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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
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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
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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
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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
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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.
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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.
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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.
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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
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"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"--
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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.
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<abstract><p>About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.</p></abstract>
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Online shopping in Macau has developed rapidly in recent years. And the success of Taobao is significantly hard to not notice. Its’ sales are breaking the record every year. However, there are a lot of negative comments towards Taobao. Various researches and data have shown that live-streaming is one of the biggest contributions towards Taobao’s sales and record breaking. This research aims to investigate deeply to understand how Taobao counters those issues and the role of live-streaming in relation to it. Based on a review of the literature in the relevant areas , qualitative methodology is adopted after thorough considerations. A small sample size of 10 were selected to conduct semi-structured in-depth interviews and the participants agreed to respond to answer the original interview questions and the follow-up questions. Analysis of the responses demonstrated e-customer service is the most influential variable towards repurchase intention. Live-streaming strategy can effectively and directly reduce constomer’s uncertainty of products and increase the efficiency of responsiveness. And product uncertainty and responsiveness speed are variables that impact purchase intention. The result demonstrated live-streaming's effectiveness in combating multiple negative aspects of Taobao and strengthening the positive aspects. On this basis, live-streaming is an impactful method to combat Taobao. In addition, e-service in terms of sufficiency of the staff’s communication skill have been found important towards customer’s satisfaction. A gap related to such an issue has been recommended in the further research recommendations along with other factors or sample groups, which are needed to explore deeply in the future
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School-age children and adolescents face several psychological conditions frequently associated with negative consequences on behavioral and mental problems. Their level of mental resilience may affect their responses to academic or interpersonal issues and coping with challenges, which in turn affects their mental health. This study aims to characterize the current status of the psychopathology and resilience of secondary students and to analyze the relationship between psychopathology and resilience in a sample of 80 girls aged 12–18 was selected by cluster sampling from one private secondary school with six grades in Macao. In this study, we used the Achenbach System of Empirically Based Assessment (ASEBA) to assess behavioral and emotional problems and the Resilience Scale for Chinese Adolescents to assess resilience. A total of 78 valid questionnaires were obtained for CBCL, 78 for TRF, 80 for YSR, and 77 for Resilience Scale and data were analyzed by using SPSS. The results reveal that clinical prevalence of Total Problems (YSR, 27.5% > CBCL, 19.2% > TRF, 15.4%) and Internalizing Problems (YSR, 22.5% > CBCL, 17.9% > TRF, 11.5%) from the perspective of adolescents was higher than that from the perspectives of parents and teachers. Senior students exhibited higher frequency on the borderline clinical range than Junior students. (χ2(2, N=80) =14.56, p<.001). The average score of resilience is 3.24±0.51, which is above the middle level. Regarding the YSR scale and Resilience scale, we found that the score of Affect Control is significantly negatively correlated with the score of Internalizing Problems (r = -.354, p<.01). Family Support is also significantly negatively correlated with the score of Internalizing Problems (r = -.302, p<.01). Good affect control and family support can reduce various emotional and behavioral problems. The results of the study found the resilience level can negatively affect internalizing problem behaviors and externalizing problem behaviors. The results are promising and can give clues for preventing and promoting measures regarding mental health issues to both family and school education contexts, as creating a sustainable development strategy of improving adolescents’ mental resilience quality
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Technology is an essential and valuable tool nowadays. New technology and innovations are introduced to the public day by day. Metaverse will be a new trend, as the Macau government has been trying to promote this technology since the COVID-19 pandemic is still here. Before this technology goes deeply to the public, this paper attempts to examine the consumer’s perspective of Metaverse and understand the cognition of Macau people about Metaverse. And bring out what factors and conditions will make them accept this technology. A qualitative research method will address the questions and problem to understand consumer perspectives towards this technology. Entrepreneurs, managers, and professionals will be invited to take part in this research. Ten interviews will be carried out in this research for data analysis, which may provide a board overview of this technology in Macau and give recommendations to local entrepreneurs. In conclusion, consumers think it is still not a well-developed technology and is not globally used at this stage. In contrast, if this technology is ready-to-use, it will be a helpful assistant in their businesses. They expect the metaverse to be fully developed in ten or more years
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The insurance companies in Macau are potentially facing a crisis of high turnover rates, exacerbated by the limited studies conducted on the insurance industry to address this issue, as the gaming and travel industries are currently experiencing a significant demand for human resources due to economic recovery. In light of this, a study was conducted to better understand the attitudes of 105 currently employed insurance agents in Macau insurance companies, specifically their commitment and thoughts of staying or leaving their employers. The study examined several independent variables (distributive justice, perceived organizational support (POS), job satisfaction, and caring climate) in relation to insurance agents’ turnover intention and the role of affective commitment. The aim was to determine the association between these variables and turnover intentions among insurance agents. The snowballing methodology was employed to effectively engage with the insurance agents and gain insights into their perspectives. The results of the statistical analysis revealed positive correlations between all independent variables and affective commitment. Job satisfaction and POS were identified as strong positive predictors. Additionally, mediated regression analyses demonstrated that affective commitment significantly mediated the relationship between all independent variables and turnover intention. Finally, the study provides implications for insurance company management to address and reduce the high turnover rate. Furthermore, the importance of future orientation is further discussed
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