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The façades of photogrammetric building mesh models frequently lack detailed semantics, such as windows, doors, and balconies. To address this, we introduce SemanticMesh, a methodology designed to enrich façades with detailed semantics by integrating discrete components seamlessly. The process begins by transforming a localized area of the building surface into a 2D planar mesh using geodesic vectors, based on the component poses. This is followed by the application of 2D constrained triangulation to map the component boundaries onto the parameterized plane, after which the mesh is reconverted into 3D space. To achieve a smooth and aesthetically pleasing integration, we employ a Laplacian mesh deformation technique along the 3D embedding boundary. Our experiments across three distinct datasets – featuring near-planar, non-planar, and noisy surfaces – demonstrate that SemanticMesh provides superior modeling outcomes compared to conventional approaches.

Abstract The economic outcomes of U.S.–China trade conflicts on Chinese enterprises remain ambiguous. Using the staggered export control lists released by the Bureau of Industry and Security (BIS) of the U.S. Department of Commerce in conjunction with data from Chinese publicly listed manufacturing firms, we identify Chinese entities subjected to these export controls. Our Difference-in-Differences (DID) estimates show that export restrictions induced by the entity lists enhance the R&D investment intensity among Chinese firms by 16.58% in the next year. The mechanism is that controls increase firms’ R&D investment through government subsidies, firms’ inventory adjustment and firms’ risk-taking. Heterogeneity tests show that the policy effect is more significant for State-owned enterprises (SOEs), firms with executives with foreign experience. In addition, the R&D promotion of firms supported by China’s industrial policy is more pronounced. Although controls significantly increase R&D inputs, they have little or negative effect on firms’ innovation outputs. Our study provides micro-level causal evidence on the economic impacts of the U.S.–China trade war.

Background and purpose: Deep learning methods can be used for automatic segmentation and detection of lymphocytes on pathological images. This study aimed to assess the performance of using variational autoencoding pre-training method for lymphocyte infiltration detection on pathological images, as well as the impact of removing tumor necrosis regions on model performance. Methods: Using variational autoencoding (VAE) pre-training method, pre-training was performed on a large number of unlabeled pathological images from the Cancer Genome Atlas (TCGA) database (TCGA-COAD and TCGA-READ) to obtain an auto-encoding pre-training model, and then a classifier model of lymphocyte infiltration was trained on the public data samples. To avoid confusion with necrotic regions, a Unet segmentation model for tumor necrotic regions was trained to remove the influence of tumor necrotic regions on lymphocyte identification. Results: The lymphocyte infiltration detection model pre-trained with the VAE model had an area under curve (AUC) of 0.979 (95% CI: 0.978-0.980), an accuracy of 92.5% (95% CI: 92.3%-92.6%), a kappa value of 0.849, sensitivity of 0.908, specificity of 0.941, precision of 0.939, recall of 0.908, and F1 of 0.923 under the receiver operating characteristic (ROC) curve on the training set. The AUC for the validation set was 0.968 (95% CI: 0.964-0.972), the accuracy was 91.3% (95% CI: 90.6%-92.0%), kappa value was 0.826, sensitivity was 0.898, specificity was 0.928, precision was 0.925, recall was 0.898, and F1 was 0.912. The results of Resnet18 model on the labeled dataset were as follows: accuracy of the validation set was 83.2 % (95% CI: 82.2%-84.1%), kappa value was 0.664, sensitivity was 0.823, specificity was 0.840, precision was 0.838, recall was 0.823 and F1 was 0.830. The Unet model that segmented the necrotic regions of the tumors had a final DICE of 0.78 for the training set, and 0.76 for the validation. After removing the necrotic region, the predictive performance of the pre-trained lymphocyte infiltration detection model using the VAE proposed in this article was improved to some extent, with the AUC on the validation set increasing from 0.968 (95% CI: 0.964-0.972) to 0.971 (95% CI: 0.968-0.975). The accuracy was 92.4% (95% CI: 91.7%-93.0%), kappa value was 0.849, sensitivity was 0.928, specificity was 0.921, precision was 0.921, recall was 0.928, and F1 was 0.925. Conclusion: Using the variational autoencoding model pre-training method to classify the pathological pictures of lymphocyte infiltration can obtain better model performance compared with direct training, and removing the influence of tumor necrosis areas through Unet can further improve the performance of the model.

The utilization of ultrasonic guided wave technology for detecting cracks in railway tracks involves analyzing echo signals produced by the interaction of cracks with guided wave modes to achieve precise crack localization, which is extremely important in a real-time railway crack robotic detection system. Addressing the challenge of selecting the optimal detection mode for cracks in various regions of railway tracks, this paper presents a method for optimal crack detection mode selection. This method is based on the sensitivity of guided wave modes to cracks. By examining the frequency dispersion characteristics and mode shapes of guided wave modes, we establish indicators for crack zone energy and crack reflection intensity. Our focus is on the railhead of the railway track, selecting guided wave modes characterized by specific cracks for detection purposes. Experimental findings validate the accuracy of our proposed mode selection method in detecting cracks in railway tracks. This research not only enhances crack detection but also lays the groundwork for exploring advanced detection and localization techniques for cracks in railway tracks.

Modulation of immune microenvironment is critical for inflammatory bowel disease (IBD) intervention. Epigallocatechin gallate (EGCG), as a natural low toxicity product, has shown promise in treating IBD. However, whether and how EGCG regulates the intestinal microenvironment is not fully understood. Here we report that EGCG lessens colitis by orchestrating Th1 polarization and self-amplification in a novel manner that required multilevel-regulated intestinal microecosystem. Mechanistically, EGCG activates GPR43 on IEC to inhibit Th1 polarization dependently of short chain fatty acid (SCFA)-producing gut microbiota. Inhibition of GPR43 activity weakens the protective effects of EGCG on colitis development. Moreover, we confirm that fecal SCFAs and/or intestinal GPR43 are limited in patients with colitis and are correlated with Th1 cell number. Taken together, our study reveals an intestinal microenvironment-dependent immunoregulatory effects of EGCG in treating IBD and provides insight into mechanisms of EGCG-based novel immunotherapeutic strategies for IBD.

Boiling is a high-performance heat dissipation process that is central to electronics cooling and power generation. However, there exists a practical limit of boiling heat transfer known as the critical heat flux (CHF), beyond which significant performance degradation is observed. Understanding the physical mechanism that triggers CHF is essential to meet the increasing cooling demands driven by power densification and device miniaturization. However, the high dimensionality, stochasticity, and dynamicity of the boiling process have led to strong challenges in the experimental characterization and modeling of boiling CHF. As such, high-frame rate, high-resolution, multi-physics boiling datasets are critical to advancing the fundamental understanding of boiling heat transfer. To this end, this paper presents a multimodal boiling dataset consisting of synchronized thermal, acoustic, and optical signals collected from five different heater surfaces under two distinct heat load conditions. With its high sampling frequency, diverse signal types, large data volume, and detailed recorded information, this dataset provides valuable ''data blood'' for the field of thermal crisis monitoring. This dataset will not only promote fundamental research on bubble dynamics during boiling but also support the implementation of advanced monitoring technologies in industrial applications such as power electronics, motors, data centers, and power plants.

Objective To analyze risk factors for perioperative blood transfusion in elderly patients undergoing unilateral primary total hip arthroplasty and develop a prediction model. Methods The study retrospectively collected 467 elderly patients receiving unilateral primary total hip arthroplasty between January 2013 and October 2021 at Peking Union Medical College Hospital. The 70% of the data were used as the training set and the 30% of the data were used as the testing set. Patients were divided into the transfusion and no-transfusion groups based on the presence or absence of perioperative blood transfusion. Univariate analysis and multivariable logistic regression were conducted to analyze patient demographic characteristics, surgical information, and preoperative laboratory tests for identifying risk factors. Clinical experience was combined to establish a prediction model and draw the nomogram. The receiver operating characteristic(ROC) curve and calibration curve were used to evaluate the model in the testing set. Results A total of 91 patients(19.5%) received perioperative blood transfusion. Multivariable logistic regression suggested the history of coronary artery disease, prolonged operation time, and lower preoperative hemoglobin were risk factors for perioperative blood transfusion(P＜0.05). The prediction model was constructed based on the results of statistical analysis and clinical experience, including the history of coronary artery disease, operation time, preoperative hemoglobin, age, and American Society of Anesthesiologists(ASA) physical status＞Ⅱ. The area under the receiver operating characteristic curve(AUC) of the model was 0.809. Conclusions The prediction model for perioperative blood transfusion in elderly patients undergoing unilateral total hip arthroplasty had a good performance and could assist in clinical practice.

BackgroundPulmonary fibrosis is a progressive and usually lethal pulmonary disease. Despite considerable research efforts, no effective therapeutic strategy for pulmonary fibrosis has been developed. NecroX-5 has been reported to possess anti-inflammatory, anti-oxidative and anti-tumor activities. In the present study, we aimed to determine whether NecroX-5 exhibits antifibrotic property in bleomycin (BLM)-induced pulmonary fibrosis.ResultsWe found that pre-treatment with NecroX-5 alleviated inflammatory response, reduced oxidative stress, inhibited epithelial-mesenchymal transition (EMT), and ameliorated pulmonary fibrosis in vivo and in vitro. Our data further indicated that NecroX-5 substantially reduced activation of NLRP3 inflammasome and TGF-β1/Smad2/3 signaling in vivo and in vitro. Additionally, NLRP3 overexpression significantly reversed the protective effects of NecroX-5 in lung epithelial cells exposed to BLM.ConclusionsOverall, our results demonstrate the potent antifibrotic properties of NecroX-5 and its therapeutic potential for pulmonary fibrosis.