Aim: Traditional artificial microscopic technologies cannot meet the current demands of automated urine detection. Furthermore, the number of cell types detected in previous studies was relatively limited; therefore, previous studies are considered to be insufficient. Methods: The present study proposes a multi-class detection method of urinary particles based on deep learning. First, we obtained an image database containing 15 types of cellular components, i.e., normal, shrinking, glomerular, and abnormal erythrocytes; leukocytes; calcium oxalate, uric acid, other types of crystals; particle and transparent casts; epithelial cells; low-transitional epithelium; Candida; Bacillus; and abnormal epithelium. The image data was then input into Resnet50 basic network and feature pyramid network (FPN) to obtain a multi-layer feature map. Thereafter, the classification sub-networks and regression sub-networks were used to classify and locate the cellular components. The network detection model was obtained after training was completed. Results: The experimental data showed that for the test set, the mean average precision (mAP) of the network model reached 82.86%, and the time required to process a single image sample was 195 ms. Therefore, we were able to perform multi-class analysis and detect urine cells with good results in terms of detection speed. Conclusion: This study applies the deep learning network model for the multi-category detection of urine cells. The method can be used to analyze and detect urinary particles in actual clinical practice and has great reference significance for the detection of other cells in the clinic.
The short-range order (SRO) structure in high-entropy alloys (HEAs) is closely associated with many properties, which can be studied through density functional theory (DFT) calculations. Atomic-scale modeling and calculations require substantial computational resources, and machine learning can provide rapid estimations of DFT results. To describe SRO information in HEAs, a new descriptor (VASE) based on Voronoi Analysis and Shannon Entropy is proposed. Based on Voronoi analysis, the Shannon entropy is introduced to directly characterize atomic spatial arrangement information except for composition and atomic interactions, which is necessary for describing the disorder atomic occupancy in HEAs. The new descriptor is used for predicting the formation energy of FeCoNiAlTiCu system based on machine learning model, which is more accurate than other descriptors (Coulomb matrices, Partial radial distribution functions, and Voronoi analysis). Moreover, the model trained based on VASE descriptors exhibits the best predictive performance for unrelaxed structures (28.94 meV/atom). The introduction of Shannon entropy provides an effective representation of atomic arrangement information in HEAs, which is a powerful tool for investigating the SRO phenomena.
Solidification and homogenization behaviors of Al-9.1Zn-2.1Mg-2.2Cu-0.1Zr-0.07Ce (wt%) alloy were investigated. Obvious grain refinement was observed in as-cast alloy by Ce modification, the main mechanism of the grain refinement is that the redistribution of solute during solidification leads to the increase of the supercooling degree of the solid/liquid interface front component and the blocking effect of primary Ce enrichment phase. Serious non-equilibrium eutectic phase (Mg(Zn, Al, Cu)2) segregation existed mainly along the grain boundary after cast. The solidification segregate phase dissolved into α (Al) matrix gradually during homogenization and a phase transformation from Mg(Zn, Al, Cu)2 to few Al2CuMg were also occurred even with 9.1 wt% Zn content at present alloy, owing to trace Ce as solute can hinder the diffusion of Cu atoms in Mg(Zn, Al, Cu)2 phase effectively. Obviously the solidification Mg(Zn, Al, Cu)2 phase was eliminated thoroughly and a larger number of disperse Al3Zr was precipitated effectively by a double-stage homogenization treatment. Thus the homogenization at 435 °C for 8 h and then at 470 °C for 32 h is identified as an optimum homogenization treatment in this experimental alloy.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
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