namely, facial expression analysis (FEA), electrodermal activity (EDA), and eye-tracking (ET), a total of thirty-eight individuals, with ages between 19 and 50 years old, from 12 different countries and regions, participated in this research study. Findings suggest that participants of different cultural backgrounds perceive multicultural commercials as more favorable than monocultural commercials. However, future research should be done with a larger sample size, as well as include a wider variety of commercials. Research would also benefit from adopting a statistical analysis to help determine the significance of the results obtained