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The determination of the pressure-bearing performance of hydrogen storage tanks (HST) is integral to enhancing their operational safety and risk management capabilities. In this study, an inaugural development of low-cost critical failure pressure (BP c) prediction model for small samples was proposed, which was based on a combination of the fuzzy grey relational analysis (FGRA) and GM(1,N). Specifically, a total of 15 of BP c -related factors were proposed for analysis and projections based on the data from bonfire tests. Results from the FGRA indicated burst pressure was most closely linked to the initial filling pressure (R ij1 = 0.939) of tanks under fire scenario. The normal working pressure (R ij2 = 0.924) and the elasticity modulus (R ij3 = 0.871) also demonstrating significant impacts. Furthermore, three GM(1,N) prediction models which could estimate BP c with multi-factor coupling were developed. Increasing the number of input feature factors could enhance the predictive capability of GM model. The GM(1,16) had optimal prediction performance among the models, achieving a prediction accuracy of 99.8 %. As well, the mean absolute error (MAE) was 0.799 MPa while the mean absolute percentage error (MAPE) was 1.56 %. This paper offered a novel option for establishing the HST critical failure criterion safely and efficiently. • Obtained factors on design features, physicochemical, and external thermal load. • Clarified the main controlling factors which affected critical failure pressure. • Developed GM(1,N)-based models to predict critical failure pressure. • Suggestions on the safety of hydrogen storage tanks in service were proposed. [ABSTRACT FROM AUTHOR]

Numerous brain oscillations are well organized into several brain rhythms to support complex brain activities within distinct frequency bands. These rhythms temporally coexist in the same or different brain areas and may interact with each other with specific properties and physiological functions. However, the identification and evaluation of these various brain rhythms derived from BOLD-fMRI signals are obscure. To address this issue, we introduced a data-driven method named Complementary Ensemble Empirical Mode Decomposition (CEEMD) to automatically decompose the BOLD oscillations into several brain rhythms within distinct frequency bands. Thereafter, in order to evaluate the performance of CEEMD in the detection of subtle BOLD signals, a novel CEEMD-based high-dimensional pattern classification framework was proposed to accurately identify mild cognitive impairment individuals from the healthy controls. Our results showed CEEMD is a stable frequency decomposition method. Furthermore, CEEMD-based frequency specific topological profiles provided a classification accuracy of 93.33%, which was saliently higher than that of the conventional frequency separation based scheme. Importantly, our findings demonstrated that CEEMD could provide an effective means for brain oscillation separation, by which a more meaningful frequency bins could be used to detect the subtle changes embedded in the BOLD signals. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the differentiation efficiency of texture analysis of T1WI, T2WI and contrasted-enhanced T1WI MRI sequences in gliomas with and without IDH1 mutation based on entire tumor region.Materials and Methods: A total of 42 patients with histopathologically confirmed gliomas, including 21 patients carrying IDH1 mutation (IDH1mutation group) and 21 with wild-type IDH1 (IDH1wt group) were included in this study. The preoperative MRI and clinical data were collected. The regions of interest (ROIs) covering the entire tumor and edema were manually delineated on axial slices using O.K. (Omni Kinetics, GE Healthcare, China) software; and the histogram and GLCM features based on T1WI, T2WI and contrasted-enhanced T1WI sequences were automatically generated.Results: Based on contrasted-enhanced T1WI features, the inertia resulted as the best feature for diagnosis, with the AUC of 0.844. Furthermore, the AUC for gliomas prediction with IDH1mutation was 0.800 for cluster prominence. IDH1-mutation was differentiated on T2WI with the highest AUC of 0.848, which corresponded to GLCM Entropy. After modeling, the accuracy of the contrasted-enhanced T1WI, T1WI, and T2WI features model was 0.952, 0.857, and 0.738, respectively. The AUC of Joint VariableT1WI+C for predicting IDH1mutation was 0.984, while the AUC of Joint VariableT1WI for predicting the same mutation was 0.927. The diagnostic efficiency of Joint VariableT2WI was also desirable.Conclusion: MRI texture analysis could be used as a new noninvasive method for identification of gliomas with IDH1 mutation. The present results show that the Joint Variable derived from conventional MR imaging histogram and GLCM features is suitable for precise detection of IDH1-mutated gliomas. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the application of conventional MRI histogram analysis based on the whole tumor measurement on assessing meningioma grading.Materials and Methods: This retrospective study was approved by the institutional review board. A total amount of 90 patients with meningioma were enrolled and the preoperative MRI of them were analyzed. To be specific, the patient group were consisted of 45 patients with grade I, 38 with grade II, and 7 with grade III meningioma. Grade I meningioma is classified as low grade meningioma (LGM), whereas Grade II and III meningioma were combined and classified as high grade meningioma (HGM). ROIs were drawn along the edge of the tumor on each section of T1WI, T2WI, and contrasted T1WI. 3D ROI signal intensity histogram and all its parameters were obtained. Independent t-test and Kruskal-Wallis test were used for comparison between two groups. Univariate logistic regression analysis and Spearman's correlation analysis were used to screen for the parameters with high predictive efficiency, while multivariate logistic regression analysis was used to determine the optimal model for the classification of meningioma.Results: There were significant differences observed between HGM and LGM groups regarding to histogram volume count, uniformity of three sequences, range of T1WI and T2WI, kurtosis, standard deviation, variance, max intensity of T2WI, skewness, mean deviation, minimum intensity, mean value, the 5th percentile, the 10th percentile, the 25th percentile, the 50th percentile, the 75th percentile, and the 90th percentile of contrasted T1WI. Volume count and uniformity were high predictive parameters in distinguishing HGM from LGM. Logistic regression model included contrasted T1WI histogram parameters (i.e. minimum intensity, volume count, skewness, uniformity, and the 75th percentile) showed the best diagnostic efficiency for meningioma grade, with a sensitivity and specificity of 83.9% and 77.4% (AUC = 0.834, cutoff value = 0.413), respectively. The optimal model was achieved with a sensitivity of 71.4% and a specificity of 78.6% in the test set (AUC = 0.791, cutoff value = 0.413).Conclusions: Histogram analysis of conventional MRI based on 3D tumor measurement can be applied in the assessment of meningioma grading in clinical. [ABSTRACT FROM AUTHOR]

Purpose: This prospective study aimed to investigate the value of kinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating uterine endometrioid adenocarcinoma (EAC) from adenocarcinoma of cervix (AdC).Methods: Seventy-five newly diagnosed patients with distinctive pathology underwent DCE-MRI. Observers independently calculated the tumor diameters and DCE-MRI parameters using both population and individual-based arterial input function (AIF). Inter-observer consistency was evaluated, and a comparative analysis between EAC (n = 47) and AdC (n = 28) was performed. Regression analysis was used to select parameters that best distinguished EAC from AdC, and to generate predictive models. Receiver operating characteristic curve (ROC) was applied to calculate the diagnostic efficiency of single parameter and the predictive models.Results: Inter-observer consistency was excellent (intra-class correlation [ICC] = 0.902-0.981), especially when calculated via population AIF with relatively higher ICC and smaller SD on Bland-Altman plot. Tumor diameters were not correlated with tumor types. All the DCE-MRI parameters were lower in EAC compared to AdC, except Kep by population AIF and TTP by both sets of AIFs. The statistical parameters were Ve, Maxslop, and Maxconc by population AIF, and Maxslop and Ktrans by individual AIF included in the predictive models, respectively. The two predictive models with combined parameters showed improved diagnostic efficiency in differentiating these two diseases compared with a single parameter.Conclusion: DCE-MRI can quantitatively evaluate the perfusion difference between EAC and AdC, thus improving the identification of uterine adenocarcinoma with uncertain biopsy pathology. [ABSTRACT FROM AUTHOR]