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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.

This paper aimed to build up the theorical and conceptual understanding of future forecasting study of Macau’s GDP and Gross Gaming Revenue (GGR) by co-movement of economic indicators. Macau GDP and GGR showed co-movements with a number of time series economic indicators, including China’s exports and imports, China’s manufacturing PMI, non-manufacturing PMI, China's electricity production growth, share price of some Macau’s gaming operators, etc. These time series data can be found in statistics departments of China, Macau and Hong Kong, stock exchanges, and international organizations such as the International Monetary Fund (IMF), the World Bank, the World Trade Organization (WTO). Burns and Mitchell’s study in 1946 identified co-movements between economic indicators and being further carried out and developed leading, coincident and lagging indicators, which is essential for future econometric models and nowcasting techniques developments to study these co-movements. In particular, with the proper application of nowcasting techniques, future studies can exploit the data of leading and coincident economic indicators to forecast Macau’s GDP and GGR within an acceptable level of error. Since Macau is a “monotown,” where the gaming revenue makes a significant contribution to the economy. The forecasting of gaming revenue is crucial as it aids the gambling and tourism industries in preparing supply and provides information to policymakers to plan for the near future. This research also contributes to understand Macau’s economy by investigating its internal and external economic variables.

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.

Microbial and hydrothermal venting activities on the seafloor are important for the formation of sediment-hosted stratiform sulfide (SHSS) deposits. Fe isotopic compositions are sensitive to both microbial and hydrothermal activities and may be used to investigate the formation of these deposits. However, to the best of our knowledge, no Fe isotopic studies have been conducted on SHSS deposits. In the Devonian Dajiangping SHSS-type pyrite deposit (389 Ma), South China, laminated pyrite ores were precipitated from exhalative hydrothermal fluids, whereas black shales were deposited during intervals with no exhalation. Pyrite grains from black shales mostly display positive δ56Fe-py (0.01–0.73‰), higher than marine sediments (ca. 0‰), due to pyrite deriving Fe from basinal shuttled Fe(III) (hydr-)oxides and slowly crystallizing in pores of sediments with equilibrium fractionation, except for negative δ56Fe-py (−0.17‰ to −0.24‰) of two samples caused by mixing of Fe from underlain laminated ores. The positive δ34S-py (3.50–24.5‰) of black shales reflect that sulfur of pyrite originated from quantitative reduction of sulfate in closed pores of sediments. In contrast, pyrite grains of laminated ores have negative δ56Fe-py (−0.60‰ to −0.21‰), which were not only inherited from the negative δ56Fe of hydrothermal fluids but also caused by kinetic fractionation during rapid precipitation of a pyrite precursor (FeS) in hydrothermal plumes. These ores have negative δ34S-py (−28.7‰ to −1.82‰), because H2S for pyrite mineralization was produced by bacterial sulfate reduction (BSR) in a sulfate-rich seawater column or shallow sediments. The δ56Fe-py values of laminated ores co-vary positively with δ34S-py and δ13C-carbonate along the ore stratigraphy, with δ13C-carbonate values ranging from −12.0‰ to −2.50‰. However, they correlate negatively with aluminum-normalized total organic carbon (TOC/Al2O3). Organic carbon is thus considered to enhance the production of H2S by BSR activities, increase pyrite precipitation rates and promote the expression of kinetic fractionation of Fe isotopes. Intriguingly, in the ore units with vigorous hydrothermal venting activities, δ56Fe-py, δ34S-py and δ13C-carbonate values display a consistently increasing trend. Such results suggest that venting hydrothermal fluids significantly inhibited the H2S production of BSR, which then reduced the pyrite crystallization rate and decreased the kinetic fractionation of Fe isotopes. Our study reveals that the formation of SHSS deposits relies on H2S from microbial activities and metals from hydrothermal exhalation on the seafloor, but that vigorous exhalation can inhibit microbial activities and thus sulfide precipitation rates. The integrated use of Fe, S, and C isotopes can effectively elucidate these dynamic interactions between hydrothermal venting and microbial activities during the formation of SHSS deposits.