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The large-scale machinery generally requires multiple accelerometers for condition monitoring. The multi-channel vibration signals carry a wealth of fault information. Synchronous fault feature extraction using multi-channel signals will significantly improve the diagnostic performance. The multivariate entropy method is able to synchronously extract the fault features from multiple sensors, but how to recognize the single fault occurring on different channels remains unexplored. Here, we propose a novel feature extraction method called variational embedding multiscale diversity entropy (veMDE) which constructs the phase space with different structures. The proposed variational phase space construction strategy will generate a different probability distribution for each channel, which results in a better separability for multi-channel feature extraction. Combined with the random forest classifier, a novel fault diagnosis scheme is developed for condition monitoring of the large-scale machinery. One simulated signal and two experimental data are designed to validate the effectiveness of the proposed strategy. The results demonstrate that the proposed method has the best multi-channel feature extraction ability compared with three existing methods: multivariate multiscale sample entropy, multivariate multiscale fuzzy entropy, and multivariate multiscale permutation entropy.

The health condition monitoring of planetary gearboxes has drawn increasing attention due to the importance for safety operation and failure prevention. A novel diagnosis methodology based on multiscale symbolic diversity entropy (SDivEn) is proposed in this article. Herein, dynamical complexity of measured data is quantified by SDivEn. Compare to other entropy-based descriptors, SDivEn has advantages in its robustness and computation efficiency. To increase the feature representation capability of entropy descriptors, multiscale analysis is performed, where the measurement data in time series is decomposed into multiple scaled series by using the coarse graining process and then processed individually by using SDivEn method. The proposed multiscale SDivEn method is applied for fault recognition of planetary gearboxes. Experimental results indicate that the proposed method obtains the highest accuracy in recognizing seven health conditions of planetary gearboxes in comparison with three other existing entropy-based methods.

To understand surrounding scenes accurately, the semantic segmentation of images is vital in autonomous driving tasks, such as navigation, and route planning. Currently, convolutional neural networks (CNN) are widely employed in semantic segmentation to perform precise prediction in the dense pixel level. A recent trend in network design is the stacking of small convolution kernels. In this work, small convolution kernels (3 × 3) are decomposed into complementary convolution kernels (1 × 3 + 3 × 1, 3 × 1 + 1 × 3), the complementary small convolution kernels perform better in the classification and location tasks of semantic segmentation. Subsequently, a complementary convolution residual network (CCRN) is proposed to improve the speed and accuracy of semantic segmentation. To further locate the edge of objects precisely, A coupled Gaussian conditional random field (G-CRF) is utilized for CCRN post-processing. Proposal approach achieved 81.8% and 73.1% mean Intersection-over-Union (mIoU) on PASCAL VOC-2012 test set and Cityscapes test set, respectively.

Using transfer learning (TL) for fault detection and diagnosis has been a hot topic in prognostic and health management (PHM) field. In this article, a systematic framework is established to solve the cross-domain fault diagnosis for rotary machines of the same type using entropy-based TL. The multiscale symbolic dynamic entropy (MSDE) is first proposed to extract features from vibration signals. Then, TL model is trained to get a mapping matrix, which can preserve the structure properties of prior distribution and minimize the discrepancies between different datasets. The mapped features are called multiscale transfer symbolic dynamic entropy (MTSDE). Finally, the support vector machine (SVM) classifier is utilized to accomplish the cross-domain intelligent fault-type recognition. To demonstrate the superiority of the proposed MTSDE method, the comparative experiments of nine methods are carried out under different datasets including gearboxes and bearings. Experimental results demonstrate that our proposed MTSDE method performs best in recognizing various fault types comparing with other nine methods. To the best knowledge of the authors, this is the first attempt of using entropy-based TL for cross-domain fault diagnosis of rotating machinery.

A novel rotating machine fault diagnosis method based on multi-scale transfer fuzzy entropy (MTFE) and support vector machine (SVM) is proposed in this paper. Compared with traditional machine learning methods, our proposed method can identify various fault types of rotating machinery with different data distribution by learning the transfer knowledge from different distribution source domains. First, multi-scale fuzzy entropy (MFE) features of all samples are extracted as input. Second, build a transfer learning model to find a projection matrix to map the MFE features of the source and target domains into a common subspace, called MTFE features. In this process, the distribution structure of training data is maintained and the distribution difference between training data and test data is reduced. Finally, the SVM classifier identifies the fault type of the test data. Two cases including gearbox and rolling bearing are used for validation. Experimental results demonstrate that our proposed MTFE method performs best in recognizing various fault types comparing with other six methods.

An energy deviation settlement (EDS) mechanism has been proposed to settle the deviation between consumption and energy contracts in China's forward electricity market. Due to high EDS costs of electricity retailers, a deviation mutual insurance (DMI) mechanism is put forward, representing an option for electricity retailers to reduce the deviation through contract transfer. Given this background, an optimal alliance strategy is proposed to maximize the utility of different types of individual electricity retailers. The alliance income model considers the EDS cost reduction and the cooperation cost analyzed based on transaction cost theory (TCT). An improved income allocation method, which considers distribution indexes selected by game theory and resource dependence theory (RDT), is proposed to ensure the comprehensive and reasonable allocation of alliance income. Considering the expected income and financial risk, an evaluation model using conditional value at risk (CVaR) as the financial risk assessment indicator is presented to measure the stability of alliances. Simulations based on data from a provincial electricity market in China illustrate that the EDS costs of retailers can be reduced by selecting appropriate partners for alliance. In addition, the proposed alliance income allocation method is more reasonable and more targeted than the cooperative game-based methods.

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method. Keywords: Convolutional neural network, Feature extraction, Infrared thermography (IRT), Intelligent fault diagnosis, Rotating machinery

A Data Processing Network (DPN) streams massive volumes of data collected and stored by the network to multiple processing units to compute desired results in a timely fashion. Due to ever-increasing traffic, distributed cache nodes can be deployed to store hot data and rapidly deliver them for consumption. However, prior work on caching policies has primarily focused on the potential gains in network performance, e.g., cache hit ratio and download latency, while neglecting the impact of cache on data processing and consumption. In this paper, we propose a novel framework, DeepChunk, which leverages deep Q-learning for chunk-based caching in wireless DPN. We show that cache policies must be optimized for both network performance during data delivery and processing efficiency during data consumption. Specifically, DeepChunk utilizes a model-free approach by jointly learning limited network, data streaming, and processing statistics at runtime and making cache update decisions under the guidance of deep Q-learning. It enables a joint optimization of multiple objectives including chunk hit ratio, processing stall time, and object download time while being self-adaptive under the time-varying workload and network conditions. We build a prototype implementation of DeepChunk with Ceph, a popular distributed object storage system. Based on real-world Wifi and 4G traces, our extensive experiments and evaluation demonstrate significant improvement, i.e., 52% increase in total reward and 68% decrease in processing stall time, over a number of baseline caching policies.

Industries across the globe are under growing pressure to rethink and redesign their supply chain operations to maintain their competitive advantage. The supplier selection process in supply chain management holds a pivotal position in its exploration of new strategies to stay competitive in global markets. This study considers supplier selection with two different strategic perspectives, including lean and agile. Selecting suppliers based on their leagile practices helps the focal industries to make their supply chain operations healthier, especially if the focal industry is a major supplier of multinational companies. China is considered as a case context in this study with the application of textile sectors since this country occupies the top position with regard to exports. The common criteria involved in leagile supplier selection were collected from existing literature resources and were fine-tuned with insights from field experts. Further, the case industrial managers also assisted with the evaluation of the influential criteria for the leagile supplier selection process. Based on the replies and the assistance of a decision-making trial and evaluation laboratory tool, the most influential criterion and interdependencies among other criteria were identified. This study helps industrial managers to evaluate their suppliers based on the resultant influential criterion and, further, it strengthens the global supply chains with agility and robustness.

Background: Calcified lumbar disc herniation (CLDH) is considered to be a special type of lumbar disc herniation (LDH). Percutaneous endoscopic interlaminar discectomy (PEID), with safety and efficacy, has been proved to be a minimally invasive surgery for LDH. However, there are few studies on PEID in the treatment of CLDH at L5-S1 level. This research aimed to analyze the clinical efficacy of PEID for L5-S1 CLDH.Methods: From August 2016 to April 2020, we retrospectively analyzed 28 consecutive patients (17 males; 11 females) with L5-S1 CLDH treated with PEID at our institution. All the patients were followed up for greater than 1 year postoperatively. The demographic characteristics, surgical results and clinical outcomes estimated by the visual analog scale (VAS) for leg pain, Oswestry disability index (ODI) and modified MacNab criteria were collected.Results: All patients were successfully performed by PEID. The mean operative time and intraoperative blood loss were 65.36 ± 5.26 minutes and 13.21 ± 4.35 ml, respectively. The VAS for leg pain and ODI scores improved remarkably from 7.54 ± 0.96 to 1.50 ± 0.51 (P < 0.05) and from 69.29 ± 9.91 to 17.43 ± 3.69 (P < 0.05) a year after operation, respectively. According to the modified MacNab criteria of the last follow-up, the excellent and good rates are 92.86%. Two of the patients had complications, one had nerve root injury and the other had postoperative dysesthesia.Conclusions: PEID achieved good clinical outcomes in the treatment of L5-S1 CLDH. And PEID was a safe and effective minimally invasive surgery for L5-S1 CLDH.

Background: Percutaneous endoscopic lumbar discectomy (PELD) is a relatively safe and effective minimally invasive surgery in the treatment of calcified lumbar disc herniation (CLDH). However, studies on percutaneous endoscopic interlaminar discectomy (PEID) and percutaneous endoscopic transforaminal discectomy (PETD) for CLDH have rarely been reported. This research aimed to compare the clinical efficacy of PEID and PETD for L5-S1 CLDH.Methods: We retrospectively analyzed 42 consecutive patients with L5-S1 CLDH treated with PELD at our institution from August 2016 to December 2019. Patients were divided into PEID group (n = 24) and PETD (n = 18) group according to the surgical methods. The demographic characteristics and surgical results of the two groups were compared. Clinical outcomes were estimated by the visual analog scale (VAS) for leg pain, Oswestry disability index (ODI) and modified MacNab criteria.Results: All patients were successfully operated on by PEID or PETD. No significant differences in the demographic characteristics, intraoperative blood loss, postoperative hospital stay and complication rate were noted between the PEID and PETD groups (P > 0.05). The excellent and good rates in the PEID group were similar to those in the PETD group (91.67% vs 88.89%, P > 0.05), whereas the PEID group exhibited superior results for operative time (m) (64.58 ± 5.88 vs 85.56 ± 9.06, P < 0.05) and fluoroscopy times (n) (2.96 ± 0.96 vs 13.33 ± 2.64, P < 0.05) compared with the PETD group.Conclusions: PEID had achieved good clinical efficacy as PETD for L5-S1 CLDH. Compared with PETD, PEID had the advantages of shorter operative time and a reduced number of fluoroscopy times in the treatment of CLDH.

TNNT2 mutation is associated with a range of cardiac diseases, including dilated cardiomyopathy (DCM). However, the mechanisms underlying the development of DCM and heart failure remain incompletely understood. In the present study, we found the expression of cardiac XIN protein was reduced in TNNT2-ΔK210 hESCs-derived cardiomyocytes and mouse heart tissues. We further investigated whether XIN protects against TNNT2 mutation-induced DCM. Overexpression of the repeat-containing isoform XINB decreased the percentage of myofilaments disorganization and increased cell contractility of TNNT2-ΔK210 cardiomyocytes. Moreover, overexpression of XINB by heart-specific delivery via AAV9 ameliorates DCM remodeling caused by TNNT2-ΔK210 mutation in mice, revealed by partially reversed cardiac dilation, systolic dysfunction and heart fibrosis. These results suggest that deficiency of XIN may play a critical role in the development of DCM. Consequently, our findings may provide a new mechanistic insight and represent a therapeutic target for the treatment of idiopathic DCM.

In China, under the planning-market double-track mechanism implemented on the generation side of electricity, unreasonable market-oriented power generation proportion may lead to unnecessary vicious competition and market price changes, and it is against the will of power exchange (PX). Given this background, in this study, a bi-level model for planning-market electricity allocation that considers the bidding game of generation companies is proposed for a smooth transition of power system reform. In the upper level of the model, the proportion of planned electricity is optimized by PX to minimize the average social electricity purchase price. In the lower level of the model, considering the impact of market power on the bidding strategy of generation companies, the bidding strategy of generation companies set as price makers is proposed using the residual demand curve analysis method, while the price takers adopt the lowest bidding strategy. Simulations based on data from a provincial electricity market in China illustrate that the proposed model can effectively reflect the impact of market-oriented electricity proportion on market power and market-clearing price, thus providing a quantitative basis for PX to determine the proportion of market-oriented electricity in total electricity consumption.

Background Modular prosthesis fracture, especially distal femoral fracture, is a rare complication of total hip arthroplasty (THA). However, it is catastrophic, and may have a serious impact on the patients. A distal femoral prosthesis fracture in a patient with developmental dysplasia of the hip (DDH) with nonunion at the subtrochanteric osteotomy site has not yet been reported in any literature. This report presents the first such case, with a purpose of analyzing the causes of modular prosthesis fractures and nonunion of the osteotomy area, so as to provide orthopedic surgeons with experience and lessons. Case presentation We report the case of a 52-year-old woman with the distal femoral prosthesis fracture after THA and subtrochanteric osteotomy for Crowe type IV DDH. The patient had severe pain in the left thigh and her activities were limited. Plain radiographs revealed fracture of the left distal femoral prosthesis and nonunion in the subtrochanteric osteotomy region of the left femur. After a revision of the THA, the patient’s symptoms were resolved. Conclusions A prosthesis fracture combined with nonunion at the subtrochanteric osteotomy site is a rare complication. Modular THA combined with a subtrochanteric osteotomy in the treatment of Crowe type IV DDH should reduce the damage to blood supply and avoid further nonunion of the osteotomy area, which may otherwise lead to modular prosthesis fractures. A detailed preoperative plan and suitable rehabilitation program may help minimize the occurrence of subtrochanteric osteotomy nonunion and reduce complications, including femoral prosthesis fractures, in patients with DDH.

Fault diagnosis of rotating machinery is crucial to improve safety, enhance reliability and reduce maintenance cost. The manual feature extraction and selection of traditional fault diagnosis methods depend on signal processing skills and expert experience, which is labor-intensive and time-consuming. As a typical intelligent fault diagnosis method, the convolutional neural network automatically learns features from original data, but it is extremely difficult to design and train a deep network architecture. This paper proposes a fault diagnosis scheme combined of hierarchical symbolic analysis (HSA) and convolutional neural network (CNN), which achieves laborsaving and timesaving preliminary feature extraction and accomplishes automatically feature learning with simplified network architecture. Firstly, hierarchical symbolic analysis is employed to extract features from original signals. The extracted features are able to identify different health conditions under various operating conditions. Then, convolutional neural network instead of human labor is used to learn the complex non-linear relationship between features and health conditions automatically. The architecture of CNN diagnosis model is simple and convenient to implement. Finally, a centrifugal pump dataset and a motor bearing dataset are adopted to validate the effectiveness of the proposed method. The diagnosis results show that the proposed method exhibits superior performance compared with shallow methods and deep learning methods.

Data-driven fault diagnosis has been a hot topic in recent years with the development of machine learning techniques. However, the prerequisite that the training data and the test data should follow an identical distribution prevents the conventional data-driven diagnosis methods from being applied to the engineering diagnosis problems. To tackle this dilemma, cross-domain fault diagnosis using knowledge transfer strategy is becoming popular in the past five years. The diagnosis methods based on transfer learning aim to build models that can perform well on target tasks by leveraging knowledge from semantic related but distribution different source domains. This paper for the first time summarizes the state-of-art cross-domain fault diagnosis research works. The literatures are introduced from three different viewpoints: research motivations, cross-domain strategies, and application objects. In addition, the corresponding open-source fault datasets and several future directions are also presented. The survey provides readers a framework for better understanding and identifying the research status, challenges and future directions of cross-domain fault diagnosis.

Bibliometric review. The spatial structure of the global spinal cord injury (SCI) research field has not been summarized or analyzed. The objective of this study was to understand the current status and global trends of SCI research, and provide scholars knowledge to integrate into their plans for future research. Not applicable. The Web of Science database was searched for articles related to SCI published between 1999 and 2019. Metrics based on publication data, including publication counts, H indices, countries, institutions, authors, and journals were extracted. Co-citation analysis, collaboration analysis, and co-occurrence analysis of keywords were conducted using CiteSpace. The search identified a total of 41,012 articles related to SCI. Overall, the number of publications increased annually. The United States was the top ranked country by publication count, H index, and citation count. Harvard University and the University of Toronto made the most contributions. M.G. Fehlings was the top ranked author. Spinal Cord published the largest number of articles, and was the most frequently cited journal. The top 5 ranked keywords that appeared most frequently were spinal cord injury, functional recovery, adult rat rehabilitation, and paraplegia. Twelve major clusters of keywords and 15 clusters of co-cited references were generated. This study comprehensively analyzed and summarized the trends in SCI research during the past 20 years. Findings should provide scholars information on the countries, institutions, authors, and journals that are active in the field of SCI research, and a knowledge base for future projects.

Due to globalization and industrialization, several industrial developments have been evident, which increases the global revenue significantly in recent years. Meanwhile, such increasing industrial activities prints many negative impacts on the environment through resources scavenging, an increase in waste, pollution and so on. Several strategies have been introduced to reduce these negative characteristics of industrial operations without comprising in production quantities. Industrial symbiosis is one such strategy that assists the industries in the industrial parks to engage in eco-efficient operations. Despite its key advantages, very limited studies exist in the developing context, especially with China industries. Though there are several attempts made in industrial symbiosis for its effective implementation, but no previous study details the success factors of implementation of industrial symbiosis even with the Chinese context. Addressing this gap, this study identified the common success factors for industrial symbiosis and analyzed the effective factors within the Chinese industrial context. The analytical hierarchy process has been adapted for this analysis, in which the China industrial eco park design managers are considered as case decision-makers. The results revealed that among considered 12 factors, technological factors are the key success factor for the effective implementation of industrial symbiosis in the Chinese context. With the findings, this study is having both scientific and societal contributions toward the implementation of industrial symbiosis in the Chinese context. Further, the future scope for research in addition to limitations has been presented.

Recent manufacturing sectors facing several challenges worldwide due to various disruptions and economic instabilities. Hence, it becomes necessary for the industries to think about new strategies that could sustain in these tough environments. Such a strategy has been discussed in this paper, Leagile manufacturing within Peru context. Despite having several benefits with the implementation of Leagile manufacturing system, since there are many tangible and intangible barriers hinders its effective implementation in Peru industries. These barriers mainly rise due to less clarity among the tools and lack of ease to implementation within the concern of an industrial perspective. This study considers addressing this gap by evaluating the most influential tools for the effective implementation of Leagile manufacturing. From the combined support of both primary and secondary data, this study considered 12 Leagile manufacturing tools, which further evaluated based on their influence in implementation on Peru context with Decision Making and Trial Evaluation Laboratory (DEMATEL).

With deregulation of electrical market in the China, the electricity retail companies (ERCs) and the electricity retail market (ERM) experiences fast development in the recent years. Different from the generation and transmission market, the retail market is more open to the participators, and has more competition and higher risk. Considering the risk factors e.g. the business scale, the capital, the employee, and the complaints, an optimization model to reduce the risk of the ERCs and ERM with higher predicted risk is newly proposed in this paper, where the risk factors are normalized by the threshold value and the time before quantifying the risk. The risk optimization problem with constraints to adjustable risk factors is nonlinear and solved by the interior point method. The numerical results validate the optimization effect on the risk of the ERCs and the ERM.

Three-dimensional hand detection from a single RGB-D image is an important technology which supports many useful applications. Practically, it is challenging to robustly detect human hands in unconstrained environments because the RGB-D channels can be affected by many uncontrollable factors, such as light changes. To tackle this problem, we propose a 3D hand detection approach which improves the robustness and accuracy by adaptively fusing the complementary features extracted from the RGB-D channels. Using the fused RGB-D feature, the 2D bounding boxes of hands are detected first, and then the 3D locations along the z-axis are estimated through a cascaded network. Furthermore, we represent a challenging RGB-D hand detection dataset collected in unconstrained environments. Different from previous works which primarily rely on either the RGB or D channel, we adaptively fuse the RGB-D channels for hand detection. Specifically, evaluation results show that the D-channel is crucial for hand detection in unconstrained environments. Our RGB-D fusion-based approach significantly improves the hand detection accuracy from 69.1 to 74.1 comparing to one of the most state-of-the-art RGB-based hand detectors. The existing RGB- or D-based methods are unstable in unseen lighting conditions: in dark conditions, the accuracy of the RGB-based method significantly drops to 48.9, and in back-light conditions, the accuracy of the D-based method dramatically drops to 28.3. Compared with these methods, our RGB-D fusion based approach is much more robust without accuracy degrading, and our detection results are 62.5 and 65.9, respectively, in these two extreme lighting conditions for accuracy.

We examine the effect of restricting executives’ outside job opportunities on corporate tax avoidance and tax risk, using a natural experiment of the staggered adoption and rejection of the inevitable disclosure doctrine (IDD). Based on a difference-in-differences analysis, we find strong evidence that the IDD decreases effective tax rates. We also find that the IDD reduces tax risk though the evidence is weaker. The effect is generally more pronounced when the risk of dismissal due to poor performance is higher. Additional analyses show that the IDD increases the use of tax haven operations. Finally, a path analysis shows that the increase in tax avoidance contributes about 6% to 7% of the total effect of the IDD on firm value. Overall, the results suggest that career concerns motivate executives to reduce their firms’ tax burden using low risk strategies.

With fast development of the retail market of China in recent years, the risk of the market needs to be quantified. This paper provides a detailed framework to assess and classify the risk of the market. The neural network is used to predict the risk. Time-dependent weight is used to describe cumulative effect. With the S-shaped curve, the risk factors are classified by safety, competition and elimination. The entropy method is used to set weights of the risk factors. The K-means clustering is used to classify the companies or periods. The risk factors, the operational risk, and the risk indicators form a hierarchical framework. The state aggregation based on serial tracking and incidence matrix finds transition rate matrix for early warning and coordination. The numerical results of a market with 9 companies are provided to validate the effectiveness of the proposed model.

The health condition monitoring of rotating machinery can avoid the disastrous failure and guarantee the safe operation. The vibration-based fault diagnosis shows the most attractive character for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) has been investigated as an effective tool for FDRM. However, the LZC only performs single-scale analysis, which is not suitable to extract the fault features embedded in vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results show that the proposed HLZC method had the best diagnosing performance compared with LZC and multi-scale Lempel-Ziv complexity methods.

Background Osteosarcoma is a primary malignant bone tumor that occurs in children and adolescents. Increasing numbers of scholars have studied its development and treatment. To fully understand the current status of osteosarcoma research and global trends therein, we performed a bibliometric and visual analysis of osteosarcoma studies published between 1999 and 2019. Methods We searched the Web of Science database for publications on osteosarcoma. The basic characteristics of this sample of publications, such as H indices, annual outputs, languages of publication, and authors, journals, institutions, and countries of origin, were determined. Co-citation, collaboration, and keyword co-occurrence were analyzed using CiteSpace software. Results The sample comprised 16,934 articles. The number of publications increased annually. H indices and total numbers of citations were far higher for articles from the United States than for those from other countries. Among institutions, the largest proportion of articles originated from Shanghai Jiaotong University. R. Gorlick was the author with the highest H index and total number of citations. Oncology Letters published the largest number of articles and Cancer Research was the most frequently cited journal. The five most frequently appearing keywords were “osteosarcoma,” “cancer,” “expression,” “apoptosis,” and “metastasis.” The analysis generated 10 major clusters of keywords and 23 clusters of co-cited references. Conclusions The findings of this study have guiding significance for researchers seeking cooperating institutions and partners for osteosarcoma research, popular journals and important literature in the field, an understanding of the knowledge base for this research, and up-to-date identification of research hotspots and trends. Keywords: osteosarcoma, bibliometric, citespace, visual analysis, co-citation

Background Even with the augmentative application of anal-preservation surgery in low rectal cancer, the role and indications of laparoscopic intersphincteric resection (Lap ISR) are still under debate, especially for T3 or node-positive (T3N0M0, T1–3N+M0) cancer, mainly due to the oncological safety and functional outcomes. INTRABEAM (Carl Zeiss, Germany) intraoperative radiotherapy (IORT) using low-energy X-rays features in accurate irradiation, less exposure, and reduced complications. Taking advantages of Lap ISR and INTRABEAM IORT, this innovative approach aims to increase the probability of the anal preservation with acceptable postoperative outcomes. Materials and methods From December 2015 to August 2019, we retrospectively analyzed the short-term outcomes of 12 patients evaluated preoperatively with T3 or node-positive (T3N0M0, T1–3N+M0) primary locally advanced low rectal cancer. They all had received Lap ISR and INTRABEAM IORT with a dose of 16–18 Gy applied by an applicator through the anus (natural orifice). Then, with no pre- or postoperative radiotherapy given, the patients were suggested to receive 6–8 cycles of the XELOX chemotherapy regimen (oxaliplatin, 130 mg/m2 and capecitabine, 1000 mg/m2). Results All patients achieved R0 resection. The median radiation time was 27 min and 15 s, and the mean radiative dose was 17.3 Gy (range 16–18 Gy). The median follow-up time was 18.5 months (range 3–45 months). Two patients experienced local recurrence. Two male patients experienced anastomotic stenosis. Furthermore, one of them experienced perianal abscess and the other one experienced pulmonary metastasis after refusing to receive chemotherapy. One female patient with internal anal sphincter invasion experienced distant metastases to the liver and gluteus maximus muscle 35 months after IORT. No acute radiation injuries or symptoms were observed. Although they experienced a reduction in anal function, every patient was satisfied with the postoperative outcomes. Conclusions For patients evaluated preoperatively with T3 or node-positive (T3N0M0, T1–3N+M0) primary locally advanced low rectal cancer, Lap ISR with INTRABEAM IORT may be a safe and feasible approach for anal preservation without compromising oncological outcomes.

Hand detection is a crucial pre-processing procedure for many human hand related computer vision tasks, such as hand pose estimation, hand gesture recognition, human activity analysis, and so on. However, reliably detecting multiple hands from cluttering scenes remains to be a challenging task because of complex appearance diversities of dexterous human hands (e.g., different hand shapes, skin colors, illuminations, orientations, and scales, etc.) in color images. To tackle this problem, an accurate hand detection method is proposed to reliably detect multiple hands from a single color image using a hybrid detection/reconstruction convolutional neural networks (CNN) framework, in which regions of hands are detected and appearances of hands are reconstructed in parallel by sharing features extracted from a region proposal layer, and the proposed model is trained in an end-to-end manner. Furthermore, it is observed that the generative adversarial network (GAN) could further boost the detection performance by generating more realistic hand appearances. The experimental results show that the proposed approach outperforms the state-of-the-art on public challenging hand detection benchmarks.

Supply chain performance evaluation problems are evaluated using data envelopment analysis. This paper proposes a fuzzy network epsilon-based data envelopment analysis for supply chain performance evaluation. In the common data envelopment analysis models which are used for evaluation of decision-maker units efficiency, there are several inputs and outputs. One of the bugs of such models is that the intermediate products and linking activities are overlooked. Considering these intermediate activities and products, the current study evaluates the performance of decision-maker units in an automotive supply chain. There are ten decision-maker units in the supply chain in which there are three suppliers, two manufacturers, two distributors, and four customers. Moreover, the overall efficiency of input-oriented (input-based) model and input-oriented divisional efficiency are calculated. In order to improve the efficiencies, the projections onto the frontiers are obtained by using the outputs of the solved model and Lingo software. In order to show the applicability of the proposed model, it is applied on automotive industry, as a case study, to evaluate supply chain performance. Then, the overall efficiencies of DMUs and each sections (divisions) of DMUs were calculated separately. Therefore, every organization can apply this evaluation method for improving the performance of alternative factors.

In this study, four 1/3-scale bridge column specimens were investigated both experimentally and numerically. The specimens consisted of two cast-in-place (CIP) reference columns and two pre...

The retail electricity market is becoming prosperous in China in the recent years. However, due to many uncertain factors, the retail providers may go bankrupt and have to withdraw from the retail market, thus the customers will suffer from unexpected supply interruption and economic loss, which will disturb the healthy operation of the electricity market. The purpose of the last resort service is to take the responsibility of the bankrupted providers, and maintain the power supply to the customers. This service is often provided by retail electric providers in foreign countries while undertaken by the power grid companies in China. In this paper, the experiences of the last resort service in several foreign countries are investigated. The qualification, pricing, customer allocation, and operating process about the last resort service are investigated. The benefit and risk of the last resort service are analyzed to determine the sharing of the responsibilities, interests, and punishments among the participants of the retail market. Based on the investigation, some practical suggestions to the last resort service are given to the provincial retail market in China.

Background In order to overcome the shortcomings of laparoscopic intersphincteric resection (Lap ISR), an alternative method of delivering intraoperative radiotherapy by Intrabeam X-rays radiotherapy system (XRS) is proposed in this paper. Intrabeam XRS is a device that uses low-energy X-rays source generated by a mobile controller unit, which is featured in accurate irradiation, reduced complications, and less exposure. The purpose of this study is to discuss the feasibility of Lap ISR with intra-operative radiotherapy using low-energy X-rays for locally advanced ultra-low rectal cancer in Asian woman. This novel proposed method will greatly increase the anus preserving probability and improved the quality of life. Methods A 53-year-old woman diagnosed with rectal adenocarcinoma had a strong desire to preserve the anal function and presented at the Jilin University Second Hospital, Jilin, China. The tumor’s size was 4 cm × 3 cm. It was located 2 cm from the anus merge and invaded the levator ani muscle. Preoperative clinical staging was T4N1M0 and could be reached R0 resection. After the consent form was signed by the patient, Lap ISR combined with the applicator put through the anus (natural orifice) to the tumor bed was performed and prophylactic ileostomy synchronized the anastomosis. Patient only received 1-cycle chemotherapy regimen of oxaliplatin with capecitabine postoperatively due to personal reasons. Pre- or postoperative radiotherapy was not given. Results After clinical follow-up, until now, there is not any sign of local recurrence. Anus function and short-term complications are acceptable. The short-term effect is satisfying and we look forward to further assess the long-term effect. Conclusion Laparoscopic intersphincteric resection with IORT using low-energy X-rays for the patients with late-stage ultra-low rectal cancer could provide an opportunity of preserving the anus function, and it is feasible for the selected patients. Trial registration Retrospectively registered; Trial registration: NCT03393234; Registered time: 05 January 2017.

Diagnosis of gearbox is crucial to prevent catastrophic failure and reduce financial losses. In this study, we introduce a novel fault diagnosis technique using the infrared thermography (IRT). The IRT-based techniques have merits of non-contact measurement and high-scalability. Since the convolutional neural network (CNN) is proven to be powerful in image processing, a fault diagnosis strategy is designed by combining the IRT and CNN. Then, the pattern identification is achieved by using softmax regression (SR) classifier. One experimental data is used to validate the effectiveness of the proposed method. Results demonstrate that this diagnosis strategy can recognize gearbox with various oil-level faults. Furthermore, some important distinguishable areas of IRT images are marked for further focused research field.

The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.

Despite rapid advances in the field of image denoising, heavy noise removal has remained an under-explored area. When the strength of noise becomes comparable to or even more dominating than that of signal, restoration of important structures from such heavily-contaminated images becomes more challenging. Existing model-based and learning-based image denoising techniques often cannot delivery satisfactory results, which call for new insights and algorithmic tools. In this paper, we propose to remove heavy noise with the help of similar images retrieved from datasets, which leads to a hybrid approach combining sparsity-based and learning-based methods. Specifically, the parametric sparse prior of underlying clean image is learned from the retrieved reference images and the input noisy image. Leveraging prior from the reference images, the estimated parametric model contains much more accurate information of image details. When compared against existing conventional methods, the proposed hybrid approach is more capable of restoring fine-detailed structures in the presence of heavy noise. Experimental results show that the proposed method dramatically outperforms current state-of-the-art image denoising methods both subjectively and objectively.

The problem of industrial pollution has increased globally at presently. Countries across the globe are aware of the concept of sustainable implementation in industries from a local to the global level to reduce pollution. Recent research supported this by focusing on Small and Medium Enterprises (SMEs) rather than Multinational Corporations (MNC). Sustainability development is a key to ensure control of the hazards and pollution in traditional activities. In India, many SMEs are involved in manufacturing varied products and services for customers. The auto component manufacturing sector is playing as an important role in the Indian economy by creating numerous job opportunities. Based on this, researchers gave special attention to auto components SMEs in sustainable development (SD) because of pollution which harms the environment. Analysis of SD is an essential activity for industries to ensure environmental improvement. Analyzing the indicators for SD helps confirm the development of sustainability in industries. However, analyzing the indicators for SD is a challenge for managers and needs detailed analysis and significant effort. Indicator analysis is a vague concept for managers who lack alternatives to evaluate SD in their industry. The aim of this present paper is to develop a set of measurement indicators for SD from literature. This paper, with the help of discussions with experts and Decision-making Trial and Evaluation Laboratory (DEMATEL) methodology analyzes sustainable development indicators in the auto components sectors. Fifteen SD indicators were considered for this study from a relevant literature background. These SD indicators indicate the industries’ sustainable development. Based on their interest, a questionnaire for this study was circulated among 35 leading auto components manufacturing SMEs in North India. This study will identify the influential indicator for SD based on experts' judgments through a pairwise comparison matrix. The results from the study clearly states that Indian auto component manufacturing industries believe that among the fifteen listed indicators, carbon management indicator helps in measuring sustainable development. Due to this reason, embedded or embodied carbon (SI2) indicator plays an influential role. Finally, the study concludes with insights about the future path of sustainability performance in a developing country like India.

First, an improved local mean decomposition (LMD) method called differential rational spline-based LMD (DRS) is developed for signal decomposition. Differential and integral operations are introduced in LMD, which can weaken the mode mixing problem. Meanwhile, an optimized rational spline interpolation is proposed to calculate the envelope functions aiming to reduce the large errors caused by moving average in the traditional LMD. A series of product functions (PFs) is obtained after the application of the proposed DRS-LMD. Then, Kullback–Leibler (K–L) divergence is adopted to select main PF components that contain most fault information. The machine fault can be easily identified from the amplitude spectrum of the selected PF component. The effectiveness of the proposed DRS-LMD and K–L strategy is tested on simulated vibration signals and experimental vibration signals. Results show that the proposed method can increase the decomposition accuracy of the signals and can be used to detect early faults on the gears and rolling bearings.

According to the performance degradation law of aeroengine in operation, we proposed a method of aeroengine performance degradation evaluation based on the fusion decision of Hierarchical Bayes method (HB) and deep neural network (DNN). Hierarchical Bayes method was used to build the aeroengine reliability model. Then Based on the aeroengine operation monitoring parameters and reliability model, we used deep learning method to extract the performance degradation law and to evaluate aeroengine performance level, realized the fusion decision of engine performance degradation assessment. 9467 monitoring samples, contaminated with sensor noise, collected from 50 aeroengines was used to evaluate the quality of the network, and the average accuracy is 92.01%. The results showed that this method showed good robustness and reduced the risk of the evaluation results error caused by noise.

This paper presents a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis (BEMD-AMMA) for early fault diagnosis of rolling bearings. In this scheme, we propose a bandwidth based method to select the best envelope interpolation method. First, multiple envelope algorithms are defined and separately subtracted from the original data to obtain the preintrinsic mode functions (PIMFs). Second, an IMF with the smallest frequency bandwidth is selected to be the optimal IMF (OIMF). Third, this OIMF is subtracted from the original signal, and then repeat the sifting process until the residual is a constant or monotonic. Since the OIMF has the smallest frequency bandwidth, the mode mixing phenomenon can be significantly weakened. After that the OIMFs with clear fault information are used to construct the main component of the original signal. Then, the AMMA is introduced to demodulate the constructed main component. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms EMD-AMMA, ensemble empirical mode decomposition-AMMA, and generalized empirical mode decomposition-empirical envelope demodulation in detecting early inner race fault.

Percussive-rotary drilling is an effective method for the hard rock drilling. As hydraulic and pneumatic hammers have many problems especially in the deep core drilling, a new electromagnetic hammer driven by a tube linear motor is introduced in this study. The study mainly covers the mechanical structure and electromagnetic design of the hammer, respectively. Due to the direct use of electric power, the electromagnetic hammer has the advantage of high efficiency while it must meet the minimal percussive energy requirement under the limitation of drill pipe diameter. In this study, the validity of electromagnetic design is verified through the finite-element analysis software Ansoft/Maxwell 2D and numerical calculation of electromagnetic force. The working temperature in deep down-hole is relatively high, the influence of high temperature in deep hole on the electromagnetic thrust of the linear motor is then analysed. A motion model of linear motor hammer is established to calculate and analyse the impact power and frequency under different conditions. The results show that the performance of linear motor hammer can reach or even exceed the one of same diameter hydraulic hammer, especially in the low-frequency range. The prototype test also confirms the validity of the design.

Health condition identification of planetary gearboxes is crucial to reduce the downtime and maximize productivity. This paper aims to develop a novel fault diagnosis method based on modified multi-scale symbolic dynamic entropy (MMSDE) and minimum redundancy maximum relevance (mRMR) to identify the different health conditions of planetary gearbox. MMSDE is proposed to quantify the regularity of time series, which can assess the dynamical characteristics over a range of scales. MMSDE has obvious advantages in the detection of dynamical changes and computation efficiency. Then, the mRMR approach is introduced to refine the fault features. Lastly, the obtained new features are fed into the least square support vector machine (LSSVM) to complete the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault types of planetary gearboxes.

A Data Processing Network (DPN) streams massive volumes of data collected and stored by the network to multiple processing units to compute desired results in a timely fashion. Due to ever-increasing traffic, distributed cache nodes can be deployed to store hot data and rapidly deliver them for consumption. However, prior work on caching policies has primarily focused on the potential gains in network performance, e.g., cache hit ratio and download latency, while neglecting the impact of cache on data processing and consumption.In this paper, we propose a novel framework, DeepChunk, which leverages deep Q-learning for chunk-based caching in DPN. We show that cache policies must be optimized for both network performance during data delivery and processing efficiency during data consumption. Specifically, DeepChunk utilizes a model-free approach by jointly learning limited network, data streaming, and processing statistics at runtime and making cache update decisions under the guidance of powerful deep Q-learning. It enables a joint optimization of multiple objectives including chunk hit ratio, processing stall time, and object download time while being self-adaptive under the time-varying workload and network conditions. We build a prototype implementation of DeepChunk with Ceph, a popular distributed object storage system. Our extensive experiments and evaluation demonstrate significant improvement, i.e., 43% in total reward and 39% in processing stall time, over a number of baseline caching policies.

Hand pose estimation is a critical technology of computer vision and human-computer interaction. Deep-learning methods require a considerable amount of tagged data. Accordingly, numerous labeled training data are required. This paper aims to generate depth hand images. Given a ground-truth 3D hand pose, the developed method can generate depth hand images. To be specific, a ground truth can be 3D hand poses with the hand structure contained, while the synthesized image has an identical size to that of the training image and a similar visual appearance to the training set. The developed method, inspired by the progress in the generative adversarial network (GAN) and image-style transfer, helps model the latent statistical relationship between the ground-truth hand pose and the corresponding depth hand image. The images synthesized using the developed method are demonstrated to be feasible for enhancing performance. On public hand pose datasets (NYU, MSRA, ICVL), comprehensive experiments prove that the developed method outperforms the existing works.

A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.

Although several human studies have examined bisphenol A (BPA) exposure in relation to routine sperm parameters, evidence of BPA’s effects on sperm movement characteristics is limited. We examined associations of BPA exposure with sperm parameters including sperm movement characteristics among fertile men. The cross-sectional study was conducted in Sandu County, Guizhou Province, China. Subjects provided semen samples analyzed by computer-aided sperm analysis (CASA) system and urine samples for BPA assay. They were invited to complete an in-person interview with a structured questionnaire to obtain demographics, lifestyle factors, etc. In final analyses, 500 subjects were included. We used multivariate linear regression analyses to estimate associations between BPA and sperm parameters after adjusting for potential confounders. BPA was detected in 73.6% of urine samples, with a geometric mean of 0.44 μg/gCreatinine. Compared with subjects of undetected BPA, subjects with detected BPA had increased Linearity (LIN, β: 2.19, 95% confidence interval (CI): 0.37, 4.0), Straightness (STR, β: 1.47, 95% CI: 0.19, 2.75), Wobble (WOB, β: 1.75, 95% CI: 0.26, 3.25), reduced Amplitude of lateral head displacement (ALH, β: −0.26, 95% CI: −0.5, −0.02) and Mean angular displacement (MAD, β: −2.17, 95% CI: −4.22, −0.11). Subjects in the highest tertile of creatinine-adjusted BPA group had lower sperm concentration than those with undetected BPA. Dose-response relationships of BPA with LIN, WOB, ALH, MAD and sperm concentration were demonstrated by statistically significant trends across tertiles of creatinine-adjusted BPA concentrations. Similar results were obtained using unadjusted BPA concentrations. Exposure to environmental BPA would decrease sperm concentration and sperm swing characteristics (ALH and MAD), and increase sperm velocity ratios (LIN, STR and WOB), which might mediate further effects on impaired male fecundity.

The increasing capacity of the grid-connected solar system has negative influence on the power grid. This paper focuses on the quality evaluation of the solar power. The quality of the solar power is quantified by the penalty function, which considers the total output, the output fluctuation and the forecast error. Firstly, the K-means clustering technique to cluster the sample data of the indices is applied. Then, the significance test using the F-distribution is applied to validate the effectiveness of the proposed model. Finally, the penalty function is proposed. The logarithmic function, the power function, and the exponential function are applied to compare the influences of each index on the penalty function. The Analytic Hierarchy Process (AHP) and the entropy method are used to determine the weight coefficients. The feasibility and application value of the proposed model are validated by the numerical results.

With the development of power market construction, in order to ensure the effective competition of the power market and the stable operation of the system, under the competitive electricity market mode, it has become an urgent problem to improve the power sales market supervision system. Firstly, the characteristics of the electricity sales market under the competitive electricity market mode is analyzed. Secondly, problems in the surveillance of the electricity sales market are pointed out. Finally, the countermeasures and suggestions for the surveillance of the electricity sales market under the competitive electricity market mode are put forward.

In this study, we investigated the contamination of 123 vegetables and 130 fruits with 11 fungicides in the mid-western region of China. The index of food safety (IFS) and risk coefficient (R) were used to assess the health hazards of the fungicides to consumers. The results showed that the qualified rates of pesticide residues in fruits and vegetables were 100 % (130/130) and 98.37 % (121/123), respectively. The detection rate of pesticides in celery, cowpea, peach, and grape was higher than 70 %. The amount of difenoconazole and procymidone pesticides exceeded the standard in vegetables, while carbendazim, difenoconazole, and tebuconazole were the most common pesticides in fruits and vegetables. The IFS of all detected pesticides in the fruits and vegetables was less than 1, indicating an acceptable safety risk of the pesticide residues. The R of difenoconazole and procymidone in the vegetables was moderate, which should be considered in the key control risk factors and continuously monitored to protect people's health.

Since the vibration signals of gearbox are non-linear and non-stationary, it is difficult to accurately evaluate the working conditions. Therefore, a fault feature extraction technique based on intrinsic characteristic-scale decomposition (ICD) and multi-scale entropy (MSE) is presented in this paper. The measured signals are firstly decomposed into a series of product components (PCs) by ICD. Secondly, the main product component is selected, and then MSE is used to extract the feature vectors from the selected PCs. Finally, the obtained feature vectors of gearbox with different scale factors are adopted as inputs of support vector machine (SVM) to fulfill the fault patterns identification. The superiority of the proposed technique is verified through comparing with three other methods.

Early fault diagnosis is crucial to reduce the machine downtime. This paper presents a novel method based on symbolic dynamic filtering (SDF) for early fault detection and intrinsic characteristic-scale decomposition (ICD) for fault type recognition. SDF is first applied to extract the fault feature for depicting bearing performance degradation. Then, a fault alarm is triggered using cumulative sum. Finally, the extracted abnormal signal is decomposed by the ICD method, and the kurtosis method is used to select a principal product component that contains most fault information for fault detection. The real life experimental results validate the effectiveness of the proposed method in early detection of bearing fault and fault diagnosis in comparison with Fourier transform, Hilbert envelope spectrum, original local mean decomposition and spectral kurtosis.