Research on Risk Prediction and Early Warning of Human Resource Management Based on Machine Learning and Ontology Reasoning

Talent is the first resource, the development of the enterprise to retain key talent is essential, the main research is based on machine learning and ontological reasoning, human resources analysis and management risk prediction and early warning methods, first of all, according to the specific situation and the target case, through the calculation of the similarity of the concept name and attribute of the similarity assessment of the source case in the case library, the matching of knowledge-based employees of the company\u27s case for the similarity prediction and human resources management risk prediction research. Then, according to the evaluation results, we can find out the most suitable job matches in specific risk problems and situations. This is a solution to the target cases and criteria for companies to evaluate candidates. Second, we have successfully developed and implemented a prediction model that applies machine learning to the early warning study of risk prediction for HR management. The model is optimized with a cross-validation function, and the convergence of the model training is accelerated by the regularization of Newton\u27s iterative method. Finally, our prediction model achieved 82% yield. Ontological reasoning and machine learning are promising in human resource management risk prediction and warning, which is proved by the high accuracy rate verified by examples. Finally, we analyze the proposed results of HRM risk prediction and early warning to contribute to the improvement of risk control and suggest measures for possible risks

CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors

ObjectiveTo investigate the feasibility and efficiency of automatic segmentation of contrast-enhanced ultrasound (CEUS) images in renal tumors by convolutional neural network (CNN) based models and their further application in radiomic analysis.Materials and methodsFrom 94 pathologically confirmed renal tumor cases, 3355 CEUS images were extracted and randomly divided into training set (3020 images) and test set (335 images). According to the histological subtypes of renal cell carcinoma, the test set was further split into clear cell renal cell carcinoma (ccRCC) set (225 images), renal angiomyolipoma (AML) set (77 images) and set of other subtypes (33 images). Manual segmentation was the gold standard and serves as ground truth. Seven CNN-based models including DeepLabV3+, UNet, UNet++, UNet3+, SegNet, MultilResUNet and Attention UNet were used for automatic segmentation. Python 3.7.0 and Pyradiomics package 3.0.1 were used for radiomic feature extraction. Performance of all approaches was evaluated by the metrics of mean intersection over union (mIOU), dice similarity coefficient (DSC), precision, and recall. Reliability and reproducibility of radiomics features were evaluated by the Pearson coefficient and the intraclass correlation coefficient (ICC).ResultsAll seven CNN-based models achieved good performance with the mIOU, DSC, precision and recall ranging between 81.97%-93.04%, 78.67%-92.70%, 93.92%-97.56%, and 85.29%-95.17%, respectively. The average Pearson coefficients ranged from 0.81 to 0.95, and the average ICCs ranged from 0.77 to 0.92. The UNet++ model showed the best performance with the mIOU, DSC, precision and recall of 93.04%, 92.70%, 97.43% and 95.17%, respectively. For ccRCC, AML and other subtypes, the reliability and reproducibility of radiomic analysis derived from automatically segmented CEUS images were excellent, with the average Pearson coefficients of 0.95, 0.96 and 0.96, and the average ICCs for different subtypes were 0.91, 0.93 and 0.94, respectively.ConclusionThis retrospective single-center study showed that the CNN-based models had good performance on automatic segmentation of CEUS images for renal tumors, especially the UNet++ model. The radiomics features extracted from automatically segmented CEUS images were feasible and reliable, and further validation by multi-center research is necessary

Two-Fold Anisotropy Governs Morphological Evolution and Stress Generation in Sodiated Black Phosphorus for Sodium Ion Batteries

Phosphorus represents a promising anode material for sodium ion batteries owing to its extremely high theoretical capacity. Recent in situ transmission electron microscopy studies evidenced anisotropic swelling in sodiated black phosphorus, which may find an origin from the two intrinsic anisotropic properties inherent to the layered structure of black phosphorus: sodium diffusional directionality and insertion strain anisotropy. To understand the morphological evolution and stress generation in sodiated black phosphorus, we develop a chemo-mechanical model by incorporating the intrinsic anisotropic properties into the large elasto-plastic deformation. Our modeling results reveal that the apparent morphological evolution in sodiated black phosphorus is critically controlled by the coupled effect of the two intrinsic anisotropic properties. In particular, sodium diffusional directionality generates sharp interphases along the [010] and [001] directions, which constrain anisotropic development of the insertion strain. The coupled effect renders distinctive stress-generation and fracture mechanisms when sodiation starts from different crystal facets. In addition to providing a powerful modeling framework for sodiation and lithiation of layered structures, our findings shed significant light on the sodiation-induced chemo-mechanical degradation of black phosphorus as a promising anode for the next-generation sodium ion batteries