Your search
Results 2,018 resources
-
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.
-
Microbial and hydrothermal venting activities on the seafloor are important for the formation of sediment-hosted stratiform sulfide (SHSS) deposits. Fe isotopic compositions are sensitive to both microbial and hydrothermal activities and may be used to investigate the formation of these deposits. However, to the best of our knowledge, no Fe isotopic studies have been conducted on SHSS deposits. In the Devonian Dajiangping SHSS-type pyrite deposit (389 Ma), South China, laminated pyrite ores were precipitated from exhalative hydrothermal fluids, whereas black shales were deposited during intervals with no exhalation. Pyrite grains from black shales mostly display positive δ56Fe-py (0.01–0.73‰), higher than marine sediments (ca. 0‰), due to pyrite deriving Fe from basinal shuttled Fe(III) (hydr-)oxides and slowly crystallizing in pores of sediments with equilibrium fractionation, except for negative δ56Fe-py (−0.17‰ to −0.24‰) of two samples caused by mixing of Fe from underlain laminated ores. The positive δ34S-py (3.50–24.5‰) of black shales reflect that sulfur of pyrite originated from quantitative reduction of sulfate in closed pores of sediments. In contrast, pyrite grains of laminated ores have negative δ56Fe-py (−0.60‰ to −0.21‰), which were not only inherited from the negative δ56Fe of hydrothermal fluids but also caused by kinetic fractionation during rapid precipitation of a pyrite precursor (FeS) in hydrothermal plumes. These ores have negative δ34S-py (−28.7‰ to −1.82‰), because H2S for pyrite mineralization was produced by bacterial sulfate reduction (BSR) in a sulfate-rich seawater column or shallow sediments. The δ56Fe-py values of laminated ores co-vary positively with δ34S-py and δ13C-carbonate along the ore stratigraphy, with δ13C-carbonate values ranging from −12.0‰ to −2.50‰. However, they correlate negatively with aluminum-normalized total organic carbon (TOC/Al2O3). Organic carbon is thus considered to enhance the production of H2S by BSR activities, increase pyrite precipitation rates and promote the expression of kinetic fractionation of Fe isotopes. Intriguingly, in the ore units with vigorous hydrothermal venting activities, δ56Fe-py, δ34S-py and δ13C-carbonate values display a consistently increasing trend. Such results suggest that venting hydrothermal fluids significantly inhibited the H2S production of BSR, which then reduced the pyrite crystallization rate and decreased the kinetic fractionation of Fe isotopes. Our study reveals that the formation of SHSS deposits relies on H2S from microbial activities and metals from hydrothermal exhalation on the seafloor, but that vigorous exhalation can inhibit microbial activities and thus sulfide precipitation rates. The integrated use of Fe, S, and C isotopes can effectively elucidate these dynamic interactions between hydrothermal venting and microbial activities during the formation of SHSS deposits.
-
As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases. Our optimal model SSL-GCN is hosted on an online server accessible through: https://app.cbbio.online/ssl-gcn/home.
-
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.
-
This paper aimed to build up the theorical and conceptual understanding of future forecasting study of Macau’s GDP and Gross Gaming Revenue (GGR) by co-movement of economic indicators. Macau GDP and GGR showed co-movements with a number of time series economic indicators, including China’s exports and imports, China’s manufacturing PMI, non-manufacturing PMI, China's electricity production growth, share price of some Macau’s gaming operators, etc. These time series data can be found in statistics departments of China, Macau and Hong Kong, stock exchanges, and international organizations such as the International Monetary Fund (IMF), the World Bank, the World Trade Organization (WTO). Burns and Mitchell’s study in 1946 identified co-movements between economic indicators and being further carried out and developed leading, coincident and lagging indicators, which is essential for future econometric models and nowcasting techniques developments to study these co-movements. In particular, with the proper application of nowcasting techniques, future studies can exploit the data of leading and coincident economic indicators to forecast Macau’s GDP and GGR within an acceptable level of error. Since Macau is a “monotown,” where the gaming revenue makes a significant contribution to the economy. The forecasting of gaming revenue is crucial as it aids the gambling and tourism industries in preparing supply and provides information to policymakers to plan for the near future. This research also contributes to understand Macau’s economy by investigating its internal and external economic variables.
-
Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.
-
With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2% and 66.6%, respectively.
-
Listening to children’s voices is still not considered an essential part of education in some schools, including many in Asian countries. The authority of schools and teachers is still highly valued under the continued influence of Confucian Heritage Culture in many Asian schools, including a significant number in Macao. Teachers in international schools in Asian countries often experience some difficulties when communicating with young children because of their low English proficiency and the traditional views supported by many parents who grew up with the Confucian Heritage Culture, which encourages children to be quiet in the classroom to be good listeners. This Action research took fifteen months between two school years, 2018- 2019 and 2019-2020, with two groups of four and five-year-old students in a kindergarten classroom. Documentation posters were created for young children to use the next morning to reflect on their learning. The pedagogy of listening and pedagogical documentation from the Reggio Emilia approach were implemented to discover and record young children’s ideas and interests, work with daily documentation posters, and help them reflect on documentation posters to improve their learning and develop their higher-order thinking skills. Photos and videos, observation notes with the children’s comments, documentation posters, and reflective discussions were used as interventions to collect the children’s ideas and record their learning activities. The children learned to use documentation posters to remember, think, share, and improve their learning. The children’s comments from Learning Centres, recess, and reflective discussions were used to examine their understanding of learning and higher-order thinking skills. During one Pilot Cycle and three structured data collection cycles, the children demonstrated improvement in learning for each learning project and development of their thinking skills both with and without the teacher’s support. The children demonstrated higher-order thinking skills more often from Learning Centres and recess when they had to solve problems. They also demonstrated higher-order thinking skills more often during the whole group reflective discussions than in small group reflections, when a bigger number of children joined or when they had enough time to think. The thinking skills when children were reflecting were observed to concentrate on remembering and understanding as they focused on remembering and sharing the previous day’s work. The children’s other higher-order thinking skills did not show an increase in frequency during reflective discussions. However, the children demonstrated active engagement and a range of higher-order thinking skills when the teacher asked openended questions and provided support and comments to help them to connect their learning to their past experiences. Findings indicated that the children’s learning from each Learning Centre showed change and improvement during their play over time according to their interests, indicated by their material use and comments. The research was limited by its small number of participants within their age group due to convenience sampling and the children’s relatively limited ability to demonstrate higher-order thinking skills. This study has shown how teachers could help children use daily documentation posters to develop their learning and thinking skills by visualizing their ideas and the teacher’s important role in supporting children’s learning with active listening and support in the classroom
-
In this chapter, a mathematical model explaining generically the propagation of a pandemic is proposed, helping in this way to identify the fundamental parameters related to the outbreak in general. Three free parameters for the pandemic are identified, which can be finally reduced to only two independent parameters. The model is inspired in the concept of spontaneous symmetry breaking, used normally in quantum field theory, and it provides the possibility of analyzing the complex data of the pandemic in a compact way. Data from 12 different countries are considered and the results presented. The application of nonlinear quantum physics equations to model epidemiologic time series is an innovative and promising approach.
-
The doctrine of original sin gives people the impression that the goodness of human nature is under-evaluated in the Christian theological tradition. The Chinese philosopher Mencius is famous for his teaching on the goodness of human nature. Reading Mencius and Thomas Aquinas side by side, this article argues that the Mencian teaching on human nature brings us to affirm the goodness of human nature by recovering the significance of the image of God for the Christian doctrine of human nature. If we seek the goodness of human nature in the possibilities to become good, it is natural to see that even in the fallen state the possibilities of becoming like God remain in human nature imprinted with the image of God. It is open to the culmination of a gradual progression to its perfection.
Explore
USJ Theses and Dissertations
- Doctorate Theses (58)
- Master Dissertations (1,043)
Academic Units
- Domingos Lam Centre for Research in Education (1)
- Faculty of Arts and Humanities (259)
- Faculty of Business and Law (194)
- Faculty of Health Sciences (40)
- Faculty of Religious Studies and Philosophy (91)
- Institute for Data Engineering and Sciences (29)
- Institute of Science and Environment (122)
- Library (3)
- Macau Ricci Institute (17)
- School of Education (180)
Resource type
- Blog Post (3)
- Book (58)
- Book Section (125)
- Conference Paper (129)
- Document (4)
- Encyclopedia Article (1)
- Film (1)
- Journal Article (412)
- Magazine Article (17)
- Manuscript (1)
- Newspaper Article (34)
- Preprint (4)
- Presentation (60)
- Radio Broadcast (5)
- Report (62)
- Thesis (1,100)
- TV Broadcast (1)
- Web Page (1)
United Nations SDGs
- 01 - No Poverty (1)
- 02 - Zero Hunger (1)
- 03 - Good Health and Well-being (33)
- 04 - Quality Education (17)
- 05 - Gender Equality (1)
- 07 - Affordable and Clean Energy (3)
- 08 - Decent Work and Economic Growth (6)
- 09 - Industry, Innovation and Infrastructure (22)
- 10 - Reduced Inequalities (1)
- 11 - Sustainable Cities and Communities (9)
- 12 - Responsable Consumption and Production (4)
- 13 - Climate Action (5)
- 14 - Life Below Water (18)
- 15 - Life on Land (4)
- 16 - Peace, Justice and Strong Institutions (2)
Cooperation
Student Research and Output
Publication year
-
Between 2000 and 2024
- Between 2000 and 2009 (155)
- Between 2010 and 2019 (961)
- Between 2020 and 2024 (902)