In this paper, the adaptive importance sampling (AIS) method is extended for hybrid reliability analysis under random and interval variables (HRA-RI) with small failure probabilities. In AIS, the ...design space is divided into random and interval variable subspaces. In random variable subspace, Markov Chain Monte Carlo (MCMC) is employed to generate samples which populate the failure regions. Then based on these samples, two kernel sampling density functions are established for estimations of the lower and upper bounds of failure probability. To improve the computational efficiency of AIS in cases with time-consuming performance functions, a combination method of projection-outline-based active learning Kriging and AIS, termed as POALK-AIS, is proposed in this paper. In this method, design of experiments is sequentially updated for the construction of Kriging metamodel with focus on the approximation accuracy of the projection outlines on the limit-state surface. During the procedure of POALK-AIS, multiple groups of sample points simulated by AIS are used to calculate the upper and lower bounds of failure probability. The accuracy, efficiency and robustness of POALK-AIS for HRA-RI with small failure probabilities are verified by five test examples.
•AIS method is extended for HRA-RI with small failure probabilities.•A combination method of POALK and AIS is proposed.•Approximation accuracy of projection outlines is improved by refining Kriging model.•The performance of POALK-AIS for HRA-RI with small failure probabilities is verified.
We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent components estimation (NICE), which we extend in numerous ways to ...improve performance and enable its application to integration problems. First, we introduce piecewise-polynomial coupling transforms that greatly increase the modeling power of individual coupling layers. Second, we propose to preprocess the inputs of neural networks using one-blob encoding, which stimulates localization of computation and improves inference. Third, we derive a gradient-descent-based optimization for the Kullback-Leibler and the χ
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divergence for the specific application of Monte Carlo integration with unnormalized stochastic estimates of the target distribution. Our approach enables fast and accurate inference and efficient sample generation independently of the dimensionality of the integration domain. We show its benefits on generating natural images and in two applications to light-transport simulation: first, we demonstrate learning of joint path-sampling densities in the primary sample space and importance sampling of multi-dimensional path prefixes thereof. Second, we use our technique to extract conditional directional densities driven by the product of incident illumination and the BSDF in the rendering equation, and we leverage the densities for path guiding. In all applications, our approach yields on-par or higher performance than competing techniques at equal sample count.
We report for the first time conical galls of Clinodiplosis capsici Gagné, 2000 (Diptera, Cecidomyiidae) on sweet pepper (Capsicum annuum L., Solanaceae) in Brazil. This report has ...agricultural importance since this midge is one of the known sweet-pepper pests.
The narcissism spectrum model synthesizes extensive personality, social–psychological, and clinical evidence, building on existing knowledge about narcissistic grandiosity and vulnerability to reveal ...a view of narcissism that respects its clinical origins, embraces the diversity and complexity of its expression, and reflects extensive scientific evidence about the continuity between normal and abnormal personality expression. Critically, the proposed model addresses three key, inter-related problems that have plagued narcissism scholarship for more than a century. These problems can be summarized as follows: (a) What are the key features of narcissism? (b) How are they organized and related to each other? and (c) Why are they organized that way, that is, what accounts for their relationships? By conceptualizing narcissistic traits as manifested in transactional processes between individuals and their social environments, the model enables integration of existing theories of narcissism and thus provides a compelling perspective for future examination of narcissism and its developmental pathways.
•The MTS network model based on the main ports and routes is established.•State of the post-disaster MTS is analyzed and the residual resilience is proposed.•Some residual resilience importance ...methods for the post-disaster MTS are given.•23 cities’ sea routes are used to demonstrate the applicability of the proposed method.
In maritime transportation system (MTS), ports and ocean routes are essential for establishing and maintaining effective international trade routes. However, the ability of the ports to send and receive goods can be easily destroyed by political and natural interferences. This will cause a significant negative socio-economic impact such as port operation suspension and route disruption. Effectively implementing resilience management in MTS can therefore improve its ability to handle interruptions and minimizing losses. Based on the post-disaster analysis, this paper proposes a new method to optimize the residual resilience management of ports and routes in MTS and proposes an optimal resilience model. The residual resilience is then applied to some importance measures. The Copeland method is used to comprehensively rank the importance of ports and routes. The restoration priority of interrupted ports and routes of different importance measures for the purpose of minimizing residual resilience is also studied. Sea routes consisting of 23 cities are used to demonstrate the applicability of the proposed method.
Importance–performance analysis (IPA) is extensively used in hospitality and tourism research because of its simplicity. However, due to the lack of critical statistical analysis, the traditional IPA ...framework is compromised by serious reliability and validity issues. Although many researchers have tried to address these problems, a comprehensive framework to guide researchers through the various stages of IPA is still needed. This study offers a research framework and a straightforward guide for the use of IPA. Experimental surveys are conducted to validate the proposed research framework.
•Offer a research framework and a straightforward guide for the use of IPA.•Provide solutions for some critical issues in conducting IPA studies.•Show how to perform IPA incorporating reliability and validity measures.•Conduct experimental surveys to validate the research framework.
The factors influencing residents health have become complex and intertwined with the development of economy and society. Traditional research with a single factor on health will not provide an ...accurate picture of the situation. This paper collects data on economic, environmental and social factors to estimate their impact on regional health. Considering the data is multi-source and complex, this paper proposes a combined feature importance algorithm, which weighted the feature importance of RF, XGB and SOIL. The algorithm does not depend on the data and adaptively approximates the true results. The results show that economic factors have a significant and direct impact on health, environmental factors have a lag correlation with health level, and social factors have a more complicated effect on health. Finally, we provide policy suggestions for health on economic, environmental, and social development.
Corporate financial distress prediction research has been ongoing for more than half a century, during which many models have emerged, among which ensemble learning algorithms are the most accurate. ...Most of the state-of-the-art methods of recent years are based on gradient boosted decision trees. However, most of them do not consider using feature importance for feature selection, and a few of them use the feature importance method with bias, which may not reflect the true importance of features. To solve this problem, a heuristic algorithm based on permutation importance (PIMP) is proposed to modify the biased feature importance measure in this paper. This method ranks and filters the features used by machine learning models, which not only improves accuracy but also makes the results more interpretable. Based on financial data from 4,167 listed companies in China between 2001 and 2019, the experiment shows that compared with using the random forest (RF) wrapper method alone, the bias in feature importance is indeed corrected by combining the PIMP method. After the redundant features are removed, the performance of most machine learning models is improved. The PIMP method is a promising addition to the existing financial distress prediction methods. Moreover, compared with traditional statistical learning models and other machine learning models, the proposed PIMP-XGBoost offers higher prediction accuracy and clearer interpretation, making it suitable for commercial use.
•The model Combines a corrected feature selection measure and XGBoost.•Permutation importance can correct the bias of feature importance.•The model is validated on Chinese listed companies datasets over five metrics.•The model is proved to outperform several benchmark techniques.•The feature importance and partial dependence plot enhance model interpretation.
Component importance measures are widely used in engineering and reliability analysis in testing the safety of running systems. There are several component importance measures to realize which ...components in a coherent system play a more important role than the others. The most common ones belong to Birnbaum and Barlow & Proschan. In this paper a new method, based on the number of path sets with exactly i working components such that the component at sth place functions, has been proposed to evaluate these two measures for all types of systems appearing in the literature.
•A novel cost-constrained reliability importance is proposed.•Mechanisms of importance to guide the design of optimization rules are explored.•A solving algorithm integrating the advantages of ...importance and GA is developed.
In the field of reliability engineering, importance measures are widely used to prioritize components within a system and facilitate the improvement of system performance. However, current multi-component importance measures, such as joint reliability importances (JRIs) and their extensions, do not fully account for the potential impact of limited resource constraints, which can significantly impede efforts to improve system reliability. To address this issue, this paper proposes a novel JRI of two components for the cost-constrained reliability optimization model (ROM), which incorporates constraint factors into the JRI calculation. This new JRI can be used to evaluate the interaction effect of two components on system reliability under cost constraints. Subsequently, a cost-constrained, ROM-based, mixed reliability importance (CRMRI) is introduced by integrating the features of single-component importance measures with the newly devised JRI. Given equivalent costs for improving each component, the CRMRI approach can identify the two components whose simultaneous improvement contributes the most to enhancing system reliability. Lastly, we develop a CRMRI-based genetic algorithm (CRMGA) to solve the cost-constrained ROM. Experimental results on systems of various scales demonstrate that CRMGA can produce superior solutions with faster convergence speed, enhanced robustness, and higher efficiency compared to other optimization algorithms.
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