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An increasing number of countries have launched their central bank digital currencies (CBDC) in recent years, but the economic impacts of CBDC adoption are underexplored. To empirically assess how CBDC adoption influences regional economic integration, this paper investigates the Greater Bay Area, where China carried out one of its first digital renminbi pilot programs. The Greater Bay Area provides a good example because the growing acceptance of digital renminbi in the area can potentially mitigate transaction costs and risks due to the exchange rate volatility of the Chinese renminbi, Hong Kong dollar, and Macao pataca. CBDC adoption can lead to greater real and financial integrations by facilitating cross-border trade in goods and services. This paper evaluates deviations from uncovered interest rate parity, purchasing power parity, and real interest rate parity across Guangdong, Hong Kong, and Macao based on monthly interest rate and price data from January 2016 to December 2022. The time series have mean values near zero, which validate the parity conditions and indicate high degrees of financial, real, and economic integrations. The Markov regime-switching regression model identifies three regimes: (1) pre-Covid, (2) post-Covid, and (3) post-CBDC. The Covid-19 outbreak brought lower integration and stability, but the launch of the CBDC restored some of the pre-Covid integration and stability. Regimes 1 and 2 are persistent, and transitions from Regime 3 back to Regime 1 are probable. Hence, this study finds evidence that CBDC adoption improves regional economic integration in the short and long run.
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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...
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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...
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The visual analysis of cardiotocographic examinations is a very subjective process. The accurate detection and segmentation of the fetal heart rate (FHR) features and their correlation with the uterine contractions in time allow a better diagnostic and the possibility of anticipation of many problems related to fetal distress. This paper presents a computerized diagnostic aid system based on digital signal processing techniques to detect and segment changes in the FHR and the uterine tone signals automatically. After a pre-processing phase, the FHR baseline detection is calculated. An auxiliary signal called detection line is proposed to support the detection and segmentation processes. Then, the Hilbert transform is used with an adaptive threshold for identifying fiducial points on the fetal and maternal signals. For an antepartum (before labor) database, the positive predictivity value (PPV) is 96.80% for the FHR decelerations, and 96.18% for the FHR accelerations. For an intrapartum (during labor) database, the PPV found was 91.31% for the uterine contractions, 94.01% for the FHR decelerations, and 100% for the FHR accelerations. For the whole set of exams, PPV and SE were both 100% for the identification of FHR DIP II and prolonged decelerations.
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Corporate leaders are constantly dealing with stress in parallel with continuous decision-making processes. The impact of acute stress on decision-making activities is a relevant area of study to evaluate the impact of the decisions made, and create tools and mechanisms to cope with the inevitable exposure to stress and better manage its impact. The intersection of leadership and neurosciences techniques is called Neuroleadership. In this work, an experiment is proposed to detect and measure the emotional arousal of two groups of business professionals, divided into two groups. The first one is the intervention/stress group, n=30, exposed to stressful conditions, and the control group, n=14, not exposed to stress. The participants are submitted to a sequence of computerized stimuli, such as watching videos, answering survey questions, and making decisions in a realistic office environment. The Galvanic Skin Response (GSR) biosensor monitors emotional arousal in real-time. The experiment design implemented stressors such as visual effects, defacement, unfairness, and time-constraint for the intervention group, followed by decision-making tasks. The results indicate that emotional arousal was statistically significantly higher for the intervention/stress group, considering Shapiro and Mann-Whitney tests. The work indicates that GSR is a reliable stress detector and may be useful to predict negative impacts on executive professionals during decision-making activities.
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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.
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The adoption of project management techniques is a crucial decision for corporate governance in construction companies since the management of areas such as risk, cost, and communications is essential for the success or failure of an endeavor. Nevertheless, different frameworks based on traditional or agile methodologies are available with several approaches, which may create several ways to manage projects. The primary purpose of this work is to investigate the adequate project management methodology for the construction industry from a general perspective and consider a case study from Macau. The methodology considered semi-structured interviews and a survey comparing international and local project managers from the construction industry. The interviews indicate that most construction project managers still follow empirical methods with no specific methodology but consider the adoption of traditional waterfall approaches. In contrast, according to the survey, most project managers and construction managers agree that the project's efficacy needs to increase, namely in planning, waste minimization, communication increase, and focus on the Client's feedback. In addition, there seems to be a clear indication that agile methodology could be implemented in several types of projects, including hospitality development projects. A hybrid development approach based on the Waterfall and Agile methodologies as a tool for the project management area may provide a more suitable methodology for project managers to follow.
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One of the purposes of this study is to evaluate how the choice of teaching method can be assisted by knowledge of a student’s personality type and learning style. The present research is undertaken in the context of Web 2.0 tools use within the Portuguese literature subject at Escola Portuguesa de Macao (EPM). The Felder-Soloman index of learning style (ILS) and the Myers-Briggs Type Indicator (MBTI) based upon Jung’s theory of psychological nature were used as measures of learning style and personality type with 8th grade EPM students. Descriptive and chi-squared correlation statistical results of the associations between personality and learners types are presented. These results are discussed positing that the knowledge of student’s personality traits and learning styles together may have a notable implication for teaching methods.
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For a long time, Geography did not hold a specific mathematical approach for any interpretation of space and this was the key reason why Geography degrees covered a wide variety of subjects such as demography, geology or topography to fulfill its curriculum. Yet from the 90’s, Geography finally created its own research agenda to meet four vital questions of any true geographer: “Where is …?”, “Is there a general spatial pattern?”, “What are the anomalies?” and “Why do these phenomena pursue certain spatial distribution?” The present review article addresses ten different spatial (point, regression and event) issues for learning and teaching aim where statistics play a major background role on the outcomes of myGeoffice© free Web GIS platform. These include cluster analysis, geographically weighted regression (GWR), ordinary least squares (OLS) regression, path analysis, minimum spanning tree, linear regression, space-time clustering and point patterns, for instance. Although the technical viewpoint of the algorithms is not explained at fully, this review paper makes a rather strong emphasis on the result’s interpretation, their respective meaning and when these techniques should be applied in a learning/teaching context.
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The knowledge of spatial distribution of grasshoppers can be very relevant for agricultural planning purposes. On the other hand, the comparison of spatial interpolators for efficiency and reliability reasons is also a key factor to understand interpolation maps outcomes (versus reality). At last, but not least, the use of open Web geographical tools to disseminate true spatial inferential methods to address spatial issues is still quite limited (if none) in high schools and universities, particularly in Geography subjects. If the latter can be addressed with myGeoffice©, the first issue will use the Utah, USA, dataset (58 samples) to layout the spatial distribution of grasshoppers and understand the counties that are more pro to this kind of agriculture infestation. Inverse Distance Weighted (IDW), Moving Average (MA), Multi-quadratic, Inverse Multi-quadratic and Nearest Neighbor (NN) will produce interpolated surfaces of grasshopper’s properties. Efficiency of spatial interpolators was assessed in this writing based on the prediction error’s statistics derived from the difference between the estimation and the real samples on a cross-validation procedure. Remarkably, results show that NN was the most accurate one when compared with the remaining deterministic approaches at sample’s locations.
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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.
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In 2000, the China-Africa relationship was further strengthened with the establishment of the Forum on China-Africa Cooperation (FOCAC). The FOCAC offers a platform for consultation and cooperation mechanisms aimed at deepening diplomatic, security, trade and investment relations between China and African countries. Later came the Belt and Road Initiative (BRI) in 2013, an international trade network initiated by China that connects the three continents of Asia, Europe and Africa. The BRI focuses on the following key areas: cultural exchange; policy coordination; facilities connectivity; trade and investment; and financial integration. The BRI shares development objectives similar to those of the United Nations’ Sustainable Development Goals (SDGs). In fact, the BRI implements part of the SDGs and provides a practical mechanism to strengthen the Sino-Africa relationship, which Africa can leverage to meet its Sustainable Goals. Africa is linked through the “Road” of the BRI plan and has received infrastructural projects funded by China to facilitate trade and integration of the national economies along the trading route. Through the establishment of Economic and Trade Zones which attracts investments from Chinese companies, and building infrastructures such as sea ports and railways, China through the BRI framework is helping Africa meet UN SGD Goal 9 concerning industry, innovation and infrastructure. A practical effect is that the BRI is helping African countries overcome the infrastructure gap, create jobs, acquire skills and promote integration between countries.
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It is argued that the role of the Chinese government to support the cross-border operations of Chinese firms is to assist these firms in overcoming their limited established brands, and their disadvantages in technology and managerial resources, which were also the reasons why such firms decided to enter emerging markets instead of developed markets. This strategic choice is preferred to avoid direct confrontation with established firms from developed countries endowed with superior ownership advantages. Therefore, Chinese resources seeking firms innovate by increasing investment in developing and emerging markets to develop unique ownership advantages for sustainable market development and competitive advantage. This research investigates the ownership advantages of resources seeking Chinese firms in these markets using the OLI theory. The paper contributes to explaining the specific advantages of Chinese MNEs when entering emerging markets. The study applied a two-stage qualitative methodology to examine Chinese firms operating in Nigeria. The first stage included an exploratory study based on interviews with key informants and experts while the second stage included a case study methodology. The study focused on resources seeking Chinese MNEs operating in Nigeria.
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This paper presents an algorithm that applies metrics derived from automatic QRS detection and segmentation in electrocardiogram signals for analyzing Heart Rate Variability to study the evolution of metrics in the frequency domain of a clinical procedure. The analysis was performed on three sets of elderly people, who are categorized according to frailty phenotype. The first set was comprised of frail elderly, the second pre-frail elderly, and the third robust elderly. Investigators from many disciplines have been encouraged to contribute to the understanding of molecular and physiological changes in multiple systems that may increase the vulnerability of frail elderly. In this work, the frailty phenotype can be characterized by unintentional weight loss, as self-reported, fatigue assessed by self-report, grip strength (measured directly), physical activity level assessed by self-report and gait speed (measured). The results obtained demonstrate the existence of significant differences between Heart Rate Variability metrics for the three groups, especially considering a higher preponderance for sympathetic nervous system for the group of robust patients in response to postural maneuver.
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This research explores innovation of traditional SMEs that do not actively invest in innovation. Elements of open innovation have been identified in these firms in their effort to build social capital which they perceive as pertinent to their businesses. The result of the research shows that instead of using social capital as means for innovation, the unintentional practice of open innovation has contributed to the development of social capital, which further opens up potential for globalization. As a result, a model of open innovation as means of developing social capital for enhancing globalization potential for SMEs was developed.
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Academic Units
- Faculty of Arts and Humanities (1)
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Faculty of Business and Law
- Alessandro Lampo (6)
- Alexandre Lobo (33)
- Angelo Rafael (2)
- Douty Diakite (8)
- Florence Lei (3)
- Ivan Arraut (8)
- Jenny Phillips (9)
- Sergio Gomes (1)
Resource type
United Nations SDGs
- 01 - No Poverty (1)
- 02 - Zero Hunger (1)
- 03 - Good Health and Well-being (8)
- 07 - Affordable and Clean Energy (1)
- 08 - Decent Work and Economic Growth (1)
- 09 - Industry, Innovation and Infrastructure (11)
- 11 - Sustainable Cities and Communities (2)
- 13 - Climate Action (2)
- 16 - Peace, Justice and Strong Institutions (1)
Publication year
- Between 2000 and 2024 (85)
- Unknown (1)