Search

Infrared thermal technology plays a vital role in the health condition monitoring of gearbox. In the traditional infrared thermal technology-based methods, Gaussian pyramid is applied as the feature extraction approach, which has disadvantages of noise influence and information missing. Focus on such disadvantages, an improved multi-scale decomposition method combined with convolutional neural network is proposed to extract the fault features of the multi-scale infrared images in this paper. It can enlarge the data length at large scales, and thus reduce the fluctuations of feature values and reserve the fault information. The effectiveness of the proposed method is validated using the experiment infrared data of one industrial gearbox. Results demonstrate that our proposed method has the best performance comparing with five methods.

Intelligent fault diagnosis of rotating machinery is a key topic for industrial equipment maintenance and fault prevention. In this study, an intelligent diagnosis approach of rotating machinery via enhanced hierarchical symbolic sample entropy (EHSSE) is proposed. Firstly, a novel indicator termed symbolic sample entropy (SSE) is proposed for complexity measure and representation of fault information. By using symbolic dynamic filtering, the raw continuous time-series will be discretized into symbolic data, and analysis of symbolic data is less sensitive to measurement noise, resulting in superior robustness. Secondly, SSE is combined with enhanced hierarchical analysis to further extract fault characteristics hidden in both low- and high-frequency components. To study the performance of SSE and EHSSE, multiple simulated signals and experimental studies are constructed and three widely used entropy methods are employed to present a comprehensive comparison. The comparison results show that EHSSE performs best in diagnosing various faults of planetary gearbox and rotor system with highest identification accuracy compared with other entropy-based approaches.

With the development of technologies such as IoT and 5G, the exponential explosion in the amount of new data has put more stringent requirements on ultrareliable and low-delay communication of services. To better meet these requirements, a resource allocation strategy using deep reinforcement learning in a cloud-edge collaborative computing environment is proposed. First, a collaborative mobile edge computing (MEC) system model, which combines the core cloud center with MEC to improve the network interaction ability, is constructed. The communication model and computation model of the system are considered at the same time. Then, the goal of minimizing system delay is modeled as a Markov decision process, and it is solved by using the deep Q network (DQN) which is improved by hindsight experience replay (HER), so as to realize the resource allocation with the minimum system delay. Finally, the proposed method is analyzed based on the simulation platform. The results show that when the number of user terminals is 80, the maximum user delay is 1150 ms, which is better than other comparison strategies and can effectively reduce the system delay in complex environment.

The transcription factor BTB and CNC homology 1(BACH1) has been linked to coronary artery disease risk by human genome-wide association studies, but little is known about the role of BACH1 in vascular smooth muscle cell (VSMC) phenotype switching and neointima formation following vascular injury. Therefore, this study aims to explore the role of BACH1 in vascular remodeling and its underlying mechanisms. BACH1 was highly expressed in human atherosclerotic plaques and has high transcriptional factor activity in VSMCs of human atherosclerotic arteries. VSMC-specific loss of Bach1 in mice inhibited the transformation of VSMC from contractile to synthetic phenotype and VSMC proliferation and attenuated the neointimal hyperplasia induced by wire injury. Mechanistically, BACH1 suppressed chromatin accessibility at the promoters of VSMC marker genes via recruiting histone methyltransferase G9a and cofactor YAP and maintaining the H3K9me2 state, thereby repressing VSMC marker genes expression in human aortic smooth muscle cells (HASMCs). BACH1-induced repression of VSMC marker genes was abolished by the silencing of G9a or YAP. Thus, these findings demonstrate a crucial regulatory role of BACH1 in VSMC phenotypic transition and vascular homeostasis and shed light on potential future protective vascular disease intervention via manipulation of BACH1., Nucleic Acids Research, 51 (9), ISSN:1362-4962, ISSN:0301-5610

To address the problem of poor detection performance of existing intrusion detection methods in the environment of high-dimensional massive data with uneven class distribution, a deep learning-based anomaly traffic detection method in cloud computing environment is proposed. First, the fuzzy C -means (FCM) algorithm is introduced and is combined with the general regression neural network (GRNN) to cluster the samples to be classified in the original space by FCM. Then, the GRNN model is trained and the center point is updated using the sample closest to the FCM clustering center until a stable cluster center is obtained. The parameters in FCM-GRNN are optimized using the global optimization feature of the modified fruit fly optimization algorithm (MFOA), and the optimal spread value is found using the three-dimensional search method through an iterative search. Finally, experiments are conducted based on the KDD CUP99 dataset, and the results demonstrate that the detection rate (DR) and false alarm rate (FAR) of the proposed FCM-MFOA-GRNN method are 91% and 1.176%, respectively, which are better than those of the comparison methods. Therefore, the proposed method has good anomaly traffic detection ability.

Aiming at the lack of feature extraction ability of rumor detection methods based on the deep learning model, this study proposes a rumor detection method based on deep learning in social network big data environment. Firstly, the scheme of combining API interface and third-party crawler program is adopted to obtain Weibo rumor information from the Weibo “false Weibo information” public page, so as to obtain the Weibo dataset containing rumor information and nonrumor information. Secondly, the distributed word vector is used to encode text words, and the hierarchical Softmax and negative sampling are used to improve the training efficiency. Finally, a classification and detection model based on the combination of semantic features and statistical features is constructed, the memory function of Multi-BiLSTM is used to explore the dependency between data, and the statistical features are combined with semantic features to expand the feature space in rumor detection and describe the distribution of data in the feature space to a greater extent. Experiments show that when the word vector dimension is 300, compared with the compared literature, the accuracy of the proposed method is improved by 4.232% and 1.478%, respectively, and the F1 value of the proposed method is improved by 5.011% and 1.795%, respectively. The proposed method can better extract data features and has better rumor detection ability.

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

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

The bearing regularly suffers from compound faults in real-world working conditions. In comparison to the single-fault feature extraction, the compound fault diagnosis is more difficult to achieve. This paper suggests an alternative signal processing strategy using the Multipoint Optimal Minimum Entropy Deconvolution method (MOMED) and B-spline based envelope-derivative operator (EDO) tools. As an upgraded version of the Minimum Entropy Deconvolution tool, the MOMEDA technique has been extensively available for bearing and gear fault detection. However, this approach results in an open problem related to how one can choose an appropriate filter size. Considering this problem, an optimized MOMED based on Salp Swarm Algorithm is proposed. Besides, a novel energy operator method called B-spline based envelope-derivative operator (B-spline EDO) is proposed to detect the corresponding fault characteristics from the two separated mono-component signals produced by the optimized MOMED. The new B-spline EDO method accomplishes higher fault detection performance in a noisy environment. Finally, the experimental results displayed that the novel compound fault detection approach can effectively identify the compound fault characteristics.

Fault diagnosis of rotary machinery plays a significant role in the prognostic and health management system, which aims to identify the root causes of system failures and provide effective information for prognostics and maintenance. Recently, Lempel–Ziv complexity (LZC) method has been employed for fault diagnosis of rotary machinery. However, one actual problem is that LZC fails to account for the multiscale information inherent in measured vibration signals. We first introduce a method to compute the multi-scale LZC for a signal. However, the variance of LZC values becomes larger as the scale factor increases. To solve this actual problem, this paper proposes refined composite multi-scale Lempel-Ziv complexity (RCMLZC) to estimate the complexity. We find that the proposed RCMLZC method consistently yields better performance when analyzing three simulated noisy and impulsive signals. Based on RCMLZC, a novel intelligent fault diagnosis method is designed to recognize various fault types of rotating machinery. Comparative experiments are performed to confirm the effectiveness of proposed method including single fault and compound fault working conditions. Experimental results indicate that RCMLZC is more accurate than multi-scale LZC, multi-scale entropy, and LZC in extracting fault features from vibration signal and that RCMLZC performs best to recognize the various fault types of rotary machinery.

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.

Ropivacaine (RVC), a common pain management drug used for clinical anesthesia and postoperative analgesia, inhibits peripheral nociceptive pain stimulation. However, the potential neurological damage resulting from RVC use must be considered. Developing a strategy to enhance the local anesthetic effect of RVC while reducing its potential acute toxicity to the central nervous system is urgently needed. In this study, a novel RVC nanocomposite drug, magnetic iron oxide/polyethylene glycol-carboxymethyl chitosan/ropivacaine nanoparticle (mCMCS-PEG/RVC NPs), was synthesized with magnetic iron oxide. The inherent shell–core structure of mCMCS-PEG retained core magnetic properties, improved the stability and biocompatibility of magnetic nanoparticles, and avoided excessive degradation. Thus, mCMCS-PEG/RVC NPs are expected to provide a new pain management strategy for perioperative patients.

In this article, a fault diagnosis scheme based on multiscale diversity entropy (MDE) and extreme learning machine (ELM) is presented. First, a novel entropy method called diversity entropy (DE) is proposed to quantify the dynamical complexity. DE utilizes the distribution of cosine similarity between adjacent orbits to track the inside pattern change, resulting in better performance in complexity estimation. Then, the proposed DE is extended to multiscale analysis called MDE for a comprehensive feature description by combining with the coarse gaining process. Third, the obtained features using MDE are fed into the ELM classifier for pattern identification of rotating machinery. The effectiveness of the proposed MDE method is verified using simulated signals and two experimental signals collected from the bearing test and the dual-rotator of the aeroengine test. The analysis results show that our proposed method has the highest classification accuracy compared with three existing approaches: sample entropy, fuzzy entropy, and permutation entropy.