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Background: The rapid spread of Coronavirus Disease-19 (COVID-19) infection has been the most important public health crisis across the globe since the end of 2019. Anxiety and depression are the most common mental health problems among people during the pandemic, and many studies have reported anxiety and depressive symptoms in college students. However, information on the mental health status of international medical students during this critical period of time has been scarce, which hinders the efforts in making proper policy or strategies to help these students. The present study aims to explore the prevalence of anxiety and depressive symptoms in international medical students in China and to find out the factors that have potential predictive value for anxiety and depressive symptoms.Method: A cross-sectional study was carried out for international medical students during November 2020 at China Medical University in Shenyang, China. Five hundred and nineteen international students were interviewed with questionnaires containing demographic variables, Stressors in school, Generalized Anxiety Disorder Assessment (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Simplified Coping Style Questionnaire (SCSQ), Perceived Stress Scale (PSS-10), the Multidimensional Scale of Perceived Social Support (MSPSS), Revised Life Orientation Test (LOT-R) and Resilience Scale-14 (RS-14). Univariate logistic regression and stepwise multiple logistic regression analyses were conducted where appropriate to explore the predictive factors of anxiety symptoms and depressive symptoms.Results: The prevalence of anxiety symptoms and depressive symptoms in the sample population was 28.5% (148/519) and 31.6% (164/519), respectively. Stressors in school (β = 0.176, OR = 1.192, CI: 1.102–1.289), negative coping style (β = 0.639, OR = 1.894, CI: 1.287–2.788) and perceived stress (β = 0.230, OR = 1.258, CI: 1.184–1.337) were found to be the predictors of anxiety symptoms among the international medical students; while gender (β = −0.594, OR = 0.552, CI: 0.315–0.968), stay up late (β = 0.828, OR = 2.288, CI: 1.182–4.431), current place of residence (β = 1.082, OR = 2.951, CI: 1.256–6.931), stressors in the school (β = 0.303, OR = 1.354, CI: 1.266–1.496), negative coping style (β = 0.866, OR = 2.377, CI: 1.516–3.725), perceived stress (β = 0.233, OR = 1.262, CI: 1.180–1.351) were found to be predictors of depressive symptoms.Conclusion: The prevalence of anxiety symptoms and depressive symptoms was moderate among international medical students in China. The communal predictors of anxiety and depressive symptoms were stressors in school, negative coping style and perceived stress; while demographic factors such as gender (male), stay up late at night and current place of residence were found associated with depressive symptoms. These results suggest that proper stress management and specific interventions are needed to help students maintain their mental health during the COVID-19 pandemic period.

Online video text detection is important in many applications, such as real-time translator and wearable camera system for visually-impaired. Existing methods for video text detection perform unsatisfactorily mainly because of the inferior text detection result and insufficient utilization of spatial and temporal information. Besides, the majority of them work in offline mode. In this paper, we propose an online video text detection method which works nearly in real time. We detect texts in each frame using a EAST based text detector, and formulate the online text tracking problem as decision making in Markov Decision Processes (MDPs). The similarity function in tracking stage can be learned by reinforcement learning. Besides, text detection and tracking are naturally unified by state transactions in the MDP. Extensive experiments on three benchmark datasets, ICDAR 2015, Minetto, and Youtube Video Text, verify the effectiveness of our method.

Scene text detection in videos has many application needs but has drawn less attention than that in images. Existing methods for video text detection perform unsatisfactorily because of the insufficient utilization of spatial and temporal information. In this paper, we propose a novel video text detection method with network flow based tracking. The system first applies a newly proposed Fully Convolutional Neural Network (FCN) based scene text detection method to detect texts in individual frames and then track proposals in adjacent frames with a motion-based method. Next, the text association problem is formulated into a cost-flow network and text trajectories are derived from the network with a min-cost flow algorithm. At last, the trajectories are post-processed to improve the precision accuracy. The method can detect multi-oriented scene text in videos and incorporate spatial and temporal information efficiently. Experimental results show that the method improves the detection performance remarkably on benchmark datasets, e.g., by a 15.66% increase of ATA Average Tracking Accuracy) on ICDAR video scene text dataset.

Integrated iron and steel enterprises is taken as the research object. Under the guidance of GHG Protocol standards for carbon emissions, the carbon dioxide accounting models on the process as a whole boundary are established, in which, the carbon emissions inventory is divided into three levels and the input data consists of CO2 emission coefficient and activity factor level data. The process model is the accounting model that the carbon emission is obtained through the balance of carbon input and carbon output outside the process boundary under the condition of considering the enterprise circulation. This article also researched one integrated iron and steel enterprise as a case. The results show that the model can quantify the emissions of different processes, provide basic value for the enterprise reduction, Identify major carbon source, identify the influence on carbon emissions by the changes of fuel structure, the structure of the process parameters and outsourcing by-product internal circulation. Therefore the model results can be used in the carbon emissions of enterprise internal assessment and decision-making.

Based on Josephson junction (JJ), superconducting quantum bit (qubit) is operated at frequencies of several GHz. Dissipation of JJs in this frequency range can cause energy relaxation in qubits, and limit coherence time, therefore it is highly concerned and needs to be determined quantitatively. The dissipation of JJs can be quantified by microwave quality factor. It is usually done at very low temperature (~mK) to determine whether a JJ is suitable for qubit devices by measuring the quality factor. In this paper, a method based on nonlinear frequency response of JJs is proposed to determine the quality factor. This method can be used in thermal activation regime, which may bring great conveniences to experiments. To analyze high frequency properties of JJs, the dynamic equation of a current-biased JJ, which describes high frequency oscillation of the JJ, is introduced. A fourth-order potential approximation is used to obtain the analytical equation of non-linear response. The dependence on quality factor, as well as on amplitude, of difference between JJ’s plasma frequency and resonant frequency, is derived from the equation. The approximate treatment is quantitatively validated by our numerical simulations with practical JJ parameters including different environment influences. Thus, based on nonlinear frequency response of JJs, a reliable and simple method to determine quality factor of JJ is proposed, which is desirable for exploring JJ based microwave devices such as parametric amplifier, superconducting qubit. Being driven well into the nonlinear microwave response regime, due to frequency-amplitude interaction, the resonant frequency of a current bias JJ deviates from the JJ’s plasma frequency. The deviation is directly related to the microwave quality factor. Hence, the quality factor can be deducted from the experimental measurement of the resonant frequency deviation, with different microwave power values applied. In comparison with linear resonance experiment, the nonlinear resonance used by the proposed method produces stronger signal. Therefore it is more robust against external noise. When being conducted at high temperature, the proposed method is more reliable. The accuracy of the measured quality factor primarily depends on those of the JJ’s parameters such as critical current and capacitance, while those parameters can be experimentally determined with high precision.

For different Manufacturing Materials, various embedded software is often needed to use to configure and implement different functions during the processing of production. In order to improve the reusability of embedded software, this paper combines the software product line technology to present a software product line reference architecture OVM_AO model, gives a detail modeling of the modify code part, meanwhile, gives the rule of transform the model to AOP model, improves the efficiency of the software automate equipped with material.

Software requirements validation is one of the hot problems of software engineering field, for the formal verification of software acceptability, this paper presents a formal verification software acceptability method. This method uses the 4-variable model to character the software system requirements and the software behavior, and gives a formal description of the 4-variable model based on the generic model of Tabular expression, and converts the Tabular expression into predicate logic knowledge base to verify the software acceptability. The analysis shows that the proposed method is effective, and the software acceptability can be verified.

The cloud computing can greatly reduce the cost of computing, but is unable to ensure either the integrality or the confidentiality of data and calculation. Therefore, this paper considers of the safety of cloud computing, combining the thought of abstract algebra group theory in modern algebra, puts forward TCCPoGT (trusted cloud computing platform based on group theory) that designs in many different respects,such as general structure, public key cryptosystems and node management,etc. The analysis results show that the platform TCCPoGT can insure service security.

A detailed study is undertaken to analyze the dispersion characteristics of a coating under laser excitation. The surface wave dispersion is one of its inherent properties. Two-dimensional Fourier transform is applied to convert the experiment data in the time-space domain into the frequency-wave-number domain, where the peaks form the dispersive curves. A coherent method is proposed to precisely measure the dispersive curves of coatings. The use of the phase information and magnitude spectrum makes the measurements more precise and stable than traditional non-coherent methods. Additionally, storage depth of oscilloscope affects the precision of dispersion curves. The technique of obtaining the dispersive curves with high precision is an efficient tool to solve the inversion problem.