A Study of Hydrogen Embrittlement of SA-372 J Class High Pressure Hydrogen Storage Seamless Cylinder (≥100 MPA).

The spinning process will lead to changes in the micro-structure and mechanical properties of the materials in different positions of the high-pressure hydrogen storage cylinder, which will show different hydrogen embrittlement resistance in the high-pressure hydrogen environment. In order to fully study the safety of hydrogen storage in large-volume seamless steel cylinders, this chapter associates the influence of the forming process with the deterioration of a high-pressure hydrogen cylinder (≥100 MPa). The anti-hydrogen embrittlement of SA-372 grade J steel at different locations of the formed cylinders was studied experimentally in three cylinders. The hydrogen embrittlement experiments were carried out according to method A of ISO 11114-4:2005. The relationship between tensile strength, microstructure, and hydrogen embrittlement is analyzed, which provides comprehensive and reliable data for the safety of hydrogen storage and transmission.

An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.

OBJECTIVES: Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID-19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination. METHODS: A three dimensional algorithm that combined multi-instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID-19 from community acquired pneumonia (CAP) while logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID-19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID-19 and 183 CAP cases) and relatively small datasets (17 COVID-19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID-19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G-mean were utilized for performance evaluation. RESULTS: In the external test cohort, the relatively large data-based 3DMTM-LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM-SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM-SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN-MMSD, LR-MMSD, SVM-MMSD, and 3DCM-MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID-19 discrimination. 3DMTM, trained with either CT or multi-modal data, presented comparably excellent performance in COVID-19 discrimination. CONCLUSIONS: The 3DMTM algorithm presented excellent robustness for COVID-19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID-19 discrimination with that trained with multi-modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID-19 discrimination, especially in the scenario with limited data for training.

Elastic-Plastic Numerical Analysis of the Spinning Process of SA-372 Steel Used in High-Pressure Hydrogen Storage Cylinders (≥100 MPA).

Elastic-plastic numerical analysis of the spinning process of SA-372 steel is used in high-pressure hydrogen storage to analyze high-pressure hydrogen storage cylinders with high precision and excellent hydrogen embrittlement resistance. The spinning process of SA-372 steel used to form such a cylinder with a pressure of 100 MPa is investigated through elastic-plastic finite element analysis. The variations in the stress, strain, pressure, temperature, and wall thickness during the spinning processes are comprehensively examined, and the optimized processing parameters are determined based on the numerical analysis results. Finally, these optimal parameters are used to conduct actual spin-forming experiments. The numerical results are found to be in excellent agreement with the experimental results, which verifies the feasibility and effectiveness of the proposed elastic-plastic numerical analysis model for the optimization of spinning process parameters. Furthermore, the hydrogen embrittlement test based on ISO 11114-4:2005 method A proves that the cylinder shoulder has a good hydrogen embrittlement resistance.

[Spatial Distribution Characteristics of Phosphorus Fractions and Release Risk in Sediments of Honghu International Importance Wetland].

According to a spatial distribution analysis of phosphorus in sediments from Honghu Wetland, it was found that TP content in sediments at the mouth of Honghu Lake was 781.31-1955.84 mg·kg-1 and the average value was(1287.21±437.28)mg·kg-1. TP content in sediments in the open water area was 438.33-1554.04 mg·kg-1, with an average value of(718.10±238.15)mg·kg-1. The TP content of sediments in lake inlet was significantly higher than that of sediments in the open water area(P<0.05), and that in the enclosed aquaculture was higher than in the open water area, although no significant difference was observed (P>0.05). The TP content of sediments to the northwest and northeast of Honghu Lake was higher than that to the southwest of Honghu Lake, and the TP content of sediments in The Four-lake main canal was significantly higher than that of Luoshan main canal(P<0.05). The phosphorus input in the Four-lake main canal may be the main source of phosphorus in Honghu Lake sediments. The phosphorus fraction composition in sediments from different sampling sites were significantly different. Fe/Al-P and Ca-P were the main forms of phosphorus in sediments from the lake inlet, while OP and Ca-P were the main forms of phosphorus in sediments from the open water area. The variation in spatial phosphorus form composition was related to the influence of human activity and the distribution of aquatic plants. Fe/Al-P and OP contents were used to estimate the content of biological available phosphorus (BAP) in evaluated sediments, and the proportion of BAP in TP was used to estimate the risk of phosphorus release in Honghu sediments. BAP/TP was 39.8%-69%, with an average of(56.5±7.23)%, indicating a high risk of phosphorus release. OP and BAP were significantly correlated with TP in overlying water(P<0.01), and the correlation between BAP and phosphate in the overlying water was the highest. The results showed that phosphorus concentration in the overlying water may be related to the release of Fe/Al-P and OP into water bodies.

Combined clinical and imaging features better predict the critical outcomes of patients with SARS-COV-2.

ABSTRACT: The purpose of this study was to investigate the predictive value of combined clinical and imaging features, compared with the clinical or radiological risk factors only. Moreover, the expected results aimed to improve the identification of severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) patients who may have critical outcomes.This retrospective study included laboratory-confirmed SARS-COV-2 cases between January 18, 2020, and February 16, 2020. The patients were divided into 2 groups with noncritical illness and critical illness regarding severity status within the hospitalization. Univariable and multivariable logistic regression models were used to explore the risk factors associated with clinical and radiological outcomes in patients with SARS-COV-2. The ROC curves were performed to compare the prediction performance of different factors.A total of 180 adult patients in this study included 20 critical patients and 160 noncritical patients. In univariate logistic regression analysis, 15 risk factors were significantly associated with critical outcomes. Of importance, C-reactive protein (1.051, 95% confidence interval 1.024-1.078), D-dimer (1.911, 95% CI, 1.050-3.478), and CT score (1.29, 95% CI, 1.053-1.529) on admission were independent risk factors in multivariate analysis. The combined model achieved a better performance in disease severity prediction (P = .05).CRP, D-dimer, and CT score on admission were independent risk factors for critical illness in adults with SARS-COV-2. The combined clinical and radiological model achieved better predictive performance than clinical or radiological factors alone.