Quantitative Method of the Structural Damage Identification of Gas Explosion Based on Case Study:The Shanxi “11. 23” Explosion Investigation

In order to present a retrospective analysis of exposition accidents using input data from investigation processes,data from a specific accident was examined,in which we analyzed possible involved gas species（ liquefied petroleum gas; nature gas） and computed their concentrations and distributions based on the interactions between the structures and the effects of the explosion. In this study,5 scenarios were created to analyze the impact effect. Moreover,a coupling algorithm was put into practice,with a practical outflow boundary and joint strength are applied. Finally,the damage effects of each scenario were simulated. Our experimental results showed significant differences in the 5 scenarios concerning the damage effects on the building structures. The results from scenario 3 agree with the accident characteristics,demonstrating the effectiveness of our proposed modeling method. Our proposed method reflects gas properties,species and the concentration and distribution,and the simulated results validates the root cause,process,and consequences of accidental explosions. Furthermore,this method describes the evolution process of explosions in different building structures. Significantly,our model demonstrates the quantatative explosion effect of factors like gas species,gas volumes,and distributions of gases on explosion results. In this study,a feasible,effective,and quantitative method for structure safety is defined,which is helpful to accelerate the development of safer site regulations.

Feasibility and value of quantitative dynamic contrast enhancement MR imaging in the evaluation of sinonasal tumors

Background Quantitative dynamic contrast enhancement MR imaging （DCE-MRI）,used to measure properties of tissue microvasculature and tumor angiogenesis,is a promising method for distinguishing benign and malignant tumors and characterizing tumor response to antiangiogenic treatment.The aim of this study was to assess the feasibility of quantitative parameters derived from clinically used DCE-MRI for distinguishing benign from malignant tumors in the sinonasal area,which may be potentially useful for prediction and monitoring of treatment response to chemoradiotherapy of sinonasal tumors.Methods One hundred and forty-three patients with sinonasal tumors,including 78 malignant tumors and 65 benign tumors and tumor-like lesions,underwent clinically used DCE-MRI.Parametric maps were obtained for quantitative parameters including Ktrans,kep and ve.Two radiologists reviewed these maps and measured Ktrans,kep and ve in the tumor tissue.Data were analyzed using independent T-test or Mann-Whitney U test analysis and receiver operating characteristic curves.Results Ktrans,kep and ve showed significant differences between benign and malignant tumors in the sinonasal area （P=-0.000 1）.The accuracy of Ktrans,kep and ve in differentiation between benign and malignant sinonasal tumors were 72.0％,76.2％ and 67.1％,respectively.There were significant differences in kep and ve between malignant epithelial sinonasal tumors and lymphomas （P ＜0.05）.Using a ve value of 0.213 as the threshold value differentiated malignant epithelial tumors from lymphomas with an accuracy of 78.3％,sensitivity of 88.2％,specificity of 68.0％,positive predictive value of 66.7％,and negative predictive value of 90.9％.However,no significant difference in Ktrans and kep was found between malignant epithelial and non-epithelial tumors in the sinonasal area （P ＞0.05）.Conclusions It is feasible that quantitative parameters of tumors can be derived from clinically used DCE-MRI in the sinonasal region.Preliminary findings suggest an increased value for quantitative DCE-MRI in the evaluation of sinonasal tumors in clinical practice.

Effect of a high strength chemical industry wastewater on microbial community dynamics and mesophilic methane generation

A high strength chemical industry wastewater was assessed for its impact on anaerobic microbial com- munity dynamics and consequently mesophilic methane generation. Cumulative methane production was 251 mL/g total chemical oxygen demand removed at standard temperature and pressure at the end of 30 days experimental period with a highest recorded methane percentage of 80.6% of total biogas volume. Volatile fatty acids （VFAs） analysis revealed that acetic acid was the major intermediate VFAs produced with propionic acid accumulating over the experimental period. Quantitative analysis of microbial communities in the test and control groups with quantitative real time polymerase chain reaction highlighted that in the test group, Eubacteria （96.3%） was dominant in comparison with methanogens （3.7%）. The latter were dominated by Methanomicrobiales and Methanobacteriales while in test groups increased over the experimental period, reaching a maximum on day 30. Denaturing gradient gel electrophoresis profile was performed, targeting the 16S rRNA gene of Eubacteria and Archaea, with the DNA samples extracted at 3 different time points from the test groups. A phylogenetic tree was constructed for the sequences using the neighborhood joining method. The analysis revealed that the presence of organisms resembling Syntrophomonadaceae could have contributed to increased production of acetic and propionic acid intermediates while decrease of organisms resembling Pelotomaculum sp. could have most likely contributed to accumulation of propionic acid. This study suggested that the degradation of organic components within the high strength industrial wastewater is closely linked with the activity of certain niche microbial communities within eubacteria and methanogens.

Correlation of quantitative perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and subtypes of breast carcinoma

Objective To investigate whether correlation existsbetween quantitative perfusion parameters obtained from dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and different prognostic factors or immunohistochemical subtypes of breast cancers.Methods A retrospective analysis of DCE-MRI was performed in