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本研究旨在探討澳門 25-34 歲年齡層個人對生育權的認知與理解,以及這 些觀點對其生涯規劃和性別平等態度的影響。澳門作為一個快速發展的特別行 政區,關於生殖權的討論仍然較為缺乏,這使得本研究特別重要,因為它有助 於填補這一領域的知識空白。本研究採用質性研究方法,通過有目的抽樣,對 20 名年齡在 25-34 歲之間的澳門居民進行半結構訪談,並以詮釋學方法分析數 據。 研究發現如下: 1. 男性參與者更關注個人角色和責任,並反思社會期望和文化價值觀。 2. 女性參與者則更關注社會期望與個人自主權的衝突,並關心性別平等和 個人權利。 這些差異突顯了性別角色和社會責任在男性和女性身上的不同詮釋,並反映 了社會、文化和個人因素對於性別觀點的塑造作用。研究結果對於推動澳門的 性別平等政策和生殖權利保障具有重要意義,並為後續研究提供了實證基礎。 This study aims to explore the awareness and understanding of reproductive rights among individuals aged 25-34 in Macau, as well as the impact of these views on their career planning and attitudes towards gender equality. As a rapidly developing Special Administrative Region, Macau still lacks extensive discussion on reproductive rights, making this research particularly important as it helps to fill the knowledge gap in this field. The study employs qualitative research methods, using purposive sampling to conduct semi-structured interviews with 20 Macau residents aged 25-34, and analyzes the data using hermeneutic methods. The findings of the study are as follows: 1. Male participants are more concerned with individual roles and responsibilities, and they reflect on societal expectations and cultural values. 2. Female participants are more focused on the conflict between societal expectations and personal autonomy, and they are concerned with gender equality and individual rights. These differences highlight the varying interpretations of gender roles and social responsibilities between men and women and reflect the influence of social, cultural, and personal factors on gender perspectives. The research results have significant implications for promoting gender equality policies and reproductive rights protection in Macau, providing an empirical basis for subsequent studies.

In the hype of multi-/inter-disciplinarity, is the voi-ce or voices of theistic religions and the attendant philosophical moral awareness (etymologically bet-ter rendered as conscientização in Portuguese) still meant to be heard? Can classical tales of saints and sinners remain part of the canon of public literacy? How existential is the threat of “organised religions” or otherwise established ecclesiastical structures posed to society when they are accused of attempting to fight proxy crusades against humanitarian enlightenment under the guise of religious literature? Are tenets pro-pounded by scholars like Gavin D’Costa in Theology and the Public Square (2005) to be politely bracketed when discussing perennial values? Values that respon-sible media strive to propagate, particularly the value of human dignity eulogised by the life exemplars of great figures in times of existential crises of whatever magnitude. With these questions in mind, this article will hearken back to the stories of two “grandees” in the Roman Catholic tradition who left their marks on the pages of the development of modern English and Chinese literacy. Newman’s Apologia pro vita sua(1865) is just but one of the tactical devises for his defense of creedal integrity, while Ma Xiangbo engaged in catholicising the Chinese national ethos through educational literacy for close to half a century. We shall phenomenologically draw inspirations from their parallel vision and experience on what lends power to the medium of words and deeds in shaping informed public conscience in regard to the core values of truth, good, and beauty.

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.

In this paper, we demonstrate that the effects of Dark-Matter, partially in the way proposed by the Modified Newtonian Dynamics (MOND), emerge naturally from the standard theory of General Relativity (without any modification) under a new proposed vacuum solution. There is a family of metric solutions able to reproduce the galaxy rotation curves and the relevant scales where the Dark-Matter effects are supposed to appear in a galaxy. This family of solutions deviate from the standard spherically symmetric solution. The proposed formulation, being relativistic by nature, opens the scenario where we can test the relativistic effects attributed to Dark-Matter and having relevance in cosmology. Among such effects, we have gravitational lensing, effects on the CMB scenario and effects on the formation of galaxies.

本書由36 位不同界別的領袖、專家和學者，分享與人口老齡化相關的精闢觀察與洞見，探索創新永續的生活和經濟模式，包括相關的政策、黃金時代經濟的發展、中國安老服務的新視野、醫康養老新發展、智齡科技的應用、永續人才和社區發展等議題，為業界提供參考，亦為45 歲以上的黃金一代應對未來退休生活提供啟發。 「我們的生活越來越受創新技術的影響，我們的社會也更加重視綠色生活和可持續發展。科技和綠色生活方式必須融入智齡產品和服務中。」 —— 陳茂波 香港特別行政區財政司司長 「我們的共同目標是在老齡化世界中不讓任何人掉隊。」 —— 威廉•史密斯博士 聯合國紐約總部老年事務非政府組織委員會主席 「我們深信人口老化為全球帶來嶄新的機遇。中、老年人是唯一正在不斷增長的人力資源，也是創新產品和服務的龐大消費群體。」 —— 容蔡美碧 黃金時代基金會創會主席

By using both the weak-value formulation as well as the standard probabilistic approach, we analyze Hardy’s experiment introducing a complex and dimensionless parameter (ϵ), which eliminates the assumption of complete annihilation when both the electron and the positron departing from a common origin cross the intersection point P. We then find that the paradox does not exist for all the possible values taken by the parameter. The apparent paradox only appears when ϵ=1, which is just a singular value. In this paper we demonstrate that this particular value is forbidden inside the scenario proposed by the experiment.

The aviation sector is transforming as electrification emerges as a promising technology. Adopting battery-electric aircraft (BEA) - aircraft that solely rely on rechargeable onboard batteries - is a sustainable alternative to conventional aviation that could change short-haul regional travel habits for business and leisure travellers. This study examines the factors influencing individuals’ public acceptance in China's Greater Bay Area (GBA) context. Given the limited research, a qualitative methodology grounded in the Theory of Planned Behaviour (TPB) examines the underlying factors influencing behavioural intentions (attitudes, subjective norms, perceived behavioural control, and perceived risks). The findings indicate that participants recognise the technology's environmental benefits and potential to enhance regional connectivity; however, they still have concerns about safety, infrastructure, and operations. The respondents’ perceived ease of access, information available, and endorsements from reputable sources also have essential roles in influencing broader acceptance. Addressing these factors with appropriate communication efforts is vital for promoting trust and accelerating technology acceptance and use. Although exploratory, this study offers insights to develop strategies for infrastructure readiness, build public confidence, and endorse sustainable aviation. The research is conducted within the GBA context. Still, the findings also apply to regions with fragmented geographies or developing transportation networks, thus contributing to global environmental sustainability and advancing regional integration goals.

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.

We derive the vacuum energy from the zero-point quantum fluctuations after imposing a natural constraint emerging from the rotational symmetry inside the de-Sitter metric. The constraint imposes a maximum azimuthal angle for each frequency mode emerging from the vacuum. In this way, the shorter the wavelength of the mode, the larger will be its suppression. The same result is derived subsequently by using the Friedmann–Lemaitre–Robertson–Walker (FLRW) metric. We then make a physical interpretation of the physical effects from the perspective of pair creations over the vacuum, where the mentioned constraint emerges, limiting then the maximum angle which each pair generated from the vacuum can rotate with respect to each other during their short existence.

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>

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.

<jats:p>Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain’s electrical activity through electrodes placed on the scalp’s surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain–computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was “subject-dependent”. In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.</jats:p>

<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>

Objetivo: Explorar a aplicação de inteligência artificial (IA) na predição da idade óssea a partir de imagens de raios-X. Método: Utilizou-se a Metodologia Interdisciplinar para o Desenvolvimento de Tecnologias em Saúde (MIDTS) para desenvolver uma ferramenta de predição. O treinamento foi realizado com redes neurais convolucionais (CNNs) usando um conjunto de dados de 14.036 imagens de raios-X. Resultados: A ferramenta alcançou um coeficiente de determinação (R²) de 0,94807 e um Erro Médio Absoluto (MAE) de 6,97, destacando sua precisão e potencial de aplicação clínica. Conclusão: O projeto demonstrou grande potencial para aprimorar a predição da idade óssea, com possibilidades de evolução conforme a base de dados aumenta e a IA se torna mais sofisticada.