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It has been proven in numerous research that mindfulness can be helpful to reduce stress and chronic pain (Hall, 2014; Lindström, n.d.; Tong et al., 2015). While interactive mindfulness has been one of the focuses in the recent mobile applications market, usually tackling three essential human senses: audio, visual, and touch, each mobile application has quite some different approaches in terms of interactivity. Some focus on the touch and visual, and some on audio (environmental sounds or instructing meditation). Immersing oneself in virtual reality (VR) creates a constant stream of interactivity. Nonetheless, what are the conditions for an (in)tangible virtual reality to be more effective? Under the COVID-19 pandemic and lockdown since the end of 2019, Macao has been facing a social concern that we cannot travel easily to visit our decedents’ graves abroad, let alone the existing concerns of expensive burial services, lack of space, and alternative burial options. Also, taking into consideration that standard funeral service in Macao is often too brief, and getting briefer, thus lacking the opportunity to properly farewell the decedent, this research is proposing a virtual reality 3D model construction of the Chapel of St. Michael, located in St. Michael the Archangel Cemetery in Macao, to be streamed on a 360 virtual tour platform, Kuula. co. By immersing in this virtual reality, the participant is to have a single user experience for mindfulness with the decedent. To ensure valid and reliable results that address the research aims and objectives, a single-user experiment is going to be set up with multiple electronic devices, namely, the smartphone iPhone X with cardboard VR, the tablet iPad Pro, and the Oculus Quest 2. The methodology to collect the data will be using observation and simulation. The experiment will be started with an introduction to the project and conducted with no instruction, allowing users to explore and examine all features in this immersive experience. Along with a post-experience survey (interview + questionnaire), we seek its conditions and impacts on Macao residents in terms of interactive mindfulness and participants’ expectation of testing, for the first time in Macao, a virtual reality grave mourning experience.

(Un)Directed Reading is an interactive installation (Edmonds, 2010) initially derived from the “Directed Reading” course, which all undergraduate students at the University must take during their final year. In this course, students are assigned different writing exercises and research assignments to open their creative and constructive minds in writing (EdwardsGroves, 2012; McVey, 2008). Every year, at the end of the course and after a selection process, a collection of stories is uploaded to a database of original stories. We then developed an open-source application to print a receipt ticket from a thermal printer with a randomly picked story every time a user pushes a button. An arcade game-style button is installed on a kiosk designed in collaboration with students and set on the university's campus (left photo). The printed receipt presents a short extract of one of the stories and a QR code that links to the full story decorated with illustrations. In a modern society where most of our interactions are audiovisual-based, young generations are less and less encouraged to read and even less to write. By offering a simple kiosk installation with short stories and graphical illustrations, we propose a new interactive interface that can easily engage passers-by to eventually stimulate their reading and creativity. By reading these stories, students, professors, staff, and visitors can be surprised by the talent of our students, as it stimulates students to write new pieces to be selected. The interactive kiosk was accessible to all for a period of 3 months with 23 stories during which we automatically collected some data to use in quantitative analysis. In this first run, we focused on data from the user's interactions such as story printed, story read, date and time. It allowed us to see the ratio between the printed receipt and the actual online reading of a story and more.

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.

Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.

The place of theology is under threat in the modern university. It is denied a place, except insofar as it is useful in the training of religious professionals or as a phenomenon in its own right, on the grounds that relate to an unscientific scientism that both makes metaphysical assumptions it itself does not recognise as scientific or denies its own epistemological commitments. This article argues that the notion of education in ‘liberal knowledge’ or ‘universal knowledge’, the idea at the heart of John Henry Newman’s The Idea of a University provides a sufficiently robust counter to these assaults on the place of theology proper in the modern university and that refusing such a place to it undermines the claim of universities to use the name at all. It is precisely the uselessness of theology that guarantees its place in the university committed to universal knowledge and universal enquiry.

This essay presents a mapping of the historical concepts that contributed to the emergence of post-digital aesthetics and their connections to the concept of post-media in historical terms. It also analyzes the transition from techno-positivism to discourse of resistance against the effects of the capital technological industrial complex and how these advances in technology influence artistic discourses, practices and are the leverage of art and technology which is nothing more than a representation of the aesthetics of capital. Following art and capitalism as an ideology of innovation. Is proposed an unstinting theory about technology, geology, and the importance of these conditions to the post-digital aesthetics in terms of material disponible and conceptual articulation. Producing a reconfiguration of the post-digital conceptual approach as I propose beyond the dysfunctional aesthetics and connected with the concept of radical ecology centered in the usability of electronic garbage and technical obsolescent technologies in the arts.