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Applications challenged by the joint problem of concept drift and class imbalance are attracting increasing research interest. This paper proposes a novel Reinforcement Online Active Learning Ensemble for Drifting Imbalanced data stream (ROALE-DI). The ensemble classifier has a long-term stable classifier and a dynamic classifier group which applies a reinforcement mechanism to increases the weight of the dynamic classifiers, which perform better on the minority class, and decreases the weight of the opposite. When the data stream is class imbalanced, the classifiers will lack the training samples of the minority class. To supply training samples, when creating a new classifier, the labeled instances buffer is used to provide instances of the minority class. Then, a hybrid labeling strategy that combines the uncertainty strategy and imbalance strategy is proposed to define whether to obtain the real label of an instance. An experimental evaluation compares the classification performance of the proposed method with semi-supervised and supervised algorithms on both real-world and synthetic data streams. The results show that the ROALE-DI achieves higher Area Under the ROC Curve (AUC) and accuracy values with even fewer real labels, and the labeling cost dynamically adjusts according to the concept drift and class imbalance ratio.

As the mobile era matures, it is increasingly competitive to market mobile apps, forcing companies to invest heavily on mobile user acquisition campaigns. This has unfortunately given birth to a new form of Internet fraud, which we refer to as ''app distribution fraud''. This new fraud involves collusion between ISPs and fraudulent app distributors where app download is hijacked/redirected. In this paper, we have the unique opportunity to cooperate with a major e-commerce company (with about 0.2 billion active users per month) to take a first peek at this problem. Through the nationwide measurement results, we find that app distribution fraud is ubiquitous yet stealthy --- about 1.55% app downloads are hijacked/redirected, affecting more than 75% of the cities we tested and causing an estimated 7.46 billion U.S. dollars financial loss per year. We follow up with additional measurements on the technical mechanism of the fraud and the scope of the fraud (i.e., what other apps are also affected). Surprisingly, we find that sometimes the original app a user intends to download can be replaced with a completely different app, rendering the user's device at risks.

An excellent routing protocol is important for wireless sensor network construction and efficient data transmission. With continuous expansion of the application scenarios and scopes of the Internet of Things, the existing wireless sensor network routing protocols are no longer suitable for the complex network structure and the huge demands of communications. Aiming at these issues of existing routing protocols, such as short network lifetime caused by high energy consumption and uneven distribution of surviving nodes, this paper proposes an energy saving clustering protocol based on adaptive Voronoi dividing, named ESCVAD (Energy Saving Clustering by Voronoi Adaptive Dividing) protocol. The innovation of ESCVAD protocol lies in the adaptive clustering algorithm based on Voronoi dividing and cluster head election optimization algorithm based on distance and energy comprehensive weighting. The advantage of proposed algorithms is effectively to balance the energy consumption between cluster head nodes and cluster member nodes. The simulation results show that, compared with the traditional routing protocols, such as LEACH (Low Energy Adaptive Clustering Hierarchy) protocol and SEP (Stable Energy Protocol), the proposed ESCVAD protocol can effectively reduce the clustering frequency and cluster head electing frequency, so as to reduce signaling interaction frequency, finally result in the energy consumption down and the network lifetime up. Among the six protocols for comparation, ESCVAD has the best network lifetime and energy efficiency.

Pipeline inner inspection technology based on Pipeline Inspection Gauge (PIG), is the primary means for ensuring the safety of submarine pipelines. The dynamic characteristics of a PIG can change abruptly with the excitation of obstacles such as girth welds inside the pipeline, which would result in failure or inaccuracy of the inspection results. This study establishes a dynamic model of the PIG sealing disc based on Kelvin spring damping in the circumferentially confined space. The axial vibration differential equations of the PIG is examined in detail. MSC/ADAMS is used to conduct the dynamic simulation of the PIG at different motion velocities and center of mass positions while passing through the girth weld process. Results indicate that the axial vibration caused by the girth weld intensifies substantially as the speed of the PIG increases, while the pitch and vertical vibrations exhibit a significant decline with an increase in the motion velocity. The change in the PIG’s center of mass positions has little effect on its axial vibration, while the pitch and vertical vibration conditions are significantly different in the same circumstances.

Background: Studies have shown that pharmacological and psychological treatments are effective for children and adolescents with obsessive-compulsive disorder (OCD). However, few network meta-analyses have examined whether pharmacological or psychological treatments on their own, or combined, are most effective. Methods: We conducted a database search and selected randomized controlled trials of pharmacological or psychological treatments, alone or in combination, for children and adolescents with OCD. The primary outcome was change in symptom severity as a result of treatment, as assessed using the Yale-Brown Obsessive Compulsive Scale (BOCS) or Children’s Yale-Brown Obsessive Compulsive Scale (CY-BOCS) . Findings: We included 18 studies with 1353 participants and 12 kinds of treatments. In terms of efficacy, all pharmacological and psychotherapy treatments were more effective than placebo. Among the 12 treatments, the efficacy of pharmacological treatment combined with cognitive behavioral therapy (CBT) was better than pharmacological treatment alone. When pharmacological treatment was used alone, escitalopram was significantly better than clomipramine(CY-BOCS average change 3.42; 95% CI 2.11, 4.65), fluvoxamine(CY-BOCS average change 3.59; 95% CI 1.09, 6.20), paroxetine(CY-BOCS average change 2 .80; 95% CI 0.01, 5.64) and sertraline(CY-BOCS average change 3.49; 95% CI 1.53, 5.64). Interpretation: The available evidence suggests that the combination of pharmacological and psychological treatment is likely to be most effective for children and adolescents with OCD. Funding: The National Nature Science Foundation of China (81801357, 81801350), the Science and Technology Education Program of the Sichuan Province (2020JDKP0013), the Key R&D Projects of the Science and Technology Department of Sichuan Province(2020YFS0259 ), and the Chengdu Key Technology R&D Program (2019-YF05– 00284-SN). Declaration of Interest: None to declare.

Accurate prediction of surrounding rock grades holds great significance to tunnel construction. This paper proposed an intelligent classification method for surrounding rock based on one-dimensional convolutional neural networks (1D CNNs). Six indicators collected in some tunnel construction sites are considered, and the degree of linear correlation between these indicators has been analyzed. The improved one-hot encoding method is put forward for transforming these non-image indicators into one-dimensional structural data and avoiding the sampling error in the indicators of surrounding rock collected in the field. We found that the 1D CNNs model based on the improved one-hot encoding method can best extract the features of surrounding rock classification indicators (in terms of both accuracy and efficiency). We applied the well-trained classification model of tunnel surrounding rock to a series of expressway tunnels in China, and the results show that our model could accurately predict the surrounding rock grade and has great application value in the construction of tunnel engineering. It provides a new research idea for the prediction of surrounding rock grades in tunnel engineering.

Due to the power interaction between the phase-shifted dual active bridges (PSDABs) and series resonant dual active bridges (SRDABs) in the hybrid modular dc Transformer (HMDCT), it is difficulty to optimize current stress based on the transferred power. An extended-state-observer based model predictive control (ESO-MPC) strategy with the extended phase shift (EPS) is proposed to optimize the current stress of the PSDABs in the HMDCT. The MPC is employed to calculate the optimal transferred power of the PSDABs. Moreover, a first-order ESO is introduced to observe the uncertain output current of the PSDABs. Based on the calculated transferred power, the optimal phase-shifted duty ratios of each PSDAB under EPS, which achieves optimal current stress, are obtained by the Lagrange multiplier method. Because of the ESO-MPC strategy, it enables to achieve the current sensorless control of the HMDCT. Simulations on a four-cell 3kV/500V HMDCT and the extensive experiments of a downscale laboratory platform, using DSP OMPL138 -EZW + FPGA XC7A75T as the core controller, validate the feasibility of the proposed control strategy.

When a laser beam induces surface tension gradient at the free surface of a liquid, a weak surface depression is expected and has been observed. Here we report giant depression and rupture in “optothermocapillary fluids” under the illumination of laser and sunlight. Computational fluid dynamics models were developed to understand the surface deformation and provided desirable physical parameters of the fluid for maximum deformation. New optothermocapillary fluids were created by mixing transparent lamp oil with different candle dyes. They can be cut open by sunlight and be patterned to different shapes and sizes using an ordinary laser show projector or a common laser pointer. Laser driving and elevation of optothermocapillary fluids, in addition to the manipulation of different droplets on their surface, were demonstrated as an efficient controlling method and platform for optofluidic operations. The fundamental understanding of light-induced giant depression and creation of new optothermocapillary fluids encourage the fundamental research and applications of optofluidics.

Modern processors often need to switch among different power states based on usage scenarios, energy availability, and thermal conditions. Dynamic voltage and frequency scaling (DVFS) is a commonly used power management strategy to trade off performance and energy. As transistor scaling is reaching its limit, the viable supply voltage range where DVFS can operate is shrinking, which limits its effectiveness. To extend the performance-energy trade-off capabilities in modern processors, this article proposes dynamic core scaling (DCS) that does not rely on voltage scaling. DCS dynamically adjusts the active superscalar datapath resources so that programs run at a given percentage of their maximum speed while minimizing energy consumption at the same time. Since DCS does not need voltage scaling, it can be combined with DVFS to achieve greater energy savings. To effectively manage performance-energy trade-offs using a combination of DCS and DVFS, this article proposes an oracle controller that demonstrates the optimal control strategy, and two practical controllers that are applicable in real implementations. Evaluations using an 8-way superscalar processor implemented in a 45-nm circuit show that DCS is more effective in performance-energy trade-offs than DVFS at the high performance end for a number of SPEC CPU2000 benchmarks. When used together with DVFS, DCS saves an additional 20 percent of a full-size core’s energy on average. At the minimum operating voltage, DVFS hits its limit, while DCS is still able to achieve an average of 46 percent further energy reduction.

In human-machine interaction systems, continuous movement estimation methods occupy an important position because they are more natural and intuitive than pattern-recognition methods. Essentially, arm position is decided by the shoulder and elbow joint angles. However, the various deformations of muscles around the shoulder and elbow often lead to difficulties in sensor fixation, which results in a loss of synchronization between the surface electromyography (sEMG) signals and joint angles. In order to accurately estimate movement angles using sEMG in situations where the sEMG is not synchronized with joint angles, we utilized a bi-directional long short-term memory (Bi-LSTM) network rather than other deep learning methods to estimate non-dominant arm movements, based on the sEMG signal from the dominant arm. This estimation protocol was designed to avoid a multiplicity of sensors and to simulate more complicated loss of synchronization problems). The performance of the Bi-LSTM was compared with multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and a long short-term memory network (LSTM). The Pearson correlation coefficient (cc) between the estimated and target joint angle sequences was calculated to evaluate the performance of each neural network. The Wilcoxon signed-rank results showed that the Bi-LSTM model significantly outperformed the MLP, CNN, and LSTM models (tested with completely untrained newly recorded free movements).

AJCC TNM stage and WHO grade (G) are two widely used staging systems to guide clinical management for pancreatic neuroendocrine neoplasms (panNENs), based on clinical staging and pathological grading information, respectively. We proposed to integrate TNM stage and G grade into one staging system (TNMG) and to evaluate its clinical application as a prognostic indicator for panNENs. Accordingly, 5254 patients diagnosed with panNENs were used to evaluate and to validate the applicability of TNMG to panNENs. The predictive accuracy of TNMG system was compared with that of each separate staging/grading system. We found that TNM stage and G grade were independent risk factors for survival in both the Surveillance, Epidemiology, and End Result (SEER) and multicenter series. The interaction effect between TNM stage and G grade was significant. Twelve subgroups combining the TNM stage and G grade were proposed in the TNMG stage, which were classified into five stages TNMG. According to the TNMG staging classification in the SEER series, the estimated median survival for stages I, II, III, IV, and V were 203, 174, 112, 61, and 8 months, respectively. The predictive accuracy of TNMG stage was higher than that of TNM stage and G grade used independently. The TNMG stage classification was more accurate in predicting panNEN patient's prognosis than either the TNM stage or G grade.

Simultaneous and proportional control (SPC) methods based on surface electromyogram (sEMG) can provide a more intuitive and natural interaction in rehabilitation assistive robots and prostheses. Recently, an increasing number of researchers have utilized deep learning methods to continuously estimate movements across multiple degrees of freedom (DoFs). It stands for higher dimensional and more complex target space, which requires more comprehensive feature extraction methods. However, the existing feature extraction methods for deep learning approaches can hardly provide adequate information for multiple DoFs complex movement estimation. Therefore, we proposed a novel sEMG feature extraction method for deep learning models to more accurately predict the joint angles simultaneously on ten DoFs and tested it on eight complex movements. The experiment was carried out on four commonly adopted deep learning methods to compare the performance of the proposed feature set with others. For the four deep learning methods, the average Pearson correlation coefficient (CC) value was around 0.85 and 0.61 for the proposed feature set and raw sEMG data, respectively. Besides, the feature extraction only takes 6 μs per 100 ms analysis window. This study suggested that the proposed feature set would improve deep learning method's application prospect on SPC for rehabilitation robotic systems.

Body temperature measurement is a very important task in the sow breeding process. The authors used an infrared camera to detect the temperature of the body surface of the sows, relying on calculating the average of the infrared image temperature in the ear root region. Based on the grayscale value of the target image of the infrared image and the corresponding temperature value of 180 infrared images, a G‐T (Gray‐Temperature) model was established by linear least squares method, which achieved temperature inversion of each pixel of the target pig. For the different growth stages and different breeds of sows, the R‐square of the all established models is greater than 0.95. The average relative error of the model inversion of the body temperature was only 0.076977%. This means that the body temperature of the sows could be detected without relying on the software. Based on the G‐T model, the authors design a kind of sow's ear root recognition and body surface temperature detection algorithm for different sow population scenarios.

The American Association for the Surgery of Trauma (AAST) appendicitis severity grading criteria use independent subscales for radiologists (Rad), surgeons (Surg), and pathologists (Path). We reviewed the EAST Multicenter Study of the Treatment of Appendicitis in America: Acute, Perforated, and Gangrenous (MUSTANG) database to determine rates of discordance and clinical consequences of inaccuracy.A confusion matrix was constructed for pairs among Rad, Surg, and Path. Accuracy was reported using chronologically latest diagnosis as gold standard. "Concordance" (C) was achieved when both agreed on the severity grade and "Discordance"(D) when they disagreed. A composite endpoint("COMP"= 30-d incidence of surgical site infection, abscess, wound complication, Clavien-Dindo complication, secondary intervention, ED[Emergency Department] visit, hospital readmission, and mortality) was compared between C versus D groups via χFor each pair and diagnosis, subjects were categorized as C or D and compared for the incidence of COMP. Incidence of COMP for Surg and/or Path in C versus D: 16% versus. 26% (p = 0.006, NS by Bonferroni) for acute (A), 39% versus 33% (p = 0.39) for gangrenous (G), and 48% versus 37% (p = 0.035, NS by Bonferroni) for perforated (P). For Rad and/or Path in C versus. D: 17% versus 42% (p0.001) for A, 27% versus 31% (p = 0.95) for G, and 56% versus 48% (p = 0.48) for P. For C versus D: 17% versus 40% (p0.001) for A, 36% versus 26% (p = 0.43) for G, and 51% versus 39% (p = 0.29) for P.In appendicitis treated by appendectomy, surgeons are most accurate at diagnosing acute appendicitis and least accurate at diagnosing gangrenous. Radiologists are less accurate for all categories. When the surgeon is wrong, clinical outcomes are not significantly worse. However, when the radiologist is wrong about acute appendicitis, patients have worse clinical outcomes.

Cardiovascular diseases (CVDs) are a large group of diseases and have become the leading cause of morbidity and mortality worldwide. Although considerable progresses have been made in the diagnosis, treatment and prognosis of CVD, communication barriers between clinicians and researchers still exist because the phenotypes of CVD are complex and diverse in clinical practice and lack of unity. Therefore, it is particularly important to establish a standardized and unified terminology to describe CVD. In recent years, there have been several studies, such as the Human Phenotype Ontology, attempting to provide a standardized description of the disease phenotypes. In the present article, we outline recent advances in the classification of the major types of CVD to retrospectively review the current progresses of phenotypic studies in the cardiovascular field and provide a reference for future cardiovascular research.

Automatic modulation recognition is a critical challenge in the field of cognitive radio. In the process of communication, radio signals are modulated in various modes and are interfered by the complex electromagnetic environment. To cope with these problems and avoid manual selection of complex expert features, we propose a multi-level feature extraction algorithm based on deep learning to adequately exploit the hidden feature information of modulated signals. Our algorithm integrates the correlation between the channels of radio signals with convolutional neural networks and Bidirectional Long Short-term Memory (Bi-LSTM), and adopts the appropriate skip connection, which avoids the loss of valid information and achieves the complementarity between spatial and temporal features. In our model, the one-dimensional convolutional layer is specially utilized to enrich the feature representation of each sample point of in-phase and quadrature (I/Q) signals and emphasize the mutual influence of I channel (in-phase signal) and Q channel (quadrature signal). In addition, the label smoothing technique is used to improve the generalization ability of the model. Our proposed method is also of certain significance for other signal processing methods based on deep learning. Experiment results demonstrate that our algorithm outperforms the popular algorithms and is of higher robustness. Specifically, the proposed method improves the recognition accuracy, reaching 92.68% at high signal-to-noise ratio (SNR). In particular, it also reduces the difficulty of recognition for multiple quadrature amplitude modulation (MQAM) signals and significantly improves the recognition accuracy for 16QAM and 64QAM.

Time series prediction refers to the learning of existing observed data of a parameter and predicting its future evolution. Based on the application of machine/deep learning methods in the field of engineering geology, it is desirable to predict the time series evolution of microseismic parameters in the process of rockburst development. Our study explores key microseismic indices that help describe the development process of rockbursts based on abundant rockburst data obtained from deep underground engineering construction. The integrated process of dynamic moving-window method and improved convolutional neural network (CNN) realizes the evolution prediction of multiple microseismic parameters or their different combinations, and the modified model structures of a univariate, multivariate input and a single-step, multi-step output are established. Various models of the multiple microseismic parameters for the CNN-based time series prediction are innovated, including a univariate prediction model, a multiple parallel series model, a multiple input series model, and a multivariate multi-step prediction model. Model training, testing, and interpretation of the rockburst risk and comparative analyses of the different models are performed for the complete process of multiple rockbursts. The results show that the proposed models can well predict the evolution trends in the various key characteristics during rockbursts. The predicted trend of multiple microseismic parameters provides time labels for rockburst prediction and risk judgement, which is conducive to rockburst early warning. This study provides a new research idea for the prediction and early warning of rockbursts in the field of deep underground and mining engineering.

Objectives: This paper analyzes and studies the relationship between the scale, structure, efficiency and economic growth of tobacco financial development in China. Methods: Through the research on the theory of supply chain finance, this paper studies the products and application of the financial chain in the tobacco industry by using the methods of case analysis, literature research and case analysis. At the same time, taking the products of a bank as an example, this paper describes the product handling and operation process in detail, and analyzes the advantages and disadvantages of the products. Results: Commercial banks at home and abroad have gradually realized the power of the vigorous development of tobacco financial supply chain and actively developed tobacco supply chain financial products. The promotion of China's bank supply chain financial products at this stage will help banks to improve and adjust the traditional credit business structure. At the same time, it helps to improve and optimize the structure of tobacco customers and enhance the business cooperation between tobacco companies and banks. This can reduce the business risk of the tobacco industry and improve the overall profitability of the tobacco industry. Conclusion: The tobacco industry should steadily take advantage of the organization and coordination advantages of the government and make use of development financial funds to overcome difficulties. Only by improving the design and enlarging the leverage of financial support policies can the tobacco industry further develop.

A reliable 235U enrichment uniformity detection system based on a compact D-D neutron generator is developed to detect the 235U enrichment uniformity of different fuel elements in the same nuclear fuel rod. The high-yield compact D-D neutron generator provides 2.45 MeV D-D neutrons, decelerated by a moderator to thermal neutrons or epithermal neutrons, thereby inducing 235U fission to produce highly excited state fission fragments that undergo de-excitation via γ-ray emission. The system detects the 235U enrichment uniformity of a nuclear fuel rod by measuring γ-rays and establishing a relationship between the γ-ray count rate and 235U enrichment in nuclear fuel. The proposed system yields a confidence probability of 99.99% for a relative 235U enrichment deviation of 10% and a neutron yield of 5 × 108 n/s, and the detection accuracy increases with increasing neutron yield. Furthermore, the developed system can satisfy quality control requirements for nuclear fuel production to promote the safe development of nuclear power.

Background The outbreak of Coronavirus Disease 2019(COVID-19) caused psychological stress in Chinese adults population. But we are unaware of whether the pandemic causes psychological stress on children. Methods We used the Children’s Impact of Event Scale questionnaire (CRIES-13) to investigate the degree of Post-traumatic Stress (PTSD) symptoms caused by the pandemic in students selected from schools in Sichuan, Jiangsu, Henan, Yunnan, and Chongqing provinces of China. Results A total of 7769 students(3692 male and 4077 female), aged 8–18 years, were enrolled in the study, comprising 1214 in primary schools, 2799 in junior high schools and 3756 in senior high schools. A total of 1639 students (21.1%) had severe psychological stress reactions. A large proportion of senior high school students (23.3%) experienced severe psychological stress, and they had the highest median total CRIES-13 score. Female students were more likely to experience severe psychological stress and had higher median CRIES-13 total scores than males. Conclusion COVID-19 has placed psychological stresses on primary and secondary school students in China. These stresses are more likely to reach severe levels among female students and senior high school students.

Background The incidence rate of adenocarcinoma of the oesophagogastric junction (AEG) has significantly increased over the past decades, with a steady increase in morbidity. The aim of this study was to explore a variety of clinical factors to judge the survival outcomes of AEG patients. Methods We first obtained the clinical data of AEG patients from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and least absolute shrinkage and selection operator (LASSO) regression models were used to build a risk score system. Patient survival was analysed using the Kaplan-Meier method and the log-rank test. The specificity and sensitivity of the risk score were determined by receiver operating characteristic (ROC) curves. Finally, the internal validation set from the SEER database and external validation sets from our center were used to validate the prognostic power of this model. Results We identified a risk score system consisting of six clinical features that can be a good predictor of AEG patient survival. Patients with high risk scores had a significantly worse prognosis than those with low risk scores (log-rank test, P-value Conclusions Our results may provide new insights into the prognostic evaluation of AEG.

Human activity recognition using WiFi signals is widespread for smart-environment sensing domain in recent years. Existing researches use learning-based methods to obtain several features of activity data and then recognize human activities. As we know, propagation characteristics of WiFi signals are different for individuals under different place conditions even in the same environment. In this paper, we focus on how to weaken the accuracy differences among individuals on activity recognition and improve the robustness in one indoor environment. Based on this, we design a novel deep learning model called LCED which consists of one LSTM-based Encoder, features image presentation, and one CNN-based Decoder to weaken the accuracy differences among individuals on activity recognition. We first use a low-pass filter to remove high-frequency noise data in time-sequence signal data and design variance-based window method to determine the start and the end of time-sequence signal data corresponding to an activity. After that, we utilize the proposed LCED model to learn informative features space of activity data and improve the accuracy of sixteen activities. Experimental results show that the average accuracy of sixteen activities is high 95 % and the accuracy differences among individuals on activity recognition averagely decreases by 3 % .

Conveyor belt dryers have been applied extensively in the drying process even though the problem of uneven drying along the dryer length exist. The general solution is to install an auxiliary device in the chamber to adjust the arrangement of air passages. A series of problems, however, are observed, such as occupying internal space and increasing flow resistance. A novel flow field improvement technology has been developed and conducted by Computational Fluid Dynamics (CFD) with a modified heat and moisture transfer model and validated by physical verification. The results show that specific stacking of feedstock characterized as vertical serration stacking with 30° angle and 43.0 mm interval can help to improve drying performance. Three indices (Integrate area, Mavg, and Mu) for the evaluation of velocity distribution confirms that most uniform moisture of products is obtained with the specific characteristics. This work presents an unconventional method to solve the problem of uneven air-distribution and thus improving drying efficiency.

Purpose To use the 4H-SiC material in integrated circuits for high temperature application, an accurate and simple circuit model of n-channel planar 4H-SiC MOSFET is required. Design/methodology/approach In this paper, a SPICE model of n-channel planar 4H-SiC MOSFET was built based on the device simulation results and measurement results. Firstly, a device model was simulated with Sentaurus TCAD, with measured parameters from fabricated planar 4H-SiC MOSFET previously. Then the device simulation results were analyzed and parameters for SPICE models were extracted. With these parameters, an accurate SPICE model was built and simulated. Findings The SPICE model exhibits the same performance as the measured results with different environment temperatures. The simulation results indicate that the maximum fitting error is 0.22 mA (7.33% approximately) at 200 °C. A common-source amplifier with this model is also simulated and the simulated gain is stable at different environment temperatures. Originality/value This paper provides a reliable modeling method for n-Channel Planar 4H-SiC MOSFET and reference value for the design of 4H-SiC high temperature integrated circuit.

Defects can affect all aspects of materials by altering their electronic structures and mediating the carrier dynamics. However, in the past decades, most research efforts were restricted to nonstoichiometric defects, while the effects of high-density defects on the carrier dynamics of semiconductors remained elusive. In this work, using transient absorption spectroscopy, we have observed for the first time a hybrid carrier relaxation dynamics with the feature of a Poisson-like retard shoulder in a time-domain profile in highly defective ZnO crystals. This novel behavior has been attributed to the spectral diffusion within continuum defect states, which is further confirmed by a proposed diffusion (in energy space) controlled carrier dynamic model. Our results thus reveal an alternative energy decay channel in highly defective crystals and may provide a new route for defect engineering.

Summary Background The benefit and safety of laparoscopic pancreatoduodenectomy (LPD) for the treatment of pancreatic or periampullary tumours remain controversial. Studies have shown that the learning curve plays an important role in LPD, yet there are no randomised studies on LPD after the surgeons have surmounted the learning curve. The aim of this trial was to compare the outcomes of open pancreatoduodenectomy (OPD) with those of LPD, when performed by experienced surgeons. Methods In this multicentre, open-label, randomised controlled trial done in 14 Chinese medical centres, we recruited patients aged 18–75 years with a benign, premalignant, or malignant indication for pancreatoduodenectomy. Eligible patients were randomly assigned (1:1) to undergo either LPD or OPD. Randomisation was centralised via a computer-generated system that used a block size of four. The patients and surgeons were unmasked to study group, whereas the data collectors, outcome assessors, and data analysts were masked. LPD and OPD were performed by experienced surgeons who had already done at least 104 LPD operations. The primary outcome was the postoperative length of stay. The criteria for discharge were based on functional recovery, and analyses were done on a modified intention-to-treat basis (ie, including patients who had a pancreatoduodenectomy regardless of whether the operation was the one they were assigned to). This trial is registered with Clinicaltrials.gov , number NCT03138213 . Findings Between May 18, 2018, and Dec 19, 2019, we assessed 762 patients for eligibility, of whom 656 were randomly assigned to either the LPD group (n=328) or the OPD group (n=328). 31 patients in each group were excluded and 80 patients crossed over (33 from LPD to OPD, 47 from OPD to LPD). In the modified intention-to-treat analysis (297 patients in the LPD group and 297 patients in the OPD group), the postoperative length of stay was significantly shorter for patients in the LPD group than for patients in the OPD group (median 15·0 days [95% CI 14·0–16·0] vs 16·0 days [15·0–17·0]; p=0·02). 90-day mortality was similar in both groups (five [2%] of 297 patients in the LPD group vs six [2%] of 297 in the OPD group, risk ratio [RR] 0·83 [95% CI 0·26–2·70]; p=0·76). The incidence rate of serious postoperative morbidities (Clavien-Dindo grade of at least 3) was not significantly different in the two groups (85 [29%] of 297 patients in the LPD group vs 69 [23%] of 297 patients in OPD group, RR 1·23 [95% CI 0·94–1·62]; p=0·13). The comprehensive complication index score was not significantly different between the two groups (median score 8·7 [IQR 0·0–26·2] vs 0·0 [0·0–20·9]; p=0·06). Interpretation In highly experienced hands, LPD is a safe and feasible procedure. It was associated with a shorter length of stay and similar short-term morbidity and mortality rates to OPD. Nonetheless, the clinical benefit of LPD compared with OPD was marginal despite extensive procedural expertise. Future research should focus on identifying the populations that will benefit from LPD. Funding National Natural Science Foundation of China and Tongji Hospital, Huazhong University of Science and Technology, China.

Objective The study aimed to (1) quantify readmission rates and common causes of readmission following endoscopic transsphenoidal pituitary surgery (ETPS); (2) identify risk factors that may predict readmission within 30 days; (3) assess postoperative care coordination with endocrinology follow-up; and (4) identify patients for whom targeted interventions may reduce 30-day readmissions. Methods Retrospective quality improvement review of patients with pituitary adenoma who underwent ETPS from December 2010 to 2018 at a single tertiary care center. Results A total of 409 patients were included in the study, of which 57 (13.9%) were readmitted within 30 days. Hyponatremia was the most common cause of readmission (4.2%) followed by pain/headache (3.9%), cerebrospinal fluid leak (3.4%), epistaxis (2.7%), hypernatremia (1.2%), and adrenal insufficiency (1.2%). Patients with hyponatremia were readmitted significantly earlier than other causes (4.3 ± 2.2 vs. 10.6 ± 10.9 days from discharge, p = 0.032). Readmitted patients had significantly less frequent outpatient follow-up with an endocrinologist than the nonreadmitted cohort (56.1 vs. 70.5%, p = 0.031). Patients who had outpatient follow-up with an endocrinologist were at lower risk of readmission compared with those without (odds ratio: 0.46; 95% confidence interval: 0.24–0.88). Conclusion Delayed hyponatremia is one of the most common causes of 30-day readmission following ETPS. Postoperative follow-up with an endocrinologist may reduce risk of 30-day readmission following ETPS. Implications for Clinical Practice A multidisciplinary team incorporating otolaryngologist, neurosurgeons, and endocrinologist may identify patients at risk of 30-day readmissions. Protocols checking serum sodium within 1 week of surgery in conjunction with endocrinologist to tailor fluid restriction may reduce readmissions from delayed hyponatremia.

The present meta-analysis was performed to compare the efficacy and safety of percutaneous cholecystostomy (PC) versus emergency cholecystectomy (EC) for the treatment of acute calculous cholecystitis (ACC) in high-risk surgical patients. Literature searches for eligible studies were performed using MEDLINE, EMBASE and the Cochrane Library. Quality assessment was conducted in each study. Meta-analyses were performed to demonstrate the pooled effects of relative risk (RR) with 95% confidence intervals (CI). A total of 8960 patients from 6 studies were finally included. PC resulted in increased risks of mortality (RR = 2.87; CI = 1.33–6.18; p = 0.007) and readmission rate (RR = 4.70; CI = 3.30–6.70; p

Wireless charging for electric vehicles has been investigated in recent years. One of the critical issues of the research is to improve the weak interoperability between two popular charging pads—rectangular pad (RCP) and double-D pad (DDP). This article intends to model the interoperability under pad misalignment and improve it by adjusting the pads’ relative position. First, the variation of interoperability with pad misalignment is analyzed by the flux model. Full output power and high transmission efficiency can be achieved when the two pads are misaligned to a certain distance. Second, the optimal misalignment is obtained by flux calculation and simulation. Third, an adaptive position adjustment method for the transmitting pad based on the variable-step perturbation and observation algorithm is proposed. By searching the maximum dc input current, the optimal position of the transmitting pad can be found. Finally, in the experiment, a 3.3 kW output with 95% coil-coil efficiency is realized when the RCP and DDP interoperate at the optimal position. Compared with the scenario without position adjustment, the improvement of output power and efficiency proves that the proposal can optimize the interoperability between the RCP and DDP.

Sarcomatoid malignant mesothelioma (MM) is a rare and aggressive disease, and its diagnosis is challenging. A 60-year-old man presented with a recurrent subcutaneous mass in his right back after the initial resection. A chest computed tomography (CT) scan found right pleural thickening, nodular pleural thickening, pleural effusion, mediastinal, and right infraclavicular lymph nodes enlargement, which indicated a right pleura MM. Immunohistochemical stains of the resected mass showed sarcomatous atypical spindle cells, which were positive for pan-CKs (clone Anti-cytokeratin cocktail AE1/AE3), cytokeratin 5/6 (CK5/6), Wilm’s tumor 1, podoplanin, vimentin and programmed death-ligand 1 (PD-L1), and negative for Napsin A, thyroid transcription factor 1, CDX 2, calretinin and desmin, and fluorescent in situ hybridization detected homozygous p16/cyclin-dependent kinase inhibitor 2A ( p16/CDKN2A) deletion. The association of the chest CT features and the pathological assessment confirmed metastatic MM in the subcutaneous layer of the back. Moreover, positron emission tomography–CT showed multiple metastases in his brain. He developed massive right pleural effusion and chest tightness soon, and the mass kept growing despite local and systemic treatments. The patient die of pulmonary failure in 3 months.

OBJECTIVESLeft ventricular systolic dysfunction (LVSD) is common and associated with adverse events in patients receiving coronary artery bypass grafting (CABG). However, the prognosis of mild LVSD has not been clearly described. We aimed to evaluate the mid-term outcomes of patients with mild LVSD following CABG.METHODSThis multicentre cohort study using propensity score matching took place from December 2012 to October 2019 in Jiangsu Province, China, with a mean and maximum follow-up of 3.2 and 7.2 years, respectively. Patients were classified to normal left ventricular systolic function (left ventricular ejection fraction ≥53%) and mild LVSD (left ventricular ejection fraction >40%/RESULTSA total of 581 pairs were formed after matching. In-hospital death (1.5% vs 2.1%, P = 0.51) did not differ between 2 cohorts. Throughout 7 years, mild LVSD was associated with higher rates of death from all causes [hazard ratio (HR) 0.59, 95% confidence interval (CI) 0.39–0.89; P = 0.012], death from cardiovascular causes (HR 0.55, 95% CI 0.36–0.90; P = 0.017), heart failure (HR 0.60, 95% CI 0.37–0.93; P = 0.023) and major adverse events (HR 0.66, 95% CI 0.49–0.91; P = 0.009). There was no difference in the rates of myocardial infarction and repeat revascularization.CONCLUSIONSMild LVSD was associated with a worse mid-term prognosis in patients following CABG.

Wireless communication with unmanned aerial vehicle (UAV) has attracted much attention due to its flexibility and low-cost in providing wireless connectivity. In this article, we investigate the spectrum sharing between UAV and terrestrial wireless communication. Two scenarios are considered in this work. Scenario 1: the UAV is energy sufficient to transmit the required data. In this case, our objective is to minimize the time that is needed to complete data transmission. Scenario 2: the battery of the UAV is energy insufficient. In this case, the goal becomes maximizing the size of data that can be transmitted. Different scenarios require different placement of the UAV. When the UAV flies towards the primary user (PU), the sensing result will be more accurate and more opportunities can be utilized to transmit data. However, the interference to primary receiver (PR) and the propulsion energy due to UAV’s movement will be increased. In this article, the sensing performance, the power allocation and the UAV’s position are jointly optimized to satisfy different requirements of the UAV system. To efficiently utilize the licensed spectrum, a hybrid transmission mode is proposed for the UAV system, in which sensing and power control are jointly employed to protect the PR. Then, efficient iterative algorithms are proposed to solve the optimization problems. Simulations results show that proposed designs can significantly reduce the time that is required to complete data transmission in energy sufficient scenario and enhance the size of data that can be transferred in energy insufficient scenario.

We presented a machine learning-based method to recognize suspension by distinguishing dispersoid-dependent speckle patterns using a convolutional neural network. The dispersoid size and concentration-related transmissive speckle patterns were recorded by a lensless camera when a coherent He-Ne laser irradiated the suspension. Firstly we realized the recognition of the polystyrene microspheres-dispersed suspensions with different particle sizes and the recognition of several common suspensions including protein powder and milk powder with similar concentration. Further recognition from three different food suspensions with unknown concentration was achieved with high accuracy of 99%. The experimental results confirm that this non-contact method could find applications in the measurement and classification of suspension liquid containing micrometers-sized particles.

Aiming at the problem of slow neighbor discovery speed of sector antenna neighbor discovery (SAND) protocol based single token, an asynchronous sector antenna neighbor discovery protocol based double tokens (D-SAND) is proposed. The double tokens include one master token and one slave token. The master token holder (MTH) starts its neighbor nodes discovery process by sending hone-in message through beam scanning, and ends this process when each neighbor node is discovered. Then, MTH passes the master token to the next hop master token holder (nMTH). At the same time, MTH sends the location information of the nMTH node to its parent master token holder (MTP) from which it obtains its token. MTP determines whether to generate a slave token according to that if there exists a neighbor node with a distance greater than 2R from nMTH, where R represents the communication distance. If there exists one such neighbor, MTP generates a slave token and passes it to that neighbor node, who can begin the neighbor discovering process. The time conditions are theoretically derived when these two tokens begin their neighbor discovering process respectively and without interference to each other. Simulation results show that compared to Q-SAND(quick-SAND), the proposed D-SAND protocol reduces the network-wide neighbor discovery time about by 10%.

Objective Adrenocorticotropic hormone-producing pancreatic neuroendocrine neoplasm (ACTHoma) is an exceedingly rare type of pancreatic neuroendocrine neoplasm (pNEN) that often causes ectopic adrenocorticotropic hormone syndrome. These neoplasms have been found to be very aggressive and challenging to treat. The current systematic review aimed to analyze the clinical features, immunohistochemical characteristics, diagnosis, therapy, and prognosis of ACTHoma. Methods A systematic review of the English- and Chinese-language literature was performed. PubMed, EMBASE, and Wanfang databases were searched to identify articles about ACTHoma in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Results A total of 210 studies encompassing 336 patients diagnosed with ACTHoma were selected for the systematic review, including 16 Chinese patients. Conclusion ACTHoma was more common in women (66.4%), and the mean age was 44.7 years. Tumors were generally large, and the mean tumor size was 4.43 cm. The incidence of clinical manifestations was: hypokalemia, 69.3%; diabetes, 63.2%; weakness, 60.1%, hypertension, 56.4%; moon face 41.1%; and edema, 37.4%. These tumors are more commonly found in the tail of pancreas, and the most frequent site of metastasis was the liver. The pNENs or other functioning pNENs could evolve into ACTHoma. ACTHoma is a very rare disease, and the mean follow-up time was 28.3 months.

Background: Researchers have pursued the new synthesis method. As a newly developed method, microwave (MW), ultrasound (US) and ultraviolet light (UV) assisted synthesis has drawn increasing interests. Under the synergistic effect, many materials with new structure, morphology and properties may be found. As an important rare-earth phosphate, DyPO4 was selected and the effect of MW, US and UV on the preparation was investigated. Method: The DyPO4·1.5H2O nanostructures were prepared by MW, US, UV and their combination. Results: Hexagonal DyPO4·1.5H2O microcrystals obtained under MW irradiation were broomstick bundles. Needle-shaped products were formed in the presence of MW and US. Interestingly, the broom-sheaf-like structures can self-assemble into flower-shaped structures upon the irradiation of MW and UV. Whereas, MW/UV/US synergetic heating results in mixed morphologies of flower-like and needle-shaped structures. Conclusion: The growth of DyPO4 nanostructures can be tuned by selecting the combination of heating method of MW, US and UV.

This paper examines the effects of finance on total factor productivity (TFP) growth based on panel data of 30 Chinese provinces from 2000 to 2016. We find that financial volatility has a significa...

In order to improve the accuracy and efficiency of Android malware detection, an Android malware detection model based on decision tree (DT) with support vector machine (SVM) algorithm (DT-SVM) is proposed. Firstly, the original opcode, Dalvik opcode, is extracted by reversing Android software, and the eigenvector of the sample is generated by using the n-gram model. Then, a decision tree is generated via training the sample and updating decision nodes as SVM nodes from the bottom up according to the evaluation result of the test set in the decision path. The model effectively combines DT with SVM. Under the premise of maintaining a high-accuracy decision path, SVM is used to effectively reduce the overfitting problem in DT and thus improve the generalization ability, and maintain the superiority of SVM for the small sample training set. Finally, to test our approach, several simulation experiments are carried out, and the results demonstrate that the improved algorithm has better accuracy and higher speed as compared with other malware detection approaches.

In recent years, the large-scale power outages caused by extreme natural disasters have caused considerable economic losses. In this paper, an optimization decision-making method is proposed to allocate emergency power resources in a distribution system ahead of an extreme natural disaster. A resource allocation optimization model is proposed to allocate different resources among microgrids and critical loads in the distribution system. The factors including capacity, rental prices, energy reserves at each stage, and so on are all considered. A heuristic method is proposed to solve the model. By transforming the optimization model into a mixed-integer quadratic program, the resource allocation plan can be obtained efficiently. A case based on the IEEE 123-bus system is performed. The results validate the effectiveness and efficiency of our method.

Multidrug resistance (MDR) remains one of the major impediments for efficacious cancer chemotherapy. Increased efflux of multiple chemotherapeutic drugs by transmembrane ATP-binding cassette (ABC) transporter superfamily is considered one of the primary causes for cancer MDR, in which the role of P-glycoprotein (P-gp/ABCB1) has been most well-established. The clinical co-administration of P-gp drug efflux inhibitors, in combination with anticancer drugs which are P-gp transport substrates, was considered to be a treatment modality to surmount MDR in anticancer therapy by blocking P-gp-mediated multidrug efflux. Extensive attempts have been carried out to screen for sets of nontoxic, selective, and efficacious P-gp efflux inhibitors. In this review, we highlight the recent achievements in drug design, characterization, structure-activity relationship (SAR) studies, and mechanisms of action of the newly synthetic, potent small molecules P-gp inhibitors in the past 5 years. The development of P-gp inhibitors will increase our knowledge of the mechanisms and functions of P-gp-mediated drug efflux which will benefit drug discovery and clinical cancer therapeutics where P-gp transporter overexpression has been implicated in MDR.

Background The incidence of underlying malignancy in appendicitis ranges between 0.5% and 1.7%. We sought to identify the subset of patients with appendicitis who are at increased risk of appendiceal malignancy. Methods Using the Eastern Association for the Surgery of Trauma Multicenter Study of the Treatment of Appendicitis in America: Acute, Perforated, and Gangrenous database, we included all patients from 28 centers undergoing immediate, delayed, or interval appendectomy between 2017 and 2018. Univariate then multivariable analyses were performed to compare patients with and without malignancy and to identify independent demographic, clinical, laboratory, and/or radiological predictors of malignancy. Akaike information criteria for regression models were used to evaluate goodness of fit. Results A total of 3,293 patients were included. The median age was 38 (27–53) years, and 46.5% were female patients. On pathology, 48 (1.5%) had an underlying malignancy (adenocarcinoma [60.4%], neuroendocrine [37.5%], and lymphoma [2.1%]). Patients with malignancy were older (56 [34.5–67] vs 37 [27–52] years, P 40 years with an appendiceal diameter >10 mm on computed tomography was 2.95% compared with 0.97% in patients ≤40 years old with appendiceal diameter ≤10 mm. The corresponding risk ratio for that population was 3.03 (95% confidence interval: 1.24–7.42; P = .02). Conclusion The combination of age >40 and an appendiceal diameter >10 mm is associated with a greater than 3-fold increased risk of malignancy in patients presenting with appendicitis.

Organic-inorganic halide perovskite materials have been demonstrated with wide applications in optoelectronics and ionization radiation detection. For bulk as-grown crystals, the existence of surface cracks and defects can significantly increase charges recombination and reduce the performance of the device. Herein, we polished the crystal surfaces with both mechanical and chemical mechanical methods at room temperature. After been chemical-mechanical polished, the crystal surface with root mean square roughness about 0.5 nm was obtained. Optical transmission and photoluminescence spectra indicate that chemical mechanical polishing technology can effectively reduce the density of crystal surface defects. The achieved low leakage current density on the surface and bulk crystal is 0.05 nA mm−2 and 0.07 nA mm−2, respectively. Furthermore, the current-voltage curve under visible photons and X-ray photons reveals that surface polishing treatment can suppress the charges recombination and increase the charges transportation.

As increasing demand for noncontact temperature sensing, the development of a high-performance optical thermometer probe is more and more urgent. In this work, an efficient dual-mode optical thermometry strategy based on the Pr3+/Dy3+ energy transfer (ET) in some typical double-perovskite oxides is presented, which offers a promising way to design FIR/lifetime dual-mode optical thermometry with excellent temperature-measuring sensitivity and signal discrimination. According to this strategy, double-perovskite La2MgTiO6:Pr3+/Dy3+ phosphors are successfully synthesized. On the basis of diverse thermal responses between Pr3+ and Dy3+, the FIR of Pr3+ to Dy3+ (four FIR mode) in this material displays outstanding optical thermometry performance from 298 to 548 K. The maximum absolute and relative sensitivities (Sa and Sr) of mode 1 are 0.09 and 2.357% K-1, being better than the current optical temperature measurement materials. For the fluorescence lifetime mode, the Sa-max and Sr-max values reach 2.85 × s 10-4 and 1.814% K-1. Furthermore, the dual-mode optical thermometry mechanism was presented and studied. It also demonstrated excellent optical thermometry performance in the other Pr3+/Dy3+ codoped double-perovskite oxides, such as LaMg0.598Nb0.402O3, NaLa(MoO4)2, NaGd(MoO4)2, and NaLa(WO4)2, proving the universality of the presented strategy. This article presents an effective Pr3+/Dy3+ ET pathway for developing new and highly sensitive FIR/lifetime dual-mode optical temperature sensing materials.

Background. Although the incidence and mortality of complicated skin and soft tissue infections have decreased, this infection is still relatively frequent and can be associated with lethal complications. In this study, the authors present our clinical experience of patients with complicated posterior cervical skin and soft tissue infections (CPCSSTIs) diagnosed and treated in a reconstructive unit in northeastern China. Methods. A retrospective chart review of patients diagnosed with CPCSSTIs from January 2009 to December 2018 was performed. To make the results objective and convincing, a data analysis was performed relating to demographic characteristics, clinical presentation, predisposing factor, bacterial culture, laboratory and radiographic evaluations, diagnostic clues, management, and complications as well as the clinical course and outcome. Results. During the ten-year period, there were 174 consecutive patients admitted to our reconstructive center with final diagnosis of CPCSSTIs included. All the patients were adults, and the majority were male (67.2%). The patient’s mean age was 51.3 years (range, 15-88 years). There were 114 patients (65.5%) that had associated systemic diseases, with diabetes mellitus (40.2%) as the most common predisposing factor. Common presented clinical symptoms were pain (90.8%), swelling (85.1%), and erythema (77%) of the neck. Surgical treatment was performed in all the patients, and most of them (83.9%) received the first surgery within 24 h. The most commonly isolated pathogen was Staphylococcus aureus (30%). Vancomycin (21.3%) was the most commonly used antibiotics, followed by cefepime (18.4%). All the patients survived and were discharged with a mean duration of hospitalization of 28.7 days. Those patients with predisposing factors (31.4±12.35 days) or complications (41.0±12.5 days) tended to have a longer hospital stay. The mean total costs of admission per patient were 47 644 RMB. Conclusion. This study highlights the high cost burden of CPCSSTI patients. Those patients with predisposing factors or complications tended to have a longer hospital stay.

Nowadays the most intriguing features of wave-particle complementarity of single-photons are exemplified by the famous Wheeler’s delayed-choice experiment with linear optics, nuclear magnetic resonance, and integrated photonic device systems in the optical platform. Until now, the delayed-choice experiments are demonstrated by either massless photons or massive particles, such as atoms, however, there is no report demonstrating Wheeler’s ideas in a hybrid system which consists of photons and atoms simultaneously. Here, we demonstrate a Wheeler’s delayed-choice experiment in an interface of light and atomic memory, in which the cold atomic memory makes the heralded single-photon divided into a superposition of atomic collective excitation and leaked pulse, thus acting as memory-based beam-splitters. We observe the intermediate states between particle and wave behavior by changing the relative proportion of the quantum random number generator, the second memory efficiency, and the relative storage time of two memories. The reported results confirm Bohr’s view that it makes no sense to illustrate the wave-like or particle-like behavior of light and matter before the measurement happens, and are helpful for improving our comprehension of the complementarity principle under the interface of light-atom interaction.

Whether infantile hemangiomas (IHs) need to be treated and which treatment should be preferred are still controversial. We aimed to compare and rank the treatments and identify the optimal treatment for IHs. We searched PubMed, EMBASE, the Cochrane Library, Web of Science, and other sources for randomized controlled trials up to August 2019. We included trials comparingdifferent treatments and reported response or adverse events rate in IH patients. Two reviewers independently evaluated studies by specific criteria and extracted data. We assessed the risk of bias with the Cochrane risk of bias tool. Random-effects were performed for pair-to-pair and Bayesian framework network meta-analyses. The primary outcomes were efficacy and safety. We deemed 20 studies eligible, including 1149 participants and eight interventions. For efficacy, oral propranolol and topical propranolol/timolol were better than observation/placebo (OR, 95% CrI: 17.05, 4.02-94.94; 9.72, 1.91-59.08). For safety, topical propranolol/timolol was significantly better tolerated than oral propranolol (0.05, 0.001-0.66). Cluster analysis demonstrated oral propranolol was the most effective treatment for IHs, while topical propranolol/timolol showed high efficacy and the highest safety. Laser, intralesional propranolol or glucocorticoid, oral glucocorticoid, or captopril had significantly lower priority than oral propranolol or topical propranolol/timolol considering both efficacy and safety. The quality of evidence was rated as moderate or low in most comparisons. This network meta-analysis found topical beta-blockers had the potential to be the most preferable and beneficial option for IHs in consideration of both efficacy and safety.

Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus–human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.

In general, the seasonal characteristics of small hydropower (SHP) especially the power transmission during the summer flood season would lead to the steady overvoltage of the distribution network. In order to suppress this steady overvoltage, a strategy for coordinated cooperation between generator phase-in operation and shunt reactors which can be called cooperative control is proposed. The distribution network of SHP stations connected to the 35 kV Bantao substation in southern Shaanxi is taken as an example to study the mechanism of overvoltage after the SHP is connected to the grid. A distribution network model with SHP units is established. Based on the model, the effect of separately installing shunt reactors on the node voltage is studied in the case of generator lag phase operation and the influence of the operation mode of SHP unit on the node voltage is also analyzed separately. Finally, the improvement measures of cooperative control between the generator phase-in operation and the parallel reactors are analyzed by simulation. Results are presented to verify the improvement measures that can effectively restrain the overvoltage of the distribution network in the SHP area.

Purpose Computer-aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that typically rely on personal experience. A CAD system has been developed to differentiate malignant thyroid nodules from benign thyroid nodules in ultrasound images based on deep learning methods. The diagnostic performance was compared between the CAD system and the experienced attending radiologists. Methods The ultrasound image dataset for training the CAD system included 651 malignant nodules and 386 benign nodules while the database for testing included 422 malignant nodules and 128 benign nodules. All the nodules were confirmed by pathology results. In the proposed CAD system, a support vector machine (SVM) is used for classification and fused features which combined the deep features extracted by a convolutional neural network (CNN) with the hand-crafted features such as the histogram of oriented gradient (HOG), local binary patterns (LBP), and scale invariant feature transform (SIFT) were obtained. The optimal feature subset was formed by selecting these fused features based on the maximum class separation distance and used as the training sample for the SVM. Results The accuracy, sensitivity, and specificity of the CAD system were 92.5%, 96.4%, and 83.1%, respectively, which were higher than those of the experienced attending radiologists. The areas under the ROC curves of the CAD system and the attending radiologists were 0.881 and 0.819, respectively. Conclusions The CAD system for thyroid nodules exhibited a better diagnostic performance than experienced attending radiologists. The CAD system could be a reliable supplementary tool to diagnose thyroid nodules using ultrasonography. Macroscopic features in ultrasound images, such as the margins and shape of thyroid nodules, could influence the diagnostic efficiency of the CAD system.