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Based on cross-entropy theory and immunity principle, the paper evaluates the health status of scraper conveying equipment. Firstly, the hierarchical analysis model and immune monitoring model of scraper conveyor equipment are established, and then the cross-entropy theory integrating AHP, fuzzy statistical method and entropy weight method is used to calculate the composite weight of scraper conveyor conveyor equipment index, and finally the health status assessment results are obtained. Combined with the case analysis, the proposed health status assessment method can effectively combine the physical characteristics and operating status of each index of scraper conveying equipment to complete the health status assessment of scraper conveying equipment.

Background: The patellar height index is important; however, the measurement procedures are time-consuming and prone to significant variability among and within observers. We developed a deep learning-based automatic measurement system for the patellar height and evaluated its performance and generalization ability to accurately measure the patellar height index. Methods: We developed a dataset containing 3,923 lateral knee X-ray images. Notably, all X-ray images were from three tertiary level A hospitals, and 2,341 cases were included in the analysis after screening. By manually labeling key points, the model was trained using the residual network (ResNet) and high-resolution network (HRNet) for human pose estimation architectures to measure the patellar height index. Various data enhancement techniques were used to enhance the robustness of the model. The root mean square error (RMSE), object keypoint similarity (OKS), and percentage of correct keypoint (PCK) metrics were used to evaluate the training results. In addition, we used the intraclass correlation coefficient (ICC) to assess the consistency between manual and automatic measurements. Results: The HRNet model performed excellently in keypoint detection tasks by comparing different deep learning models. Furthermore, the pose_hrnet_w48 model was particularly outstanding in the RMSE, OKS, and PCK metrics, and the Insall–Salvati index (ISI) automatically calculated by this model was also highly consistent with the manual measurements (intraclass correlation coefficient [ICC], 0.809–0.885). This evidence demonstrates the accuracy and generalizability of this deep learning system in practical applications. Conclusion: We successfully developed a deep learning-based automatic measurement system for the patellar height. The system demonstrated accuracy comparable to that of experienced radiologists and a strong generalizability across different datasets. It provides an essential tool for assessing and treating knee diseases early and monitoring and rehabilitation after knee surgery. Due to the potential bias in the selection of datasets in this study, different datasets should be examined in the future to optimize the model so that it can be reliably applied in clinical practice. Trial registration: The study was registered at the Medical Research Registration and Filing Information System (medicalresearch.org.cn) MR-61-23-013065. Date of registration: May 04, 2023 (retrospectively registered). [ABSTRACT FROM AUTHOR]

All‐solid‐state batteries suffer from high interface resistance and lithium dendrite growth leading to low Li plating/stripping Coulombic efficiency (CE) of <90% and low critical current density at high capacity. Here, simultaneously addresses both challenges are simultaneously addressed and the Li plating/stripping CE is significantly increased to 99.6% at 0.2 mA cm−2/0.2 mAh cm−2, and critical current density (CCD) of > 3.0 mA cm−2/3.0 mAh cm−2 by inserting a mixed ionic‐electronic conductive (MIEC) and lithiophobic LiF‐C‐Li3N‐Bi nanocomposite interlayer between Li6PS5Cl electrolyte and Li anode. The highly lithiophobic LiF‐C‐Li3N‐Bi interlayer with high ionic conductivity (10−5 S cm−1) and low electronic conductivity (3.4×10−7 S cm−1) enables Li to plate on the current collector (CC) surface rather than on Li6PS5Cl surface avoiding Li6PS5Cl electrolyte reduction. During initial Li plating on CC, Li penetrates into porous LiF‐C‐Li3N‐Bi interlayer and lithiates Bi nanoparticles into Li3Bi. The lithiophilic Li3Bi and Li3N nanoparticles in LiF‐C‐Li3N‐Li3Bi sub‐interlayer will move to CC along with plated Li, forming LiF‐C/Li3N‐Li3Bi lithiophobic/lithiophilic sublayer during the following Li stripping. This interlayer enables Co0.1Fe0.9S2/Li6PS5Cl/Li cell with an areal capacity of 1.4 mAh cm−2 to achieve a cycle life of >850 cycles at 150 mA g−1. The lithiophobic/lithiophilic interlayer enables solid‐state metal batteries to simultaneously achieve high energy and long cycle life. [ABSTRACT FROM AUTHOR]

Background: Zygotic genome activation (ZGA) is an important event in the early embryo development, and human embryo developmental arrest has been highly correlated with ZGA failure in clinical studies. Although a few studies have linked maternal factors to mammalian ZGA, more studies are needed to fully elucidate the maternal factors that are involved in ZGA. Methods and results: In this study, we utilized published single-cell RNA sequencing data from a Dux-mediated mouse embryonic stem cell to induce a 2-cell-like transition state and selected potential drivers for the transition according to an RNA velocity analysis. Conclusions: An overlap of potential candidate markers of 2-cell-like-cells identified in this research with markers generated by various data sets suggests that Trim75 is a potential driver of minor ZGA and may recruit EP300 and establish H3K27ac in the gene body of minor ZGA genes, thereby contributing to mammalian preimplantation embryo development. [ABSTRACT FROM AUTHOR]