Relaxation-based importance sampling for structural reliability analysis

This study presents an importance sampling formulation based on adaptively relaxing parameters from the indicator function and/or the probability density function. The formulation embodies the prevalent mathematical concept of relaxing a complex problem into a sequence of progressively easier sub-problems. Due to the flexibility in constructing relaxation parameters, relaxation-based importance sampling provides a unified framework for various existing variance reduction techniques, such as subset simulation, sequential importance sampling, and annealed importance sampling. More crucially, the framework lays the foundation for creating new importance sampling strategies, tailoring to specific applications. To demonstrate this potential, two importance sampling strategies are proposed. The first strategy couples annealed importance sampling with subset simulation, focusing on low-dimensional problems. The second strategy aims to solve high-dimensional problems by leveraging spherical sampling and scaling techniques. Both methods are desirable for fragility analysis in performance-based engineering, as they can produce the entire fragility surface in a single run of the sampling algorithm. Three numerical examples, including a 1000-dimensional stochastic dynamic problem, are studied to demonstrate the proposed methods

Seismic Reliability Analysis of Energy-dissipation Structures by PDEM-ETDM

Energy-dissipation devices have been widely used for improving the performance of civil structures exposed to seismic hazard. In this study, a hybrid approach, which combines the probability density evolution method (PDEM) and the explicit time-domain method (ETDM), is proposed for the seismic reliability analysis of large-scale energy-dissipation structures with uncertain parameters of nonlinear energy-dissipation devices subjected to random seismic excitations. To demonstrate the feasibility of the proposed approach, a dynamic reliability analysis under random seismic excitations is carried out for a suspension bridge with a main span of 1,200 m equipped with 4 nonlinear viscous dampers with uncertain parameters.The research is funded by the National Natural Science Foundation of China (51678252) and the Science and Technology Program of Guangzhou, China (201804020069)

RGB-NIR image categorization with prior knowledge transfer

Abstract Recent development on image categorization, especially scene categorization, shows that the combination of standard visible RGB image data and near-infrared (NIR) image data performs better than RGB-only image data. However, the size of RGB-NIR image collection is often limited due to the difficulty of acquisition. With limited data, it is difficult to extract effective features using the common deep learning networks. It is observed that humans are able to learn prior knowledge from other tasks or a good mentor, which is helpful to solve the learning problems with limited training samples. Inspired by this observation, we propose a novel training methodology for introducing the prior knowledge into a deep architecture, which allows us to bypass the burdensome labeling large quantity of image data to meet the big data requirements in deep learning. At first, transfer learning is adopted to learn single modal features from a large source database, such as ImageNet. Then, a knowledge distillation method is explored to fuse the RGB and NIR features. Finally, a global optimization method is employed to fine-tune the entire network. The experimental results on two RGB-NIR datasets demonstrate the effectiveness of our proposed approach in comparison with the state-of-the-art multi-modal image categorization methods.https://deepblue.lib.umich.edu/bitstream/2027.42/146762/1/13640_2018_Article_388.pd

Seismic Reliability Analysis of Complex Nuclear Power Plants by Explicit Time Domain Method

Seismic reliability evaluation is of great importance in nuclear power engineering. The task remains an open challenge since it will involve the dynamic reliability analysis of large-scale complex structures of nuclear power plants on a global structure level under random seismic excitations, and in particular in the presence of structural uncertainties. The traditional random vibration methods with coupling treatment of the physical and the probabilistic evolution mechanism are hardly capable of executing such a difficult task. In this study, the explicit time-domain method (ETDM) developed in recent years is applied to the seismic global reliability analysis of complex nuclear power plants in consideration of structural uncertainties. The time-domain explicit expressions of the critical responses involved are first constructed based on the impulse response functions, and on this basis, the subsequent random vibration and reliability analysis can then be conducted just focusing on the selected critical responses. The uncoupling treatment of the two sets of mechanism in ETDM will lead to a real-sense dimensional reduction in terms of degrees of freedoms and time instants involved in random vibration analysis of structures, and thus a high efficiency in dynamic reliability analysis even in the presence of large-scale structural models. The engineering application to a nuclear power plant with over 2 million degrees of freedom, which is now being built in China, shows the feasibility of the present approach.The research is funded by the National Natural Science Foundation of China (51678252) and the Science and Technology Program of Guangzhou, China (201804020069)

Analysis of forensic autopsy cases associated with epilepsy: Comparison between sudden unexpected death in epilepsy (SUDEP) and not-SUDEP groups

Background and aimsEpilepsy is a common and chronic neurological disorder characterized by seizures that increase the risk of mortality. SUDEP is the most common seizure-related category of death. The study aimed to evaluate the key characteristics between SUDEP and not-SUDEP death cases.MethodsA retrospective study of forensic autopsy cases from 2002 to 2021, performed by the Academy of Forensic Science (Ministry of Justice, China), identified a total of 31 deaths associated with epilepsy. We compared the different characteristics between individuals who died of SUDEP (SUDEP group) and individuals with epilepsy died suddenly due to unrelated causes (not-SUDEP group).Results and conclusions13 cases met the general accepted definition of SUDEP; and 18 cases were classified as not-SUDEP. The mean age of the not-SUDEP group was significantly higher than that of the SUDEP groups (p < 0.05) and there were more cases without a clear cause of epilepsy in the SUDEP group than in the not-SUDEP group (p < 0.05). Death position differed significantly between the two groups, with more cases dying in the prone position in the SUDEP group (p < 0.05). Complete autopsies were performed in 24 of the 31 cases. There were no significant differences in heart, lungs and brain weights, or in ventricular thickness (p > 0.05) between the SUDEP and not-SUDEP groups. In addition, compared to the not-SUDEP group, the SUDEP group featured a significantly more cases with coronary lesions (grades 1-3, p < 0.05). Neuropathological lesions were identified in 12 of the 13 SUDEP cases (92.3%), cardiac lesions were present in 10 cases (76.9%) and pulmonary edema and pulmonary congestion were present in all cases. The primary cause of death in 13 of the 31 cases was seizure disorder or epilepsy. The primary mechanism of death in SUDEP group was mainly asphyxia while that in the not-SUDEP group was cardiopulmonary failure (p < 0.05). Patients in the prone position had a significantly higher risk of asphyxia than those who were not. Here, we investigated the key characteristics between SUDEP and not-SUDEP death cases, which may help to facilitate forensic diagnosis in presumed SUDEP cases

Regional Homogeneity and Multivariate Pattern Analysis of Cervical Spondylosis Neck Pain and the Modulation Effect of Treatment

Objects: We investigated brain functional alteration in patients with chronic cervical spondylosis neck pain (CSNP) compared to healthy controls (HCs) and the effect of intervention.Methods: 104 CSNP patients and 96 matched HCs were recruited. Patients received 4 weeks of treatment. Resting-state fMRI and Northwick Park Neck Pain Questionnaire (NPQ) were collected before and after treatment. Resting state regional homogeneity (rs-ReHo) and multivariate pattern analysis (MVPA) were applied to (1) investigate rs-ReHo differences between CSNP patients and controls and the effect of longitudinal treatment and (2) classify CSNP patients from HCs and predict clinical outcomes before treatment using MVPA.Results: We found that (1) CSNP patients showed decreased rs-ReHo in the left sensorimotor cortex and right temporo-parietal junction (rTPJ), and rs-ReHo at the rTPJ significantly increased after treatment; (2) rs-ReHo at rTPJ was associated with NPQ at baseline, and pre- and post-treatment rs-ReHo changes at rTPJ were associated with NPQ changes in CSNP patients; and (3) MVPA could discriminate CSNP patients from HCs with 72% accuracy and predict clinical outcomes with a mean absolute error of 19.6%.Conclusion: CSNP patients are associated with dysfunction of the rTPJ and sensorimotor area.Significance: rTPJ plays on important role in the pathophysiology and development of CSNP

Joint analysis of three genome-wide association studies of esophageal squamous cell carcinoma in Chinese populations

We conducted a joint (pooled) analysis of three genome-wide association studies (GWAS) 1-3 of esophageal squamous cell carcinoma (ESCC) in ethnic Chinese (5,337 ESCC cases and 5,787 controls) with 9,654 ESCC cases and 10,058 controls for follow-up. In a logistic regression model adjusted for age, sex, study, and two eigenvectors, two new loci achieved genome-wide significance, marked by rs7447927 at 5q31.2 (per-allele odds ratio (OR) = 0.85, 95% CI 0.82-0.88; P=7.72x10−20) and rs1642764 at 17p13.1 (per-allele OR= 0.88, 95% CI 0.85-0.91; P=3.10x10−13). rs7447927 is a synonymous single nucleotide polymorphism (SNP) in TMEM173 and rs1642764 is an intronic SNP in ATP1B2, near TP53. Furthermore, a locus in the HLA class II region at 6p21.32 (rs35597309) achieved genome-wide significance in the two populations at highest risk for ESSC (OR=1.33, 95% CI 1.22-1.46; P=1.99x10−10). Our joint analysis identified new ESCC susceptibility loci overall as well as a new locus unique to the ESCC high risk Taihang Mountain region

Non-stationary non-Gaussian random vibration analysis of Duffing systems based on explicit time-domain method

Non-stationary non-Gaussian random vibration problems of structures are challenging and drawing increasing attention. In the present study, firstly, an explicit time-domain method (ETDM) is proposed to determine the higher-order response statistics of linear systems subjected to non-stationary non-Gaussian random excitations, in which the first four orders of cumulants of dynamic responses are directly formulated through the cumulant operation rule based on the explicit expressions of responses. Secondly, an equivalent linearization – explicit time-domain method (EL-ETDM) is further developed to solve the non-stationary non-Gaussian random vibration problems of Duffing systems, in which the equivalent linear system is derived discarding the assumption of Gaussian response, and the corresponding higher-order cumulant analyses of the linearized system are accomplished by the efficient ETDM. The present approach can account for non-Gaussian random excitations with arbitrary forms, and two specific applications to the Poisson white noise and the square form of Gaussian random process are investigated. Four numerical examples are presented to demonstrate the effectiveness of the proposed methods

Improved estimation of fixed effects panel data partially linear models with heteroscedastic errors

Fixed effects panel data regression models are useful tools in econometric and microarray analysis. In this paper, we consider statistical inferences under the setting of fixed effects panel data partially linear regression models with heteroscedastic errors. We find that the usual local polynomial estimator of the error variance function based on residuals is inconsistent, and develop a consistent estimator. Applying this consistent estimator of error variance and spline series approximation of the nonparametric component, we further construct a weighted semiparametric least squares dummy variables estimator for the parametric and nonparametric components. Asymptotic normality of the proposed estimator is derived and its asymptotic covariance matrix estimator is provided. The proposed estimator is shown to be asymptotically more efficient than those ignoring heteroscedasticity. Simulation studies are conducted to demonstrate the finite sample performances of the proposed procedure. As an application, a set of economic data is analyzed by the proposed method.16 page(s