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Mangrove forests are one of the most ecologically valuable ecosystems in the world and provide a wide variety of ecosystem services to coastal communities, including cities. Macao, a highly urbanized coastal city located on the southern coast of China west of the Pearl River, is home to several species of mangroves with many associated flora and fauna. Mangrove forests in Macao are vulnerable to threats due to pressure from rapid and massive urban developments in the area, which led to mangrove loss in the past decades. To address this issue, the local authorities established special Ecological Zones for the management of the local mangroves. To reinforce local conservation efforts, educating the local population about the value of mangroves, especially school students, is of utmost importance. To evaluate the impact of environmental education activities on the environmental orientation, knowledge, and values of students toward mangrove conservation in Macao, a quasi-experimental study was undertaken. The effectiveness of a mangroves exhibition and field visit were evaluated using the New Environmental Paradigm (NEP) Scale—Macao version in a group of local school students who participated in the activities. Overall, the results provided consistently positive evaluations of the impact of the environmental education program. The strongest improvements were found in the students’ pro-environmental orientations, knowledge about mangroves, and value for environmental protection.
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Fast and efficient malaria diagnostics are essential in efforts to detect and treat the disease in a proper time. The standard approach to diagnose malaria is a microscope exam, which is submitted to a subjective interpretation. Thus, the automating of the diagnosis process with the use of an intelligent system capable of recognizing malaria parasites could aid in the early treatment of the disease. Usually, laboratories capture a minimum set of images in low quality using a system of microscopes based on mobile devices. Due to the poor quality of such data, conventional algorithms do not process those images properly. This paper presents the application of deep learning techniques to improve the accuracy of malaria plasmodium detection in the presented context. In order to increase the number of training sets, deep convolutional generative adversarial networks (DCGAN) were used to generate reliable training data that were introduced in our deep learning model to improve accuracy. A total of 6 experiments were performed and a synthesized dataset of 2.200 images was generated by the DCGAN for the training phase. For a real image database with 600 blood smears with malaria plasmodium, the proposed Deep Learning architecture obtained the accuracy of 100% for the plasmodium detection. The results are promising and the solution could be employed to support a mass medical diagnosis system.
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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).
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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.
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The Black-Scholes equation is famous for predicting values for the prices of Options inside the stock market scenario. However, it has the limitation of depending on the estimated value for the volatility. On the other hand, several Machine learning techniques have been employed for predicting the values of the same quantity. In this paper we analyze some fundamental properties of the Black-Scholes equation and we then propose a way to train its free-parameters, the volatility in particular. This with the purpose of using this parameter as the fundamental one to be learned by a Machine Learning system and then improve the predictions in the stock market.
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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.
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