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Noise suppression is an important part of microseismic monitoring technology. Signal and noise can be separated by denoising and filtering to improve the subsequent analysis. In this paper, we propose a new denoising method based on convolutional blind denoising network (CBDNet). The method is partially modified for image denoising network CBDNet to make it suitable for one–dimensional data denoising. At present, most of the existing filtering methods are proposed for the Gaussian white noise denoising. In contrast, the proposed method also learns the wind noise, construction noise, traffic noise and mixed noise through the strategy of residual learning. The full convolution subnetwork is used to estimate the noise level, which significantly improves the signal-to-noise ratio and its performance of removing the correlated noise. The model is trained with different types of real noise and random noise. The denoising result is evaluated by corresponding indexes and compared with other denoising methods. The results show that the proposed method has better denoising performance than traditional methods, and it has a superior noise suppression level for oil well construction noise and mixed noise. The proposed method can suppress the interference of time–frequency overlapped end to end and still have noise suppression and event detection capability even if the signal is superimposed on other types of noise.

Abstract A robust tube‐based distributed model predictive control method is proposed for spatially interconnected systems with constraints and disturbances. The system contains multiple discrete‐time piecewise affine subsystems, which are coupled to each other through states. The predictive states of each subsystem are dependent on its states, inputs, and neighbouring states, and shrinking constraints are constructed to deal with the disturbances and the model mismatches. The differences between the predicted states at the current time instant and the optimal states at the previous time instant are constrained, which improves the accuracy of the predicted model and enhances the control performance. The conditions which ensure the recursive feasibility of optimization problems and the asymptotic stability of closed‐loop system are obtained. The effectiveness of the proposed method is verified by the simulation results.

Cf/TaxHf1−xC–SiC composites are ideal thermal structural materials for service under extreme conditions of hypersonic vehicles. However, how to synthesize TaxHf1-xC powders and efficiently fabricate Cf/TaxHf1-xC–SiC composites still faces some challenges. Furthermore, mechanical properties and thermophysical properties of TaxHf1−xC vary with the composition, but not monotonically. In-depth analysis of mechanical behaviors of the Cf/TaxHf1−xC–SiC composites is extremely important for their development and applications. In this study, the TaxHf1−xC powders (x = 0.2, 0.5, 0.8) were successfully synthesized via solid solution of TaC and HfC at a relatively low temperature of 1800 ℃, with a small amount of Si as an additive. Subsequently, the efficient fabrication of 2D-Cf/TaxHf1–xC–SiC composites was achieved by slurry impregnation and lamination (SIL) combined with precursor infiltration and pyrolysis (PIP). In addition, the mechanical behavior of the composites was investigated systematically. It is demonstrated that the composites present remarkable non-brittle fractures, including a large number of fiber pull out and interphase debonding. Also, the fracture failure involves a complex process of microcrack generation and propagation, matrix cracking, and layer fracture. Moreover, the interfacial bonding between the fibers and the matrix is enhanced as the Ta∶Hf ratio decreases from 4∶1 to 1∶4. As a result, Cf/Ta0.2Hf0.8C–SiC composites exhibit exceptional flexural strength of 437±19 MPa, improved by 46% compared with Cf/Ta0.8Hf0.2C–SiC (299±19 MPa). This study provides a new perception of design and fabrication of ultra-high-temperature ceramic (UHTC) matrix composites with high performance.

Abstract Introduction Immunoglobulin G4-related disease (IgG4-RD) is a debilitating multiorgan disease characterized by recurring flares leading to organ dysfunction, decreased quality of life, and mortality. Glucocorticoids, the standard of care for IgG4-RD, are associated with substantial treatment-related toxicity. Inebilizumab, an antibody directed against CD19, mediates the rapid and durable depletion of CD19+ B cells thought to be involved in IgG4-RD pathogenesis. We describe the first international, prospective, double-blind, placebo-controlled trial to evaluate the safety and efficacy of B-cell depletion for flare prevention in IgG4-RD (MITIGATE). Methods The study was designed by an international panel of physicians with expertise in IgG4-RD. Critical trial design decisions included the selection of participants, definition of clinically meaningful primary and secondary endpoints, accommodation of standard of care, and development of flare diagnostic criteria. The study is approved for conduct in 22 countries. Planned Outcomes The primary efficacy endpoint is time from randomization to the occurrence of the first centrally adjudicated and investigator-treated disease flare during the 1-year randomized controlled period. A set of novel, organ-specific flare diagnostic criteria were developed specifically for this trial, incorporating symptoms and signs, laboratory findings, imaging study results, and pathology data. MITIGATE aims to accrue 39 flares for the primary endpoint, which provides sufficient power to detect a relative risk reduction of 65% in the inebilizumab group. It is anticipated that enrollment of 160 participants will achieve this goal. Additional endpoints include safety, annualized flare rate, flare-free complete remission, quality-of-life measures, and cumulative glucocorticoid use. MITIGATE represents the first randomized, double-blind, placebo-controlled trial of any treatment strategy conducted in IgG4-RD. Data from this study will provide insights into the natural history and pathophysiology of IgG4-RD and the efficacy and safety of B-cell depletion as a therapeutic avenue. Trial Registration NCT04540497.

Abstract Background Follicular microenvironment has been proposed as an important factor for oocyte grown and maturation. We sought to evaluate the oxidative stress and inflammatory levels in follicular fluid (FF) and association with embryo quality in patients with diminished ovarian reserve (DOR). Methods The current research included 46 DOR cases and 56 normal ovarian reserve (NOR) cases. Twelve representative oxidative stress markers and eight representative inflammatory factors were measured in the FF. Results Oxidative stress markers total GSH (T-GSH) was decreased in the FF from women with DOR compared with that in NOR group (P = 0.041). More modest differences were observed for reduced GSH (rGSH) and rGSH/GSSG. Women with DOR compared to controls had higher level of TNF-α (P = 0.000) and lower level of IL-18 (P = 0.013). Correlation analysis revealed that GSSG was negatively correlated with normal fertilization rate in NOR group (r = -0.358, P = 0.008), and reduced GSH was negatively correlated with normal fertilization rate in DOR group (r = -0.299, P = 0.049). Moreover, as the regression analysis data showed, the GSSG level was significantly associated with embryo quality indicator. Conclusions The FF in DOR patients was accompanied by increased oxidative stress and inflammatory levels. Follicular development of women with DOR might be influenced by unusual IL-18 and TNF-α levels in FF. And oxidative stress marker GSSG in NOR group was a negative predictor for embryo quality.

The technology for object detection in remote sensing images finds extensive applications in production and people’s lives, and improving the accuracy of image detection is a pressing need. With that goal, this paper proposes a range of improvements, rooted in the widely used YOLOv7 algorithm, after analyzing the requirements and difficulties in the detection of remote sensing images. Specifically, we strategically remove some standard convolution and pooling modules from the bottom of the network, adopting stride-free convolution to minimize the loss of information for small objects in the transmission. Simultaneously, we introduce a new, more efficient attention mechanism module for feature extraction, significantly enhancing the network’s semantic extraction capabilities. Furthermore, by adding multiple cross-layer connections in the network, we more effectively utilize the feature information of each layer in the backbone network, thereby enhancing the network’s overall feature extraction capability. During the training phase, we introduce an auxiliary network to intensify the training of the underlying network and adopt a new activation function and a more efficient loss function to ensure more effective gradient feedback, thereby elevating the network performance. In the experimental results, our improved network achieves impressive mAP scores of 91.2% and 80.8% on the DIOR and DOTA version 1.0 remote sensing datasets, respectively. These represent notable improvements of 4.5% and 7.0% over the original YOLOv7 network, significantly enhancing the efficiency of detecting small objects in particular.

In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang–Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.

Idiopathic pulmonary fibrosis (IPF) is a fatal disease with limited treatment options. The role of the developmental transcription factor Sine oculis homeobox homolog 1 (SIX1) in the pathophysiology of lung fibrosis is not known. IPF lung tissue samples and IPF-derived alveolar type II cells (AT2) showed a significant increase in SIX1 mRNA and protein levels, and the SIX1 transcriptional coactivators EYA1 and EYA2 were elevated. Six1 was also upregulated in bleomycin-treated (BLM-treated) mice and in a model of spontaneous lung fibrosis driven by deletion of Telomeric Repeat Binding Factor 1 (Trf1) in AT2 cells. Conditional deletion of Six1 in AT2 cells prevented or halted BLM-induced lung fibrosis, as measured by a significant reduction in histological burden of fibrosis, reduced fibrotic mediator expression, and improved lung function. These effects were associated with increased macrophage migration inhibitory factor (MIF) in lung epithelial cells in vivo following SIX1 overexpression in BLM-induced fibrosis. A MIF promoter-driven luciferase assay demonstrated direct binding of Six1 to the 5-TCAGG-3 consensus sequence of the MIF promoter, identifying a likely mechanism of SIX1-driven MIF expression in the pathogenesis of lung fibrosis and providing a potentially novel pathway for targeting in IPF therapy.