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Aiming at the problem of high fault similarity in the transmission chain of wind turbines, this study proposes a single-sample wind turbine bearing fault diagnosis method based on empirical mode decomposition and signal equalization processing. In this method, the signal components of different modes are obtained by empirical mode decomposition of the actual monitoring fault signal. The energy value and peak value of each modal component are calculated, and some modal components with large energy and high peak value are selected for signal reconstruction to obtain a new fault signal. The new fault signal decomposed by wavelet packet, and the wavelet packet is decomposed into the third layer component for signal reconstruction. The variance of the reconstructed signal is used as the eigenvalue of the fault diagnosis. Non-linear equalization is performed on the eigenvalues, which solves the problem of signal mutual submergence. The concept of discrimination is introduced to quantify the difference between different fault signals. The experimental results show that the fault diagnosis method proposed is effective, and the discrimination between the four faults on the transmission chain of the wind turbine before and after the treatment is significantly increased, which shows that the experimental method has a strong robustness. Compared with the improved fuzzy clustering method and the deep learning method based on the improved AlxeNet network, the method performs better. The fault diagnosis method only uses a single sample to realize the fault diagnosis of the transmission chain of wind turbines, which is in line with the characteristic of the low failure rate of wind turbines, and is of great significance for improving the troubleshooting efficiency of wind turbines in actual projects.

Abstract Glioblastoma stem cells (GSCs) play a key role in glioblastoma (GBM) resistance to temozolomide (TMZ) chemotherapy. With the increase in research on the tumour microenvironment, exosomes secreted by GSCs have become a new focus in GBM research. However, the molecular mechanism by which GSCs affect drug resistance in GBM cells via exosomes remains unclear. Using bioinformatics analysis, we identified the specific expression of ABCB4 in GSCs. Subsequently, we established GSC cell lines and used ultracentrifugation to extract secreted exosomes. We conducted in vitro and in vivo investigations to validate the promoting effect of ABCB4 and ABCB4-containing exosomes on TMZ resistance. Finally, to identify the transcription factors regulating the transcription of ABCB4, we performed luciferase assays and chromatin immunoprecipitation-quantitative PCR. Our results indicated that ABCB4 is highly expressed in GSCs. Moreover, high expression of ABCB4 promoted the resistance of GSCs to TMZ. Our study found that GSCs can also transmit their highly expressed ABCB4 to differentiated glioma cells (DGCs) through exosomes, leading to high expression of ABCB4 in these cells and promoting their resistance to TMZ. Mechanistic studies have shown that the overexpression of ABCB4 in GSCs is mediated by the transcription factor ATF3. In conclusion, our results indicate that GSCs can confer resistance to TMZ in GBM by transmitting ABCB4, which is transcribed by ATF3, through exosomes. This mechanism may lead to drug resistance and recurrence of GBM. These findings contribute to a deeper understanding of the mechanisms underlying drug resistance in GBM and provide novel insights into its treatment.

Abstract BackgroundRemovable partial denture (RPD) design is crucial to long-term success in dental treatment, but shortcomings in RPD design training and competency acquisition among dental students have persisted for decades. Digital production is increasing in prevalence in stomatology, and a digital RPD (D-RPD) module, under the framework of the certified Objective Manipulative Skill Examination of Dental Technicians (OMEDT) system reported in our previous work, may improve on existing RPD training models for students. ObjectiveWe aimed to determine the efficacy of a virtual 3D simulation–based progressive digital training module for RPD design compared to traditional training. MethodsWe developed a prospective cohort study including dental technology students at the Stomatology College of Chongqing Medical University. Cohort 1 received traditional RPD design training (7 wk). Cohort 2 received D-RPD module training based on text and 2D sketches (7 wk). Cohort 3 received D-RPD module pilot training based on text and 2D sketches (4 wk) and continued to receive training based on 3D virtual casts of real patients (3 wk). RPD design tests based on virtual casts were conducted at 1 month and 1 year after training. We collected RPD design scores and the time spent to perform each assessment. ResultsWe collected the RPD design scores and the time spent to perform each assessment at 1 month and 1 year after training. The study recruited 109 students, including 58 (53.2%) female and 51 male (56.8%) students. Cohort 1 scored the lowest and cohort 3 scored the highest in both tests (cohorts 1-3 at 1 mo: mean score 65.8, SD 21.5; mean score 81.9, SD 6.88; and mean score 85.3, SD 8.55, respectively; PPPPPPP ConclusionsCohort 3 obtained the highest score at both time points with retention of competency at 1 year, indicating that progressive D-RPD training including virtual 3D simulation facilitated improved competency in RPD design. The adoption of D-RPD training may benefit learning outcomes.

Due to the low-complexity implementation, direction-of-arrival (DOA) estimation-based one-bit quantized data are of interest, but also, signal processing struggles to obtain the demanded estimation accuracy. In this study, we injected a number of noise components into the receiving data before the uniform linear array (ULA) composed of one-bit quantizers. Then, based on this designed noise-boosted quantizer unit (NBQU), we propose an efficient one-bit multiple signal classification (MUSIC) method for estimating the DOA. Benefiting from the injected noise, the numerical results show that the proposed NBQU-based MUSIC method outperforms existing one-bit MUSIC methods in terms of estimation accuracy and resolution. Furthermore, with the optimal root mean square (RMS) of the injected noise, the estimation accuracy of the proposed method for estimating DOA can approach that of the MUSIC method based on the complete analog data.

Developing an efficiently supported Cu-based catalyst with promoters to substitute the existing non-supported Cu-based catalysts is highly desirable to the Rochow-Müller reaction. Using a simple ball-milling method and CeO2 support, we prepared a high-performance CuO-ZnO-P-Sn/CeO2 catalyst by integrating highly dispersed multicomponent promoters of ZnO, Sn, and P with the active component CuO. This catalyst shows a significantly enhanced dimethyldichlorosilane selectivity because these promoters can substantially increase the Cu+ content and the formation of an active CuxSi phase. This work provides a new approach to efficiently designing Cu-based catalysts for the Rochow-Müller reaction.

The monitoring capability of microseismic monitoring network depends on many factors, such as network layout, velocity model, seismic phase reading error, regional anomaly of travel time, positioning algorithm, equipment running state and environmental noise. Among these factors, the network layout can be artificially optimized at present stage. In order to effectively evaluate the monitoring capacity of microseismic monitoring network and optimize the network layout, the analysis and optimization method of monitoring capacity of coal mine microseismic monitoring network is proposed. This study analyzes four factors which have the greatest and most direct influence on the monitoring capability of the microseismic monitoring network. The four factors are the number of effective waveforms, the maximum gap angle, the near-station epicenter distance and the height difference between stations. It is pointed out that the number of effective waveforms, the near-station epicenter distance and the height difference between stations play a decisive role in the error of hypocenter depth solution. The number of effective waveforms and the maximum gap angle play a decisive role in the precision of epicenter positioning. According to the situation of the existing network and the working face, the distribution cloud pictures of the four factors are obtained. The monitoring capability of the microseismic network is evaluated item by item through the distribution cloud pictures of the four factors. The new network arrangement scheme is obtained through optimization of the evaluation result. The positioning error and sensitivity of the new scheme are analyzed. The epicenter positioning error, hypocenter positioning error and regional sensitivity of the whole mine are obtained. The second evaluation of the new scheme is carried out. If the secondary evaluation results meet the requirements, the new scheme can be regarded as the best network layout scheme. If the secondary evaluation results do not meet the requirements, the four factors sub item evaluation will be carried out again and the scheme will be optimized until the requirements are met. The field test results show that after the proposed method is used to optimize the microseismic monitoring network of 5307 working face in Tangkou Coal Mine, the average value of blasting hypocenter positioning error is reduced from 59.2 m to 37.2 m. The maximum value of positioning error is reduced to less than 100 m, and the blasting events with error less than 50 m account for 69.0% of the total. The results show that the proposed method can effectively improve the microseismic positioning precision and optimize the monitoring capability of the network.

Abstract Background Several studies have demonstrated autophagy was involved in the process of esophageal adenocarcinoma (EAC). The aim of this study was to explore autophagy-related genes (ARGs) correlated with overall survival (OS) in EAC patients. Methods Expressions of ARGs in EAC and normal samples were downloaded from TCGA database. GO and KEGG enrichment analyses were used to investigate the ARGs bioinformatics functions. Univariate and multivariate cox regressions were performed to identify prognostic ARGs and the independent risk factors. ROC curve was established to evaluate the feasibility to predict the prognosis. Finally, the correlations between ARGs and clinical features were further explored. In addition, significantly different ARGs were verified in EAC specimens and normal esophageal mucosal tissues. Results Thirty significantly different ARGs were selected from EAC and normal tissues. Functional enrichments showed these ARGs were mainly related apoptosis. Multivariate cox regression analyses demonstrated eight ARGs were significantly associated with OS. Among these eight genes, BECN1 (HR = 0.321, P = 0.046), DAPK1 (HR = 0.636, P = 0.025) and CAPN1 (HR = 0.395, P = 0.004) played protective roles in survival. Gender (HR = 0.225, P = 0.032), stage (HR = 5.841, P = 0.008) and risk score (HR = 1.131, P < 0.001) were independent prognostic risk factors. ROC curves showed better efficacy to predict survival using the risk score. Additionally, we found BECN1, DAPK1, VAMP7 and SIRT1 genes were correlated significantly with survival status, gender, primary tumor and tumor stage (all P < 0.05). The experimental results confirmed the BIRC5 was overexpressed and the ITPR1, PRKN were downregulated in the EAC tissues compared with the normal esophageal mucosal tissues (all P < 0.05). Conclusion Our findings suggested that autophagy was involved in the process of EAC. Several ARGs probably could serve as diagnostic and prognostic biomarkers and may help facilitate therapeutic targets in EAC patients.

In this paper, we investigate the benefit of intentionally added noise to observed data in various scenarios of Bayesian parameter estimation. For optimal estimators, we theoretically demonstrate that the Bayesian Cramér-Rao bound for the case with added noise is never smaller than for the original data, and the updated minimum mean-square error (MSE) estimator performs no better. This motivates us to explore the feasibility of noise benefit in some useful suboptimal estimators. Several Bayesian estimators established from one-bit-quantizer sensors are considered, and for different types of pre-existing background noise, optimal distributions are determined for the added noise in order to improve the performance in estimation. With a single sensor, it is shown that the optimal added noise for reducing the MSE is actually a constant bias. However, with parallel arrays of such sensors, bona fide optimal added noise, no longer a constant bias, is shown to reduce the MSE. Moreover, it is found that the designed Bayesian estimators can benefit from the optimal added noise to effectively approach the performance of the minimum MSE estimator, even when the assembled sensors possess different quantization thresholds.