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The estimation of the seismic vulnerability of regional structures has contributed positively to the development of seismic risk and resilience in large-scale building clusters. However, the empirical structural seismic damage and ground motion data used to validate the risk model are relatively outdated, resulting in insufficient evaluation accuracy of the established risk model and an urgent use for retrofitting. This study analysed the actual failure peculiarities of typical structures during a recent typical destructive earthquake in China (the Luding earthquake in Sichuan Province on September 5, 2022). A total of 300,000 acceleration records from ten actual monitoring seismic stations were processed and analysed. Nonlinear dynamic time history curves, response spectra, and combined seismic acceleration cloud images were generated considering different directions. Moreover, the traditional instrument intensity calculation equation has been updated, and an instrument intensity curve bunch considering 300,000 earthquake acceleration records has been developed. To study the probabilistic seismic risk of large-scale regional buildings, the developed vulnerability database of typical regional structures (Sichuan Province) was supplemented and optimized, and an updated vulnerability database (413 damage probability matrix) was established, considering the failure of five structures in 32 destructive earthquakes. An updated structural seismic vulnerability model based on extended vulnerability grades was proposed, and a mean vulnerability probability matrix based on structural risk datasets was established. A membership index function for estimating the correlation between different vulnerability grades was innovatively proposed, and the rationality of the model was verified using an improved empirical dataset. A multidimensional failure prediction model was developed based on an improved mean seismic damage index. [ABSTRACT FROM AUTHOR]

Seismic risk estimation for urban building clusters is essential when developing regional seismic resilience models. A masonry structure is a type of building with a large stock, broad applicability, and simplified construction technology. The traditional seismic intensity measures and fuzzy quantification of structural vulnerability levels result in low accuracy in estimating and predicting seismic damage to masonry structure clusters. This paper proposes a method for evaluating bivariate seismic intensity measure. Nonlinear dynamic and spectral analyses were conducted on real acceleration records monitored by ten stations during the Wenchuan earthquake in Sichuan, China, on May 12, 2008. The outdated seismic vulnerability level of masonry structures is updated, and an optimized structural seismic risk model based on the seismic damage database of 2407 masonry structures in Dujiangyan city under the influence of the Wenchuan earthquake is established. An innovative seismic risk membership index algorithm was developed, and a vulnerability curve was generated for masonry structures considering multiple risk membership indices. An improved seismic risk index calculation function is proposed using the proposed updated vulnerability level, and the seismic risk index curve and stripe model of typical urban masonry building clusters are established. According to the field investigation of the actual structures in Dujiangyan city affected by the Wenchuan earthquake, the typical failure features and disaster mechanism of masonry structures are reported and analysed, and measures and methods to improve and enhance the seismic capacity of such buildings are proposed. The developed seismic risk model for urban masonry structures can contribute positively to the development of relatively accurate seismic resilience models for urban buildings and can improve the accuracy of seismic risk estimation for masonry structures. [ABSTRACT FROM AUTHOR]

The Panxi area in Sichuan Province is the main area for the production of truffles in China, and several species of truffle are known to exist in this region. Nevertheless, it is unclear what the differences in chemical composition between the truffles are. Using an ultra‐high‐performance liquid chromatography quadrupole/orbitrap high‐resolution mass spectrometry coupled with Compound Discoverer 3.0, we identified chemical components in three mainly known truffles from the Panxi region. Further analysis of chemical composition differences was conducted using principal component analysis, and orthogonal partial least squares discriminant analysis. Note that, 78.9% of the variance was uncovered by the principal component analysis model. As a result of the orthogonal partial least squares discriminant analysis model, the three species of truffles (Tuber pesudohimalayense, Tuber indicum, and Tuber sinense) from Panxi were better discriminated, with R2X, R2Y, and Q2 being 0.821, 0.993, and 0.947, respectively. In this study, 87 components were identified. T. pesudohimalayense contained significantly higher levels of nine different compounds than the other two species. Hence, it was possible to identify similarities and differences between three species of truffles from Panxi in terms of chemical composition. This can be used as a basis for quality control. [ABSTRACT FROM AUTHOR]

Probability-based seismic risk and random ground motion theories are used to develop high-precision seismic risk functions and models of regional building portfolios. Seismic intensity measures and structural demand parameters are critical in developing seismic fragility models for building clusters. Earthquake damage data indicate that ambient temperature may influence structural fragility, but the effect of temperature on seismic risk is commonly ignored. A method for quantifying seismic intensity is proposed in this study and optimized using 450,000 acceleration records detected by 15 stations during the Luding earthquake in Sichuan, China, on September 5, 2022. Considering the influence of different temperature fields, the proposed seismic intensity quantification method was used to evaluate structural damage during two destructive earthquakes in Xinjiang, China (the Aktao earthquake on September 2, 2003 and the Zhaosu earthquake on December 1, 2003). Three structural empirical seismic risk matrices and cloud maps were developed, and a vulnerability membership curve was generated using fuzzy decision-making, a membership algorithm, and nonlinear regression. A structural seismic risk index function was proposed, and vulnerability cloud diagrams and curves based on actual structural seismic vulnerability datasets were generated. Together, these analyses demonstrated the impact of temperature and intensity measures on the seismic risk to regional buildings. A model was established to evaluate the seismic risk to typical structures and was validated using historical earthquake damage data from China. This method improves the accuracy of earthquake risk and vulnerability assessments for typical building clusters given the influence of temperature. The developed seismic risk prediction model enables the evaluation of vulnerability and resilience of typical structures under low and high temperatures. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to develop a nomogram model based on multiparametric magnetic resonance imaging (MRI) radiomics features, clinicopathological characteristics, and blood parameters to predict the progression-free survival (PFS) of patients with nasopharyngeal carcinoma (NPC). Methods: A total of 462 patients with pathologically confirmed nonkeratinizing NPC treated at Sichuan Cancer Hospital were recruited from 2015 to 2019 and divided into training and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomics feature dimension reduction and screening in the training cohort. Rad-score, age, sex, smoking and drinking habits, Ki-67, monocytes, monocyte ratio, and mean corpuscular volume were incorporated into a multivariate Cox proportional risk regression model to build a multifactorial nomogram. The concordance index (C-index) and decision curve analysis (DCA) were applied to estimate its efficacy. Results: Nine significant features associated with PFS were selected by LASSO and used to calculate the rad-score of each patient. The rad-score was verified as an independent prognostic factor for PFS in NPC. The survival analysis showed that those with lower rad-scores had longer PFS in both cohorts (p < 0.05). Compared with the tumor–node–metastasis staging system, the multifactorial nomogram had higher C-indexes (training cohorts: 0.819 vs. 0.610; validation cohorts: 0.820 vs. 0.602). Moreover, the DCA curve showed that this model could better predict progression within 50% threshold probability. Conclusion: A nomogram that combined MRI-based radiomics with clinicopathological characteristics and blood parameters improved the ability to predict progression in patients with NPC. [ABSTRACT FROM AUTHOR]

An optimized macroseismic intensity index, which integrates the traditional macroseismic intensity scale and the instrument intensity evaluation model, is proposed to evaluate regional buildings' empirical resilience and vulnerability. The traditional failure probability of the group structure is modified and improved. The parameters and probability indexes used to assess the resilience and vulnerability of group structure in urban and rural areas are innovatively proposed. The empirical group structure seismic loss data from the actual field investigation of the Jiuzhaigou earthquake in Sichuan Province, China, on August 8, 2017, were statistically analysed, the vulnerability of the sample data was assessed by using the optimized Chinese seismic intensity scale (OCSIS-20), European Macroseismic Scale (EMS-98) and the Modified Mercalli Intensity (MMI), and the assessment and comparison model of multidimensional modal resilience and probability index was established. To improve the rationality and accuracy of typical building resilience in the macrointensity quantification region, a new quantitative model of resilience and vulnerability based on optimizing the macrointensity index and updating the lognormal distribution is proposed, and the group structure resilience comparison models for typical macrointensity standard evaluation are established. The structural vulnerability database of the Jiuzhaigou earthquake is utilized to verify the rationality of the innovative model. Ultimately, field loss inspection and damage mechanism analysis are conducted based on the characteristics of regional structural seismic resilience and the actual vulnerability of the Jiuzhaigou earthquake. • The rapid resilience and vulnerability prediction and evaluation domain, plane, surface, and matrix based on quantity and failure index parameters are established. • An innovative regional domain vulnerability and resilience assessment model is conducted. • A new quantitative model of resilience and vulnerability based on optimizing the macrointensity index and updating the lognormal distribution is proposed. [ABSTRACT FROM AUTHOR]