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To improve the density of polycrystalline diamond, a study was conducted to investigate the changes in diamond powder under different pressure conditions, including initial loading, cold isostatic pressing, and six-sided die pressing. The study focused on the particle size distribution, powder density, and microstructural rearrangement before and after applying pressure to different diamond powder sizes and ratios. The process involved the initial random arrangement of particles, followed by the filling of fine particles into voids and rearrangement at 220 MPa during cold isostatic pressing. Subsequently, under ultra-high pressure, large particles (G20～30) were crushed and gradually filled the voids. The buffering effect of fine particles resulted in fewer fractures in the dual particle size formula (G2～4 and G20～30) compared to the single particle size formula (G20～30), which facilitated higher stacking density of the diamond powder. These findings provide valuable data support for optimizing the particle size and ratio design of diamond powders for the high-temperature high-pressure (HPHT) synthesis of high-performance polycrystalline diamond composite.

A new type of poly-diamond plate without a catalyst was produced via the high-pressure high-temperature (HPHT) compression of diamond powders. The densification of diamond powders and sp3 to sp2 carbon on the surface under HPHT compression was investigated through the characterization of the microstructure, Raman spectroscopy analysis and electrical resistance measurement. The densification and sp3-sp2 transformation on the surface are mainly affected by the pressure, temperature and particle size. The quantitative analysis of the diamond sp3 and sp2 carbon amount was performed through the peak fitting of Raman spectra. It was found that finer diamond particles under a higher temperature and a lower pressure tend to produce more sp2 carbon; otherwise, they produce less. In addition, it is interesting to note that the local residual stresses measured using Raman spectra increase with the diamond particle size. The suspected reason is that the increased particle size reduces the number of contact points, resulting in a higher localized pressure at each contact point. The hypothesis was supported by finite element calculation. This study provides detailed and quantitative data about the densification of diamond powders and sp3 to sp2 transformation on the surface under HPHT treatment, which is valuable for the sintering of polycrystalline diamonds (PCDs) and the HPHT treatment of diamonds.

Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method.

Large congenital melanocytic nevus has a high risk of malignancy. However, few studies have summarized its characteristics, treatments, outcomes and malignancy incidence in Chinese patients. This paper reviews a retrospective cohort study evaluating 1,171 patients from Shanghai Ninth People's Hospital between 1 January 1989 and 31 August 2019 using electronic medical records and phone calls to collect clinical and pathological data in which 133 patients were diagnosed with a large congenital melanocytic nevus. Three patients relapsed, and none developed melanoma among the qualified patients. Besides, a new “7B” rule for distribution patterns of large congenital melanocytic nevi was proposed, including bonce, bolero, back, bathing trunk, breast/belly, body extremity, and body. The most common distribution pattern of large congenital melanocytic nevi was bonce, and all blue nevi distributed as bonce. Statistical analysis showed a significant difference (P = 0.0249) in the “7B” patterns between the melanocytic nevus and the neuronevus. In conclusion, the malignancy rate of large congenital melanocytic nevi is much lower in China than in other regions and people of other races. The pathology of large congenital melanocytic nevus may decide its “7B” distribution pattern.

Objectives: This study compared the diagnostic ability of image-based parameters with texture parameters in the differentiation of hepatocellular carcinoma (HCC) and hepatic lymphoma (HL) by positron emission tomography–computed tomography (PET/CT).Methods: Patients with pathological diagnosis of HCC and HL were included in this study. Image-based and texture parameters were obtained by manual drawing of region of interest. Receiver operating characteristic (ROC) was used to test the diagnostic capacity of each parameter. Binary logistic regression was used to transform the most discriminative image-based parameters, texture parameters, and the combination of these parameters into three regression models. ROC was used to test the diagnostic capacity of these models.Result: Ninety-nine patients diagnosed with HCC (n = 76) and HL (n = 23, 10 primary HL, 13 secondary HL) by histological examination were included in this study (From 2011 to 2018, West China hospital). According to the AUC and p-value, 2 image-based parameters and five texture parameters were selected. The diagnostic ability of texture-based model was better than that of image-based model, and after combination of those two groups of parameters the diagnostic capacity improved.Conclusion: Texture parameters can differentiate HCC from HL quantitatively and improve the diagnostic ability of image-based parameters.