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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.

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.

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.

AimsVascular calcification is a common clinical complication of chronic kidney disease (CKD), atherosclerosis (AS), and diabetes, which is associated with increased cardiovascular morbidity and mortality in patients. The transdifferentiation of vascular smooth muscle cells (VSMCs) to an osteochondrogenic phenotype is a crucial step during vascular calcification. The transcription factor CCAAT/enhancer-binding protein alpha (C/EBPα) plays an important role in regulating cell proliferation and differentiation, but whether it regulates the calcification of arteries and VSMCs remains unclear. Therefore, this study aims to understand the role of C/EBPα in the regulation of vascular calcification.Methods and ResultsBoth mRNA and protein expression levels of C/EBPα were significantly increased in calcified arteries from mice treated with a high dose of vitamin D3 (vD3). Upregulation of C/EBPα was also observed in the high phosphate- and calcium-induced VSMC calcification process. The siRNA-mediated knockdown of C/EBPα significantly attenuated VSMC calcification in vitro. Moreover, C/EBPα depletion in VSMCs significantly reduced the mRNA expression of the osteochondrogenic genes, e.g., sex-determining region Y-box 9 (Sox9). C/EBPα overexpression can induce SOX9 overexpression. Similar changes in the protein expression of SOX9 were also observed in VSMCs after C/EBPα depletion or overexpression. In addition, silencing of Sox9 expression significantly inhibited the phosphate- and calcium-induced VSMC calcification in vitro.ConclusionFindings in this study indicate that C/EBPα is a key regulator of the osteochondrogenic transdifferentiation of VSMCs and vascular calcification, which may represent a novel therapeutic target for vascular calcification.

The research explores the perceptions of five secondary school students with special education needs (SEN) about their participation in learning, group membership, and agency within an inclusive school in Macau SAR. This goal is achieved by using students' voices documented in open-ended interviews and is underpinned by the conceptual framework of heutagogy. The aim is to shed light on students' perceptions on school effectiveness in supporting their needs through successful participation and agentic possibilities. Findings showed that students were more prone to social rejection and being isolated or bullied than their peers. They were struggling to feel included or participate, their needs were only partially being met, and they had few opportunities to exert influence on their educational trajectories. Recommendations are provided to assist educators and schools in enhancing students with SEN to connect to the learning process and community, with the provision of appropriate learning adjustments and more active approaches to ensure their acceptance by mainstream students, including the formation of coaching peers to assist in developing social and academic skills under teacher's scaffolding practices. This study highlights the contribution of the heutagogical perspective to advance research on the participation and agency of students with SEN in mainstream schools.

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.

In this essay we argue that, based on current scientific data, the most prudential course of future actions that an American conservative can take, is one that assumes what we call climate change alarmism. In order to establish this thesis, we first provide a basic overview of the relevant climate change science, as well as give an analysis of the alarmist and lukewarming dialectic (the two primary interpretations of the data). We then move to develop our environmental wager. Finally, following Roger Scruton, we end this work by proposing what sort of policies conservatives should endorse going further.

Launonen and Mullins argue that if Classical Theism is true, human cognition is likely not theism-tracking, at least, given what we know from cognitive science of religion. In this essay, we develop a model for how classical theists can make sense of the findings from cognitive science, without abandoning their Classical Theist commitments. We also provide an argument for how our model aligns well with the Christian doctrine of general revelation.

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.

In southeast Asia, males of the Siamese fighting fish, Betta splendens, have been selected across centuries for winning paired staged fights and previous work has shown that males from fighter strains are more aggressive than wild-types. This strong directional selection for winners is likely to have targeted aggression-related endocrine systems, and a comparison between fighter and wild-type strains can bring into evidence the key hormones implicated in aggression. Here, we compared the plasma levels of the androgen 11-ketotestosterone (KT) and of the corticosteroid cortisol (F) in F2 males of a fighter and a wild-type strain raised under similar laboratory conditions. We show that F was generally lower in fighter as compared with wild-type males, while no overall differences in KT levels were detected between strains. When presented with a mirror-induced aggressive challenge, post-fight levels of F increased but more significantly so in wild-type males, while KT increased in males of both strains. After the challenge, fighter males had higher levels of KT as compared with wild-type males, while the pattern for F was opposite. As compared with animals in social groups, wild-type males placed under social isolation had lower F levels, while KT decreased for fighters. Taken together, this data suggests that while wild-type males responded to aggression with an increase in circulating levels of both androgens and corticosteroids, males selected for winning fights maintained a blunt F response, increasing only KT levels. These data agree with the hypothesis that a combination of high levels of androgens and low levels of corticosteroids is associated with high aggression. Overall, these results seem to indicate that selection for winning had a stronger impact in the hypothalamus-pituitary-interrenal axis than in the hypothalamus-pituitary–gonadal axis in B. splendens.

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.

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.

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.

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.