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

In-situ recommendation of alternative soil samples during field sampling based on environmental similarity

Field sampling is an essential step for digital soil mapping and various sampling strategies have been designed for achieving desirable mapping results. Some unpredictable complex circumstances in the field, however, often prevent some samples from being collected based on the pre-designed sampling strategies. Such circumstances include inaccessibility of some locations, change of land surface types, and heavily-disturbed soil at some locations, among others. This may result in the missing of some essential samples, which could impact the quality of digital soil mapping. Previous studies have attempted to design alternative samples for the selected ones beforehand to address this issue. It cannot solve the problem completely as those pre-designed alternative samples could also be inaccessible. In this paper, we propose a dynamic method to recommend alternative samples for those unavailable samples in the field. The identification of alternative samples is based on the environmental similarity between an unavailable soil sample and its alternative candidates, as well as the spatial accessibility of these candidates. For the convenience of fieldwork, the proposed method was implemented to be a mobile application on the Android platform. A simulated soil sampling study in Xuancheng county, Anhui province of China was used to evaluate its performance. From a sample set for the study are, 30 samples were assumed to be inaccessible. For each of them, an alternative soil sample from the set was recommended using the proposed method. A deviation analysis of silt and sand content at the depth of 20 ~ 40 cm between the soil samples and their alternatives shows that the deviation on silt content is less than 20% for half of the soil samples. A larger deviation on sand content might be attribute to the limited alternative candidates in this virtual experiment. In a second experiment, we randomly selected a number of existing soil samples and replaced them with their corresponding alternative soil samples. This created 1000 hybrid sample sets. Each hybrid sample set was then used for digital soil mapping with iPSM. An evaluation using 59 independent soil samples indicated that the RMSE and MAE with the hybrid sample sets were close to that with the original sample set. The proposed method proved to be able to recommend effective alternative samples for those unavailable samples in the field