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Background Drug-coated balloon (DCB) is a novel and effective device for coronary artery disease patients with in-stent restenosis (ISR). However, the incidence and possible influencing factors associated with binary restenosis have not yet been adequately assessed. Methods The data are extracted from a prospective, multicenter, randomized controlled trial. A total of 211 patients with ISR were enrolled at 13 centers from August 2017 to October 2018 and treated with DCB. At the 9-month coronary angiographic follow-up, patients were divided into restenosis and non-restenosis groups, and demographic data, lesion features, and laboratory tests were retrospectively reviewed. Furthermore, logistic regression analysis was used to identify possible influencing factors. Results All patients successfully underwent treatment, and 166 patients with 190 lesions took part in angiography follow-ups at 9 months. Of these, 41 patients with 44 target lesions developed restenosis following treatment, and the incidence of ISR was 24.7%. There were significant differences in the average length of target lesions and the number of multivessel lesions and fasting plasma glucose (FBG) between the two groups ( p  < 0.05). Demographic data, cardiac risk factors, left ventricular ejection fractions (LVEF), blood routine tests, biochemical tests, and other features of devices and lesions showed no difference. Logistic regression analyses showed that FBG > 6.1 mmol/L (OR: 7.185 95% CI: 2.939–17.567 P  < 0.001) and length of lesion (OR:1.046 95% CI: 1.001–1.093 P  = 0.046) were associated risk factors. Conclusions The longer length of lesions, more target lesions and FBG > 6.1 mmol/L per individual may be characteristics of patients showing ISR following treatment. Studies with larger sample size, and more complete follow-up data are needed in the future to expend on these findings. Trial registration No.: NCT04213378, first posted date (30/12/2019).

In order to assist physicians in diagnosis and treatment planning, accurate and automatic methods of organ segmentation are needed in clinical practice. UNet and its improved models, such as UNet +  + and UNt3 + , have been powerful tools for medical image segmentation. In this paper, we focus on helping the encoder extract richer features and propose a N-Net for medical image segmentation. On the basis of UNet, we propose a dual encoder model to deepen the network depth and enhance the ability of feature extraction. In our implementation, the Squeeze-and-Excitation (SE) module is added to the dual encoder model to obtain channel-level global features. In addition, the introduction of full-scale skip connections promotes the integration of low-level details and high-level semantic information. The performance of our model is tested on the lung and liver datasets, and compared with UNet, UNet +  + and UNet3 + in terms of quantitative evaluation with the Dice, Recall, Precision and F1 score and qualitative evaluation. Our experiments demonstrate that N-Net outperforms the work of UNet, UNet +  + and UNet3 + in these three datasets. By visual comparison of the segmentation results, N-Net produces more coherent organ boundaries and finer details.

Novel coronavirus disease 2019 (COVID-19) is an infectious disease that spreads very rapidly and threatens the health of billions of people worldwide. With the number of cases increasing rapidly, most countries are facing the problem of a shortage of testing kits and resources, and it is necessary to use other diagnostic methods as an alternative to these test kits. In this paper, we propose a convolutional neural network (CNN) model (ULNet) to detect COVID-19 using chest X-ray images. The proposed architecture is constructed by adding a new downsampling side, skip connections and fully connected layers on the basis of U-net. Because the shape of the network is similar to UL, it is named ULNet. This model is trained and tested on a publicly available Kaggle dataset (consisting of a combination of 219 COVID-19, 1314 normal and 1345 viral pneumonia chest X-ray images), including binary classification (COVID-19 vs. Normal) and multiclass classification (COVID-19 vs. Normal vs. Viral Pneumonia). The accuracy of the proposed model in the detection of COVID-19 in the binary-class and multiclass tasks is 99.53% and 95.35%, respectively. Based on these promising results, this method is expected to help doctors diagnose and detect COVID-19. Overall, our ULNet provides a quick method for identifying patients with COVID-19, which is conducive to the control of the COVID-19 pandemic. •We built a new deep learning model (ULNet) and applied it to two classification and three classification tasks.•Multiple experiments showed that the use of ULNet can improve the classification accuracy.•The proposed model helps researchers continue to develop advanced deep learning methods to respond to COVID-19 epidemic.

The Belt and Road Initiative, as an important measure for China in terms of opening up and participating in international economic and trade cooperation, has become a new driving force for the sustainable development of China’s forest products trade. This paper takes the Belt and Road Initiative as a policy event and evaluates its policy effects on the development of China’s forest products trade from the causal level through the difference-in-differences model (DID), explores the policy effect in detail from the perspectives of product heterogeneity and regional heterogeneity, and clarifies the specific impact mechanism. The main results are as follows: (1) there is a significant policy promotion effect of the Belt and Road Initiative on the growth of the bilateral trade scale of forest products between China and the countries along the route. (2) In terms of product structure, the policy promotion effect of the Belt and Road Initiative is mainly manifested in processed wood products. (3) In terms of regional distribution, the policy promotion effects of the Belt and Road Initiative are mainly concentrated in Europe, Africa, and Asia. (4) The “logistics performance, political partnership with China, and Internet penetration” of trading countries play a significant positive mediating role in the policy effects of the Belt and Road Initiative. Therefore, in view of the significant role of the Belt and Road Initiative in promoting the development of bilateral forest products trade, China should promote more countries to participate in the joint construction of the Belt and Road and tap new momentum for the development of the forest products trade by focusing on key countries, priority areas, and key products.

Concerning a 750kV power plant circuit breaker refused to move accident, combined with the action process of accident circuit breaker and the waveform of fault current, analyze the cause of the breakout of the circuit breaker. This paper described the process of the accident from four stages and analyzed the fault waveform in detail. The result shows that the circuit breaker in the DC converter station recloses when the current of circuit breaker in power plant reaches the maximum reverse. The recloses of the circuit breaker in the converter station causing the power plant of the circuit breaker to produce a larger DC component and the DC component caused the short-circuit current-zero offset of the circuit breaker in power plant and the breaker on the power plant side failed to open due to no zero crossing. The simulation result shows that the circuit breaker of power plant does not appear DC component while the reclosing time of the circuit breaker in the DC converter station ahead of 5ms. It verifies that the analysis results are correct.

National distance (ND) is the key factor that affects international trade but the traditional trade gravity model only considers spatial distance, which is not enough. This paper therefore constructs a trade gravity model and a Generalized Moment Estimation Model (GMM) based on four dimensions—spatial distance (SD), economic distance (ED), institutional distance (ID) and cultural distance (CD)—comprehensively analyzing the impact of the heterogeneity represented by national distance on exports of wooden forest products (EWFP) from China to countries along the “Belt and Road” using panel data from 2001 to2018. The results show that the impacts of the four types of ND on China’s EWFP are different and that a major change has taken place since the “Belt and Road” initiative was proposed, within which CD has become the key factor that hinders exports, while the traditional SD is not significant. Therefore, using NDs instead of the SD of the traditional trade gravity model is much more reasonable. Finally, this paper proposes some suggestions to reduce the ND between China and the route countries and to promote cooperation among them.

To solve problems such as insufficient energy and effective arc burning during the injection of a working medium in a short arc discharge channel, the application of a W-Cu alloy as the electrode material in short electrical arc machining (SEAM) is explored in this paper. Using the surface roughness, material removal rate, and tool electrode wear rate as technical indices, the effects of voltage, frequency, and duty cycle on W-Cu alloy materials are studied to explore machining characteristics in SEAM. Contrast experiments on a graphite electrode were conducted using a SU8010 scanning electron microscope and energy spectrum analysis to study the erosion mechanism of the W-Cu alloy material. This paper addresses applications of copper-based composites in SEAM and provides a theoretical basis for the development of electrode materials in the SEAM.

An emerging method for effectively improving the catalytic activity of metal oxide hybrids involves the creation of metal oxide interfaces for facilitating the activation of reagents. Here, we demonstrate that bilayer vesicles formed from a hexavanadate cluster functionalized with two alkyl chains are highly efficient catalysts for the oxidation of 3,3＇,5,5＇-tetramethylbenzidine （TMB） with H2O2 at room temperature, a widely used model reaction mimicking the activity of peroxidase in biological catalytic oxidation processes. Driven by hydrophobic interactions, the double-tailed hexavanadate-headed amphiphiles can self-assemble into bilayer vesicles and create hydrophobic domains that segregate the TMB chromogenic substrate. The reaction of TMB with H2O2 takes place at the interface of the hydrophilic and hydrophobic domains, where the reagents also make contact with the catalytic hexavanadate clusters, and it is approximately two times more efficient compared with the reactions carried out with the corresponding unassembled systems. Moreover, the assembled vesicular system possesses affinity for TMB comparable to that of reported noble metal mimic nanomaterials, as well as a higher maximum reaction rate.

Facial micro-expressions recognition has attracted much attention recently. Micro-expressions have the characteristics of short duration and low intensity, and it is difficult to train a high-performance classifier with the limited number of existing micro-expressions. Therefore, recognizing micro-expressions is a challenge task. In this paper, we propose a micro-expression recognition method based on attribute information embedding and cross-modal contrastive learning. We use 3D CNN to extract RGB features and FLOW features of micro-expression sequences and fuse them, and use BERT network to extract text information in Facial Action Coding System. Through cross-modal contrastive loss, we embed attribute information in the visual network, thereby improving the representation ability of micro-expression recognition in the case of limited samples. We conduct extensive experiments in CASME II and MMEW databases, and the accuracy is 77.82% and 71.04%, respectively. The comparative experiments show that this method has better recognition effect than other methods for micro-expression recognition.

Slot filling and intent detection have become a significant theme in the field of natural language understanding. Even though slot filling is intensively associated with intent detection, the characteristics of the information required for both tasks are different while most of those approaches may not fully aware of this problem. In addition, balancing the accuracy of two tasks effectively is an inevitable problem for the joint learning model. In this paper, a Continual Learning Interrelated Model (CLIM) is proposed to consider semantic information with different characteristics and balance the accuracy between intent detection and slot filling effectively. The experimental results show that CLIM achieves state-of-the-art performace on slot filling and intent detection on ATIS and Snips.