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Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method.

With the increasing demand for rail transit, wireless communication technologies are playing a growing significant role in train control systems, which enables the railway systems to provide a higher capacity and more efficient services. However, due to the nature of radio frequency propagation, the quality of the train-to-ground wireless connections is highly dependent on a well-planned deployment of the wayside access points. To improve both the accuracy and the efficiency in railway network planning, in this paper, a deep learning technology is exploited to model the wireless propagation, which was very difficult to deterministically predict at a fast speed in our previous research due to the high computation demanding. In this proposed wireless propagation model, Kalman filter is utilized to update the neural network parameters online, which makes this model can meet the variation of the environment. The numeric evaluation result shows that the deep neural network based wireless channel model can precisely predict the outage probability with a very low computational cost.

Reconfigurable intelligent surfaces (RISs) can shape the wireless environment for enhancing the communication performance. In this letter, we propose a cooperative multi-RIS assisted transmission scheme for a millimeter-wave multi-antenna orthogonal frequency division multiplexing system. We first put forward a delay matching based scheme for simultaneously estimating the multipath channels and the transmission delays of distributed RISs, which requires limited training overhead and feedback. Based on this scheme, we obtain a closed-form solution for the RIS phase shift. Then, we derive an analytical expression of the downlink rate, which is proved to increase logarithmically with the square number of RIS reflecting elements. Simulations are conducted to verify these observations.

With the development of intelligent transportation system, the maintenance of railway turnout is an essential daily task which was required to be efficiency and automatically. This paper presents an intelligent diagnosis method based on deep learning curve segmentation and the Support Vector Machine. Firstly, we studied the curve segmentation approach of the real-time monitoring power data collected form turnout, for which is an essential step and do a great help to improve the diagnose accuracy. Then based on the well pre-processed data sets, the SVM algorithm was applied to classify the samples and report the health states of the turnout which under testing. At last, the experiments were taken on the power data curve collected from the real turnouts, during which we compared the new diagnose method with conventional ones, and the results showed that the diagnose accuracy of proposed method can averaged to 98.5%. Compared with traditional SVM based frameworks, the proposed diagnosis method dramatically improves the accuracy which is more suitable for railway turnout.

When a sensor data-based detection method is used to detect the potential defects of industrial products, the data are normally imbalanced. This problem affects improvement of the robustness and accuracy of the defect detection system. In this work, welding defect detection is taken as an example: based on imbalanced radiographic images, a welding defect detection method using generative adversarial network combined with transfer learning is proposed to solve the data imbalance and improve the accuracy of defect detection. First, a new model named contrast enhancement conditional generative adversarial network is proposed, which is creatively used as a global resampling method for data augmentation of X-ray images. While solving the limitation of feature extraction due to low contrast in some images, the data distribution in the images is balanced, and the number of the image samples is expanded. Then, the Xception model is introduced as a feature extractor in the target network for transfer learning, and based on the obtained balanced data, fine-tuning is performed through frozen–unfrozen training to build the intelligent defect detection model. Finally, the defect detection model is used to detect five types of welding defects, including crack, lack of fusion, lack of penetration, porosity, and slag inclusion; an F1-score of 0.909 and defect recognition accuracy of 92.5% are achieved. The experimental results verify the effectiveness and superiority of the proposed defect detection method compared to conventional methods. For other similar applications to defect detection, the proposed method has promotional value.

Percutaneous computed tomography (CT)-guided transthoracic needle biopsy (TTNB) is a valuable procedure for obtaining tissue or cells for diagnosis, which is especially indispensable in thoracic oncology. Pneumothorax and hemoptysis are the most common complications of percutaneous needle biopsy of the lung. According to reports published over the past decades, pneumothorax incidence in patients who underwent TTNB greatly varies. The morbidity of pneumothorax after CT-guided TTNB depends on several factors, including size and depth of lesions, emphysema, the number of pleural surfaces and fissure crossed, etc. Attention to biopsy planning and technique and post-biopsy precautions help to prevent or minimize potential complications. Many measures can be taken to help prevent the progression of a pneumothorax, which in turn might reduce the number of pneumothoraces requiring chest tube placement. A multitude of therapeutic options is available for the treatment of pneumothorax, varying from observation and oxygen treatment, simple manual aspiration, to chest tube placement. When a pneumothorax develops during the biopsy procedure, it can be manually aspirated after the needle is retracted back into the pleural space or by inserting a separate needle into the pleural space. Biopsy side down positioning of the patient after biopsy significantly reduces the incidence of pneumothorax and the requirement of chest tube placement. Aspiration in biopsy side down position is also recommended for treating pneumothorax when simple manual aspiration is unsuccessful or delayed pneumothorax occurred. Chest tube placement is an important treatment strategy for patients with a large or symptomatic pneumothorax. Clinicians are encouraged to understand the development, prevention, and treatment of pneumothorax. Efforts should be made to reduce the incidence of pneumothorax in biopsy planning and post-biopsy precautions. When pneumothorax occurs, appropriate treatment should be adopted to reduce the risk of worsening pneumothorax.

With the rapid growth of China's economy, technology import plays a crucial role in enhancing regional innovation capability. Based on inter-provincial panel data from 2007 to 2016, this paper uses three benchmark linear regression models of Ordinary Least Squares (OLS), Difference-Generalized Method of Moments (DIFF-GMM), and System-Generalized Method of Moments (SYS-GMM) to explore the improvement of China's regional innovation capability by technology import. Then, the regional institutional quality level is measured from the three dimensions of politics, economy, and law, and the two-step difference GMM threshold panel model is used to analyze the effect of technology import on regional innovation capability under different institutional quality conditions. The results show that: (1) In the benchmark linear regression model, technology import has a significant role in promoting regional innovation capability. (2) With the rise of regional corruption, the quality of political institution declines, and the promotion effect of technology import on regional innovation ability is weakened. (3) The improvement of the marketization level and intellectual property protection level strengthen the role of technology introduction in promoting regional innovation capability. On the contrary, in regions with low economic and legal institutional quality, technology import has no significant impact on regional innovation capability.

Currently, the fault diagnosis with balanced data and distinct characteristics has received mass concern, and the related research achievements are remarkable. However, because of the weakness and scarcity of incipient fault signals, the diagnosis of incipient fault commonly existing in industrial systems is still an intractable problem. In order to solve the problem, an incipient fault diagnosis method based on a sliding-scale resampling strategy and improved sparse autoencoder with multi-particle noise addition (MpNA-SAE) is proposed in this paper. Firstly, the original time domain signals are preprocessed, and a sliding-scale resampling strategy is designed to construct the balanced sample sets. Secondly, a multi-particle noise addition strategy and an adaptive loss function are designed, and then an improved sparse autoencoder with multi-particle noise addition (MpNA-SAE) fault diagnosis model is constructed to identify the fault pattern and determine the severity degree. Thirdly, a diagnostic performance evaluation criterion is proposed to quantify the application range of the model. Finally, the effectiveness and practicability of the proposed method are verified by incipient artificial damage and real fault experiments, respectively.

Since December 2019, COVID-19 has occurred unexpectedly and emerged as a health problem worldwide. Despite the rapidly increasing number of cases in subsequent weeks, the clinical characteristics of pediatric cases are rarely described. A cross-sectional multicenter study was carried out in 10 hospitals across Hubei province. A total of 25 confirmed pediatric cases of COVID-19 were collected. The demographic data, epidemiological history, underlying diseases, clinical manifestations, laboratory and radiological data, treatments, and outcomes were analyzed. Of 25 hospitalized patients with COVID-19, the boy to girl ratio was 1.27:1. The median age was 3 years. COVID-19 cases in children aged

What is already known on this topic? Triatomines, also known as kissing bugs, are widespread vectors for Chagas disease which affects 6–8 million people worldwide. Two species of triatomines have been previously reported in China. What is added by this report? This study showed data from the first investigation of triatomine distribution in China. Triatoma rubrofasciata and a novel species of triatomine in 170 habitats in 30 cities in southern China were recorded in this investigation. What are the implications for public health practice? Considering the worldwide spread of Chagas disease and new species of trypanosomiasis, strengthening the monitoring of triatomines and their associated diseases in southern China is vital to prevent and control these diseases.

PIWI-interacting RNA (PiRNA) was discovered in 2006 and is expected to become a new biomarker for diagnosis and prognosis of various diseases. The purpose of this study is to explore functions of piRNAs and identify cancer subtypes on the basis of the pattern of transcriptome and somatic mutation data. A total of 285 510 SNPs in piRNAs and genes, which might affect piRNA biogenesis or piRNA targets binding were identified. Significant co-expression networks of piRNAs were then constructed separately for 12 major types of cancer. Finally, mutational matrices were mapped to piRNA network, propagated, and clustered for identification of cancer-related piRNAs and cancer subtypes. Findings showed that subtypes of three types of cancer (COAD, STAD and UCEC), which are significantly associated with survival were identified. Analysis of differentially expressed piRNAs in UCEC subtypes showed that piRNA function is closely related to cancer hallmarks “Enabling Replicative Immortality” and contributes to initiation of cancer.

Background : The purpose of this study is to illustrate whether the electro-cauterization and transient tourniquet enhanced efficiency of local anesthesia with epinephrine in surgery of metacarpal fractures. Methods : Forty-four consecutive cases of metacarpal fractures with estimated major operative time more than 30 minutes were enrolled. Local anesthesia with epinephrine, electro-cauterization and transient tourniquet were performed. Data regarding anesthesia effect, bleeding in the surgical field (with inflating and deflating tourniquet), time when patient felt uncomfortable with tourniquet, inspection of the surgical outcome, and some other surgical tricks were collected and evaluated. Results : All patients felt no pain for whole surgical period in the surgical field. Time when patient felt uncomfortable with tourniquet was about 16 minutes. Mean tourniquet time was about 29 minutes. There was no or less bleeding in the surgical field for whole surgical period. The anatomical structures, such as nerve, tendon and vessel can be easily distinguished and dissected. Outcome of repair and reconstruction could be examined with instructed movement. Postoperative inspection showed no symptom of inflammation. Conclusions : Local anesthesia with epinephrine, electro-cauterization and transient tourniquet for major hand surgery can save time and obtain wide-awake effect, which would satisfy both doctors and patients.

The image denoising discriminant model has received extensive attention in recent years due to its good denoising performance. In order to solve the problems of denoising of traditional generative adversarial networks, which are difficult to train and easy to collapse, traditional denoising methods will damage the visibility of important structural details after the image is denoised, this paper proposes an improved generative adversarial network algorithm. The novel algorithm obtains more image features by adding multi-level convolution of the generative network, and adds multiple residual blocks and global residuals to extract and learn the features of the input noisy image to avoid the loss of features. The network uses the weighted sum of feature loss and perceptual loss, which can effectively retain the detailed information of the image while removing noise. In the experiments, we selected PSNR and SSIM as the indicators, and the experimental results show that the novel algorithm can effectively remove image noise and improve the visual perception of the image.

SAR (Synthetic Aperture Radar) is a kind of active microwave sensor, and has been widely used in the field of ocean ship detection. Because SAR imaging is affected by the speckle noise, sea clutter and complex environment, it is still a challenging task about ship target detection in SAR image processing. In this paper a fast ship target detecting method based on saliency is proposed. Firstly, the SAR image is separated to land and sea in order to remove the influence of land scattering. In the next step, the sea candidate area is segmented by super pixels, the global and local contrast of super pixels are calculated by using Bhattacharyya dissimilarity measure, and then combine them to generate the saliency map. Finally, the Otsu algorithm is used to separate the target regions in the saliency maps. The experimental results show that the proposed method effectively suppressed the influence of sea and land clutter, enhanced the saliency of the target, which greatly improves the detection efficiency and accuracy.

In order to effectively measure the image quality of different distortion types, a non-reference image quality evaluation algorithm based on natural scene statistics (NSS) model is proposed. This algorithm uses the statistical characteristics of the normalized brightness coefficients of natural images to tend to obey the characteristics of the generalized Gaussian probability distribution. Firstly, the regions of interest and non-interest regions of natural images are extracted, and then the image features are extracted in the corresponding regions, and finally realized image quality evaluation method based on visual regions of interest. Based on the LIVE2 image database, a large number of experiments are performed and compared with other classic algorithms. Experimental results show that the improved algorithm in this paper has high subjective consistency in the evaluation results of degraded images with multiple types of distortion coexisting, and can accurately reflect the visual perception quality of the human eye.

In view of the clinical status of the evaluation of functional coronary stenosis that requires invasive surgery to determine the fractional flow reserve (FFR), this study aims to use an in vitro non-invasive detection method, which uses computer tomography to obtain two-dimensional CT images of patients, uses fluid dynamics simulation methods to establish patient-specific models in vitro, and then performs hemodynamic values on the patient’s real blood flow. Analyze and explore the relationship between different hemodynamic parameters and coronary artery stenosis, non-invasively calculate the blood flow reserve score to assess the degree of coronary artery stenosis lesions, and dig out hidden data. A total of 3 patients with real CT data whose image quality meets the modeling requirements were included in the study. After calculation, the noninvasive blood flow reserve fraction of each stenosis site was obtained. SPSS software was used to analyze the calculated value of the model and the blood flow reserve fraction measured at each stenosis site, and the Pearson correlation coefficient of the two was. 1. Meanwhile, the dynamic changes of the stenosis can be obtained by analyzing the main parameters of hemodynamics.This study shows that the method of evaluating coronary stenosis lesions using fluid mechanics non-created model has a good correlation with the measured blood flow reserve (FFR) during angiography, which can provide an important reference for clinical diagnosis and greatly reduce the pain that patients need to bear during surgery.

Overtaking decision under dynamic environment is one of the key research directions. Traditional methods are difficult to solve such a complex optimization decision-making problem. In recent years, reinforcement learning algorithm is developing continuously, which can be applied to solve the overtaking decision problem of unmanned vehicles. Reinforcement learning generates new data through the continuous interaction between agent and environment, and feeds back to the agent for corresponding learning behavior, so as to improve its own behavior strategy. In this paper, Deep Q-learning (DQN) is used to solve the overtaking decision-making problem on one-way through two lanes. A one-way through two lane traffic environment is built in Python to train and test the algorithm.

With the increasing complexity of industrial processes, process big data inevitably has strong nonlinear, dynamic and noise problems, which restrict the accurate establishment of data-driven or mechanism-driven soft sensor models. Therefore, a soft sensor based on the denoising autoencoder and mechanism-introduced gated recurrent units is proposed. The denoising autoencoder is used to denoise the data. The mechanism-introduced gated recurrent units are used to introduce the information contained in the mechanism model and extract the dynamic characteristics of the process data by deep learning. This soft sensing method can deal with various problems of complex process data and introduce mechanism model for hybrid-driven modeling, which improves the prediction performance of the soft sensor. The effectiveness and superiority of the method are verified by the industrial case of predicting the thermal deformation of an air preheater rotor.

In this article, we fabricated PbS quantum-dots doped P3HT thin films, and their thermoelectric properties were enhanced by introducing the MoO3 interface layer into the ITO/PbS-doped P3HT/Al devic...

Objective The aim of this study was to evaluate the clinical features, surgical complications, and functional outcomes of the surgical treatment of patients with cavernous malformations (CMs) involving the medulla oblongata. Methods The charts of 69 patients undergoing surgical treatment of CMs in the medulla oblongata, pontomedullary, and cervicomedullary junctions between 2011 and 2017 were retrospectively reviewed. Patient demographics, lesion characteristics, operative documents, and patient outcomes were examined. Results Of the 69 patients, the male-to-female ratio was 1.3. The mean patient age was 32.6 years, and the mean mRS score was 2.7 on admission. Postoperatively, 21 patients (30.4%) had deficits of cough reflexes, and 6 patients (8.7%) experienced respiratory rhythm disorder and dyspnea. The mean follow-up duration was 35.3 months. At the last follow-up evaluation, the mean mRS score was 1.8, and 53 patients (80.3%) had favorable outcomes, with mRS scores ≤2. The conditions of the patients improved in 45 cases (68.2%), remained unchanged in 11 cases (16.7%), and worsened in 10 cases (15.1%) relative to their preoperative baseline. The independent adverse factors for long-term functional outcome were increased age, multiple hemorrhages, presence of developmental venous anomalies, and lack of perilesional edema. Conclusions Respiratory dysfunction and deficits of cough reflexes can commonly occur during the early postoperative period for surgical resection of CMs involving the medulla oblongata. Favorable functional outcomes can be achieved by surgery, especially for younger patients who experience fewer hemorrhages and have lesions with perilesional edema and the absence of developmental venous anomalies.

Objective To evaluate surgical management of cavernous malformations (CMs) involving the medulla oblongata and to predict risk factors of postoperative respiratory dysfunction (RDF). Methods Patient data from individuals who underwent surgical treatment for CMs involving the medulla oblongata were retrospectively reviewed. Patients with postoperative RDF and/or deficits of the cough reflex (CR, ≥7 days) were deemed as having bad respiratory statuses. A binary logistic regression analysis tested the association of preoperative predictors with bad postoperative respiratory status. Results The study consisted of 69 patients. Preoperatively, 9 patients (13.0%) had dyspnea, and 4 (5.8%) had hypoxemia. Postoperatively, 11 patients (15.9%) had bad respiratory statuses, including RDF as a respiratory rhythm disorder and/or dyspnea in 6 patients, and ≥7 days of CR deficits in 5 patients. With a mean follow-up duration of 35.3 months, the neurologic status improved in 45 patients (68.2%), remained unchanged in 11 (16.7%), and worsened in 10 (15.1%) relative to the preoperative baseline. A multivariate logistic regression analysis identified that the independent adverse factors of bad postoperative respiratory status were multiple preoperative hemorrhages, large lesion size, and surgical intervention during the chronic period (>8 weeks). Conclusions Postoperative RDF and CR deficits could commonly occur in patients with CMs involving the medulla oblongata. However, patients with fewer preoperative hemorrhages, small lesion size, and operation within 8 weeks of the last bleeding are prone to be associated with a reduced possibility of bad postoperative respiratory status.

Information and communication technology supported by the internet has become an important driving force that promotes the intelligent development of environmental governance in China. Using Chinese provincial panel data for the period 2006–2017, this study investigates whether the internet has improved China's green total factor energy efficiency (GTFEE) using a dynamic spatial Durbin model, mediation effect model and dynamic threshold panel model. The empirical results indicate that the GTFEE has a significant positive spatial correlation. Internet development can not only directly improve local GTFEE but also improve GTFEE in neighboring regions. After accounting for potential endogeneity, this conclusion is still valid. Meanwhile, internet development can indirectly improve regional GTFEE by reducing the degree of resource mismatch while enhancing GTFEE by improving regional innovation capabilities and promoting industrial structure upgrades. In addition, the regression results of the dynamic threshold model show that there is a nonlinear relationship between the influence of the internet development and GTFEE. Specifically, due to an increase in the degree of labor resource mismatch and capital resource mismatch, the impact of the internet on GTFEE has gradually decreased, and this effect has gradually increased with the improvement of regional innovation capabilities and the industrial structure.

Image Inpainting aims to use the technical methods to repair and reconstruct the corrupted region of the corrupted image, so that the reconstructed image looks more authentic. In this paper, the improved Wasserstein Generative Adversarial Network combined with the perceived loss and context loss function is used to repair the corrupted region of the image. By finding the nearest neighbor coding method from the defective image in the latent space and using the improved WGAN to add the spectral normalization constraint, the two main problems of the original GAN including the pattern collapse and gradient dispersion can be greatly reduced, in which can produce more clear as well as realistic images. Test experiments on the CelebA face data set indicate that the method has a good inpaint result.

To solve the problem of fault diagnosis of rolling bearing caused by large amount of data and difficulties of processing those data on to bearing set, based on Convolution Neural Network, a new method of data processing is proposed in this paper. With this method, one-dimensional time domain signal can be transformed into two-dimensional images, which is more suitable for Convolutional Neural Network processing. Meanwhile, the traditional machine learning method has the disadvantage of low robustness and low recognition rate with noise interference. Therefore, based on the feature extraction of Convolution Neural Network, in this paper we proposed an improved LeNet-5 Convolution Neural Network model, that is, adding a convolution layer and a pooling layer to the classic LeNet-5 model. The hidden layer features are extracted by using the trainable convolution kernel, while the extracted implicit features are reduced by the pooling layer, the Softmax classifier is used for classification and recognition of rolling bearing faults. In this paper we verified the effectiveness of the improved LeNet-5 model for fault diagnosis of rolling bearing by using the rolling bearing data to train the classic LeNet-5 model and the improved model.

Objective To evaluate the effectiveness and safety of allogeneic tendons for functional reconstruction of severe hand injuries. Methods From August 2007 to July 2014, we performed functional reconstruction with tendon allografts for severe hand injuries affecting two or more tendons. At the final follow-up, we assessed total active motion (TAM); pincer pinch strength; grip strength; Disabilities of the Arm, Shoulder, and Hand (DASH) score; degree of satisfaction; and adhesion. We measured the white blood cell count, C-reactive protein concentration, erythrocyte sedimentation rate, total T-cell count, and CD4+T/CD8+T ratio to evaluate the immune response and check for infection. Results Ten patients received 26 allogeneic tendons to reconstruct hand function. The average follow-up period was 50.0 months (range, 24–82 months). The TAM was 126.4° (12°–253°), pincer pinch strength was 0.83 kg (0–4.5 kg), and grip strength was 13.69 kg (4–41.5 kg). The DASH score was 14.25 (3.3–30.8), and seven and three patients were satisfied and partially satisfied, respectively. One patient developed tendon adhesion. All immune and infectious parameters were within the reference range. Conclusion Functional reconstruction using allogeneic tendons for severe hand injuries with multiple tendon defects was effective and safe; however, more research is needed.

Background Malaria is an insect-borne infectious disease that spreads through bites from infected Anopheles mosquitos. The disease seriously endangers human health and economic development. Against the background of eliminating malaria in Fujian Province, in order to understand the cost and benefit of malaria monitoring measures, the health economics of the monitoring measures should be examined..Methods The malaria epidemic data during the malaria elimination stage (2005–2019) in Fujian Province were collected and sorted. The basic economic data were obtained from the Fujian Statistical Yearbook. A cost-benefit analysis was conducted for this study, based on the fundamental theories in health economics. Moreover, the Cost (C) of monitoring measure during the malaria elimination stage was calculated, and the Benefit of malaria elimination stage (B) was analyzed.Results The total cost of monitoring measures in the malaria elimination stage was about 607,878,783.36 yuan ( USD $ 87,534,544.80). The proportions of the costs were as follows: case detection and treatment was 66.62%, health education was 13.31%, epidemic monitoring was 10.05%, supervision and assessment was 5.15%, and training and meeting was 4.87%. The total benefit of the malaria elimination stage was about 116,123,417,911.81 yuan (USD$ 16,721,772,179.30), the economic benefit was 22.61%, and the social benefit was 77.39%. The cost-benefit analysis of the malaria elimination stage showed that the NB of monitoring measures was about 115,515,539,128.45 yuan (USD$ 16,634,237,634.50) and the BCR was 191.03. The monitoring cost for residents was 1.08 yuan (USD$ 0.16) per capita, and the benefit for residents was 206.12 yuan (USD$ 29.68) per capita. Conclusion In the past 15 years, malaria control work has achieved excellent economic and social benefits in Fujian, China. The work should continue in strengthening the monitoring and control of imported malaria cases, increasing health education in high-risk locations (such as those frequented by entry-exit personnel), and enhancing residents’ awareness about prevention and personal protection. The work should continue to be consolidated for the elimination of malaria. The framework and results of this study conform to the principles of health economics and have a certain reference value for current malaria monitoring practices.

In this paper a method based on the combination of product quantization and pruning to compress deep neural network with large size model and great amount of calculation is proposed. First of all, we use pruning to reduce redundant parameters in deep neural network, and then refine the tune network for fine tuning. Then we use product quantization to quantize the parameters of the neural network to 8 bits, which reduces the storage overhead so that the deep neural network can be deployed in embedded devices. For the classification tasks in the Mnist dataset and Cifar10 dataset, the network models such as LeNet5, AlexNet, ResNet are compressed to 23 to 38 times without losing accuracy as much as possible.

Introduction Little is known of acupuncture patients’ experiences and opinions of clinical trials, and what may influence their compliance when participating in an acupuncture trial. Objectives To explore the potential factors that influence patients’ choice and determinants to participate in acupuncture clinical trials. Methods Ten qualitative, in-depth interviews were conducted with patients from acupuncture clinics in Beijing, who had previously participated in acupuncture clinical trials. Results Four main themes emerged from the interview data: effectiveness of the treatment, convenience of participating in a trial, doctor-participant communication, and participant acceptance of the treatment (or the trial). Effectiveness of acupuncture in treating the health condition was the most important factor for participant adherence. Pragmatics of treatment schedules, travel and attendance burden, together with confidence in the doctor’s ability additionally influenced trial and treatment compliance. Conclusions In-depth interviews suggest that treatment effectiveness, the pragmatics of attending treatment sessions, and the expertise and attitudes of acupuncturists are determining factors of participation and compliance in acupuncture clinical trials. Participants’ confidence in, and expectation of, acupuncture may facilitate compliance, while their fear of acupuncture and negative perceptions of the trial’s purpose may reduce treatment compliance. Compliance may be facilitated by enhanced doctor-patient communication, personalized treatment programs, and feedback on treatment outcomes.

The Local Group dwarf elliptical galaxy M32 hosts one of the nearest and most under-luminous super-massive black holes (SMBHs) ever known, offering a rare opportunity to study the physics of accreting SMBHs at the most quiescent state. Recent Very Large Array (VLA) observations have detected a radio source at the nucleus of M32, which is suggested to be the radio counterpart of the SMBH. To further investigate the radio properties of this nuclear source, we have conducted follow-up, high-resolution VLA observations in four epochs between 2015--2017, each with dual frequencies. At 6 GHz, the nuclear source is resolved under an angular resolution of $\sim$0\farcs4, exhibiting a coreless, slightly lopsided morphology with a detectable extent of $\sim$2.5 \arcsec ($\sim$10 parsec). No significant variability can be found among the four epochs. At 15 GHz, no significant emission can be detected within the same region, pointing to a steep intrinsic radio spectrum (with a 3\,$\sigma$ upper limit of -1.46 for the spectral index). We discuss possible scenarios for the nature of this nuclear source and conclude that a stellar origin, in particular planetary nebulae, X-ray binaries, supernova remnants or diffuse ionized gas powered by massive stars, can be ruled out.Instead, the observed radio properties can be explained by synchrotron radiation from a hypothetical wind driven by the weakly accreting SMBH., Comment: 12 peges, 3 figures, accepted for publication in ApJ. Comments welcome

A wireless monitoring network is an effective way to monitor and transmit information about railway infrastructure conditions. Its lifetime is significantly affected by the energy usage among all sensors. This paper proposes a novel cluster-based valid lifetime maximization protocol (CVLMP) to extend the lifetime of the network. In the CVLMP, the cluster heads (CHs) are selected and rotated with the selection probability and energy information. Then, the clusters are determined around the CHs based on the multi-objective optimization model, which minimizes the total energy consumption and balances the consumption among all CHs. Finally, the multi-objective model is solved by an improved nondominated sorting genetic algorithm II. The simulation results show that, compared with two other strategies in the prior literature, our proposed CVLMP can effectively extend the valid lifetime of the network as well as increase the inspected data packets received at the sink node.

Objective Primary intracranial angioleiomyoma is a rare and distinct neoplasm. Only 29 cases have been reported previously, and we aimed to investigate the clinical and radiopathologic features of these lesions. Methods Medical records and radiographs of 8 patients (7 male and 1 female; mean age: 48.7 years) at our institution were reviewed retrospectively. Patient follow-up and a literature review were performed. Results The most common preoperative symptom was a visual defect (n = 2), followed by diplopia (n = 1) and abducens paralysis (n = 1). Three patients were asymptomatic. The parasellar area (particularly the cavernous sinus) was the predilection site (n = 4; 50.0%). Radiographically, all lesions were solid without cystic degeneration. All lesions appeared with T1 hypointensity and T2 hyperintensity, and they were gradually heterogeneously enhanced after the administration of gadolinium. Complete resection was achieved in 7 patients (87.5%) without recurrence after 26.8 months of follow-up. Mitosis was rarely observed, and the Ki-67 labeling index was less than 1%; pathologically, the cavernous type was the most common. Conclusions Primary intracranial angioleiomyomas were prevalent in middle-aged men, and they usually involved the cavernous sinus and were frequently pathologically identified as the cavernous type. Preoperative symptoms varied depending on lesion location. The preoperative diagnosis of primary intracranial angioleiomyomas is difficult without pathology. Digital subtraction angiography and preoperative embolization are useful for differential diagnosis and surgery. Given the indolent biology of these tumors, a favorable outcome can be achieved using total resection without recurrence. A larger sample size with long-term follow-up is needed to verify our findings.

Background: To assess the effect of aspiration in the biopsy-side down position to deal with delayed pneumothorax after computed tomography (CT)-guided lung biopsy. Methods: A retrospective review was performed of the 236 delayed pneumothorax patients who underwent CT-guided transthoracic needle biopsies (TTNBs). Asymptomatic minimal pneumothorax patients were managed conservatively. Manual aspirations were applied for symptomatic cases with minimal pneumothorax and all cases with moderate to large pneumothorax. Patients were included into two groups: in group A (35 patients), aspiration was performed in the same position as the biopsy, while in group B (54 patients), patients were turned to the biopsy-side down position (from supine to prone or vice versa), and aspiration was conducted. The ef cacy of two approaches was evaluated. Results: One hundred forty-seven (62.3%) asymptomatic cases resolved without treatment. Distance between parietal and visceral pleura before and after aspiration were 4.24±1.87 and 1.93±2.33 cm for group A, 3.92±1.31 and 0.98±1.50 cm for group B, respectively. Volume of aspirated air in group A and group B were 735.4±231.8 and 434.8±320.3 mL, respectively. Complete lung expansion was detected in 28.6% (10/35) and 38.9% (21/54) for group A and group B, respectively. The overall effective rate and failure rate were 74.3% (26/35) and 25.7%(9/35) for group A, 92.6% (50/54) and 7.4% (4/54) for group B, respectively, which have signi cant statistic difference (P Conclusions: Manual aspiration in biopsy-side down position demonstrates the safety and efficacy in treating delayed pneumothorax after CT-guided TTNBs. Thus reduce the rate of pneumothorax requiring drainage catheter placement.

Thermoelectric generator (TEG) has been proved as a promising technology for directly converting heat into electricity based on Seebeck effect. On the contrary, this electricity can trigger a solid-state cooling based on conventional Peltier effect. However, these two effects induce a coupling between heat and electric flux, especially for the quantitative relationship still remaining a mystery. Here, we show experimental evidence and theoretical calculation for the coupling by monitoring transient response of fluid temperature and output power. The experimental maximum heat flow in open circuit is 1162 W at cold fluid flow rate V c = 0.3 m3/h and fluid temperature difference ΔTf = 70 °C, enhanced by 13% owing to heat compensation from intrinsic coupling in closed-loop circuit. Meanwhile, the measured maximum output power of TEG is 18.2 W, and subsequently decreases to 15.4 W due to the objective existence of coupling. This double-edged sword in coupling vigorously inspires the potential applications in heat-dissipation situation such as spacecraft, electronic components, photovoltaic, refrigerator and etc. Present findings open a novel avenue for manipulating heat-electricity conversion in practical engineering.

Floating solar still is considered as a land-saving, easy-operating and environmental-friendly approach for freshwater production. However, the low yield and work instability in actual weather conditions limits its application. To overcome these disadvantages, a bionic floating solar still inspired by water intake of plant roots was presented in this paper. The channels for water supply were distributed in array and wrapped in a whole thermal insulation sheet. With the design of simulating roots, the present desalination system achieves low heat losses, water-absorbing uniformity and structural stability. Cellulose fabric and glass were separately selected as evaporation and condensation materials of the experimental prototype after preliminary test. The outdoor experiment was conducted in Xingtai, China, and the accumulated radiation in the test day was 21.96 MJ/m2. The maximum evaporation temperature and daily freshwater yield reached 59.2 °C and 1.5 kg/m2/d, respectively, which were the highest in comparison with existing floating solar stills. Next, the effect of rolling motion on humid air flow inside the still was investigated by CFD simulation. The results of this study will contribute to the further development of floating solar still, as well as the application of such small-scale desalination technologies.

As the key component of the locomotive bogie, the axles have an important influence on the railway safety. The axles are usually maintained or replaced entirely and regularly after a certain running mileage or running time. This means that some damaged axles may not be replaced in time, and some others may be replaced before their running life, which not only poses potential safety risks but also result in wastage of resources. To address this problem, we propose an aging analysis method for high-speed train axles based on the variation rate of axle temperature, which can diagnose each individual axle. The main steps in this are as follows: (i) preprocessing the original data to correct the isolated zero points and complementing the missing values; (ii) eliminating the measurement error and noise for the automatic extraction of the beginning and end of every temperature rising stage; (iii) calculating the temperature rising rate and evaluating the health status (or the aging level) of axle based on the temperature rising rate. Finally, the proposed method is validated based on the data from an actual operating line of a high-speed train. The results demonstrate the effectiveness and practicability of the proposed method. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Numerical simulation work was carried out on flow distribution inside a Self-Similarity Heat sink (SSHS) due to its good heat removal ability for electronic systems. Effect of the geometrical parameters of the overflow channels on flow distribution are discussed. The simulation results proved that a significant flow maldistribution exists inside the SSHS, which has to be avoided in application. Modifications to the structure of a SSHS was put forward to overcome the problem and improve its overall performance. Both the inlet manifold channel and the inlet plenum were modified with tapered contracting structure to mitigate flow maldistribution inside the SSHS. Based on the analysis on a single unit of the modified SSHS, an optimized SSHS was presented with a heat exchange area of 10 × 10 mm 2 , based on which the obtained data could be extended to a larger heating area with high heat dissipation requirement.

A novel conceptual design of a low temperature multi-effect desalination (LT-MED) system is presented in this paper. The LT-MED system operates at vacuum pressure and is composed of several modules of tubular solar still (TSS) cells, each of which can independently produce freshwater. To verify the performance of the modules in different stages of the system, an experimental TSS was fabricated and tested under different vacuum pressures and heating conditions from 20 to 80 kPa and 100 to 300 W, respectively. The results showed that the energy utilization efficiency exceeded 0.7 in most cases, with a maximum value of approximately 0.81, which was nearly double that of a basin still operated at normal pressure. A 5-effect modular MED system for practical application with a capacity of 86 kg/d was proposed according to the presented theoretical and experimental work. A structural design and control scheme were also presented. Performance and economic analyses indicated a wide applicability of the system, especially for remote and desert regions.

Background To evaluate the effective and safety of reconstruction of flexor and extensor tendon in hand using allogenic tendon with 2- to 7.6-year fellow-up. Methods Between August 2007 and July 2014, we performed tendon allografts for 14 patients who suffered from severe hand injury with 2 or more tendon defects. 10 patients have been followed-up, 6 cases of flexor tendon rupture with defect, 3 cases of extensor tendon rupture with defect, 1 case with flexor and extensor tendon rupture with defect. Tendon allografts were used to repair tendon defects in order to reconstruct the function of flexion or extension. At the final follow-up visit, the total active motion (TAM), grip strength, pinch strength, DASH and the degree of satisfaction were measured. The WBC, C-reactive protein (CRP), Erythrocyte sedimentation rate (ESR), Total T cell and CD4+T/CD8+T were measured to evaluate the response related to immune and infection. Results The average follow-up period was 50.0 months (range 24-82 months). The mean motion of TAM was 129.9°(rang 12-259°), pinch strength was 0.76Kg (rang 0-4.5Kg), grip strength was 18.67Kg (rang 4-46Kg), the score of DASH was 14.25 (rang 3.3-30.8), 7 patients were satisfied and 3 patients were partially satisfied with the results. The results of WBC, CRP, ESR, Total T cell and CD4+ T/CD8+ T were mostly in normal field. Conclusion In severe hand injuries with multiple tendon defects, reconstruction of flexor and extensor tendon in hand using allogenic tendon is an effective and safe treatment.

The recently developed deep belief network (DBN) has been shown to be an effective methodology for solving time series forecasting problems. However, the performance of DBN is seriously depended on the reasonable setting of hyperparameters. At present, random search, grid search and Bayesian optimization are the most common methods of hyperparameters optimization. As an alternative, a state-of-the-art derivative-free optimizer-negative correlation search (NCS) is adopted in this paper to decide the sizes of DBN and learning rates during the training processes. A comparative analysis is performed between the proposed method and other popular techniques in the time series forecasting experiment based on two types of time series datasets. Experiment results statistically affirm the efficiency of the proposed model to obtain better prediction results compared with conventional neural network models.

Train plug doors play a vital role to keep train operation safe and reliable. Taking the advantages of sound signals based fault diagnosis method into consideration, a novel fault diagnosis method for train plug doors based on multiscale normalized permutation entropy (MNPE) and an improved particle swarm optimization based multi-class SVM (IPSO-MSVM) is proposed. First, empirical mode decomposition (EMD) is used to decompose each sound signal into a series of intrinsic mode functions (IMFs) and a residue for stationary processing. Then, MNPE features are extracted from the IMFs. And Fisher discrimination criterion is utilized to obtain the most significant features as feature vectors. Further, an improved PSO (IPSO) is used to search for the optimal parameters of multi-class SVM. Finally, the IPSO-MSVM model is trained and verified using training set and test set, respectively. The identification accuracy of the proposed method reaches 90.54%, which is higher than backpropagation (BP) neural network classifier and 1 Nearest Neighbor (1NN) classifier, indicating the proposed method for fault diagnosis on train plug doors is feasible.

Background Despite the availability of pharmacological intervention, patients with burn injuries experience pain during the treatment of wounds. Supplementary rehabilitation nursing intervention are required to enhance the wellbeing of patients sustaining injuries from burns. The present study aims to conduct a systematic exploration of the impact of rehabilitation nursing intervention on the wellbeing in patients sustaining burn injuries. Methods The electronic databases listed below will be searched systematically: PubMed, EMBASE, CINAHL, Cochrane Library, Web of Science, Scopus, China National Knowledge Infrastructure, and WanFang database. All the databases will be searched from their inauguration to November 2020. There will be no language constraints. Independent undertaking by 2 authors will select studies, extract data from selected studies, and assess the quality of the included studies. All disagreements will be resolved through discussion, or by consulting a third independent author. This study will make use of RevMan 5.3 software to perform statistical analysis. Results The present protocol summarizes high-quality evidence to assess the impact of rehabilitation nursing intervention on the wellbeing of patients sustaining burn injuries. Conclusion The results of the present protocol has the potential to present evidence to assess whether rehabilitation nursing intervention can enhance the wellbeing of patients sustaining burn injuries. Registration number November 17, 2020.osf.io/t6b8c/. (https://osf.io/t6b8c/).

The samples representing abnormal situation is usually very few in the dataset, which makes it difficult to learn the features of abnormal samples by machine-learning-based methods. To improve the accuracy of anomaly detection, the number of abnormal samples should be expanded to ensure the balance of the dataset. In this paper, a discrete synthetic minority oversampling technique (D-SMOTE) is proposed to generate new samples. A closed area is constructed using the three nearest abnormal samples in the dataset. The new samples are then uniformly interpolated in a closed area. By this means, the problem of the imbalance for the original dataset is handled, thus improving the data quality. Based on the expanded datasets, a two-dimensional convolutional neural network (2D CNN) is constructed to detect abnormal samples. In experiments, three cases and different machine learning methods are considered for comparison. Several indexes including accuracy, precision, confusion matrix, F1-score, and Recall have been used to evaluate the detection effectiveness. The results show that the abnormal samples can be detected accurately using oversampling data obtained from the proposed D-SMOTE method.

Background and purpose The aim of this study was to investigate the feasibility of applying a partially randomized patients’ preference (PRPP) trial in the clinical evaluation of acupuncture versus cupping therapy for fibromyalgia. Materials and methods The final study included 126 participants. Participants without a treatment modality preference were randomly assigned to either the cupping therapy group or the acupuncture group. Patients with strong preferences were assigned to their treatment modality of choice. Ashi points were used for treatment. Outcome measures were both qualitative (patient expectation and satisfaction) and quantitative (drop-out rate, pain intensity, quality of life, depression assessment). Results The recruitment of the non-randomized participants was completed 8 months before the randomized participants were recruited. There was no statistical difference related to the grouping method in the adjusted drop-out rate, patient expectation, and satisfaction. Conclusion The PRPP model is suitable for use in the clinical evaluation of non-pharmaceutical therapies.

The control problem for single-input single-output (SISO) systems in the presence of mixed uncertainties, both stochastic and deterministic uncertainties, is considered. The stochastic uncertainties are modeled as exogenous noises, while the deterministic uncertainties are time invariant and appear as the unknown parameters which lie in a bounded interval. Based on a subdivision for the continuous interval, a robust adaptive controller is designed. The controller can not only realize the system output to track the desired output, but also learn a more accurate interval which contains the true value of the unknown parameter with a learning error given in advance. An example is given finally to demonstrate the effectiveness of the proposed method.

This paper presents a bulk-driven CMOS amplifier with high immunity to electromagnetic interference (EMI). The approach is based on a bulk-driven input stage of MOSFET, thereby providing a low-voltage realization of a gate-driven stage. The bulk-driven amplifier includes the positive feedback, an input voltage drop, and dual input-stage symmetrical topology to improve the equivalent transconductance, dc linearity, and ac performance. As demonstrated by the measurements, the dc offset of the proposed amplifier is less than 70 mV, the measured PSD is about −66 dBm with 1 − V pp EMI conveyed. The amplifier chip is implemented using a 0.35 μm standard CMOS process with a 1-V power supply.

Solar stills are one of the most promising and eco-friendly solutions for providing fresh water in arid regions. This study used computational fluid dynamics (CFD) modelling to investigate the effects of operating pressure pop and geometrical parameters on the performance of a Tubular Solar Still (TSS), with an emphasis on vapor flow in the enclosure. The simulation results indicated that water vapor has a higher circulation velocity when operated under vacuum compared with atmospheric conditions, resulting that the yield rate increased by more than 50% at pop

Skewed distribution and incompleteness of monitored data might cause feature submergence and information loss, rendering the fault diagnosis from imbalanced and incomplete data commonly existing in industrial systems is still an intractable problem. Therefore, in order to improve the accuracy of fault diagnosis from imbalanced and incomplete data, this paper proposes a fusion autoencoder (FAE) network and an ensemble diagnosis scheme. A designed multi-level denoising strategy and a variable-scale resampling strategy are adopted as compensation for information loss and skewed distribution. The multiple FAE networks are constructed by combining the advantages of improved sparse autoencoder (SAE) with denoising autoencoder (DAE) to enhance the adaptability. Different hyper parameters are configured for each FAE to ameliorate the diagnostic flexibility, and Bagging strategy is employed to integrate each network into a complete FAE fault diagnosis model. Furthermore, evaluation criteria are suggested and the application range of the model is tested. Finally, different experiments are conducted to verify the effectiveness and practicability of the proposed method.