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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).

This book is a study of Eric Voegelin (1901-1985) and of the role played by metaxy in his vision of political philosophy. Metaxy, already defined by ...

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.

There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model.

La crisi del Covid-19 ha evidenziato il disagio e il divario sempre più ampio tra ricchi e poveri. La crisi finanziaria del 2007-2009 era già risuonata come un campa­nello d'allarme sulla necessità di

Current global shifts in education towards inclusive early childhood education are deeply engineered by the crisis of educational exclusion. In responding to exclusion, teachers have mainly utilized dominant western theories to plan and implement inclusive teaching. In this chapter, we draw on a non-western philosophy, a Nichiren Buddhist (Soka) philosophy, to provide a ‘kaleidoscopic’ lens through which to create inclusive educational learning spaces that engender full participation of all children. The Soka education philosophy is a humanist concept which can guide teachers when preparing to create inclusive education. The aims of this chapter are threefold: The first is an exploration of the Nichiren Buddhist (Soka) philosophy. The second aim is to highlight how this philosophy can enable teachers to unleash the unlimited potential of children in inclusive learning settings. Thirdly, we argue that grounding early childhood teacher education in this philosophy can help improve the effectiveness of inclusive educational experience for all children.

The article analyses the media system in Macao, a special administrative region of China that transitioned from Portuguese to Chinese sovereignty in 1999, becoming one of cities in the world with the largest number of published newspapers per capita. Combining historical research with the analysis of contemporary empirical data collected through interviews with journalists working on the ground, the research demonstrates how there is a long tradition of state control that goes back to the colonial era and that has assumed different forms, ranging from outright censorship to physical intimidation of journalists and economic dependence on the government. Limitations and control strategies imposed on news reporting during the Portuguese administration continue to be practiced today by the Chinese authorities. Even so, journalists operating on the Macao media market tend to overstate the level of freedom they are given, which can be attributed to media outlets being economically dependent on the state. Nevertheless, the level of freedom attributed to the press is today higher than it had been during the colonial period with some critical voices being allowed to reach the media. This needs to be understood in the context of what has been defined as the Chinese safety valve strategy.

Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease's behavior and the progression of the virus's natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with R2 score of 0.99 to short-term predictions and 0.93 to long-term predictions.

Abstract Much controversy has occurred in the past few decades regarding the nature of the sources, the petrogenetic processes, and the tectonic regime(s) of the Jurassic magmatism within the Southeast China magmatic belt. This study aims to contribute to the discussion with mineral chemistry, and whole-rock element and Sr-Nd-Hf-Pb isotopic geochemical data from granitic rocks and microgranular mafic enclaves from Macao, where two discrete groups of I-type biotite granites have been identified (referred to as Macao Group I [MGI] and Macao Group II [MGII]). It is proposed that the granitic magmas were generated by partial melting of infracrustal medium- to high-K, basaltic Paleoproterozoic to Mesoproterozoic protoliths (Nd depleted mantle model age [TDM2] = 1.7–1.6 Ga and Hf TDM2 = 1.8–1.6 Ga), triggered by underplating of hot mantle-derived magmas in an extensional setting related to the foundering of a previously flat slab (paleo–Pacific plate) beneath the SE China continent. The main differences between the two groups of Macao granites are attributed to assimilation and fractional crystallization processes, during which upper-crustal Paleozoic metasediments were variably assimilated by MGI magmas. This is evidenced by an increase in initial 87Sr/86Sr ratios with degree of evolution, presence of metasedimentary enclaves, and high percentage of zircon xenocrysts with Paleozoic ages. In addition, other processes like late-stage fluid/melt interaction and magma mixing also left some imprints on granite compositions (rare earth element tetrad effect plus non–charge-and-radius-controlled behavior of trace elements and decoupling between different isotope systems, respectively). The distribution of isotopically distinct granites in SE China reflects the nature of the two Cathaysia crustal blocks juxtaposed along the Zhenghe-Dapu fault.