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There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model.

To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people’s choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5% and the convergence speed is increased by 21.2% compared with the basic algorithm, which proves that the improved algorithm is notably effective.

Abstract With its large population and natural resources, Africa needs investors who can sustain its development. At the same time, foreign investors expect returns on their investments. In ...

Objective: Over the past decade, arbitration has grown in popularity as a method of resolving commercial disputes worldwide. However, this practice is relatively new in Macao SAR. Recently, official plans were announced to make Macao as a seat of arbitration for commercial disputes between China and Portuguese-speaking countries (Hereinafter PSCs). This article is dedicated to explores the possibility of Macao undertaking and implementing such a role. Accordingly, this article addresses the following issues: What are the strengths and weaknesses of Macao as a seat and eventually as venue for hosting international commercial arbitration between Chinese and PSCs entrepreneurs?Methodology: A mixed-method approach of legal doctrinal and empirical research was used in this article. We first included a thorough study of the concept of arbitration followed by analysis of various legal journals and legislations, including Macao, China, and PSCs’ arbitration laws. An empirical research was then used to collect data by surveying and interviewing with both lawyers and arbitration practitioners from Macao, China and PSCs.Results: This article argues that the strength of Macao resides in the similarities between its legal system and that of the China and PSCs and the languages advantage (Chinese and Portuguese both official languages). In spite of this, arbitration is still relatively underutilized in the region, and there is a limited number of arbitrators and legal professionals with bilingual proficiency.Contributions: This article contributes to the identification of the opportunities and challenges that Macao faces in its potential future development as a seat/venue of arbitration between China and the PSCs.

Stock price prediction has always been challenging due to its volatility and unpredictability. This paper performs a preliminary exploratory comparison that utilizes Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms to forecast the stock market in Hong Kong. It considers a public dataset publicly available and uses feature engineering to extract relevant features. Then, LSTM and SVM algorithms are applied to predict stock prices. Our results show that the proposed machine learning techniques can predict stock prices in Hong Kong's share market with the error metrics presented, and, for this purpose, LSTM achieved better results than SVM, with MSE = 0.0026, RMSE = 0.0508, MAE = 0.0406, and MAPE = 1.325.

Predicting stock prices is difficult because of their multiple input variables, volatility, and unpredictable nature. To provide a suitable model for forecasting the global stock market, this study conducts an exploratory analysis comparing two models based on Artificial Intelligence: Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Networks. The work considers a publicly accessible dataset and uses feature engineering to extract time-series features. Stock price predictions are made using the SVM and LSTM algorithms. For this purpose, Accuracy (ACC) and Root Mean Squared Error (RMSE) are considered accuracy and performance measures. According to the results, LSTM with mean accuracy (ACC) = 0.9061 achieved better accuracy than SVM with mean accuracy (ACC) = 0.881. SVM with mean RMSE = 0.729 achieved better performance and the degree of fit to the data than LSTM with mean RMSE = 427.1. According to the results, the study demonstrates the effectiveness and applicability of machine learning methods for estimating the values of the global stock market and providing valuable models for researchers, analysts, and investors.

This qualitative study explores parental perspectives and decision-making processes regarding young children's engagement with electronic devices in Macao. Data was collected through semi- structured, in-depth interviews with 15 parents of young children. The researcher analysed the interview contents and synthesized the findings with relevant literature and theoretical support, resulting in the following key insights: 1. Parental attitudes towards children's use of electronic devices were found to be distinctly polarised. Parents with a more open attitude viewed electronic devices as an inevitable part of modern life, suggesting that excessive restrictions might hinder children's development. In contrast, parents with a more conservative perspective emphasised the developmental risks of early exposure. Three primary factors influenced parental attitudes; the parents’ level of professional knowledge in education, their own usage habits, and their sense of parental self-efficacy. Additionally, parental behaviours had a modelling effect on children's usage patterns, directly correlating with the stringency of their management strategies. 2. Regarding child development, parents generally recognised the educational value of electronic devices, particularly the ability of multimedia interactivity to enhance learning engagement and facilitate knowledge acquisition. However, they also expressed concerns about the potential risks in three developmental domains: physiological development (e.g., visual eye strain, sedentary behaviour), psychological development (e.g., addiction, reduced social interaction), and cognitive development (e.g., diminished attention span, fewer opportunities for practical learning). Parents with professional knowledge of child development tended to adopt more comprehensive assessment perspectives and balanced usage strategies. 3. The study identified three primary contexts in which parents provided electronic devices to their children: first, as a response to parenting stress, where parents used devices to gain brief respite or manage household tasks; second, as behavioural management tools, using devices for reward and punishment mechanisms, such as encouraging good behaviour, boosting learning motivation, or soothing children in specific situations (e.g., during illness or mealtime); and third, as influenced by external environmental factors, including caregiving differences (e.g., grandparents, domestic helpers), the technological emphasis in educational settings, and peer influence on children's social interactions. III 4. In terms of management strategies, parents predominantly employed three approaches: restrictive mediation, focusing on controlling screen time and filtering content; technical mediation, involving the use of device security settings and application restrictions; and alternative activity planning, such as encouraging children to engage in constructive play and artistic activities. However, the study revealed that parents less frequently utilised active educational mediation (e.g., discussing content, sharing experiences) and co-viewing strategies (e.g., joint parent-child usage, interactive learning). Based on these findings, the researcher provides recommendations for parents, schools, educators, and government agencies to establish a more comprehensive and supportive framework for young children's use of electronic devices and related services. 本研究採用質性研究方法，探討澳門家長對幼兒使用電子產品的觀點及決策因由，透過半 結構式深度訪談，收集 15 位育有幼兒之家長的經驗與看法。研究者根據訪談內容進行分析 討論，並輔以相關文獻和理論支持，歸納出以下研究結果: (一) 家長對幼兒使用電子產品的態度呈現明顯的兩極分化:持開放態度的家長視其為時代 趨勢，認為過度限制可能阻礙幼兒發展;持保守態度的家長則強調提早接觸電子產品可能帶 來發展風險。研究發現，家長的態度主要受三個因素影響:教育專業知識水平、個人使用習 慣，以及親職效能感。此外，家長的行為會通過示範效應影響幼兒的使用模式，並直接關係 到其管理策略的嚴格程度。 (二) 在幼兒發展方面，家長普遍認同電子產品的教育價值，尤其是其多媒體互動特性能提 升學習投入度及知識獲取。然而，家長同時關注其潛在風險，這些風險主要集中在三個層 面:生理發展(如視力損害、久坐問題)、心理發展(如沉迷、社交互動)、認知發展(如 專注力、實踐機會)。研究發現，具備幼兒發展專業知識的家長更傾向採取全面的評估視角 和均衡的使用策略。 (三) 家長提供電子產品予幼兒的三個主要情境包括:首先是因應育兒壓力，家長表示會透 過電子產品獲得短暫休息及處理家務的時間。其次是行為管理工具，家長運用電子產品進行 獎懲，包括鼓勵良好行為、提升學習動機，以及在特定情境下(如幼兒不適或進食)作為安 撫工具。第三是受外在環境因素的影響，主要來自三方面:替代照顧者(如祖父母、家傭) 的管教差異、教育環境的科技化趨勢，以及同儕使用經驗對幼兒社交需求的影響。 (四) 在管理策略方面，家長主要採取三種調解方式:限制性調解，著重於使用時間管控與 內容篩選;技術性調解，運用設備安全管理功能與應用程式限制;替代活動規劃，包括引導 幼兒參與建構性遊戲與藝術創作等。研究發現，家長較少採用具教育意義的指導性調解(如 內容討論、經驗分享)和社交共賞(如親子共同使用、互動學習)策略。 基於上述研究發現，研究者分別對家長，學校及教師、以及政府部門提出建議，期望建立 更適切且全面的幼兒電子產品使用框架及相關支援服務。

This study provides an empirical assessment of public servants’ integrity in Macao’s public sector, exploring how integrity is perceived, practiced, and promoted amid Macao’s unique administrative and socio-cultural context. Drawing on in-depth, semi-structured interviews with ten public servants from diverse departments and hierarchical levels, the research adopts a qualitative approach guided by the Theory of Public Service Motivation (PSM). The findings reveal that integrity fundamentally prioritizes public interest above personal gain, grounded in honesty, self-discipline, and ethical commitment. While organizational missions and formal codes offer guidance, individual values, professional ethics, and personal motivation are central to upholding integrity. Key enablers include mission-driven values, ethical leadership, supportive organizational culture, non-monetary incentives, and recognition. However, integrity is undermined by inconsistent ethics training, weak supervision, ineffective reporting mechanisms, and cultural factors such as close social networks and fear of retaliation. The effectiveness of mechanisms, such as internal regulations, anti-corruption agencies, and ethics training, varies across departments, often hindered by procedural gaps and societal norms. The study concludes that sustaining integrity in Macao’s public sector requires strengthening ethics education, enhancing leadership and culture, improving internal systems, and addressing systemic and societal risks. Limitations include the small, non-generalizable sample and focus on qualitative insights. Recommendations are targeted at policymakers, the supervisory institution, the Commission Against Corruption (CCAC), and institutional leaders for future reforms.

Today, social media has become one of the main marketing strategies used by marketers and practitioners to target travelers from all over the world. Macau is a city that relies heavily on tourism for economic development, however, little is known about how social media affects tourists’ choices to travel to Macau. This study explores the role of the popular social media platform Xiaohongshu in influencing Chinese tourists’ travel decisions and information acquisition when visiting Macau. This article uses qualitative research methods to conduct semi-structured interviews with 15 participants. Research shows travelers rely on Xiaohongshu’s user-generated content and detailed guides. Food, route selection and itinerary arrangements, and short-term itinerary arrangements are the most frequently mentioned search tags by travelers. The platform’s personalized recommendations and efficient content presentation enhance the user experience, but concerns about content credibility remain, especially the potential for commercial bias in food recommendations. Travelers assess the credibility of content through a variety of strategies, including looking at multiple posts, evaluating user interactions, and the platform’s IP address display capabilities. Although travelers pay more attention to positive reviews when deciding to travel to Macau, users also consider travel pitfalls posts to improve their travel experience. In addition, travelers are less willing to share content on the Xiaohongshu platform after the trip, which is attributed to personal habits and perceived creativity complexity. The research results provide relevant practitioners with the perspectives and decision-making behaviors of Chinese travelers who use Xiaohongshu when making itineraries to Macau, and provide guidance for their marketing strategies.

The manifestation of generating digital visuals through an algorithm is gaining worldwide attention in the graphic design industry. It is a new form of computing that visualizes data input by the designer or collected in the physical environment and turns them into artwork. The generative design of...

Macau has long been considered to be an example of remarkable economic growth. With the opening of the gaming sector in 2002, the casino and hospitality sector flourished, creating employment opportunities but also imposing several challenges on managers. Since Macau endeavors to be positioned as the center for international business with Portuguese-speaking countries and a platform for trading with China’s Greater Bay Area (GBA), it becomes essential for international enterprises to understand the local dynamics. In light of the limited research available, this study aims to identify management challenges from the perspectives of senior executives in different industries based in Macau. Our findings point out that managers must contend with several issues, such as the lack of a skilled local talent pool, high turnover rates, employees' work attitudes, and a tightly controlled immigration policy. It is also imperative for international managers to nurture relationships and pay attention to the local culture. Our results suggest that Macau has to develop a highly skilled local workforce to attract international companies, while local organizations also have to create an attractive working environment to compete in the marketplace.

The area of clinical decision support systems (CDSS) is facing a boost in research and development with the increasing amount of data in clinical analysis together with new tools to support patient care. This creates a vibrant and challenging environment for the medical and technical staff. This chapter presents a discussion about the challenges and trends of CDSS considering big data and patient-centered constraints. Two case studies are presented in detail. The first presents the development of a big data and AI classification system for maternal and fetal ambulatory monitoring, composed by different solutions such as the implementation of an Internet of Things sensors and devices network, a fuzzy inference system for emergency alarms, a feature extraction model based on signal processing of the fetal and maternal data, and finally a deep learning classifier with six convolutional layers achieving an F1-score of 0.89 for the case of both maternal and fetal as harmful. The system was designed to support maternal–fetal ambulatory premises in developing countries, where the demand is extremely high and the number of medical specialists is very low. The second case study considered two artificial intelligence approaches to providing efficient prediction of infections for clinical decision support during the COVID-19 pandemic in Brazil. First, LSTM recurrent neural networks were considered with the model achieving R2=0.93 and MAE=40,604.4 in average, while the best, R2=0.9939, was achieved for the time series 3. Second, an open-source framework called H2O AutoML was considered with the “stacked ensemble” approach and presented the best performance followed by XGBoost. Brazil has been one of the most challenging environments during the pandemic and where efficient predictions may be the difference in saving lives. The presentation of such different approaches (ambulatory monitoring and epidemiology data) is important to illustrate the large spectrum of AI tools to support clinical decision-making.