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• An efficient framework for structural time-variant reliability analysis is proposed. • The adaptive accuracy of outcrossing rate is provided by the FOTRE method. • The explicit formulation of reliability index and sensitivity direction is given. Time-variant reliability problems are frequently encountered in engineering due to factors like material degradation or random loading modeled as random processes. The PHI2 method, which employs the First Order Reliability Method (FORM), is commonly used to solve such problems. However, it requires repeated searches for Most Probable Points (MPPs), making it computationally expensive. To improve efficiency with little sacrifice of accuracy, this study proposes a First Order Time-variant Reliability Expansion (FOTRE) method, which provides an efficient explicit formulation for MPP regarding time, in contrast to the expensive optimization approach of the PHI2 method. It requires only a single accurate search for the so-called "worst MPP" over the whole lifespan and offers the " adaptive accuracy of outcrossing rate ", which avoids the repeated search for MPPs ensuring computational accuracy. The inspiration behind the FOTRE method stems from the observation that the outcrossing rate tends to be small at time points with relatively large reliability indexes compared to the minimum reliability index β min , which has a negligible impact on the subsequent structural failure probability over the entire lifespan. This innovative approach significantly improves the efficiency of solving time-variant reliability problems without compromising much of the numerical accuracy. The effectiveness and accuracy of the FOTRE method are demonstrated through several numerical examples. [ABSTRACT FROM AUTHOR]

During the decommissioning process of nuclear facilities, workers are exposed to radiation and face the risk of exceeding safe dose limits. Ensuring the safety of personnel requires not only enhancing radiation protection measures but also optimizing work paths to minimize exposure time and avoid high-radiation areas. This paper proposes a nested optimization algorithm that combines an ant colony optimization (ACO) with an improved A* algorithm for the decommissioning of a nonradiation source. The algorithm aims to minimize the total radiation dose and transforms the original path optimization problem into an equivalent traveling salesperson problem. The improved A* algorithm is employed in the inner layer to calculate the path with the lowest radiation dose for any given sales order. The ACO operates in the outer layer to determine a set of optimal working paths that traverse all target points. The provided solution example demonstrates that the proposed path optimization algorithm effectively integrates the radiation field and obstacles. It successfully identifies a sequence for dismantling with the lowest dose and corresponding optimal work path while ensuring the completion of the dismantling task. These findings are expected to offer valuable insights for optimizing personnel work paths during the subsequent decommissioning process of nuclear facilities. [ABSTRACT FROM AUTHOR]

• The spatially uncertain parameters are described by the interval field model with upper and lower bounds. • An interval iterative method is proposed for static response analysis of structures with spatial uncertainties which can effectively avoid the overestimation existing in traditional interval analysis. • The upper and lower bounds of the responses obtained by the proposed method could envelop the actual response bounds. Parameters with spatial uncertainties are very common in practical engineering, which could have a significant effect on structural performances. Different from the framework of probability theory, the interval field model describes the spatial uncertainty with upper and lower bounds rather than the probability distribution function or other statistical characteristics. By introducing the interval field model into finite element analysis and solving the resulting interval finite element equilibrium equation, the upper and lower bounds of structural responses can then be obtained. This paper proposes an interval iterative method for static displacement response analysis of structures with spatial uncertainties, effectively improving the overestimation existing in traditional interval analysis due to dependency problems and obtaining a compact interval envelope of the exact response bounds. Firstly, the spatially uncertain parameters described by the interval field are represented by the interval Karhunen–Loève (K-L) expansion, based on which the interval finite element equilibrium equation is formulated. Secondly, the interval finite element problem is decomposed into several subproblems, and an interval iterative method is developed for an envelope solution of the structural response bounds. Finally, the accuracy and efficiency of the proposed method are verified by several numerical examples. [ABSTRACT FROM AUTHOR]

• A transient temperature response analysis method based on the interval K-L expansion is proposed. • The time-variant uncertain parameters are quantified by an interval process model due to lack of sufficient sample data. • The analytical solution for the upper and lower bounds of the transient temperature responses are given. A transient temperature response analysis method for heat transfer problems with dynamic uncertain parameters is proposed. The time-variant or dynamic uncertain parameters are quantified by an interval process model using only the variation bounds of uncertainty, providing an effective way for problems where probabilistic modeling of the dynamic uncertainties is unfeasible due to lack of sufficient sample data. The interval Karhunen–Loève (K-L) expansion is adopted to represent the interval processes, thus describing accurately the continuous uncertainty over time by a series of interval variables. For convenience of solution, the Laplace transformation is then applied to transform the heat transfer uncertainty analysis over time domain to the complex field, where the analytical solution for the upper and lower bounds of the transient temperature responses can be obtained. Finally, the computational effectiveness and efficiency of the proposed method are verified by three typical numerical examples. [ABSTRACT FROM AUTHOR]

The Reliability-Based Design Optimization (RBDO) provides an effective way to obtain the optimum design in the presence of random uncertainties which follow the precise probability distribution function in the structural optimization design. However, in many practical engineering problems, the probability distribution which describes the stochastic nature of the uncertainties cannot be precisely obtained due to limited information. To quantify this kind of imprecise uncertainties, a probability-interval hybrid model emerged, where all uncertain variables are treated as random variables while some distribution parameters can only be given variation intervals. For such kind of uncertainties, this paper establishes a hybrid reliability-based design optimization model and proposes a single-loop solution algorithm. The interval parameters lead to an interval of reliability for each constraint function, thus giving rise to a triple-loop optimization problem for the hybrid RBDO, which is difficult to solve due to the unaffordable computational effort and the hinder of convergency. In this paper, the Karush–Kuhn–Tucker (KKT) optimality conditions of the inner loops are imposed as equivalent deterministic equality constraints. The original triple-loop optimization is thus converted into an equivalent single-loop problem, which alleviates the computational demand significantly. The efficiency and accuracy of the proposed Single-Loop Method (SLM) is verified through several practical engineering problems. [ABSTRACT FROM AUTHOR]

This paper presents an effective sequence interval and correlation inverse strategy for the uncertain inverse problem, aiming to identify the uncertainties and non-probabilistic correlations of the structural parameters simultaneously. First, an ellipsoidal convex model is adopted to quantify the uncertainty boundary of the measured responses with limited samples. Then, the uncertain inverse problem based on the ellipsoidal convex model is decoupled into an interval inverse problem and a correlation inverse problem. For the interval inverse problem, a subinterval decomposition analysis method constrained by the ellipsoidal convex model is developed to evaluate the intervals of the structural responses with a low computational cost. For the correlation inverse problem, the correlation propagation equations are derived to calculate the non-probabilistic correlation coefficient matrix of the structural responses. After that, by using optimization algorithms to circularly reduce the errors of the intervals and the correlation coefficients between the measured responses and calculated structural responses, the intervals and the non-probabilistic correlation coefficient matrix of the structural parameters are identified effectively, and an ellipsoidal convex model of the structural parameters can be established eventually. Two numerical examples and one experimental example are investigated to verify the effectiveness and accuracy of the proposed sequence interval and correlation inverse strategy. [ABSTRACT FROM AUTHOR]

• Correlation propagation method is proposed to obtain the uncertain responses domain. • The ME model is utilized to quantify the uncertainties in parameters and responses. • Subinterval decomposition analysis method is adopted to evaluate response intervals. • The non-probabilistic correlation propagation equations are theoretically derived. • The propagated correlations are completely accurate for the second-order systems. Traditional non-probabilistic methods for uncertainty propagation problems evaluate only the lower and upper bounds of structural responses, lacking any analysis of the correlations among the structural multi-responses. In this paper, a new non-probabilistic correlation propagation method is proposed to effectively evaluate the intervals and non-probabilistic correlation matrix of the structural responses. The uncertainty propagation process with correlated parameters is first decomposed into an interval propagation problem and a correlation propagation problem. The ellipsoidal model is then utilized to describe the uncertainty domain of the correlated parameters. For the interval propagation problem, a subinterval decomposition analysis method is developed based on the ellipsoidal model to efficiently evaluate the intervals of responses with a low computational cost. More importantly, the non-probabilistic correlation propagation equations are newly derived for theoretically predicting the correlations among the uncertain responses. Finally, the multi-dimensional ellipsoidal model is adopted again to represent both uncertainties and correlations of multi-responses. Three examples are presented to examine the accuracy and effectiveness of the proposed method both numerically and experimentally. [ABSTRACT FROM AUTHOR]

In this paper, an electromagnetic stochastic finite element method is presented to calculate the statistical moments of electromagnetic problems with spatially uncertain dielectric parameters. First, the random field model of the dielectric material is represented by the Karhunen–Loève expansion and inserted into the scalar Helmholtz wave equations. The stochastic equilibrium equations are thus constructed according to the node-based finite element method. Second, the first-order and second-order perturbation stochastic finite element methods are developed for calculating the statistical moments of electromagnetic responses. Finally, the feasibility and validity of the proposed method are verified by three numerical examples. [ABSTRACT FROM AUTHOR]

Background: In response to China's rapidly aging population and increasing healthcare service demands, the Chinese government is developing a universal medical insurance system. This study aimed to assess healthcare utilization patterns and analyze the impacts of medical insurance schemes on healthcare utilization among the middle-aged and elderly in China.Methods: Data was extracted from the China Health and Retirement Longitudinal Study in 2011, 2013 and 2015. Healthcare utilization was measured by outpatient and inpatient service utilization. Univariate analysis was deployed to examine the impacts of different medical insurance schemes on healthcare utilization. The factors associated with healthcare utilization were estimated using a random-effects logistic regression model.Results: During the study period, the number of individuals involved was 17,250, 18,195 and 19,842, respectively. The proportion of individuals who received outpatient service was 18.6, 20.7 and 18.7% and those who used inpatient service was 9.6, 13.8 and 14.3%, respectively. We identified that medical insurance was a major protective factor for improving healthcare utilization but different medical insurance schemes exerted various impacts on the middle-aged and the elderly.Conclusions: Despite the growing population coverage, the Chinese government should make every effort to bridge the gap among people with different medical insurance schemes. Further evaluation is needed to assess whether the expanded medical insurance schemes could protect the middle-aged and elderly households from catastrophic health expenditure. [ABSTRACT FROM AUTHOR]

• A more general parallelotope-formed evidence theory model is proposed for uncertainty quantification considering correlation. • The constructed frame of discernment and joint focal element can reflect a same degree of correlation. • The proposed model can transform the correlated evidence variables into the uncorrelated evidence variables. • The proposed model can handle the correlated and uncorrelated evidence variables within a unified framework. Due to the flexibility of evidence framework, evidence theory is recognized as a more general uncertainty quantification tool. However, the traditional evidence theory model only can deal with the uncorrelated evidence variables, which restricted its applicability in practical engineering problems. In this paper, a concept of evidence correlation coefficient is firstly defined to characterize the correlation between evidence variables. In view of that, a new parallelotope-formed evidence theory model which consists of the parallelotope-formed frame of discernment and the parallelotope-formed joint focal elements with basic probability assignments is proposed for effectively quantifying the correlated and uncorrelated evidence variables. Because of the consistency of frame of discernment and joint focal elements, the parallelotope-formed evidence theory model can freely realize affine transformation from correlated evidence variables to uncorrelated evidence variables, and can deal with the correlated and uncorrelated evidence variables in unified framework. Therefore, for the structural uncertainty quantification based on the proposed parallelotope-formed evidence theory model, the calculation process can be implemented in the transformed uncorrelated evidence space, and then the belief and plausibility measures can be conveniently obtained. Finally, two numerical examples and one engineering application are utilized to demonstrate the validity of the proposed parallelotope-formed evidence theory model. [ABSTRACT FROM AUTHOR]

Rare diseases are an important global public health issue. One significant challenge is to ensure the access to orphan drugs for patients with rare disease. This study aims to evaluate the accessibility of orphan drugs in China. Information pertaining to the availability and costs of each orphan drug in each hospital was obtained from the Chinese Medicine Economic Information database during 2011-2017. We measured the accessibility of orphan drugs from 3 aspects: availability, daily costs, and affordability to patients.The market availability rate of orphan drugs in China was 28.8% by June 30, 2017. The median availability rate at the hospital level was less than 15% but was increasing over time. The cost of a defined daily dose of orphan drugs varied significantly with a decreasing trend in all areas. Less than half of all surveyed orphan drugs had a cost of a defined daily dose no more than residents' average daily income.This study reveals the challenges of access to orphan drugs in China. The availability of marketed orphan drugs in China was relatively low and most orphan drugs placed a heavy financial burden on patients with rare disease. It is necessary to develop legislation for orphan drugs and encourage domestic generics. [ABSTRACT FROM AUTHOR]

In this paper, a new non-probabilistic reliability-based topology optimization (NRBTO) method is proposed to account for interval uncertainties considering parametric correlations. Firstly, a reliability index is defined based on a newly developed multidimensional parallelepiped (MP) convex model, and the reliability-based topology optimization problem is formulated to optimize the topology of the structure, to minimize material volume under displacement constraints. Secondly, an efficient decoupling scheme is applied to transform the double-loop NRBTO into a sequential optimization process, using the sequential optimization & reliability assessment (SORA) method associated with the performance measurement approach (PMA). Thirdly, the adjoint variable method is used to obtain the sensitivity information for both uncertain and design variables, and a gradient-based algorithm is employed to solve the optimization problem. Finally, typical numerical examples are used to demonstrate the effectiveness of the proposed topology optimization method. [ABSTRACT FROM AUTHOR]

• A hybrid reliability-based heat dissipation structural design optimization model is established for the practical heat dissipation problem with the interval distribution parameters. • Two equivalent triple-loop hybrid reliability-based heat dissipation structural design optimization models are established. • An efficient single-loop method is proposed to solve hybrid reliability-based heat dissipation structural design optimization the model. The conventional reliability-based heat dissipation structural design optimization, which fully considers the influence of the random uncertainties in the optimization procedure, can provide an optimum design satisfying the reliability requirements of the heat radiating device. However, in practical heat dissipation design problems, especially in the early stage of structural design which lacks the corresponding experimental data, some crucial distribution parameters of the random uncertainties, may not be determined precisely. This paper establishes a hybrid reliability-based heat dissipation structural design optimization model for the problem with limited information based on a kind of probability-interval hybrid quantification model and proposes a single-loop method to solve this model efficiently. In this optimization model, the interval parameters coupled to the random uncertainties lead to an interval of reliability for each constraint function, thus giving rise to a triple-loop optimization problem. The proposed single-loop method firstly converts the original nested triple-loop optimization model into an equivalent double-loop optimization model through monotonic analysis. Then, the Karush-Kuhn-Tucker (KKT) optimality conditions of the inner loop are enforced to convert the double-loop model into an equivalent single-loop optimization model. Through this treatment, the original triple-loop optimization model can be then solved by a series of design deterministic optimization and the computational demand can be alleviated significantly. The efficiency and accuracy of the proposed single-loop method are verified through several numerical problems. [ABSTRACT FROM AUTHOR]