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Gearbox diagnosis is critical for avoiding catastrophic failure and minimizing financial damages. Aiming at the problem that the vibration-based fault diagnosis methods cannot effectively identify the non-structural failure mode and the diagnosis model based on the infrared thermal image is not robust enough, a fusion fault diagnosis method for gearboxes using vibration signals and infrared images is proposed. By fusing these two kinds of heterogeneous data, the proposed method can identify both structural and unstructured health states while maintaining high robustness. In addition, CNN has powerful image processing capabilities, which can directly process two-dimensional infrared images and achieve high accuracy. Finally, a gearbox experiment is carried out to test the performance of our method. The results suggest that the proposed fusion CNN can obtain the highest accuracy compared with some methods based on single signals, shallow learning methods SVM and deep unsupervised learning methods SAE.

To overcome the problem that traditional feature extraction algorithms are sensitive to noise, a bearing fault signature extraction scheme is proposed in this paper with the help of oscillation-based signal decomposition and time frequency manifold (TFM) learning. Firstly, an oscillation-based signal component separation method based on tunable Q factor wavelet transform (TQWT) is utilized to separate the low oscillatory component from vibration signals. Then, concept of TFM is utilized on the separated low oscillatory component to generate the low oscillatory time frequency manifold signature. The proposed method is termed as oscillatory time frequency manifold (OTFM). Compared to that of traditional short time Fourier transform (STFT) and original TFM algorithm, results of experiment show that the proposed algorithm has better time frequency characterization ability for bearing fault signature.

The frequency domain electromagnetic sounding method is one of the important means of exploring mineral resources. This paper combined tensor controlled source audio frequency magnetotelluric (CSAMT) and audio frequency magnetotelluric(AMT) methods to investigate Xiarihamu copper-nickel ore in Qinghai Province. Spliced tensor CSAMT high frequency (200 Hz-8533 Hz) and AMT low frequency (1 Hz-200 Hz) apparent resistivity and impedance phase data, as well as spliced data, were used for a 2D inversion and to generate a 2D geoelectric model. Coupled with the known geological, geophysical, and drilling data, the inversion results were analyzed. The results show that the combined use of tensor CSAMT and AMT can overcome the inherent defects of these methods, such as limited exploration depth and the "dead band" effect. This method produced good results in terms of exploring the underground electrical structure, finding the low resistivity body, and determining the thickness of the overburden, as well as the occurrence and the buried depth of the ore body. These results provide an effective method and exploration strategy to find similar potential deposits in the surrounding areas. (C) 2018 Elsevier B.V. All rights reserved.

Multiscale symbolic dynamic entropy (MSDE) has been recently proposed to characterize the dynamical behavior of time series, which has merits of high computational efficiency and robustness to noise comparing with multiscale sample entropy (MSE) and multiscale permutation entropy (MPE). However, the variance of the MSDE values increases as the length of a time series becomes shorter using multiscale analysis. To address this shortcoming, a new method, namely refined composite multi-scale symbolic dynamic entropy (RCMSDE), is proposed to extract the fault information of rotating machinery. Then, Laplacian score (LS) is utilized to reduce the dimension of eigenvectors. In the end, the selected features are taken as the input of the improved support vector machine based on binary tree (ISVM-BT) for fault type identification. The effectiveness of the proposed method is validated using both the simulation and two experimental tests. Results demonstrate that the proposed method generates highest classification accuracy in comparison with existing methods such as MSDE, refined composite multiscale sample entropy (RCMSE) and refined composite multiscale permutation entropy (RCMPE).

Intelligent fault diagnosis provides great convenience for the prognostic and health management of the rotating machinery. Recently, the entropy-based feature extraction method has aroused researchers’ attentions due to its independence with prior knowledge, unnecessary of preprocessing, and easy to perform. The multiscale diversity entropy has been proven to be a promising feature extraction method for the intelligent fault diagnosis. Compared to the existing entropy methods, the multiscale diversity entropy has advantages of high consistency, strong robustness and high calculation efficiency. However, the multiscale diversity entropy encounters the challenge to extract features for early fault diagnosis due to the weak fault symptoms and strong noise. This can be attributed to the multiscale diversity entropy only concerns the fault information embedded in the low frequency, which ignores the information hidden in the high frequency. To address this defect, the hierarchical diversity entropy (HDE) is proposed, which can synchronously extract fault information hidden in both high and low frequency. Based on HDE and random forest, a novel intelligent fault diagnosis frame has been proposed. The effectiveness of the proposed method has been evaluated through simulated and experimental bearing signals. The results show that the proposed HDE has the best feature extraction ability compare to multiscale sample entropy, multiscale permutation entropy, multiscale fuzzy entropy, and multiscale diversity entropy.

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).

Gearbox is one of the most important parts of the rotating machinery, so health monitoring of the gearbox is essential. The accurate positioning of tooth failure of gear is an important function of the fault diagnosis system. This paper proposes a detection strategy based on designed convolutional neural networks to detect and locate gear tooth failure. The detection strategy aims to compare the characteristic gap between the normal gear and the faulty gear in the same period extracted by the convolutional neural network, and assign weights to the faulty gear vibration signal to obtain the weight sequence of the faulty vibration signal, so as to obtain the faulty tooth weight. Finally, the health condition of the gear can be evaluated by comparing the weight between all teeth of the gear. The proposed detection strategy is tested through simulation vibration signal and experiment vibration signal. The result shows that the proposed method can successfully identify gear failure and effectively detect single tooth failure on gear.

Incorporating with inhomogeneous phases with high electroluminescence (EL) intensity to prepare smart meta-superconductors (SMSCs) is an effective method for increasing the superconducting transition temperature (Tc) and has been confirmed in both MgB2 and Bi(Pb)SrCaCuO systems. However, the increase of ΔTc (ΔTc = Tc ‒ Tcpure) has been quite small because of the low optimal concentrations of inhomogeneous phases. In this work, three kinds of MgB2 raw materials, namely, aMgB2, bMgB2, and cMgB2, were prepared with particle sizes decreasing in order. Inhomogeneous phases, Y2O3:Eu3+ and Y2O3:Eu3+/Ag, were also prepared and doped into MgB2 to study the influence of doping concentration on the ΔTc of MgB2 with different particle sizes. Results show that reducing the MgB2 particle size increases the optimal doping concentration of inhomogeneous phases, thereby increasing ΔTc. The optimal doping concentrations for aMgB2, bMgB2, and cMgB2 are 0.5%, 0.8%, and 1.2%, respectively. The corresponding ΔTc values are 0.4, 0.9, and 1.2 K, respectively. This work open a new approach to reinforcing increase of ΔTc in MgB2 SMSCs.

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.

To solve the defects that the generalization ability of the traditional data-driven fault diagnosis model reduces or even fails in mechanical system diagnosis, a multi-scale transfer sample entropy is proposed based on the idea of transfer learning. First, the multi-scale transfer sample entropy method is proposed to extract fault features based on multi-scale sample entropy and feature transfer learning. Second, the parameters of the multiscale transfer sample entropy method are optimized to further improve the fault identification accuracy. Finally, the experiment results of rolling bearing show that the proposed multi-scale transfer sample entropy can effectively improve the generalization ability of the data-driven model and realize the accurate identification of different fault locations of the rolling bearing under a small number of samples.

Planetary gearboxes play a critical role in aerospace and heavy industry fields, such as wind turbines, heavy vehicles and construction machines. Intelligent fault diagnosis is significant for safe operation and fault prevention of planetary gearboxes. Recently, multiscale diversity entropy and related entropy methods are proposed to extract features of time series and applied for the fault diagnosis. However, there are still some limitations in fault feature representation and stability for multiscale diversity entropy. To solve the problem, in this paper, a novel planetary gearboxes fault diagnosis method via refined time-shift multiscale diversity entropy (RTSMDE) is proposed. First, a novel entropy algorithm called RTSMDE is proposed to measure the complexity of time series and extract fault features of the vibration signals, which is robust and efficient in performance. Then, the obtained features are utilized to fulfil automatically the fault pattern identifications using support vector machine. To confirm the superiority of the RTSMDE-based fault diagnosis method, simulated signals and experimental studies are constructed and three used widely methods are employed to present a comprehensive comparison. The results indicate that RTSMDE performs best and obtains the highest 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.

The entropy-based method has been demonstrated to be an effective approach to extract the fault features by estimating the complexity of signals, but how to remove the strong background noises in analyzing early weak impulsive signal remains unexplored. To solve this problem, this paper proposes symbolic fuzzy entropy (SFE) based on symbolic dynamic filtering and fuzzy entropy to eliminate the noises and improve the calculation efficiency. The main idea of SFE is to use symbolic dynamic filtering to remove the noise-related fluctuations while significantly simplifying the circulation calculation, thereby, generating better performance in resisting the background noises and high computation efficiency. The superiority of SFE is verified via two simulated signals and other three entropy methods. For comprehensive feature description, we further extend SFE into multiscale analysis by incorporating with the coarse gaining process, called MSFE. Experimental results demonstrate that the proposed MSFE method has the best performance in extracting weak fault characteristics compared with three existing MSE, MFE, and MPE methods.

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.

Sample entropy (SE) has been employed for fault diagnosis of rotary machinery (FDRM). However, SE has low computation efficiency for long time series. To solve this problem, symbolic sample entropy (SSE), a novel measure of time series regularity, is proposed to estimate the complexity. However, SSE fails to account for the multiple scale information inherent in measured vibration signals. Therefore, we combine the concept of multi-scale analysis with SSE, called multi-scale SSE (MSSE). To evaluate the effectiveness of the proposed MSSE method, we apply several simulated signals to verify the merits of SSE in impulsion detection and calculation efficiency. Furthermore, we utilize one experimental data to validate its effectiveness in recognizing several fault types of rotary machinery. Experimental results indicate that MSSE has an advantage in extracting fault features compared with multi-scale entropy (MSE), multi-scale fuzzy entropy (MFE), and multi-scale permutation entropy (MPE) methods.

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.

This paper examines whether ties to politicians by corporate boards of directors weaken the effectiveness of tax authorities in constraining tax avoidance in China. We use a unique data set to measure geographic time-variant tax enforcement, including the probability of income tax audit, the expertise of tax officers, and the consequences of underreporting tax liabilities. Based on a sample of 11,121 firm-years from 2003 to 2013, we find that the deterrent effect of the probability that a firm's taxable income understatement will be detected and lead to heavy penalties is significantly undermined if the board is politically connected. To enhance our analysis, we use opportunities for income shifting, the most likely mechanism through which Chinese firms avoid taxes on an ongoing basis, to illustrate how connected boards exert power to unwind the constraining effect of tax enforcement. Overall, our results suggest that a board's ties to politicians can be a significant challenge to the effective enforcement of tax compliance in a politically controlled economy. This article is protected by copyright. All rights reserved.

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.

Early fault diagnosis of rolling bearings is crucial to operating and maintenance cost reduction of the equipment with bearings. This paper aims to propose a novel early fault feature extraction method based on the proposed hierarchical symbol dynamic entropy (HSDE) and the binary tree support vector machine (BT-SVM). Multiscale symbolic dynamic entropy (MSDE) has been recently proposed to characterize the dynamical behavior of time series. MSDE has several merits comparing with multiscale sample entropy (MSE) and multiscale permutation entropy (MPE), such as high computational efficiency and robustness to noise. However, MSDE only utilizes the fault information in the low frequency components and consequently the fault information hidden in the high frequency components is discarded. To address this shortcoming, a new method, namely HSDE, is proposed to extract the fault information in the high frequency components. Then, the BT-SVM is utilized to automatically complete the fault type identification. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. Meanwhile, a comparison is conducted between MPE, hierarchical permutation entropy (HPE), MSE, hierarchical sample entropy (HSE), MSDE and HSDE. Results show that the proposed method performs best to recognize the early fault types of rolling bearings.

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.

Unmanned Aerial Vehicles (UAVs) are widely used for various civilian, security and military applications. UAVs are equipped with a number of sensors such as accelerometers and gyroscopes, the measured data reliability of these sensors is critical and data obtained from these sensors must be accurate. Sometimes, sensors are prone to damage because of various electric/communication problems. This paper proposes a statistical scheme for catering real-time experimental results of fault detection for inertial measurement unit sensors (accelerometer and gyroscope) by using statistical analysis measures. The objective is to detect sensor bias fault, total sensor failure and drift or additive type fault in gyroscope and accelerometer measurements by processing their data via statistical measures. The scheme of processing the sensor data aims to suggest best statistical measures for fault detection in a multiple-sensor setting, after graphical signal analysis (comparison of the graphs of signal overture of all sensors) has been completed. This scheme can be helpful in diagnosis and subsequent isolation of sensors with fault in high-reliability applications where accuracy of data is critical.

We consider the problem of optimally mapping an edge computing service that is modeled as a tree with multiple processing sub-tasks and data flows onto the underlying physical network. As new computing and data analytics applications require more complicated data processing structures, and different types of data (e.g., images, videos, and numbers) sensed at geographically distributed locations must be collected and processed to obtain a complex and comprehensive result, highly intelligent algorithms are needed to solve this challenging problem. In this paper, we propose a learning-based hierarchical service tree placement strategy that aims to optimize the net utility, defined as achieved utility minus network congestion. The key idea is to decouple a service tree into appropriate sub-trees each containing a single computing sub-task as well as associated data flows and to recursively leverage Q-learning to place each sub-tree while maintaining the dependencies of sub-tasks in the service tree structure. It enables a scalable solution for large networks with unknown arrival statistics and complex service structures. Numerical results show that our solution can significantly outperform baseline heuristics in online service tree placement.

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.

An efficient and sensitive analytical method for the simultaneous determination of five plant growth regulators (PGRs) has been successfully developed, including 3-Indoleacetic acid (IAA), 2-Naphthylacetic acid (2-NAA), Beta-indolebutyric acid (IBA), 2-(2,4-dichlorophenoxy), acetic acid (2.4-D), and 4-Chlorophenoxyacetic acid (4-CPA) in bean sprouts. Using 10% boron trifluoride of methanol as the derived reagent and the reaction was derivatized at 70 °C for 30 min, the derivatives of five PGRs have been completely separated within 19 min. It has a linear relationship in the concentration range of 0.01–0.20 mg/kg and the correlation coefficients (r2) in excess of 0.997. The average recoveries were ranged from 72.7–98.6% at three spiking different concentration levels of 0.05, 0.10, and 0.15 mg/kg, respectively. The relative standard deviations (RSDs) were ranged from 1.2 to 9.0%. The limits of detection and the limits of quantification were ranged from 0.00024 to 0.00172 mg/kg and 0.0008–0.0052 mg/kg, respectively. On the basis of these, the measurement has been successfully applied to the analysis of the exogenous phytohormones in the bean sprouts. Except for 4-CPA and IAA, the rest of the analytes were undetected in samples. In sum, the current method can be used for accurate determination and confirmation of PGRs in many other crops, such as bean sprouts.

Product recovery, accompanied by cradle to cradle policies from the contemporary supply chain, becomes an essential element in meeting environmental compliance and waste management policies. Incorporation of reverse logistics into the traditional supply chains becomes a complementary factor for efficient product recovery. To begin with product recovery, consumers are encouraged to return their end-of-use/end-of-life products, and the steps of collecting and planning the movement of returned products are crucial decisions. The efficient planning of a cost effective recovery process in reverse logistics requires dealing with the uncertainty underlying in the quantity and quality of the returned products. In this paper, we propose establishing an initial collection point within a permissible radius of the customer zones to overcome some of the issues of uncertainty. The uncertainty in the quantity and quality of the returned products are modelled using fuzzy triangular numbers. To capture the real world conditions of the proposed problem, our model aims at maximizing the profit incurred in the recovery process in an uncertain environment. The model was solved with the help of fuzzy mathematical programming. The model is validated by a company case belonging to the manufacturing of electronic products. To increase the applicability of the product recovery process in the industry, we propose a recovery process for the planning horizon consisting of multi periods and multi products. The outcomes of the proposed model indicate that for the successful realisation of such network, customers need to be legally enforced to return their end of used products in the channels established for value recovery.

The inhomogeneous phase of a smart meta-superconductor has a great effect on its superconductivity. In this paper, the effect of concentration, dimensions, electroluminescence (EL) intensity, and distribution of the inhomogeneous phase on the superconducting critical temperature $$(T_{C})$$ has been systematically investigated. An ex situ solid sintering was utilized to prepare smart meta-superconducting $$\hbox {MgB}_{2}$$ doped with six kinds of electroluminescent materials, such as $$\hbox {YVO}_{4}{:}\hbox {Eu}^{3+}$$ and $$\hbox {Y}_{2}\hbox {O}_{3}{:}\hbox {Eu}^{3+}$$ flakes. Elemental mappings through energy dispersive spectroscopy (EDS) show that the inhomogeneous phase is comparatively uniformly dispersed around the $$\hbox {MgB}_{2}$$ particles; thus V, Y, and Eu were accumulated at a small area. The measurement results show that the optimum doping concentration of the meta-superconducting $$\hbox {MgB}_{2}$$ is 2.0 wt%. The offset temperature ( $$T_{C}^{{ off}}$$ ) of the sample doped with 2.0 wt% dopant A is 1.6 K higher than that of pure $$\hbox {MgB}_{2}$$ . The improvement in $$T_{C}^{{ off}}$$ is likely related to the sizes, thickness, and EL intensity of the inhomogeneous phase of $$\hbox {MgB}_{2}$$ smart meta-superconductor. This experiment provides a novel approach to enhance $$T_{C}$$ .

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.

Rolling bearings' operation under variable speed conditions exhibits complex time-varying modulations and spectral structures, resulting in difficulty in the fault diagnosis. In order to effectively remove the influence of the rotational speed and extract the fault characteristics, this paper develops a fault diagnosis scheme based on the Vold-Kalman filter (VKF), refined composite multi-scale fuzzy entropy (RCMFE), and Laplacian score (LS). In the proposed method, the VKF is first adopted to remove the fault-unrelated components and give a clear representation of the fault symptoms. Second, the RCMFE is applied to extract fault features from the denoised vibration signal. Third, the LS approach is introduced to refine the fault features by sorting the scale factors. In the end, the selected features are fed into the logistic regression to automatically complete the fault pattern identifications. The proposed method is experimentally demonstrated to be able to recognize the localized defect on the inner race, outer race, and rolling element under variable speed conditions.

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.

The size and shape of functional micro/nanomaterials have crucial influence on their physical and chemical properties. The monoclinic Y4O(OH)9NO3 with nanosheets, submicrospheres, and microrods have been successfully prepared by a non-aqueous solvothermal method. The precursors Y4O(OH)9NO3 decompose into cubic Y2O3:Eu3+ phosphors after sintering at 800 °C for 2 h. The morphologies and sizes of Y2O3:Eu3+ particles can be easily controlled by changing the growth rates of various crystallographic facets during solvothermal process. With the increment of the rare-earth nitrates concentration, the morphologies transform from 2D nanosheets to sub-microspheres, and eventually to microrods. The bright red emission, owing to Eu3+ f-orbital transitions, can be easily observed under ultraviolet excitation. Furthermore, the dependence of Y2O3:Eu3+ luminescence performances on different morphologies are discussed in the present work.

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.

The non-contact alternating current (AC) impedance method is widely used in the study of electrochemical properties of cement-based materials,the instrument of this paper provide a new way for the study of pore structure of cement-based materials. Due to the loss of magnetic materials and the parasitic capacitance of measuring circuit, the ratio difference and phase difference of current sensor inevitably exit. Traditionally the standard resistor calibration method is used to determine the ratio error and phase error under different frequencies based on polynomial fitting (FT). In this paper, a machine-learning support vector machine (SVM) model is used to predict the calibration data with a small test sample. For the accuracy assessment, SVM model has the best comprehensive performance among FT, artificial neural network (ANN). The actual impedance test of typical cement-based materials under different frequencies is given and the results verify the excellent performance of SVM.

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.