Your search

Malacca Portuguese Creole (MPC) (ISO 639-3; code: mcm), popularly known as Malacca Portuguese or locally as (Papiá) Cristang, belongs to the group of Portuguese-lexified creoles of (South)east Asia, which includes the extinct varieties of Batavia/Tugu (Maurer 2013) and Bidau, East Timor (Baxter 1990), and the moribund variety of Macau (Baxter 2009). MPC has its origins in the Portuguese presence in Malacca, and like the other creoles in this subset, it is genetically related to the Portuguese Creoles of South Asia (Holm 1988, Cardoso, Baxter & Nunes 2012).

The widespread W-(Mo)-Sn-Nb-Ta polymetallic mineralization in Southeast (SE) China is genetically associated with Mesozoic highly fractionated granitic rocks. Such rocks have enigmatic mineralogical and geochemical features, making its petrogenesis an intensely debated topic. To better understand the underlying magma evolution processes, petrography, garnet chemistry and whole-rock major and trace element data are reported for Jurassic highly fractionated granitic rocks and associated microgranite and aplitepegmatite dikes from Macao and compared with coeval similar granitic rocks from nearby areas in SE China. Despite the fact that the most evolved rocks in Macao are garnet-bearing aplite-pegmatite dikes, the existence of coeval two-mica and garnet-bearing biotite and muscovite granites displaying more evolved compositions (e.g, lower Zr/Hf ratios) indicates that the differentiation sequence reached higher degrees of fractionation at a regional scale. Although crystal fractionation played an important role, late-stage fluid/melt interactions, involving F-rich fluids, imparted specific geochemical characteristics to Macao and SE China highly fractionated granitic rocks such as the non-CHARAC (CHArge-and-RAdius-Controlled) behavior of trace elements, leading, for example, to non-chondritic Zr/Hf ratios, Rare Earth Elements (REE) tetrad effects and Nb-Ta enrichment and fractionation. Such process contributed to the late-stage crystallization of accessory phases only found in these highly evolved facies. Among the latter, two populations of garnet were identified in MGI (Macao Group I) highly fractionated granitic rocks: small grossular-poor euhedral grains and large grossular-rich skeletal garnet grains with quartz inclusions. The first group was mainly formed through precipitation from highly evolved Mn-rich slightly peraluminous melts under low-pressure and relatively low temperature (∼700 °C) conditions. Assimilation of upper crust metasedimentary materials may have contributed as a source of Mn and Al to the formation of garnet. The second group has a metasomatic origin related to the interaction of magmatic fluids with previously crystallized mineral phases and, possibly, with assimilated metasedimentary enclaves or surrounding metasedimentary strata. The highly fractionated granitic rocks in Macao represent the first stage in the development of granite-related W-(Mo)-Sn-Nb-Ta mineralization associated with coeval more evolved lithotypes in SE China.

Objective: This study highlights the potential of an Electrocardiogram (ECG) as a powerful tool for early diagnosis of COVID-19 in critically ill patients with limited access to CT–Scan rooms. Methods: In this investigation, 3 categories of patient status were considered: Low, Moderate, and Severe. For each patient, 2 different body positions have been used to collect 2 ECG signals. Then, from each collected signal, 10 non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) were extracted every 1s ECG time-series length to serve as entries for 19 Machine learning classifiers within a leave-one-out cross-validation procedure. Four different classification scenarios were tested: Low vs. Moderate, Low vs. Severe, Moderate vs. Severe and one Multi-class comparison (All vs. All). Results: The classification report results were: (1) Low vs. Moderate - 100% of Accuracy and 100% of F1–Score; (2) Low vs. Severe - Accuracy of 91.67% and an F1–Score of 94.92%; (3) Moderate vs. Severe - Accuracy of 94.12% and an F1–Score of 96.43%; and (4) All vs All - 78.57% of Accuracy and 84.75% of F1–Score. Conclusion: The results indicate that the applied methodology could be considered a good tool for distinguishing COVID-19’s different severity stages using ECG signals. Significance: The findings highlight the potential of ECG as a fast and effective tool for COVID-19 examination. In comparison to previous studies using the same database, this study shows a 7.57% improvement in diagnostic accuracy for the All vs All comparison.

This article discusses the new gaming law in Macau with emphasis on the critical aspects concerning the gaming operators, concession regime, and other regulatory obligations.1 Thanks to the gaming liberalization commenced in 2001,2 Macau has experienced tremendous economic growth. The past two decades have seen the rapid development of large-scale integrated resorts, and Macau now ranks among the world's major gaming jurisdictions.3 Policy and regulatory challenges have also emerged along with the growth of the junket-driven VIP business in casinos.4 With the recent amendment of Law No. 16/2001 and the subsequent enactment of Law No. 16/2022, Macau has strengthened the legal underpinnings of its system of gaming regulation to oversee various groups involved in casinos and their industry practices. The present study is among the first to review the scope and impact of the revised gaming law, and associated managerial and operational implications for Macau casinos. Topics covered include policy directions, concession requirements, industry participants, gaming taxes, and fair business practices. This study could provide insights into the “Macau 2.0” project and how casinos are to be operated and managed over the next decade. This article could also provide practical guidance for policy makers charged with formulating gaming policy and regulation in other jurisdictions.

In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account.

A growing focus on God’s mercy and forgiveness emerged in the wake of the recent Pontificates of John Paul II, Benedict XVI, and Francis. Our time with its multiple crises cries for healing, forgiveness, and the experience of God’s mercy. In social, political, and global terms, humanity craves for “lasting peace, born of the marriage of justice and mercy” (John Paul II, 2001, no. 15). The experience of God’s forgiveness, merciful healing and new life has been expressed many times in the Bible. But, theologically, it has never been formulated as directly as in Hosea 11:8, when God’s own heart becomes “turned over”, “converted” following the blaze of his own overwhelming compassion, paving the way for a fundamental spiritual transformation, rooted in forgiveness and mercy, that opens wellsprings of dignity, healing, and new life for all.

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.

We are delighted to present this special issue editorial for Neural Computing and Applications special issue on LatinX in AI research. This special issue brings together a collection of articles that explore machine learning and artificial intelligence research from various perspectives, aiming to provide a comprehensive and in-depth understanding of what LatinX researchers are working on in the field. In this editorial, we will introduce the overarching theme of the special issue, highlight the significance of the selected papers, and offer insights into the contributions made by the authors. The LatinX in AI organization was launched in 2018, with leaders from organizations in Artificial Intelligence, Education, Research, Engineering, and Social Impact with a purpose to together create a group that would be focused on “Creating Opportunity for LatinX in AI.” The main goal is to increase the representation of LatinX professionals in the AI industry. LatinX in AI Org and programs are volunteer-run and fiscally sponsored by the Accel AI Institute, 501(c)3 Non-Profit.

The land of the potiguara indians of Brazil: a social and political construction The space in which the potiguaras of Brazil live is, today as in the past, the result of a longterm process, many negotiations and well-managed refuges. Paradoxically, despite their recurrent discourse invoking the ancestry of their lands of origin, the Potiguara fight and continue to fight politically for the return to the spaces where their colonial refuge took place.

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.

China’s return to social work education, after a nearly 35-year absence, opened the door for partnerships like the 2012 China Collaborative partnership between the Council on Social Work Education’s (CSWE) Katherine A. Kendall Institute, the China Association of Social Work Education (CASWE) and the International Association of Schools of Social Work (IASSW). The University of Alabama School of Social Work (UA SSW) was selected to participate in the collaborative and was connected to the Southwest China Region, specifically partnered with Yunnan University. This manuscript will share the strategies used to engage faculty and students from each partnering institution. Data collected by UA SSW over the five-year partnership will be utilised to contribute to the discussion of the extent to which Western knowledge and theory about social work education might usefully be applied to the Chinese context.

Stock movement prediction is one of the most challenging problems in time series analysis due to the stochastic nature of financial markets. In recent years, a plethora of statistical methods and machine learning algorithms were proposed for stock movement prediction. Specifically, deep learning models are increasingly applied for the prediction of stock movement. The success of deep learning models relies on the assumption that massive training data are available. However, this assumption is impractical for stock movement prediction. In stock markets, a large number of stocks do not have enough historical data, especially for the companies which underwent initial public offering in recent years. In these situations, the accuracy of deep learning models to predict the stock movement could be affected. To address this problem, in this paper, we propose novel instance-based deep transfer learning models with attention mechanism. In the experiments, we compare our proposed methods with state-of-the-art prediction models. Experimental results on three public datasets reveal that our proposed methods significantly improve the performance of deep learning models when limited training data are available.

Peer-rewieved journal

Education for learners with special education needs has become one of the major concerns of education policies in every corner of the world. In Macau, however, the transformation of schools into inclusive environments is reported to be slow because many teachers in Macau have not accepted the key values of inclusive education and possess little knowledge of their responsibilities as inclusive education teachers. Despite being nonempirical, the aim of this article is twofold: to inform inclusive education teachers, especially those in Macau and other developing regions, of the necessary knowledge, skills and strategies of working collaboratively with parents of children with SEN and provide policy makers concerned with practical ideas of designing effective professional development programmes for teachers working in the inclusive environment. The ultimate aim is to ensure that children with SEN benefit from an education process that includes quality learning opportunities.

Macao inhabit a population of 683,100. The birth rate has been dropping while the death rate has risen compared to two years ago. Cemeteries are becoming crowded, and burial spots are demanding. In this case, video calls and social media can be the solution. How about our beloved ancestors? Can we video call them on their memorial days? This paper presents a VR experience of immersing oneself in the 3D VR of the Chapel of St. Michael of Macao to create a peaceful atmosphere for grave mourning. The chapel is also a personal space where we can be truly isolated in serenity. It is a retreat to pray, disconnect, and reconnect to the beloved deaths that may not be buried in an easily accessible location. The authors propose a possible future of mourning our loved ones through virtual reality and telepresence: an immersive experience connected with Macao's extraordinary and cultural unicity.

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.

Noise pollution is increasingly present in aquatic ecosystems, causing detrimental effects on growth, physiology and behaviour of organisms. However, limited information exists on how this stressor affects animals in early ontogeny, a critical period for development and establishment of phenotypic traits. We tested the effects of chronic noise exposure to increasing levels (130 and 150 dB re 1 μPa, continuous white noise) and different temporal regimes on larval zebrafish (Danio rerio), an important vertebrate model in ecotoxicology. The acoustic treatments did not affect general development or hatching but higher noise levels led to increased mortality. The cardiac rate, yolk sac consumption and cortisol levels increased significantly with increasing noise level at both 3 and 5 dpf (days post fertilization). Variation in noise temporal patterns (different random noise periods to simulate shipping activity) suggested that the time regime is more important than the total duration of noise exposure to down-regulate physiological stress. Moreover, 5 dpf larvae exposed to 150 dB continuous noise displayed increased dark avoidance in anxiety-related dark/light preference test and impaired spontaneous alternation behaviour. We provide first evidence of noise-induced physiological stress and behavioural disturbance in larval zebrafish, showing that both noise amplitude and timing negatively impact key developmental endpoints in early ontogeny.

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.