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Fuzzy entropy (FE) can be regarded as an effective measure for nonlinear characterization of rolling element bearing (REB) health condition by quantifying the complexity of vibration signals. However, during continuous monitoring operation under heavy noise, transient impulses corresponding to a REB fault get submerged under unnecessary random noise components. As a consequence, FE algorithm not only fails to detect a REB fault at the earliest point of inception but also performs poorly in monitoring the development of the incepted fault in an efficient manner. Aiming at solving the aforementioned limitations of FE in continuous monitoring of REB health, background noise associated with collected vibration signals is eliminated by weighting the corresponding square envelope signal. Due to the utilization of weighted squared envelope signal, the proposed measure is termed as weighted square envelope-based FE (WSEFE). One simulated case and two different run-to-failure experimental cases are used for validation. The comparison results demonstrate that the proposed WSEFE not only overcomes the limitations of original FE but also performs better than conventional permutation entropy and advanced FE-based measure multiscale FE (MFE) in continuous monitoring of REB health.

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.

In this article, a novel intelligent fault diagnosis method based on multiscale symbolic Lempel-Ziv (MSLZ) is proposed to identify several faults of railway vehicle systems (RVSs). The proposed MSLZ is essentially for the purpose of estimating the irregularity of a given time series. In the proposed MSLZ method, the symbolization and multiscale techniques are combined with Lempel–Ziv (LZ) to enhance its feature extraction ability. First, the symbolization can facilitate LZ to remove the noises and reserve the fault information. Second, multiscale analysis can extend LZ to multiple time scales, which can further enhance the description ability of dynamic characteristics. Using numerical data and experimental signals collected from RVSs, the performance of the MSLZ method is demonstrated to be sensitive to periodical impulses and robust to environmental noise. Moreover, it has been demonstrated that MSLZ has superiority in extracting fault information of the RVS compared with LZ, symbolic LZ, and multiscale LZ methods.

Multisource domain adaptation (MDA) methods have been preliminarily applied in cross-domain fault diagnosis of rotating system due to its correlation ability between different but related fields. However, it still remains challenging to learn domain-invariant representations under multisource scenarios. This article proposes a multi-representation symbolic convolutional neural network (MR-SCNN) for multisource cross-domain fault diagnosis of rotating system. The novelty of our work lies in three aspects. First, the proposed method combines symbolic dynamics with CNN to obtain a coarse-grained description of vibration signals, which could eliminate the negative transfer caused by subtle changes in dynamic characteristics among different source domains. Second, considering that most MDA methods ignore the significant limitations brought by statistical properties of the specific working condition, a multi-representation Softmax (MR-Softmax) is developed to learn domain-invariant discriminative representations by allowing the diversity of the predictions of samples with the same label. In addition, an undifferentiated adversarial training strategies is proposed to narrow the domain discrepancies and reasonably assess the residual negative transfer risk of different source domains. Based on the assessment, confidence coeffients are defined and embedded into MR-Softmax to extract and utilize the useful diagnostic knowledge on each source domain. Compared with several state-of-the-art diagnostic models, the experimental results confirm the validation of the proposed MR-SCNN method.

Multiscale-based entropy methods have proven to be a promising tool for extracting fault information due to their high feature extraction ability and easy application. Despite multiscale analysis showing great potential in extracting fault characteristics, it has some drawbacks, such as cutting the data length and neglecting high-frequency information. This paper proposes a bi-filter multiscale diversity entropy (BMDE) to filter comprehensive fault information and address the data length problem. First, the low-frequency information is filtered out by moving average in a multi-low procedure and the high-frequency information is filtered out by an adjacent subtraction in a multi-high procedure. Second, a modified coarse-grained process is introduced to overcome the issue of data length. The validity of the BMDE method is evaluated using both simulation signals and experimental measurements. Results demonstrate that the proposed method offers optimal feature extraction capability with the highest diagnostic accuracy compared with four other traditional entropy-based diagnosis methods.

Compared with the deepening development of time-space coding metasurfaces in the electromagnetic and acoustic field, the research on time-space coding metasurface in the field of elastic vibration is still in the infancy. In order to enrich the designed schemes of spatiotemporal coding for elastic vibration, a time-space coding metasurface based on the random modulation strategy of wavefront phase is proposed in this paper. Compared with traditional homogeneous materials with highly correlated vibration transmission, the proposed coding metasurfaces with homogeneous materials realizes extremely low correlated vibration transmission in different vibration directions, which provides a physical realization for the measurement matrix in compression sensing. Furthermore, combined with compressed sensing technology, the potential application value of the proposed time-space coding metasurfaces in single-sensor vibration location and impact identification is verified through simulation.

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.

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

On the basis of the idea that the injecting energy will improve the conditions for the formation of Cooper pairs, a smart meta-superconductor (SMSC) was prepared by doping luminescent nanocomposite Y2O3:Eu3+/Ag in MgB2. To improve the superconducting transition temperature (TC) of the MgB2-based superconductor, two types of Y2O3:Eu3+/Ag, which has the strong luminescence characteristic, with different sizes were prepared and marked as m-Y2O3:Eu3+/Ag and n-Y2O3:Eu3+/Ag. MgB2 SMSC was prepared through an ex situ process. Results show that when the dopant content was fixed at 2.0 wt.%, the TC of MgB2 SMSC increased initially then decreased with the increase in the Ag content in the dopant. When the Ag content is 5%, the TC of MgB2 SMSC was 37.2–38.0 K, which was similar to that of pure MgB2. Meanwhile, the TC of MgB2 SMSC doped with n-Y2O3:Eu3+/Ag increased initially then decreased basically with the increase in the content of n-Y2O3:Eu3+/Ag, in which the Ag content is fixed at 5%. The TC of MgB2 SMSC doped with 0.5 wt.% n-Y2O3:Eu3+/Ag was 37.6–38.4 K, which was 0.4 K higher than that of pure MgB2. It is thought that the doping luminescent nanocomposite into the superconductor is a new means to improve the TC of SMSC.

The smart meta-superconductor MgB

High viscosity cement (HVC) and low viscosity cement (LVC) have been used to treat osteoporotic vertebral compression fractures (OVCFs). Our study was to assess the safety and efficacy of HVC and LVC in treating OVCFs.We searched the electronic database for randomized controlled trials of HVC and LVC to treat OVCFs. Random-effects model was performed to pool the outcomes about operation time, visual analogue scale (VAS), bone cement injection volume, oswestry disability index (ODI), bone cement leakage and adjacent vertebral fractures.Twelve randomized trials were included in the meta-analysis. The 2 groups had similar changes in terms of bone cement injection volume, ODI and adjacent vertebral fractures. The HVC group showed shorter operation time and better VAS score improvement. The bone cement leakage rate of the HVC group was significantly better than LVC group (P .00001).According to the location of bone cement leakage, in the leakages of the veins (P .00001), the intervertebral disc (P .00001), the paravertebral area (P = .003) and the intraspinal space (P = .03), the HVC group were significantly better than the LVC group.In terms of bone cement injection volume, ODI and adjacent vertebral fractures, the 2 group are equivalent. HVC had a shorter operation time, lower bone cement leakage rate and better VAS score improvement, compared with LVC.

Heart development is controlled by a complex transcriptional network composed of transcription factors and epigenetic regulators. Mutations in key developmental transcription factor MESP1 and chromatin factors, such as PRC1 and cohesin components, have been found in human congenital heart diseases (CHDs), although their functional mechanism during heart development remains elusive. Here, we find that MESP1 interacts with RING1A/RING1, the core component of PRC1. RING1A depletion impairs human cardiomyocyte differentiation, and cardiac abnormalities similar to those in patients with MESP1 mutations were observed in Ring1A knockout mice. Mechanistically, MESP1 associates with RING1A to activate cardiogenic genes through promoter-enhancer interactions regulated by cohesin and CTCF and histone acetylation mediated by p300. Importantly, CHD mutations of MESP1 significantly affect such mechanisms and impair target gene activation. Together, our results demonstrate the importance of MESP1-RING1A complex in heart development and provide insights into the pathogenic mechanisms of CHDs caused by mutations in MESP1, PRC1, and cohesin components.

Remaining useful life (RUL) prediction of rolling bearings plays a critical role in reducing unplanned downtime and improving machine productivity. The existing prediction methods primarily provide point estimates of RUL without quantifying uncertainty. However, uncertainty quantification of RUL is crucial to conduct reliable risk analysis and make maintenance decision, which can significantly decrease the maintenance costs. To solve the uncertainty quantification problem and improve prediction accuracy at the same time, a novel diversity entropy-based Bayesian deep learning (DE-BDL) method is proposed. First, start degradation time (SDT) of bearings is adaptively determined using diversity entropy, which can extract early degradation information. Then, multi-scale diversity entropy (MDE) is developed to extract dynamic characteristics over multiple scales. Third, the obtained features using MDE are fed into the BDL model for degradation tracking and prediction. By doing this, the proposed DE-BDL method has merits in subsequent decision making, which can not only provide point estimation but also offer uncertainty quantification with epistemic uncertainty and aleatoric uncertainty. The superiority of the proposed method is validated using run-to-failure data. The experimental results and comparison with state-of-art prediction methods have demonstrated that the proposed DE-BDL method is promising for RUL of rolling bearings.

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.

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.

A smart meta-superconductor Bi(Pb)SrCaCuO (B(P)SCCO) may increase the critical transition temperature (TC) of B(P)SCCO by electroluminescence (EL) energy injection of inhomogeneous phases. However, the increase amplitude ΔTC (ΔTC=TC−TC,pure) of TC is relatively small. In this study, a smart meta-superconductor B(P)SCCO with different matrix sizes was designed. Three kinds of raw materials with different particle sizes were used, and different series of Y2O3:Sm3+, Y2O3, Y2O3:Eu3+, and Y2O3:Eu3++Ag-doped samples and pure B(P)SCCO were prepared. Results indicated that the TC of the Y2O3 or Y2O3:Sm3+ non-luminescent dopant doping sample is lower than that of pure B(P)SCCO. However, the TC of the Y2O3:Eu3++Ag or Y2O3:Eu3+ luminescent inhomogeneous phase doping sample is higher than that of pure B(P)SCCO. With the decrease of the raw material particle size from 30 to 5 μm, the particle size of the B(P)SCCO superconducting matrix in the prepared samples decreases, and the doping content of the Y2O3:Eu3++Ag or Y2O3:Eu3+ increases from 0.2% to 0.4%. Meanwhile, the increase of the inhomogeneous phase content enhances the ΔTC. When the particle size of raw material is 5 μm, the doping concentration of the luminescent inhomogeneous phase can be increased to 0.4%. At this time, the zero-resistance temperature and onset transition temperature of the Y2O3:Eu3++Ag doped sample are 4 and 6.3 K higher than those of pure B(P)SCCO, respectively.

Superionic conductors, which exhibit liquid-like phonon transport but crystal-like carrier transport, have attracted great attention and broad research interest in the thermoelectric community. Ag2Te is a superionic conductor; however, its small band gap and large Ag vacancy formation energy impede its application as a prominent p-type thermoelectric material. In this work, synergistic optimization of the thermoelectric performance of Ag2Te through Cu substitution is realized through a combination of experimental and theoretical efforts. For the electrical transport, Cu substitution systematically increases the band gap of Ag2Te and reduces the cation vacancy formation energy. These two beneficial effects simultaneously increase the electrical conductivity and suppress the bipolar effect, thereby greatly enhancing the p-type electrical transport properties of Ag2Te. For the thermal transport, alloying Cu2Te with Ag2Te significantly reduces the thermal conductivity through not only point defect scattering but also softening of the interatomic interactions. The latter is attributed to the relatively small Cu atoms vibrating in the oversized 8c sites. This two-fold optimization results in maximum thermoelectric figure of merit zT values of over 1.3 at 773 K for both Ag1.2Cu0.8Te and AgCuTe, demonstrating the great potential of Ag2−xCuxTe as a promising p-type thermoelectric material system. Keywords: Ag2Te, Band gap, Carrier concentration, Thermoelectric material

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.

Vehicle routing problem (VRP) is one of the widely researched areas in transportation science, mainly due to the potential cost savings and service improvement opportunities which brings to organizations involved in physical distribution of goods. In this paper, we develop a multi-depot green vehicle routing problem (MDGVRP) by maximizing revenue and minimizing costs, time and emission, and then, apply an improved ant colony optimization (IACO) algorithm that aims to efficiently solve the problem. The IACO model developed in this research uses an innovative approach in updating the pheromone that results in better solutions. The results achieved through the IACO demonstrate satisfying performance, which have higher solution quality when compared to the conventional ACO. The IACO algorithm used in this paper demonstrated a good level of responsiveness and simplicity when solving MDGVRP with multiple objectives.

Vapor–liquid equilibrium (VLE) data for the systems 2-propanol + water + calcium chloride, 2-propanol + water + magnesium chloride, 2-propanol + water + calcium nitrate, and 2-propanol + water + ma...

This paper assesses the interrelationships between hazards/risks (considered as barriers) for the effective implementation of Occupational Health and Safety (OHS) measures using Interpretive Structural Modeling (ISM). We investigate within and among hazard/risk classes and formulate a structural model that identifies and prioritizes their levels. Mitigation strategies for the identified barriers make the research outcome more useful and applicable. Results reveal that insulation failure, barrier (B7), is the most influential hazard; hence, it must be addressed first for the effective implementation of OHS measures in coal-fired thermal power plants. The second barrier in the ISM hierarchy, (B6) fire (mill/pool/jet), and additional hazards and risks require further assessment.

With the integration of the ultra-high-voltage direct-current (UHVDC) of large capacity, the scheduled outputs of the power plants are limited, and the direct power purchase (DPP) by large consumers is influenced. The negative impact caused by the failure modes of the UHVDC on the economic dispatch (ED) and the security of the power system is to be studied. Here, the derated capacities of the UHVDC are described together with their state probabilities. To maximise the profit of power plants, an ED model with the DPP is proposed. The relationship between the amount of the DPP and the power transmission by the UHVDC is decided based on two contract strategies for the DPP. The interior-point method is applied to solve the ED problem. The simulation results validate the feasibility and accuracy of the proposed model, which is beneficial to the development of the power market and the UHVDC in China.

This paper presents a novel signal processing scheme by combining an improved Vold-Kalman filter and the multi-scale sample entropy (IVKF-MSSE) for planetary gearboxes under non-stationary working conditions. In this scheme, we propose a method based on the characteristic frequency ratio (CFR) to select the VKF bandwidth. First, a CFR is adopted to select a VKF bandwidth with the largest CFR value as the optimal VKF bandwidth. Second, IVKF is used to extract fault-induced information under time-varying speed conditions. Because an optimal bandwidth is used in VKF, the feature extraction capability of VKF is enhanced. Then, the MSSE is applied to extract gearbox fault features. After that, the Laplacian score (LS) approach is introduced to refine the fault features by sorting the scale factors. At the end, the selected features are fed into the least square support vector machine (LSSVM) for effective fault pattern identification. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the auto-regressive AR-MSSE, VKF-MSSE and EEMD-MSSE in identifying fault types of planetary gearboxes.

Digital teaching resources are the core cornerstone of educational informationization. Their construction and application level determine the pace of educational modernization. Digital teaching resources for basic education are an effective guarantee to solve the problem of educational equity in urban and rural areas and promote the co-construction and sharing of high-quality scientific and technological education information resources. Based on the reality of uneven distribution of basic education resources in different regions of China, taking physical education courses in Yueyang District of Hunan Province as an example, this paper studies the problems existing in the construction and application of digital teaching resources in regional basic education, and puts forward corresponding countermeasures and suggestions, which can provide reference for the reform and development of information-based teaching in basic education in relevant regions of China.

A novel hybrid fault diagnosis method based on ensemble empirical mode decomposition and weighted adaptive multi-scale morphological analysis (WAMMA) is proposed to detect the early damage of gearboxes. In this method, we propose a characteristic frequency ratio (CFR) method to determine the weighted coefficient for each scale of AMMA. First, multiple scales are obtained using the AMMA method. Second, the weighted coefficient of each scale in the AMMA method is calculated using the CFR. Third, the final results can be obtained by multiplying the weighted coefficients and filtering results with all scales. Since the performance of each scale of AMMA is evaluated using the CFR, the demodulation ability can be effectively improved. However, the WAMMA is easily disturbed by heavy noise when extracting early fault feature directly. A method combined EEMD with the WAMMA is proposed. The effectiveness of the proposed method has been verified using two experimental vibration signals of gearboxes. The results demonstrate that the proposed method has a superior performance in the extraction of weak fault characteristics of gearboxes in comparison with the WAMMA and EEMD-AMMA 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.