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Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.

It is plausible to assume that the component waves in ECG signals constitute a unique human characteristic because morphology and amplitudes of recorded beats are governed by multiple individual factors. According to the best of our knowledge, the issue of automatically classifying different ’identities’ of QRS morphology has not been explored within the literature. This work proposes five alternative mathematical models for representing different QRS morphologies providing the extraction of a set of features related to QRS shape. The technique incorporates mechanisms of combining the mathematical functions Gaussian, Mexican-Hat and Rayleigh probability density function and also a mechanism for clipping the waveform of those functions. The searching for the optimal parameters which minimize the normalized RMS error between each mathematical model and a given QRS search window enables to find an optimal model. Such modeling behaves as a robust alternative for delineating heartbeats, classifying beat morphologies, detecting subtle and anomalous changes, compression of QRS complex windows among others. The validation process evaluates the ability of each model to represent different QRS morphology classes within 159 full ECG signal records from QT database and 584 QRS search windows from MIT-BIH Arrhythmia database. From the experimental results, we rank the winning rates for which each mathematical model best models and also discriminates the most predominant QRS morphologies Rs, rS, RS, qR, qRs, R, rR’s and QS. Furthermore, the average time errors computed for QRS onset and offset locations when using the corresponding winner mathematical models for delineation purposes were, respectively, 12.87±8.5 ms and 1.47±10.06 ms.

Crowdsensing exploits the sensing abilities offered by smart phones and users' mobility. Users can mutually help each other as a community with the aid of crowdsensing. The potential of crowdsensing has yet to be fully realized for improving public health. A protocol based on gamification to encoura...

The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and th...

The adoption of IoT for smart health applications is a relevant tool for distributed and intelligent automatic diagnostic systems. This work proposes the development of an integrated solution to monitor maternal and fetal signals for high-risk pregnancies based on IoT sensors, feature extraction based on data analytics, and an intelligent diagnostic aid system based on a 1-D convolutional neural network (CNN) classifier. The fetal heart rate and a group of maternal clinical indicators, such as the uterine tonus activity, blood pressure, heart rate, temperature, and oxygen saturation are monitored. Multiple data sources generate a significant amount of data in different formats and rates. An emergency diagnostic subsystem is proposed based on a fog computing layer and the best accuracy was 92.59% for both maternal and fetal emergency. A smart health analytics system is proposed for multiple feature extraction and the calculation of linear and nonlinear measures. Finally, a classification technique is proposed as a prediction system for maternal, fetal, and simultaneous health status classification, considering six possible outputs. Different classifiers are evaluated and a proposed CNN presented the best results, with the F1-score ranging from 0.74 to 0.91. The results are validated based on the diagnosis provided by two specialists. The results show that the proposed system is a viable solution for maternal and fetal ambulatory monitoring based on IoT.

Background and objective Intrauterine Growth Restriction (IUGR) is a condition in which a fetus does not grow to the expected weight during pregnancy. There are several well documented causes in the literature for this issue, such as maternal disorder, and genetic influences. Nevertheless, besides the risk during pregnancy and labour periods, in a long term perspective, the impact of IUGR condition during the child development is an area of research itself. The main objective of this work is to propose a machine learning solution to identify the most significant features of importance based on physiological, clinical or socioeconomic factors correlated with previous IUGR condition after 10 years of birth. Methods In this work, 41 IUGR (18 male) and 34 Non-IUGR (22 male) children were followed up 9 years after the birth, in average (9.1786 ± 0.6784 years old). A group of machine learning algorithms is proposed to classify children previously identified as born under IUGR condition based on 24-hours monitoring of ECG (Holter) and blood pressure (ABPM), and other clinical and socioeconomic attributes. In additional, an algorithm of relevance determination based on the classifier is also proposed, to determine the level of importance of the considered features. Results The proposed classification solution achieved accuracy up to 94.73%, and better performance than seven state-of-the-art machine learning algorithms. Also, relevant latent factors related to HRV and BP monitoring are proposed, such as: day-time heart rate (day-time HR), day-night systolic blood pressure (day-night SBP), 24-hour standard deviation (SD) of SBP, dropped, morning cortisol creatinine, 24-hour mean of SDs of all NN intervals for each 5 minutes segment (24-hour SDNNi), among others. Conclusion With outstanding accuracy of our proposed solutions, the classification system and the indication of relevant attributes may support medical teams on the clinical monitoring of IUGR children during their childhood development.

Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.

The spontaneous symmetry breaking phenomena applied to Quantum Finance considers that the martingale state in the stock market corresponds to a ground (vacuum) state if we express the financial equations in the Hamiltonian form. The original analysis for this phenomena completely ignores the kinetic terms in the neighborhood of the minimal of the potential terms. This is correct in most of the cases. However, when we deal with the martingale condition, it comes out that the kinetic terms can also behave as potential terms and then reproduce a shift on the effective location of the vacuum (martingale). In this paper, we analyze the effective symmetry breaking patterns and the connected vacuum degeneracy for these special circumstances. Within the same scenario, we analyze the connection between the flow of information and the multiplicity of martingale states, providing in this way powerful tools for analyzing the dynamic of the stock markets.

There is considerable evidence to suggest that the human capital needs of the world city differ from what Robinson calls “ordinary cities” or what Markusen and associates term as “second tier cities”. This path is blazed most notably in the field of world cities and the flow of skilled labour, in the work by Sassen and with case examples (finance, law, accountancy) provided in the work by Beaverstock and his associates. This focuses on producer services and migration flows needs to be matched by an accompanying look at city-based strategies. This paper represents an attempt to provide this by providing a case history analysis of Singapore in three stages of growth – as port city, industrial city and as world city – in order to show how the evolving infrastructure associated with human capital (education, immigration and labour policies) allows human capital to be developed, attracted, harnessed, deployed, released and retained.

In the context of Asia, the changing dynamics of higher education has increased the visibility and significance of the group of intraregional education migrants. There are several methodological issues which need to be addressed in conducting research for this group of migrants. First, how does the particular type of migrant group and Asian context influence the research design? Second, in order to capture the scale and diversity of this migrant group, how should research be conducted across multiple sites? Third, how does a mixed method design allow researchers to learn more about the behaviour, practice and orientations of education migrants? Our paper aims to make contributions to the discussions on the methods of education migration research in Asia through answering these questions. We use research experiences and preliminary data from a multinational project to illustrate the issues involved in the selection of methods, research design and project management.

Since the launch of the One Belt and One Road Initiative (BRI) in 2013, the internationalisation of China’s tertiary education has entered a new stage. Central to the BRI is investment and strategic planning for talent cultivation, knowledge production, and transmission. This paper explains how the BRI redirects, reinforces, and intensifies China’s strategic planning and actions for internationalising its education. It adopts a policy analysis approach and reviews three key aspects of development and shifting emphasis of internationalisation under the impact of the BRI: international education networks along the Six BRI Economic Corridors, vocational colleges as new players in international education, and promotion of the Chinese language as a new global language. The analysis captures an important moment in which international education processes are being visibly altered through China’s strategies to take the lead in economic globalisation and to compete for a central place in the world via the BRI.

In government studies, electronic government has become a hot topic in recent decades. Many scholars believe that soon, the government might not be able to operate smoothly without the help of ICTs as the Internet has been overwhelming people's daily lives already. In analyzing people's behavioral factors towards adopting e-government services, most studies targeted the adult population, while those in the hard-to-reach groups are minimal. This study was designed especially to understand the behavioral factors of the younger generation aged between 18 and 24 and the senior citizens above 60 on their adoption of e-government services in Macao SAR. Sixteen in-depth interviews were conducted based on the semi-structured interview questions developed from the prior literature on the Theory of Planned Behavior and e-government studies. Six significant findings are yielded, which could serve as an important reference for policymakers designing e-government policy and promoting its implementation strategy. These behavioral factors also contribute empirical data to support the theoretical framework of TPB in the context of Macao SAR e-government services.

This essay presents a mapping of the historical concepts that contributed to the emergence of post-digital aesthetics and their connections to the concept of post-media in historical terms. It also analyzes the transition from techno-positivism to discourse of resistance against the effects of the capital technological industrial complex and how these advances in technology influence artistic discourses, practices and are the leverage of art and technology which is nothing more than a representation of the aesthetics of capital. Following art and capitalism as an ideology of innovation. Is proposed an unstinting theory about technology, geology, and the importance of these conditions to the post-digital aesthetics in terms of material disponible and conceptual articulation. Producing a reconfiguration of the post-digital conceptual approach as I propose beyond the dysfunctional aesthetics and connected with the concept of radical ecology centered in the usability of electronic garbage and technical obsolescent technologies in the arts.

In the face of the Covid-19 pandemic, the fashion industry was surprised and quickly had to adapt to digital media. However, the relationship between fashion and the multiplicity of screens is not new. Fashion emerged and took its first steps with Cinema, in Modernity. Although there are times when these two systems are further apart from each other, the alliance survived. To analyse contemporaneity, we take as main reference the studies of Gilles Lipovetsky, and his reflections on aesthetic capitalism. The fashion system has many Western fields of life, including art and technology. In this article we discuss how this relationship of fashion adapts and develops with aesthetic capitalism and post-digital art while we analyse representative artefacts from/about fashion. We propose to put the recent digital fashion artefacts in dialogue with post-digital aesthetics theories, discussing the blurred boundaries between the digital and the post-digital, and proposing the instantiation of a post-digital creation cycle applied to fashion artefacts.

In this essay we argue that, based on current scientific data, the most prudential course of future actions that an American conservative can take, is one that assumes what we call climate change alarmism. In order to establish this thesis, we first provide a basic overview of the relevant climate change science, as well as give an analysis of the alarmist and lukewarming dialectic (the two primary interpretations of the data). We then move to develop our environmental wager. Finally, following Roger Scruton, we end this work by proposing what sort of policies conservatives should endorse going further.