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Reaction of ultramafic rocks with seawater and subsequent serpentinization has been considered one of the most important factors controlling the formation of ultramafic-hosted seafloor massive sulfide (UM-SMS) deposits. However, the mineralization processes responsible for these deposits remain poorly understood, in particular because they are less abundant as compared with their basaltic counterparts. In this work, serpentinites with different alteration grades collected at the Tianzuo hydrothermal field (THF), Southwest Indian Ridge, were studied. Mineralogical and chemical analyses were performed in the secondary opaque minerals resulting from serpentinization to understand the role of this process during the formation of UM-SMS deposits. Our results show that these opaque minerals mainly consist of magnetite, hematite, pentlandite, and minor pyrite, suggestive of high but varying oxygen and sulfur fugacities. The hematite is characterized by an enrichment in Mg, Si, Ni, and Co as compared with magnetite. Pentlandite associated with hematite has elevated and consistent Ni contents as compared with that associated with magnetite. These results indicate that breakdown and decomposition of primary silicate and sulfide minerals during serpentinization has controlled the sources of ore-forming materials. Concentrations of Te are variable and show a positive correlation with Ni in pentlandite associated with magnetite or hematite, suggesting that gabbroic intrusions provided additional material to the hydrothermal system. Oxidation and sulfidation conditions are ideal for the formation of trisulfur ion S3− in THF, which can significantly improve the capability of hydrothermal fluids for leaching ore-forming metals from the wall rocks, promoting the formation of THF. In addition of reduced systems, hydrothermal fluids with high oxygen and sulfur fugacities triggered by extensive seawater infiltration can most likely also develop in ultramafic-hosted systems. These results suggest that the areas with well-developed fractures are promising candidates for further exploration of UM-SMS deposits along mid-oceanic ridges.

Electronic government is increasingly dominant in the study of public administration. In analysing people's behavioural factors towards the adoption of e-services, most previous studies targeted the adult population, while those on government employees are minimal. Government employees have an essential function in the process of government operation; they can be regarded as the principal medium of communication between the service provider (government) and the end-users (citizens). This study was designed to understand the government employees' behavioural factors on their intentions towards adopting e-government services. A set of semi-structured interview questions was developed based on the prior literature on the Theory of Planned Behaviour (TPB) and e-government studies. Ten in-depth interviews were conducted in Macao SAR (Special Administrative Region). In addition to analysing the three primary constructs of TPB, the factor of Trust and some enablers and hindrances were identified. Significant findings were yielded while investigating how the government employees perceived the e-services and how they regarded the general public's perception of this issue. This contextualisation would help policymakers look at this issue from different perspectives and design feasible interventions according to group alignment strategies.

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.

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 […]

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.

China’s economy has entered a critical period of structural adjustment. The developing green industries and the transforming traditional industries have increasing demand for finance, making ""green finance"" increasingly essential. While China's green finance is in the development stage, some newly developed zones serve as pilots for the launch of green financial products. An example is Tongzhou District of Beijing, which aims to expand Beijing’s space, promote the coordinated development of Beijing-Tianjin-Hebei, and explore the optimal development mode of the densely populated economic areas. This thesis aims to study consumer acceptance of green financial technology (fintech) in the case of Tongzhou District. This thesis extended the commonly applied theoretical model for the problem of study, the Energy Augmented Technology Acceptance Model (EA-TAM), to analyze the impacts of perceived usefulness, perceived ease of use, attitude toward use, intention, usage intention, environmental awareness, and green knowledge on the acceptance of green fintech in Tongzhou District. The survey collected 403 valid responses from people that had been active in Tongzhou District. The quantitative analysis is based on structural equation modeling techniques, including reliability analysis, validity analysis, standard method deviation test, and hypothesis testing. The analytical results show that all the hypothesized factors are significant. In addition, the sample is divided into different gender groups and education groups, so that the impacts of the socio-demographic characteristics can be explored. Males’ environmental awareness and green knowledge are insignificant in determining their acceptance of green fintech. The low-educated group’s acceptance of green fintech does not significantly depend on environmental awareness and perceived usefulness

The present study aimed to analyse the differences in the internalising problems (anxiety, depression, somatic complaints), assessed by different informants (teachers and students), according to the level of academic achievement and school adaptation level in secondary students. Furthermore, we examine the gender difference in the level of internalising symptoms. Finally, we analyzed the differences between teacher-rated and adolescents' self-reported internalising symptoms. The Achenbach System of Empirically Based Assessment (ASEBA) was used for collecting informants’ data. The sample consisted of 882 secondary students (349 males and 473 females), while 50 came from public schools and 772 from private schools. No significant differences are found in internalising problems according to the level of academic achievement from both teachers’ and students’ perspectives. Generally, students who are well-adapted to the school context have the least symptoms of internalising problems compared to average and less-adapted groups from the teachers' perspective. In addition, from students’ perspectives, adolescent females present more internalising problems than males. Finally, teachers rated fewer internalising problems when compared to the students. In conclusion, the low level of awareness of teachers towards the internalising problems of students arouse attention. It is suggested that teachers should attend professional development programs in order to address to students’ internalising problems

Teacher turnover is a global issue that has not received much research attention in Macau despite studies indicating that teachers in the region experience high levels of stress and burnout. Given that private school teachers account for a significant proportion (88.6%) of the non-tertiary education system in Macau, this qualitative study focused on this specific group who voluntarily resigned from their positions. Through in-depth interviews with 13 former teachers from different kindergartens, primary, and secondary schools, the research identified 50 reasons categorized into 15 factors under three categories. Although schoolrelated factors account for the most, personal reasons were found to be the primary driver. The findings of the study highlight the complex nature of teacher turnover which can be attributed to both single and multiple factors, in both direct and indirect forms. The factors could also interplay in both unidirectional and mutual relationships. A conceptual framework for teacher turnover in Macau was developed to address the 15 contributing factors and the complex interplay of these factors. This study could fill the gap in the literature and serve as a valuable resource for policymakers and school leaders seeking to reduce teacher attrition rates in the region

The primary research focus of this dissertation revolves around the concept of a "plugin" program. It raises a fundamental question about whether a building can attain long-term usability through metabolic flexibility (plugin units and their reconfigurable space), promoting adaptability (accommodating various program transfers), and meeting sustainable future criteria. Specifically, this dissertation inquires whether this "plugin" building design, with its reconfigurable units and metabolic system, can adapt to different spatial programs and become sustainable architecture

The use of computational tools for medical image processing are promising tools to effectively detect COVID-19 as an alternative to expensive and time-consuming RT-PCR tests. For this specific task, CXR (Chest X-Ray) and CCT (Chest CT Scans) are the most common examinations to support diagnosis through radiology analysis. With these images, it is possible to support diagnosis and determine the disease’s severity stage. Computerized COVID-19 quantification and evaluation require an efficient segmentation process. Essential tasks for automatic segmentation tools are precisely identifying the lungs, lobes, bronchopulmonary segments, and infected regions or lesions. Segmented areas can provide handcrafted or self-learned diagnostic criteria for various applications. This Chapter presents different techniques applied for Chest CT Scans segmentation, considering the state of the art of UNet networks to segment COVID-19 CT scans and a segmentation experiment for network evaluation. Along 200 epochs, a dice coefficient of 0.83 was obtained.

COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94.

This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity.

Although there is a substantial body of research on the second language acquisition of adults, there is little specific research on the learning experiences of senior and very senior adults. This thesis investigates and discovers the experience of being a senior from a traditional Confucian Heritage Culture aged between 55 and 75 years old, learning English as a foreign language through various interventions, including, the introduction of an adapted version of synthetic phonics to improve pronunciation, alongside the use of andragogical and geragogical principles to accommodate and encourage the development of agency and self-directed learning. This research adopted a case study methodology to investigate the lived experiences of seniors, and investigated the participants’ subjective constructions of the situation, learning experiences, challenges, circumstances, needs, and wants with regard to the situation. Therefore, an open and exploratory case study design was selected to understand the participants and report the findings. Furthermore, this thesis identifies the challenges faced by senior and very senior learners who are post-work and post-family rearing to make recommendations from the findings to complement, enhance and empower their learning

"Student engagement is a catch-all term, irresistible to educators and policy makers, and serving many agendas and purposes. This ground-breaking book provides a powerful theory of student engagement, rooted in critical theory and social justice. It sets out a compelling argument for student engagement to promote social justice and to repel neoliberalism in, and through, higher education, addressing three key questions: -Student engagement in what? -Student engagement for what? -Student engagement for whom? The answers draw on Habermas, Honneth, Gramsci, Foucault, and Giroux in examining ideology, power, recognition, resistance, and student engagement, with examples drawn from across the world. It sets out key features, limitations and failures of neoliberalism in higher education, and indicates how student engagement can resist it. Student engagement calls for higher education institutions to be sites for challenge, debate on values and power, action for social justice, and for students to engage in the struggle to resist neoliberalism, taking action to promote social justice, democracy, and the public good. This book is essential reading for educators, researchers, managers and students in higher education, social scientists and social theorists. It is a call to reawaken higher education for social justice, human rights, democracy and freedoms"--

Continuous cardiac monitoring has been increasingly adopted to prevent heart diseases, especially the case of Chagas disease, a chronic condition that can degrade the heart condition, leading to sudden cardiac death. Unfortunately, a common challenge for these systems is the low-quality and high level of noise in ECG signal collection. Also, generic techniques to assess the ECG quality can discard useful information in these so-called chagasic ECG signals. To mitigate this issue, this work proposes a 1D CNN network to assess the quality of the ECG signal for chagasic patients and compare it to the state of art techniques. Segments of 10 s were extracted from 200 1-lead ECG Holter signals. Different feature extractions were considered such as morphological fiducial points, interval duration, and statistical features, aiming to classify 400 segments into four signal quality types: Acceptable ECG, Non-ECG, Wandering Baseline (WB), and AC Interference (ACI) segments. The proposed CNN architecture achieves a $$0.90 \pm 0.02$$accuracy in the multi-classification experiment and also $$0.94 \pm 0.01$$when considering only acceptable ECG against the other three classes. Also, we presented a complementary experiment showing that, after removing noisy segments, we improved morphological recognition (based on QRS wave) by 33% of the entire ECG data. The proposed noise detector may be applied as a useful tool for pre-processing chagasic ECG signals.