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Brain-computer interface (BCI) based on motor imagery electroencephalogram (EEG) has been widely used in various applications. Despite the previous efforts, the remained major challenges are effective feature extraction and the time-consuming calibration procedure. To address these issues, a novel multiattention adaptation network integrating the multiple attention mechanism and transfer learning is proposed to classify the EEG signals. First, the multiattention layer is introduced to automatically capture the dominant brain regions relevant to mental tasks without incorporating any prior knowledge about the physiology. Then, a multiattention convolutional neural network is employed to extract deep representation from raw EEG signals. Especially, a domain discriminator is applied to deep representation to reduce the differences between sessions for target subjects. The extensive experiments are conducted on three public EEG datasets (Dataset IIa and IIb of BCI Competition IV, and High Gamma dataset), achieving the competitive performance with average classification accuracy of 81.48%, 82.54%, and 93.97%, respectively. All the results outperform the state-of-the-art algorithms demonstrate the effectiveness and robustness of the proposed method. Importantly, we confirm that it is easier and more appropriate to transfer the information from local brain regions than from the whole brain. This enhances the transfer ability of deep features and, hence, it improves the performance of BCI systems.

Visual evoked potential (VEP)-related EEG signals have attracted widespread attention in the construction of brain-computer interface (BCI) systems. However, long-term use of the BCI system is prone to fatigue. Effectively coping with fatigue is the key to expanding BCI application scenarios. In this brief, we conduct two VEP experiments and propose a novel, concise and practical convolutional neural network (CNN) model to study and solve this problem. In detail, firstly, in terms of the stimulus frequencies in VEP experiments, we intercept the spectrum sequences of the fundamental waves and two harmonics. All these spectrum sequences are then paralleled input into the proposed multi-harmonic linkage CNN (MHLCNN) model, through three branches. Finally, the linkage characteristics of multiple harmonics are combined for VEP signal classification. The experimental results demonstrate that the proposed model shows good scalability, and can respectively improve the average classification accuracy by 23.17% and 15.85% when applied to steady-state VEP (SSVEP) signals in fatigue state and steady-state motion VEP (SSMVEP) signals, compared with the benchmark methods. All these provide a feasible and high-quality solution for reducing the impact of fatigue on the performance of VEP-based BCI systems.

Epilepsy makes the patients suffer great pain and has a very bad impact on daily life. In this paper, a novel method is proposed to implement electroencephalogram (EEG)-based epilepsy detection, in which multi-frequency multilayer brain network and deep learning are jointly utilized. Firstly, based on the multi-frequency characteristics of brain, we construct a multilayer brain network from the multi-channel EEG signals. The time, frequency and channel-related information from EEG signals are all mapped into the multilayer network topology, making it an effective feature for epilepsy detection. Subsequently, with multilayer brain network as input, a multilayer deep convolutional neural network (MDCNN) model is designed. MDCNN model has two blocks and uses a parallel multi-branch design in the first block, which exactly matches the multilayer structure of the proposed brain network. The experimental results on publicly available CHB-MIT datasets show that the proposed method can accurately detect epilepsy, with a high average accuracy of 99.56%, sensitivity of 99.29%, and specificity of 99.84%. All these provide an efficient solution for characterizing the complex brain states using multi-channel EEG signals.

Brain–computer interface (BCI) systems based on electroencephalography (EEG) signals have been extensively used in medical practice. To enhance the BCI performance, improving the classification accuracy of EEG signals is the key, which has always been the focus of research and development. In this article, a novel method integrating complex network and broad learning system (BLS) is proposed for visual evoked potential (VEP)-based BCI research. First, systematic VEP-based brain experiments are conducted for obtaining EEG signals, including steady-state VEP (SSVEP) and steady-state motion VEP (SSMVEP). Then, limited penetrable visibility graph (LPVG) and its degree sequence are employed to implement the preliminary feature extraction. All these features are finally fed into a BLS to study and classify the SSVEP and SSMVEP signals, respectively. The classification results show that our LPVG-based BLS can effectively classify VEP-based EEG signals, with average classification accuracy 96.22% for SSVEP and 74.54% for SSMVEP. These results are significantly better than other comparison methods as well as traditional CCA-based methods. All these open up new venues for studying EEG-based BCI systems via the fusion of network science and BLS.

Gas–liquid two-phase flow is of great importance in various industrial processes. How to accurately measure the flow parameters in the gas–liquid two-phase flow remains a challenging problem. In this article, we develop a novel deep learning based soft measure technique to predict the gas void fraction, which is one key parameter in a gas–liquid two-phase flow. We conduct the vertical upward gas–liquid two-phase flow experiments to measure the flow signals by using the four-sector distributed conductance sensor. Then, we design a novel multitask-based temporal-channelwise convolutional neural network (MTCCNN) to predict the gas void fraction. In MTCCNN, we first utilize the decomposed convolutional block to extract temporal dependence and channel connection from fluid data. After further fusion by the dense layer, we apply multitask learning to make full use of the extracted features through both classification branch and gas void fraction prediction branch. We compare our MTCCNN with its variations to demonstrate the proposed improvements. We also present other competitive methods for comparisons, which shows that our MTCCNN presents a better performance in gas void fraction prediction.

Driver fatigue detection is of great significance for guaranteeing traffic safety and further reducing economic as well as societal loss. In this article, a novel complex network (CN) based broad learning system (CNBLS) is proposed to realize an electroencephalogram (EEG)-based fatigue detection. First, a simulated driving experiment was conducted to obtain EEG recordings in alert and fatigue state. Then, the CN theory is applied to facilitate the broad learning system (BLS) for realizing an EEG-based fatigue detection. The results demonstrate that the proposed CNBLS can accurately differentiate the fatigue state from an alert state with high stability. In addition, the performances of the four existing methods are compared with the results of the proposed method. The results indicate that the proposed method outperforms these existing methods. In comparison to directly using EEG signals as the input of BLS, CNBLS can sharply improve the detection results. These results demonstrate that it is feasible to apply BLS in classifying EEG signals by means of CN theory. Also, the proposed method enriches the EEG analysis methods.

Sleep is an essential integrant in everyone’s daily life. Thereby, it is an important but challenging problem to construct a reliable and stable system, that can monitor user’s sleep quality automatically. In this brief, we combine complex network and deep learning to propose a novel Graph-Temporal fused dual-input Convolutional Neural Network (CNN) method to detect sleep stages by using the Sleep-EDF database. Firstly, we segment each single-channel EEG signal into non-overlapping 30s epochs to set up the network. For that, we map each epoch into a Limited Penetrable Visibility Graph (LPVG) and obtain the corresponding Degree Sequence (DS) by calculating the node degree. Finally, the DSs and the 30s EEG epochs are combined as inputs of the novel Graph-Temporal fused dual-input CNN to learn about the graph topology and about the temporal feature representations of the raw data for the purpose of classifying the sleep stages into the two-, three-, four-, five-, and six-state. Notably, the classification accuracy of six-state stage detection is 87.21% and the corresponding Kappa value is 0.80. The results demonstrate the effectiveness of our model structure in detecting sleep states, whereby they further provide a basic strategy for future sleep research.

Two-phase flow exists widely in the industrial field, and its research is of great significance for industrial production. In this brief, we design a wire-mesh sensor system with 256 measurement points and conduct vertical upward gas-liquid two-phase flow experiments to capture the flow information under different flow conditions. We find that flow structure can be characterized by the gas-liquid distribution imaging obtained through the wire-mesh sensor. To obtain more detailed imaging, we apply super-resolution methods based on deep learning to improve the imaging quality and choose No-Reference Structural Sharpness to evaluate the effects. The results show that compared with traditional interpolation method, the deep learning-based super resolution can restore more abundant details such as edges and textures, thereby characterizing the flow structure more finely. This provides an important support for further analysis of the flow characteristics and flow regularity, which can effectively optimize the industrial process.

Purpose: The present meta-analysis was designed to compare the relative clinical efficacy of unilateral and side-by-side metal stenting for the treatment of malignant hilar biliary obstruction (MHB...

In this article, we propose a method for emotion classification based on multilayer convolutional neural network (MCNN) and combining differential entropy (DE) and brain network. First, we use continuous wavelet transform (CWT) to get the time–frequency representation (TFR) of electroencephalogram (EEG) signals on each channel and extract rich information from different frequency bands for subsequent analysis. Brain networks are then constructed in multiple bands to characterize the spatial connections hidden in the multichannel EEG signals. Based on brain networks, we further develop core brain networks through a set of key nodes determined by DE. These core brain networks are associated with brain activities and differ markedly between different emotional states. The final designed MCNN model takes DE features and core brain networks as inputs for emotion recognition. We evaluate our method on the SJTU emotion EEG dataset, and the average accuracy of 15 subjects achieves 91.45%. Utilizing the complementary features of DE and brain network, the proposed method provides an efficient framework for accurate emotion recognition from EEG signals.

Gas–liquid two-phase flow widely exists in petroleum, natural gas, and other industries. Recognition of flow structure is an important issue in the study of two-phase flow, and it is of great significance for the optimization of industrial processes. Therefore, how to recognize complex flow structures effectively represents a challenge. To cope with this problem, we develop a novel deep learning network to recognize flow structures under different flow conditions. In particular, we conduct vertical upward gas–liquid two-phase flow experiments to obtain the flow structure data set on the basis of images collected by a high-speed camera. Then, we design a branch-aggregation network (BAN), where the branch structure is utilized to increase the width of the network, and multilevel aggregation structure is used to fuse features of different levels. In recognition of flow structure, the proposed network achieves an accuracy of 99.60% with a fast convergence speed and shows the advantages in antinoise ability, which is significant for online recognition in industrial processes. The results indicate that BAN can be a feasible method to recognize complex flow structures.

Electroencephalogram (EEG) has been widely used in brain computer interface (BCI) due to its convenience and reliability. The EEG-based BCI applications are majorly limited by the time-consuming calibration procedure for discriminative feature representation and classification. Existing EEG classification methods either heavily depend on the handcrafted features or require adequate annotated samples at each session for calibration. To address these issues, we propose a novel dynamic joint domain adaptation network based on adversarial learning strategy to learn domain-invariant feature representation, and thus improve EEG classification performance in the target domain by leveraging useful information from the source session. Specifically, we explore the global discriminator to align the marginal distribution across domains, and the local discriminator to reduce the conditional distribution discrepancy between sub-domains via conditioning on deep representation as well as the predicted labels from the classifier. In addition, we further investigate a dynamic adversarial factor to adaptively estimate the relative importance of alignment between the marginal and conditional distributions. To evaluate the efficacy of our method, extensive experiments are conducted on two public EEG datasets, namely, Datasets IIa and IIb of BCI Competition IV. The experimental results demonstrate that the proposed method achieves superior performance compared with the state-of-the-art methods.

Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject’s intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.

Sleep is an essential integrant in everyone’s daily life; therefore, it is an important but challenging problem to characterize sleep stages from electroencephalogram (EEG) signals. The network motif has been developed as a useful tool to investigate complex networks. In this study, we developed a multiplex visibility graph motif‐based convolutional neural network (CNN) for characterizing sleep stages using EEG signals and then introduced the multiplex motif entropy as the quantitative index to distinguish the six sleep stages. The independent samples t‐test shows that the multiplex motif entropy values have significant differences among the six sleep stages. Furthermore, we developed a CNN model and employed the multiplex motif sequence as the input of the model to classify the six sleep stages. Notably, the classification accuracy of the six‐state stage detection was 85.27%. Results demonstrated the effectiveness of the multiplex motif in characterizing the dynamic features underlying different sleep stages, whereby they further provide an essential strategy for future sleep‐stage detection research.

Steady State Motion Visual Evoked Potential (SSMVEP)-based Brain Computer Interface (BCI) is widely studied and has been used to varies of occasions on account of its good performance, mild stimulation, and free of additional training. We design a trolley control system based on SSMVEP signals and observe a phenomenon named “BCI Illiterate,” in which case some subjects present unsatisfactory performance with low classification accuracies. In order to cope with this challenging problem in real-world contexts, we introduce a deep learning (DL) method. The method allows improving the accuracies for both EEG literate and EEG illiterate. In particular, we firstly conduct SSMVEP experiments to obtain EEG signals from 10 subjects, including 5 EEG literates and 5 EEG illiterates. Then we construct a convolutional neural network with long short-term memory (CNN-LSTM) framework, which allows extracting the spectral, spatial, and temporal features of EEG signals, to realize the high classification accuracies of SSMVEP signals. The results show that DL method can reach 96.83% and 91.86% for EEG literate and EEG illiterate respectively, which are 12.68% and 31.08% higher than the results of traditional methods. These results indicate that DL method is not only suitable for EEG literate, but more importantly, can greatly improve the performance for EEG illiterate, which finally can enhance the robustness and universality of the SSMVEP-based BCI.

Electroencephalogram (EEG), obtained by wearable devices, can realize effective human health monitoring. Traditional methods based on artificially designed features have achieved valid results in EEG-based recognition, and numerous studies start to apply deep learning techniques in this area. In this article, we propose a coincidence-filtering-based method to build a connection between artificial-features-based methods and convolutional neural networks (CNNs), and design CNNs through simulating the information extraction pattern of artificial-features-based methods. Based on this method, we propose a novel, simple, and effective CNNs structure for EEG-based classification. We implement two experiments to obtain EEG data, and perform experiments based on the two health monitoring tasks. The results illustrate that the proposed network can achieve a prominent average accuracy on the emotion recognition and fatigue driving detection task. Due to its generality, the proposed framework design of CNNs is expected to be useful for broader applications in health monitoring areas.

Gas–liquid two-phase flow widely exits in production and transportation of petroleum industry. Characterizing gas–liquid flow and measuring flow parameters represent challenges of great importance, which contribute to the recognition of flow regime and the optimal design of industrial equipment. In this paper, we propose a novel complex network-based deep learning method for characterizing gas–liquid flow. Firstly, we map the multichannel measurements to multiple limited penetrable visibility graphs (LPVGs) and obtain their degree sequences as the graph representation. Based on the degree distribution, we analyze the complicated flow behavior under different flow structures. Then, we design a dual-input convolutional neural network to fuse the raw signals and the graph representation of LPVGs for the classification of flow structures and measurement of gas void fraction. We implement the model with two parallel branches with the same structure, each corresponding to one input. Each branch consists of a channel-projection convolutional part, a spatial–temporal convolutional part, a dense block and an attention module. The outputs of the two branches are concatenated and fed into several full connected layers for the classification and measurement. At last, our method achieves an accuracy of 95.3% for the classification of flow structures, and a mean squared error of 0.0038 and a mean absolute percent error of 6.3% for the measurement of gas void fraction. Our method provides a promising solution for characterizing gas–liquid flow and measuring flow parameters.

Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human's physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain-computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.

As a global and grievous mental disease, major depressive disorder (MDD) has received much attention. Accurate detection of MDD via physiological signals represents an urgent research topic. Here, a frequency-dependent multilayer brain network, combined with deep convolutional neural network (CNN), is developed to detect the MDD. Multivariate pseudo Wigner distribution is firstly introduced to extract the time-frequency characteristics from the multi-channel EEG signals. Then multilayer brain network is constructed, with each layer corresponding to a specific frequency band. Such multilayer framework is in line with the nature of the workings of the brain, and can effectively characterize the brain state. Further, a multilayer deep CNN architecture is designed to study the brain network topology features, which is finally used to accurately detect MDD. The experimental results on a publicly available MDD dataset show that the proposed approach is able to detect MDD with state-of-the-art accuracy of 97.27%. Our approach, combining multilayer brain network and deep CNN, enriches the multivariate time series analysis theory and helps to better characterize and recognize the complex brain states.

An urgent problem in the field of deep learning is the optimization of model construction, which frequently hinders its performance and often needs to be designed by experts. Optimizing the hyper-parameters remains a substantial obstacle in designing deep learning models, such as CNNs, in practice. In this paper, we propose an automatical optimization framework using binary coding system and GPSO with gradient penalties to select the structure. Such swarm intelligence optimization approaches have been used but not extensively exploited, and the existing work focuses on models with a fixed depth of networks. We design an experiment to arouse three types of emotion states for each subject, and simultaneously collect EEG signals corresponding to each emotion category. The GPSO-based method efficiently explores the solution space, allowing CNNs to obtain competitive classification performance over the dataset. Results indicate that our method based on the GPSO-optimized CNN model enables us to achieve a prominent classification accuracy, and the proposed method provides an effective automatic optimization framework for CNNs of the emotion recognition task with an uncertain depth of networks.

Making full use of solar energy is a feasible way to reduce building heating energy consumption in winter. A mathematical model is developed to investigate the dynamic heat transfer process in building external walls in the hot summer and cold winter zone of China by employing numerical simulation. Solar energy entering rooms through opaque walls is quantitatively analyzed by excluding the influence of outdoor air temperature on the room's net heat gain. The results indicate that solar energy entering rooms through opaque walls is of the same importance as the solar energy entering the room through a window for reducing building heating energy consumption. The solar absorptance of a south-facing room through the opaque wall can reach 17% when the heat-transfer coefficient of the south wall outer surface is low (i.e., under conditions of calm wind or high building density). With the consideration of solar energy entering rooms through the opaque walls, the reduction of the building heat loss index accounts for 20–80% and 10–45% of the total building heat loss index required by the national design standard ( JGJ26, 2010 ) for the wall with internal and external thermal insulations, respectively. Therefore, a significant reduction in building heating energy consumption can be achieved by appropriate building layout such that the wind speed is reduced near the south walls. In addition, semi-empirical formulas for estimating the room solar absorptance through an opaque wall are given for the condition of consecutive sunny days in winter.

Background Sleep deprivation (SD) among young adults is a major public health concern. In humans, it has adverse effects on mood and results in serious health problems. Faced with SD, persons may take precautionary measures to try and reduce their risk. The aim of this study is to evaluate the efficacy and safety of electroacupuncture (EA) for the prevention of negative moods after SD. In addition, we will do a comparison of the effects of EA on mood after SD at different time points. Methods This randomized controlled trial (RCT) will be performed at the First Affiliated Hospital of Changchun University of Chinese Medicine in China. The Standards for Reporting Interventions in Clinical Trials of Acupuncture 2010 will be strictly adhered to. Forty-two healthy male volunteers will be distributed into acupoints electroacupuncture (AE) group, non-acupoints electroacupuncture (NAE) control group, or blank control group. This trial will comprise 1-week baseline (baseline sleep), 1-week preventative treatment, 30-h total sleep deprivation (TSD), and 24-h after waking follow-up period. Participants in the AE group and the NAE control group during the preventative treatment period will be administered with EA treatment once daily for 1 week. Participants in the blank control group will not be administered with any treatment. The primary outcome will be the Profile of Mood States (POMS) Scale. Secondary outcome measures will include changes in the Noldus FaceReader (a tool for automatic analysis of facial expressions) and Positive and Negative Affect Schedule (PANAS) Scale. Total sleep deprivation will be 30 h. During the 30-h TSD period, participants will be subjected to 11 sessions of assessment. Adverse events will be recorded. Discussion This study is designed to evaluate the efficacy and safety of EA for the prevention of negative moods after SD. The results of this trial will allow us to compare the effects of EA on mood after SD at different time points. Moreover, the findings from this trial will be published in peer-reviewed journals. Trial registration Chinese Clinical Trial Registry Chi2000039713. Registered on 06 November 2020

Background To compare the changes in quantitative parameters and the size and degree of 18F-fluorodeoxyglucose ([18F]FDG) uptake of malignant tumor lesions between Bayesian penalized-likelihood (BPL) and non-BPL reconstruction algorithms. Methods Positron emission tomography/computed tomography images of 86 malignant tumor lesions were reconstructed using the algorithms of ordered subset expectation maximization (OSEM), OSEM + time of flight (TOF), OSEM + TOF + point spread function (PSF), and BPL. [18F]FDG parameters of maximum standardized uptake value (SUVmax), SUVmean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and signal-to-background ratio (SBR) of these lesions were measured. Quantitative parameters between the different reconstruction algorithms were compared, and correlations between parameter variation and lesion size or the degree of [18F]FDG uptake were analyzed. Results After BPL reconstruction, SUVmax, SUVmean, and SBR were significantly increased, MTV was significantly decreased. The difference values of %ΔSUVmax, %ΔSUVmean, %ΔSBR, and the absolute value of %ΔMTV between BPL and OSEM + TOF were 40.00%, 38.50%, 33.60%, and 33.20%, respectively, which were significantly higher than those between BPL and OSEM + TOF + PSF. Similar results were observed in the comparison of OSEM and OSEM + TOF + PSF with BPL. The %ΔSUVmax, %ΔSUVmean, and %ΔSBR were all significantly negatively correlated with the size and degree of [18F]FDG uptake in the lesions, whereas significant positive correlations were observed for %ΔMTV and %ΔTLG. Conclusion The BPL reconstruction algorithm significantly increased SUVmax, SUVmean, and SBR and decreased MTV of tumor lesions, especially in small or relatively hypometabolic lesions.

Due to the inherent disturbances associated with flow structures, measurement of the complicated flow parameters in multiphase flows remains a challenging problem of significant importance. The flow dynamical behaviors are still elusive. In this paper, a multichannel complex impedance measurement system is designed to cope with this difficult issue. First, the geometry of the distributed multielectrode impedance sensor is optimized and a matched hardware measurement system is developed. After performance evaluation, a convolutional neural network and long short-term memory based measurement model is formulated for measuring flow parameters with high accuracy. The mean absolute error is only 0.36% for water cut and 0.77% for total flow velocity. Further, from the perspective of Lempel–Ziv complexity and mutual information, the relationship between the diverse flow structures and spatial flow behaviors is explored, leading to a deeper understanding of oil-water flows. All the experimental and analytical results demonstrate that the combination of deep learning and the designed impedance sensor measurement system allows measuring the complicated flow parameters, thereby characterizing the flow structures and behaviors. This opens up a new venue for exploring industrial multiphase flows and serving for an efficient oilfield exploitation as well.

Early screening of COVID-19 is essential for pandemic control, and thus to relieve stress on the health care system. Lung segmentation from chest X-ray (CXR) is a promising method for early diagnoses of pulmonary diseases. Recently, deep learning has achieved great success in supervised lung segmentation. However, how to effectively utilize the lung region in screening COVID-19 still remains a challenge due to domain shift and lack of manual pixel-level annotations. We hereby propose a multi-appearance COVID-19 screening framework by using lung region priors derived from CXR images. Firstly, we propose a multi-scale adversarial domain adaptation network (MS-AdaNet) to boost the cross-domain lung segmentation task as the prior knowledge to the classification network. Then, we construct a multi-appearance network (MA-Net), which is composed of three sub-networks to realize multi-appearance feature extraction and fusion using lung region priors. At last, we can obtain prediction results from normal, viral pneumonia, and COVID-19 using the proposed MA-Net. We extend the proposed MS-AdaNet for lung segmentation task on three different public CXR datasets. The results suggest that the MS-AdaNet outperforms contrastive methods in cross-domain lung segmentation. Moreover, experiments reveal that the proposed MA-Net achieves accuracy of 98.83 $\%$ and F1-score of 98.71 $\%$ on COVID-19 screening. The results indicate that the proposed MA-Net can obtain significant performance on COVID-19 screening.

Iatrogenic nerve injury is a risk in many surgical procedures. We present our experience with a novel fluorescent agent to enhance the visualization of at-risk nerves during a prostatectomy in a canine model. Illuminare-1 is an intravenously administered myelin-binding fluorophore. After IRB approval and successfully visualizing target nerves in murine and porcine tests, we undertook a robotically-assisted prostatectomy on a dog, which has comparable genitourinary anatomy to a human. Dogs were positioned supine, anesthetized, the abdomen was insufflated, and the peri-prostatic and obturator nerves were exposed with a DaVinci SI Surgical System. A modified FDA-approved laparoscope with white and blue (370 – 425nm wave length) light settings was positioned via an assistant port to illuminate these nerves. A bolus of 1mg/kg of Illuminare-1 was administered intravenously with uninterrupted visualization of the presumed nerves. Fluorescent structures were resected for histological assessment. With 1mg/kg of Illuminare-1, nerves rapidly fluoresced under blue light, displaying a distinct hue. Fluorescence was seen within 90 seconds of administration and sustained for over three hours in the obturator and smaller periprostatic nerves. Tiny linear structures that were not initially seen under white light conditions were clearly identified as fluorescent tissue after injection with Illuminare-1. Seven fluorescent peri-prostatic structures were resected and all were histologically confirmed to be myelinated nerves. The cross-sectional nerve fiber diameters ranged from 64 – 247nm. Illuminare-1 enhanced the visualization of the neurovascular bundle in a dog. Phase-1 in-human trials with Illuminare-1 will follow to address the unmet need to reduce unintended surgical morbidity.

Electroencephalography (EEG) decoding is an important part of Visual Evoked Potentials-based Brain-Computer Interfaces (BCIs), which directly determines the performance of BCIs. However, long-time attention to repetitive visual stimuli could cause physical and psychological fatigue, resulting in weaker reliable response and stronger noise interference, which exacerbates the difficulty of Visual Evoked Potentials EEG decoding. In this state, subjects’ attention could not be concentrated enough and the frequency response of their brains becomes less reliable. To solve these problems, we propose an attention-based parallel multiscale convolutional neural network (AMS-CNN). Specifically, the AMS-CNN first extract robust temporal representations via two parallel convolutional layers with small and large temporal filters respectively. Then, we employ two sequential convolution blocks for spatial fusion and temporal fusion to extract advanced feature representations. Further, we use attention mechanism to weight the features at different moments according to the output-related interest. Finally, we employ a full connected layer with softmax activation function for classification. Two fatigue datasets collected from our lab are implemented to validate the superior classification performance of the proposed method compared to the state-of-the-art methods. Analysis reveals the competitiveness of multiscale convolution and attention mechanism. These results suggest that the proposed framework is a promising solution to improving the decoding performance of Visual Evoked Potential BCIs.

Fatigue driving is the main cause of traffic accidents. Analysis of electroencephalogram (EEG) signals has attracted wide attention for identifying fatigue driving. With the development of the Internet of Vehicles (IoV), we hope to establish an EEG-based IoV traffic management system to improve traffic safety. In the proposed system, real-time diagnosis is a significant factor, and improvement of the detection speed is our main concern. EEG signals generate a large amount of spatially oriented data over a relatively short duration; hence, their dimension needs to be reduced effectively before being analysed. We proposes a feature reduction method, based on a novel weighted principal component analysis (WPCA) algorithm for EEG signals. First, the EEG features are extracted by an autoregressive (AR) model. Second, we calculate the influence of different features on the classified performance of fatigue state. The accuracy reduction values of different features are normalised as the weights of the features. Finally, these weights are assigned to the WPCA to reduce the EEG features. To verify the effectiveness of the algorithm, we carried out a simulated driving experiment involving eight participants. For comparison, power spectral density and differential entropy models were also introduced to extract EEG features. Support Vector Machine was adopted as a classifier to establish a fatigue driving classification experiment. The experimental results show that the WPCA method can effectively reduce the feature dimension of different EEG feature extraction methods, speed up calculations, and achieve a much higher classification accuracy of fatigue driving.

Xiakemycin A (XKA), a new antibiotic in the pyranonaphthoquinone family, shows antitumor activity. However, the type of cell death induced by XKA remains elusive. In this study, we aim to investigate the type of death induced by XKA in hepatic cancer. The apoptotic features, such as chromatic agglutination, reactive oxygen species generation and membrane potential of mitochondria, in HepG2 cells treated by XKA were measured by Hoechst 33342 staining and flow cytometry. Apoptosis of HepG2 cells treated with XKA was determined by Annexin V-FITC/propidium iodide double staining and Western blot analysis, respectively. XKA had a significant dose-dependent elevation of chromatic agglutination, reactive oxygen species generation, Annexin V and propidium iodide staining, decrease of membrane potential. Meanwhile, in apoptotic HepG2 cells induced by XKA, robust increment was noticed in p53 expression, cleavage of PARP, caspase-3, and caspase-9. XKA showed potent inhibitory effects on the proliferation of HepG2 cells. Such phenomenon may be related to activation of the apoptotic pathway.

Brain–computer interface (BCI) enables users to interact with the environment without relying on neural pathways and muscles. P300 based BCI systems have been extensively used to achieve human–machine interaction. However, the appearance of fatigue symptoms during operation process leads to the decline in classification accuracy of P300. Characterizing brain cognitive process underlying normal and fatigue conditions constitutes a problem of vital importance in the field of brain science. We in this paper propose a novel wavelet decomposition based complex network method to efficiently analyze the P300 signals recorded in the image stimulus test based on classical ‘Oddball’ paradigm. Initially, multichannel EEG signals are decomposed into wavelet coefficient series. Then we construct complex network by treating electrodes as nodes and determining the connections according to the 2-norm distances between wavelet coefficient series. The analysis of topological structure and statistical index indicates that the properties of brain network demonstrate significant distinctions between normal status and fatigue status. More specifically, the brain network reconfiguration in response to the cognitive task in fatigue status is reflected as the enhancement of the small-worldness.

The Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system has seen extensively applications in many fields, such as physical recovery of handicap persons, obstacle avoidance of intelligent vehicles, entertainment and smart homes. However, subjects easily get fatigued because of the involving long-time operations. The presence of fatigue symptoms typically affect the efficiency of the BCI system, so investigating the effects of fatigue on the SSVEP classification accuracy from the perspective of brain network becomes a challenging issue of significant importance. In this paper, we develop an adaptive optimal-Kernel time-frequency representation (AOK-TFR)-based complex network method for characterizing fatigued behavior using the SSVEP-based BCI system. We apply the traditional Canonical Correlation Analysis (CCA) and Fisher Linear Discriminant Analysis (FLDA) to classify SSVEP signals. We find that the classification accuracy at the fatigue states is significantly lower than that at the normal states. To reveal the reasons, we infer and analyze the AOK-TFR-based functional brain network with SSVEP signals. In particular, we calculate the AOK-TFR of the acquired 30-channel SSVEP signals under both normal and fatigue conditions and then construct a brain network in terms of the two-norm distance between different channels. Our results suggest that the small-world-ness of the network at normal states is prominent, and the main brain regions associated with SSVEP are in the prefrontal cortex and occipital lobe. Our analysis sheds new insights into the understanding and management of the fatigued behavior using the SSVEP-based BCI system.

Epilepsy is one of the most common brain diseases, and seizures usually occur randomly. Accurately predicting seizures enable doctors and patients to carry out medical prevention timely. In seizure prediction studies, single-domain information input (time domain or frequency domain, Etc.) neglects some parts’ information from signals. In this paper, we propose a novel deep learning framework named channel attention dual-input convolutional neural network (CADCNN) to obtain the signal’s useful information fully. The spatial–temporal features extracted by short-time Fourier transform (STFT) are fed to the CADCNN, and the raw EEG signals are fed for further feature extraction. With the fusion of two inputs from different domains and the combination of channel attention, CADCNN can learn faithful and distinguishable representations of EEG signals and boost the temporal, spectrum, and spatial information utilization capability. We evaluate the proposed method using the Boston Children’s Hospital-MIT scalp EEG public datasets. Compared with other state-of-the-art methods, the sensitivity, false prediction rate, specificity, and AUC of our proposed method reach 97.1%, 0.029h, 95.6%, and 0.917, respectively, presenting better performance and higher prediction accuracy.

Brain is the most complex organ of human, which serves as the center of controlling most activities. A novel methodology called complex network is capable of characterizing the functional connectivity of human brain by means of graph theoretical measures. We designed and conducted experiments to record electroencephalograph (EEG) signals during left and right hand movement imagery tasks and then probed into brain activities by analyzing multichannel motor imagery signals from the perspective of complex networks. More specifically, we first utilized wavelet time–frequency analysis to calculate energy sequence of each channel and then modeled human brain as a graph by treating the channels of scalp EEG as nodes and determining interconnections according to the distance between energy sequences of each channel. The functional connectivity of derived brain networks could be interpreted with characteristics of nodes and edges. Results demonstrated that when subjects imagined left hand movements, the node betweenness centrality (BC) of right sensorimotor area was greater than that of left sensorimotor area. The node BC distribution was roughly opposite when imagining right hand movements. It could be concluded that nodes of contralateral sensorimotor regions were more likely to be activated to control information flows during motor imagery tasks.

The mechanical performance of epoxy asphalt concrete has been well reported in existing studies. This study investigated the surface characteristics of asphalt concrete, including the permeability, friction coefficient, moisture damage resistance and surface texture. The permeability test, dynamic friction test, Cantabro test, Hamburg test and laser texture scanning test were carried out for such investigations. Test results show that the permeability of the epoxy asphalt concrete was very low, which is desirable for protecting the steel deck. The friction coefficient can satisfy the requirement for steel deck bridge according to the Pendulum Number. However, it was found that the skid resistance could be a concern when the vehicle speed is higher than a certain level according to the dynamic friction test. The results suggested to control the traffic speed when epoxy asphalt concrete is applied as steel bridge pavement. In terms of the raveling and stripping, it was found that the epoxy asphalt mixture has an excellent anti-stripping performance based on the Cantabro test and Hamburg wheel tracking test results. The yield point was not reached after 20,000 loading cycles at 60 °C in the immersed Hamburg wheel tracking test. The mean profile depth of the epoxy asphalt concrete is 0.305 mm, much lower than conventional asphalt concrete indicating a very fine surface texture. Keywords: Surface characteristics, Epoxy asphalt, Skid resistance, Permeability, Anti-stripping, Surface texture

How to get the best uniformity in the shortest time has always been a hot and difficult point in the quantitative analysis of bulk mixing process using rotating drum. With the advent of Discrete Element Method (DEM), the mixing behavior of particle material in blender has been extensively studied these years. In this paper, the reasonable rotary speeds for blending process as well as the ideal process durations of a horizontally rotating drum with internal baffles in batch mode are quantitatively determined by running simulations based on DEM software PFC3D. The particle-counting codes, developed in PFC3D built-in programming language FISH, take important roles in evaluating the corresponding effects of different process parameters, which overcomes the uncertainty of usual qualitative analysis of simulation results.

Driver fatigue is an important cause of traffic accidents, which has triggered great concern for detecting drivers' fatigue. Numerous methods have been proposed to fulfill this challenging task, including feature methods and machine learning methods. Recently, with the development of deep learning techniques, many studies achieved better results than traditional feature methods, and the combination of traditional methods and deep learning techniques gradually received attention. In this paper, we propose a recurrence network-based convolutional neural network (RN-CNN) method to detect fatigue driving. To be specific, we first conduct a simulated driving experiment to collect electroencephalogram (EEG) signals of subjects under alert state and fatigue state. Then, we construct the multiplex recurrence network (RN) from EEG signals to fuse information from the original time series. Finally, CNN is employed to extract and learn the features of a multiplex RN for realizing a classification task. The results indicate that the proposed RN-CNN method can achieve an average accuracy of 92.95%. To verify the effectiveness of our method, some existing competitive methods are compared with ours. The results show that our method outperforms the existing methods, which demonstrate the effect of the RN-CNN method.

The steady state motion visual evoked potential (SSMVEP)-based brain computer interface (BCI), which incorporates the motion perception capabilities of the human visual system to alleviate the negative effects caused by strong visual stimulation from steady-state VEP, has attracted a great deal of attention. In this paper, we design a SSMVEP-based experiment by Newton's ring paradigm. Then, we use the canonical correlation analysis and Support Vector Machines to classify SSMVEP signals for the SSMVEP-based electroencephalography (EEG) signal detection. We find that the classification accuracy of different subjects under fatigue state is much lower than that in the normal state. To probe into this, we develop a multiplex limited penetrable horizontal visibility graph method, which enables to infer a brain network from 62-channel EEG signals. Subsequently, we analyze the variation of the average weighted clustering coefficient and the weighted global efficiency corresponding to these two brain states and find that both network measures are lower under fatigue state. The results suggest that the associations and information transfer efficiency among different brain regions become weaker when the brain state changes from normal to fatigue, which provide new insights into the explanations for the reduced classification accuracy. The promising classification results and the findings render the proposed methods particularly useful for analyzing EEG recordings from SSMVEP-based BCI system.

According to statistics from the World Health Organization, China has always been among the countries with high incidence of traffic accidents, and the main reason is fatigue driving. In recent years, regression analysis of electroencephalogram (EEG) signals has already been a topic of interest within the field of fatigue driving research, yet, it has not been effectively resolved. In this paper, we designed a platform for the collection of EEG signals for fatigue driving that monitors the brain's fatigue state through multiple sensors. Based on the collected EEG data, a framework of fatigue driving regression based on EEG has been proposed. In order to determine the driver's fatigue level, we produced the data set label, which calculates the fatigue index of EEG signals to characterize the level of fatigue. In order to better cope with individual differences, the fatigue index curve was fitted by least squares. At the same time, we proposed an Ensemble Learning driver fatigue index regression analysis method based on the Bayesian model combination, with the support vector regression algorithm as a base learner. By increasing the diversity and difference of the base learners, the performance of the regression analysis method during the process of driver fatigue index regression analysis has been improved. The experimental results showed that the proposed regression analysis method was reliable and could accurately and reliably characterize the driver's fatigue index.

Objective.Electroencephalogram (EEG) data, as a kind of complex time-series, is one of the most widely-used information measurements for evaluating human psychophysiological states. Recently, numerous works applied deep learning techniques, especially the convolutional neural network (CNN), into EEG-based research. The design of the hyper-parameters of the CNN model has a great influence on the performance of the model. Therefore, automatically designing these hyper-parameters can save the time and labor of experts. This leads to the appearance of the neural architecture search technique. In this paper, we propose a reinforcement learning (RL)-based step-by-step framework to efficiently search for CNN models.Approach.Specifically, the deep Q network in RL is first used to determine the depth of convolutional layers and the connection modes among layers. Then particle swarm optimization algorithm is used to fine-tune the number and size of convolution kernels. Through this step-by-step strategy, the search space can be narrowed in each step for saving the overall time cost. This framework is employed for both EEG-based sleep stage classification and driver drowsiness evaluation tasks.Main results.The results show that compared with state-of-the-art methods, the high-performance CNN models identified by the proposed optimization framework, can achieve high overall accuracy and better root mean squared error in the two tasks.Significance.Therefore, the proposed optimization framework has a great potential to provide high-performance results for other kinds of classification and prediction tasks. In this way, it can greatly save researchers’ time cost and promote broader applications of CNNs.

Distinguishing brain cognitive behavior underlying disabled and able-bodied subjects constitutes a challenging problem of significant importance. Complex network has established itself as a powerful tool for exploring functional brain networks, which sheds light on the inner workings of the human brain. Most existing works in constructing brain network focus on phase-synchronization measures between regional neural activities. In contrast, we propose a novel approach for inferring functional networks from P300 event-related potentials by integrating time and frequency domain information extracted from each channel signal, which we show to be efficient in subsequent pattern recognition. In particular, we construct brain network by regarding each channel signal as a node and determining the edges in terms of correlation of the extracted feature vectors. A six-choice P300 paradigm with six different images is used in testing our new approach, involving one able-bodied subject and three disabled subjects suffering from multiple sclerosis, cerebral palsy, traumatic brain and spinal-cord injury, respectively. We then exploit global efficiency, local efficiency and small-world indices from the derived brain networks to assess the network topological structure associated with different target images. The findings suggest that our method allows identifying brain cognitive behaviors related to visual stimulus between able-bodied and disabled subjects.

Measuring water holdup and characterizing the flow behavior of an oil–water two-phase flow is a contemporary and challenging problem of significant importance in industry. To address this problem, we develop a new method to design a new four-sector distributed conductance sensor. Specifically, we first use the finite-element method (FEM) to investigate the sensitivity distribution of the electric field and then calculate its response on the measurement electrodes. Based on the FEM analysis results, we extract two optimizing indexes to describe and find the optimum geometry for the four-sector distributed conductance sensor. We carry out oil–water two-phase flow experiments in a vertical upward pipe to validate the designed sensor implemented in the measurement of water holdup. In addition, we use the multivariate pseudo Wigner distribution (MPWD) method to analyze the multivariate signals from the four-sector distributed sensor. Our analytical and experimental results indicate that the four-sector distributed conductance sensor enables measuring water holdup and the MPWD allows uncovering local flow behavior revealing different oil–water flow patterns.

Driver fatigue has caused numerous vehicle crashes and traffic injuries. Exploring the fatigue mechanism and detecting fatigue state are of great significance for preventing traffic accidents, and further lessening economic and societal loss. Due to the objectivity of EEG signals and the availability of EEG acquisition equipment, EEG-based fatigue detection task has raised great attention in recent years. Although there exist various methods for this task, the study of fatigue mechanism and detection of fatigue state still remain much to be explored. To investigate these problems, a multivariate weighted ordinal pattern transition (MWOPT) network is proposed in this paper. To be specific, a simulated driving experiment was first conducted to obtain the EEG signals of subjects in alert state and fatigue state respectively. Then the MWOPT network is constructed based on a novel Shannon entropy. To probe into the mechanism underlying fatigue behavior, the small-worldness index is extracted from the generated MWOPT network. Furthermore, the nodal degree index is input into a classifier to distinguish the fatigue state from alert state. The obtained high accuracy indicates the effectiveness of the proposed network for EEG-based fatigue detection. Besides, four nodes are found to play an important role in identifying fatigue state. These results suggest that the proposed method enables to analyze nonlinear multivariate time series and investigate the driving fatigue behavior.

Driver fatigue is an important contributor to road accidents, and driver fatigue detection has attracted a great deal of attention on account of its significant importance. Numerous methods have been proposed to fulfill this challenging task, though, the characterization of the fatigue mechanism still, to a large extent, remains to be investigated. To address this problem, we, in this work, develop a novel Multiplex Limited Penetrable Horizontal Visibility Graph (Multiplex LPHVG) method, which allows in not only detecting fatigue driving but also probing into the brain fatigue behavior. Importantly, we use the method to construct brain networks from EEG signals recorded from different subjects performing simulated driving tasks under alert and fatigue driving states. We then employ clustering coefficient, global efficiency and characteristic path length to characterize the topological structure of the networks generated from different brain states. In addition, we combine average edge overlap with the network measures to distinguish alert and mental fatigue states. The high-accurate classification results clearly demonstrate and validate the efficacy of our multiplex LPHVG method for the fatigue detection from EEG signals. Furthermore, our findings show a significant increase of the clustering coefficient as the brain evolves from alert state to mental fatigue state, which yields novel insights into the brain behavior associated with fatigue driving.

Driver fatigue evaluation is of great importance for traffic safety and many intricate factors would exacerbate the difficulty. In this paper, based on the spatial–temporal structure of multichannel electroencephalogram (EEG) signals, we develop a novel EEG-based spatial–temporal convolutional neural network (ESTCNN) to detect driver fatigue. First, we introduce the core block to extract temporal dependencies from EEG signals. Then, we employ dense layers to fuse spatial features and realize classification. The developed network could automatically learn valid features from EEG signals, which outperforms the classical two-step machine learning algorithms. Importantly, we carry out fatigue driving experiments to collect EEG signals from eight subjects being alert and fatigue states. Using 2800 samples under within-subject splitting, we compare the effectiveness of ESTCNN with eight competitive methods. The results indicate that ESTCNN fulfills a better classification accuracy of 97.37% than these compared methods. Furthermore, the spatial–temporal structure of this framework advantages in computational efficiency and reference time, which allows further implementations in the brain–computer interface online systems.