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The objective of this case study is to analyze how two groups of parents, a group who have newly arrived in Macau from Mainland China and the other who have resided in Macau for more than three decades, interact with the class teachers at the levels of ?two-way communication,? ?supervision of children at home,? and ?participating in decision making? in a secondary school. The findings will redound to the benefits of school leaders, teachers, and indirectly the parents in a sense that looking closely at the ethnic and cultural differences between parents can promote effective cooperation between parents and teachers.

Medical classification is affected by many factors, and the traditional medical classification is usually restricted by factors such as too long text, numerous categories and so on. In order to solve these problems, this paper uses word vector and word vector to mine the text deeply, considering the problem of scattered key features of medical text, introducing long-term and short-term memory network to effectively retain the features of historical information in long text sequence, and using the structure of CNN to extract local features of text, through attention mechanism to obtain key features, considering the problems of many diseases, by using hierarchical classification. To stratify the disease. Combined with the above ideas, a deep DLCF model suitable for long text and multi-classification is designed. This model has obvious advantages in CMDD and other datasets. Compared with the baseline models, this model is superior to the baseline model in accuracy, recall and other indicators.

Traditional text classification models have some drawbacks, such as the inability of the model to focus on important parts of the text contextual information in text processing. To solve this problem, we fuse the long and short-term memory network BiGRU with a convolutional neural network to receive text sequence input to reduce the dimensionality of the input sequence and to reduce the loss of text features based on the length and context dependency of the input text sequence. Considering the extraction of important features of the text, we choose the long and short-term memory network BiLSTM to capture the main features of the text and thus reduce the loss of features. Finally, we propose a BiGRU-CNN-BiLSTM model (DCRC model) based on CNN, GRU and LSTM, which is trained and validated on the THUCNews and Toutiao News datasets. The model outperformed the traditional model in terms of accuracy, recall and F1 score after experimental comparison.

Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness.

Parental nutrient reserves are directly related to reproductive performance in sea cucumbers. This study focused on the lipid requirements of male and female sea cucumbers Apostichopus japonicus during the reproductive stage and analyzed their physiological responses to a high-fat diet (HFD). The intestinal lipid metabolites and microbiome profile changed significantly in animals fed with the HFD, as given by an upregulation of metabolites related to lipid metabolism and an increase in the predominance of Proteobacteria in the microbiome, respectively. The metabolic responses of male and female sea cucumbers to the HFD differed, which in turn could have triggered sex-related differences in the intestinal microbiome. These results suggest that the lipid content in diets can be differentially adjusted for male and female sea cucumbers to improve nutrition and promote reproduction. This data contributes to a better understanding of the reproductive biology and sex differences of sea cucumbers.

Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution episodes to warn the public ahead of time. Five different state-of-the-art machine learning (ML) algorithms were applied to create predictive models to forecast PM2.5, PM10, and CO concentrations for the next 24 and 48 h, which included artificial neural networks (ANN), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR), to determine the best ML algorithms for the respective pollutants and time scale. The diurnal measurements of air quality data in Macau from 2016 to 2021 were obtained for this work. The 2020 and 2021 datasets were used for model testing, while the four-year data before 2020 and 2021 were used to build and train the ML models. Results show that the ANN, RF, XGBoost, SVM, and MLR models were able to provide good performance in building up a 24-h forecast with a higher coefficient of determination (R2) and lower root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). Meanwhile, all the ML models in the 48-h forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-h forecast. The 48-h forecasting model could be further improved by proper feature selection based on the 24-h dataset, using the Shapley Additive Explanations (SHAP) value test and the adjusted R2 value of the 48-h forecasting model. In conclusion, the above five ML algorithms were able to successfully forecast the 24 and 48 h of pollutant concentration in Macau, with the RF and SVM models performing the best in the prediction of PM2.5 and PM10, and CO in both 24 and 48-h forecasts.

Employees work long hours in an environment where the ambient air quality is poor, directly affecting their work efficiency. The concentration of particulate matters (PM) produced by the interior renovation of shopping malls has not received particular attention in Macao. Therefore, this study will investigate the indoor air quality (IAQ), in particular of PM2.5, in large-scale shopping mall renovation projects. This study collected on-site PM data with low-cost portable monitoring equipment placed temporarily at specific locations to examine whether the current control measures are appropriate and propose some improvements. Prior to this study, there were no measures being implemented, and on-site monitoring to assess the levels of PM2.5 concentrations was non-existent. The results show the highest level of PM2.5 recorded in this study was 559.00 μg/m3. Moreover, this study may provide a reference for decision-makers, management, construction teams, design consultant teams, and renovation teams of large-scale projects. In addition, the monitoring of IAQ can ensure a comfortable environment for employees and customers. This study concluded that the levels of PM2.5 concentration have no correlation with the number of on-site workers, but rather were largely influenced by the processes being performed on-site.

The extraction of 21 insecticides and 5 metabolites was performed using an optimized and validated QuEChERS protocol that was further used for the quantification (GC–MS/MS) in several seafood matrices (crustaceans, bivalves, and fish-mudskippers). Seven species, acquired from Hong Kong and Macao wet markets (a region so far poorly monitored), were selected based on their commercial importance in the Indo-Pacific region, market abundance, and affordable price. Among them, mussels from Hong Kong, together with mudskippers from Macao, presented the highest insecticide concentrations (median values of 30.33 and 23.90 ng/g WW, respectively). Residual levels of fenobucarb, DDTs, HCHs, and heptachlors were above the established threshold (10 ng/g WW) for human consumption according to the European and Chinese legislations: for example, in fish-mudskippers, DDTs, fenobucarb, and heptachlors (5-, 20- and tenfold, respectively), and in bivalves, HCHs (fourfold) had higher levels than the threshold. Risk assessment revealed potential human health effects (e.g., neurotoxicity), especially through fish and bivalve consumption (non-carcinogenic risk; ΣHQLT > 1), and a potential concern of lifetime cancer risk development through the consumption of fish, bivalves, and crustaceans collected from these markets (carcinogenic risk; ΣTCR > 10–4). Since these results indicate polluted regions, where the seafood is collected/produced, a strict monitoring framework should be implemented in those areas to improve food quality and safety of seafood products.

Sinã, conhecido entre os seus parentes pelo nome de “Luz Lilás”, homem de um imenso metro e noventa, cacique, mandou reunir a sua comunidade na oca central, a maior, situada no ponto mais alto do cimo da falésia, feita de massaranduba e sucupira – as madeiras mais resistentes – entrelaçadas com cipós de fogo.   Pintaram-lhe o longo rosto com traços firmes, a geometria dos signos direcionada ao céu e à terra, protetores das suas gentes. Dessa vez as linhas marcadas na face de “Luz Lilás” eram mais simétricas do que o habitual, as tintas extraídas das sementes de urucu […]

The various volumes coordinated by Pierre Nora to pursue a history of the places of memory in France have become a multidisciplinary theoretical reference for those who, like us, seek to reconstruct the memories with which the land of the Potiguara aborigines of Brazil is organized today. In the introduction to the voluminous work that he directed for eight years, Nora explained his epistemic understanding of the notion of “places of memory”, stressing that a “lieu de mémoire” is any significant entity that, material or immaterial in nature, through a human will or the wear and tear of time, has become a symbolic element of a community's memorial heritage. The French historian also added that, since memory is the fundamental structure of this generally lengthy process, it was convenient to understand it as a phenomenon of emotions and magic that only accommodates the facts that feed it. Strictly speaking, memory is always vague, and reminiscent, stirring both general impressions and fine symbolic details. Furthermore, memory is always vulnerable to transference, repressed and imagined memories, censorship, and all kinds of projections. (Nora, 1984). In this article, we try to understand that the places of memory are also almost always what comes to us, stays, and selects the past. The reserve where they live appears as a symbolic locus to which the Potiguara aborigines cling with all their strength to preserve what remains of their past.

Higher Education in Macau has been benefiting from a multi-layered institutional environment under China’s One-Country-Two-Systems. This presentation introduces research and education policies and practices of Macau universities under China’s national plan of the Greater Bay Area development. It aims to demonstrate and analyze how higher education actors collaborate with local and regional governments and industrial sectors in human capital formation and research innovations.

By using both, the weak-value formulation as well as the standard probabilistic approach, we analyze the Hardy's experiment introducing a complex and dimensionless parameter ($\epsilon$) 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 $\epsilon=1$; however, even in this case we can interpret this result as a natural consequence of the fact that the particles can cross the point $P$, but at different times due to a natural consequence of the energy-time uncertainty principle.

Macau, Macau Business, MAG, MB, MB Featured, Opinion | Despite the welcome optimism expressed at the government’s plans to resurrect Macau’s economy, its economic recovery will continue to suffer from having had the rug pulled from under its feet by the zero-Covid policy, however well intentioned.

Macau, Macau Business, MAG, MB, MB Featured, Opinion | As Macau strives to revive its post-pandemic economy and to reinject life into its ailing society, calls for investment in human capital resurface, alongside endless mantras of economic diversification which, for years, seem to have fallen on deaf ears, and together with plans for further infrastructure development and construction which have already turned Macau into a concrete jungle.

Macau, Macau Business, MAG, MB, MB Featured, Opinion | Macau’s development of international and tertiary sector industries is the watchword for its long-overdue diversification. Is Macau ready for this?

In the last few years, the tourism industry has experienced rapid expansion and diversification, making it one of the fastest-growing financial industries in the world. Consequently, the hotel industry has significantly affected the environment's long-term viability. Many hotels have begun voluntarily implementing environmentally sustainable practices as they become more aware of their ecological footprint. There has been a great deal of discussion about the effects of hotel operations on the environment and tourism sustainability in Macau. It is because of these negative impacts that hoteliers have adopted green practices in an attempt to minimize them. By developing sustainability reports, hotels can set goals, measure performance, and manage change, resulting in better sustainability. It could also be viewed as a strategy to enhance the company’s sustainability reporting to ensure stakeholders know what the company does. The objective of this study is twofold based on the analysis of the official sustainability reports of four major hotel chains. Firstly, seven categories of sustainable practices effectively adopted by these chain hotels are identified and clusterized. Second, it is presented in which areas some hotels performed more efficiently than others, considering the UN Sustainable Development Goals (SDGs) as a reference. The results allow a comprehensive clusterized analysis of the industry in a highly developed gaming and entertainment area of South China and create a clear comparison between relevant players and their concerns about sustainability practices.