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This paper proposes an integrated approach to group decision-making (GDM) by using stochastic preference analysis (SPA) and best-worst method (BWM). BWM preparation algorithm is proposed to obtain the best and worst of experts and the relative importance degree. Meanwhile, expert weights model and expert's priority vector model are proposed. Furthermore, the stochastic composite rank acceptability index, stochastic composite expected priority vector, stochastic composite expected rank and stochastic composite confidence factor are developed based on SPA to describe the ranks of alternatives based on SPA. Finally, a group stochastic preference analysis-best worst method (GSPA-BWM) algorithm is developed by analyzing the judgments space through Monte Carlo simulation. The experts can use this method to choose some of the outcomes which they find most useful to make reliable decisions. Examples and comparison analyses show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

In this article, a novel metaheuristic technique named Far and Near Optimization (FNO) is introduced, offering versatile applications across various scientific domains for optimization tasks. The core concept behind FNO lies in integrating global and local search methodologies to update the algorithm population within the problem-solving space based on moving each member to the farthest and nearest member to itself. The paper delineates the theory of FNO, presenting a mathematical model in two phases: (i) exploration based on the simulation of the movement of a population member towards the farthest member from itself and (ii) exploitation based on simulating the movement of a population member towards the nearest member from itself. FNO's efficacy in tackling optimization challenges is assessed through its handling of the CEC 2017 test suite across problem dimensions of 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results underscore FNO's adeptness in exploration, exploitation, and maintaining a balance between them throughout the search process to yield viable solutions. Comparative analysis against twelve established metaheuristic algorithms reveals FNO's superior performance. Simulation findings indicate FNO's outperformance of competitor algorithms, securing the top rank as the most effective optimizer across a majority of benchmark functions. Moreover, the outcomes derived by employing FNO on twenty-two constrained optimization challenges from the CEC 2011 test suite, alongside four engineering design dilemmas, showcase the effectiveness of the suggested method in tackling real-world scenarios. [ABSTRACT FROM AUTHOR]

The stochastic EXtended finite‐fault ground‐motion SIMulation algorithm (EXSIM) has been widely applied in simulating and predicting broadband strong ground‐motion. However, an increasingly number of researchers have found that EXSIM may overestimate ground‐motions at low frequencies for some large‐magnitude earthquakes and/or thrust earthquakes, for which the far‐field source model has been explained by a double‐corner‐frequency model. Despite controversy, the double‐corner‐frequency model is now being accepted as one of the main categories of the far‐field source model. This study demonstrated the limited applicability of EXSIM to earthquakes explained by the double‐corner‐frequency source model, by presenting the equivalence between motions generated by EXSIM and those generated by EXSIM's point‐source version, SMSIM, which adopts the ω‐square single‐corner‐frequency model. Furthermore, two improvements to EXSIM have been proposed: (a) the incorporation of the asperity‐distributed stress‐drop compound faults model and (b) the hybrid application of EXSIM with the proposed model. The effects of the two improvements have been verified by comparing EXSIM‐generating motions with recorded ground‐motions for the 2013 Mw 6.7 Lushan thrust earthquake. Significantly, consistent simulation accuracy has been achieved across high‐ and low‐frequency bands as well as in far‐ and near‐fields. The consistent accuracy of the improved EXSIM in simulating high‐ and low‐frequency ground motions enables its direct and independent application to broadband ground motion simulations. Moreover, the first validation of this consistent accuracy in both near‐ and far‐field scenarios further enhances its application in earthquake engineering practices. Key Points: EXtended finite‐fault ground‐motion SIMulation algorithm (EXSIM) could overestimate the low‐frequency ground motions of earthquakes with double‐corner‐frequency source model characteristicsTwo improvements to EXSIM were proposed: the asperity‐distributed stress‐drop compound faults model and the hybrid application of EXSIMThe improvements have been validated by comparing EXSIM‐generating motions with records for Mw 6.7 Lushan thrust earthquake [ABSTRACT FROM AUTHOR]

The article presents the results of the application of the stochastic method used to predict hydrocarbon-prospective geostructures of a concentric type within the Barents Sea region. According to the calculated values of stochastic parameters for the 21 geostructures of a concentric view in the waters of the Barents region, based on four different geoparameters a summary table of stochastic parameters was compiled and an encouraging forecast was made for 13 concentric geostructures promising for the search for new oil deposits within the Barents Sea region.

This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC. [ABSTRACT FROM AUTHOR]

This article presents models to predict median horizontal elastic response spectral accelerations for 5% damping from earthquakes with moment magnitudes ranging from 3.5 to 7.25 occurring in the United Kingdom. This model was derived using the hybrid stochastic-empirical method based on an existing ground-motion model for California and a stochastic model for the UK that was developed specifically for this purpose. The model is presented in two consistent formats, both for two distance metrics, with different target end-users. Firstly, we provide a complete logic tree with 162 branches, and associated weights, capturing epistemic uncertainties in the depth to the top of rupture, geometric spreading, anelastic path attenuation, site attenuation and stress drop, which is more likely to be used for research. The weights for these branches were derived using Bayesian updating of a priori weights from expert judgment. Secondly, we provide a backbone model with three and five branches corresponding to different percentiles, with corresponding weights, capturing the overall epistemic uncertainty, which is tailored for engineering applications. The derived models are compared with ground-motion observations, both instrumental and macroseismic, from the UK and surrounding region (northern France, Belgium, the Netherlands, western Germany and western Scandinavia). These comparisons show that the model is well-centred (low overall bias and no obvious trends with magnitude or distance) and that the branches capture the body and range of the technically defensible interpretations. In addition, comparisons with ground-motion models that have been previously used within seismic hazard assessments for the UK show that ground-motion predictions from the proposed model match those from previous models quite closely for most magnitudes and distances. The models are available as computer subroutines for ease of use. [ABSTRACT FROM AUTHOR]

Abstract The stochastic EXtended finite‐fault ground‐motion SIMulation algorithm (EXSIM) has been widely applied in simulating and predicting broadband strong ground‐motion. However, an increasingly number of researchers have found that EXSIM may overestimate ground‐motions at low frequencies for some large‐magnitude earthquakes and/or thrust earthquakes, for which the far‐field source model has been explained by a double‐corner‐frequency model. Despite controversy, the double‐corner‐frequency model is now being accepted as one of the main categories of the far‐field source model. This study demonstrated the limited applicability of EXSIM to earthquakes explained by the double‐corner‐frequency source model, by presenting the equivalence between motions generated by EXSIM and those generated by EXSIM's point‐source version, SMSIM, which adopts the ω‐square single‐corner‐frequency model. Furthermore, two improvements to EXSIM have been proposed: (a) the incorporation of the asperity‐distributed stress‐drop compound faults model and (b) the hybrid application of EXSIM with the proposed model. The effects of the two improvements have been verified by comparing EXSIM‐generating motions with recorded ground‐motions for the 2013 Mw 6.7 Lushan thrust earthquake. Significantly, consistent simulation accuracy has been achieved across high‐ and low‐frequency bands as well as in far‐ and near‐fields. The consistent accuracy of the improved EXSIM in simulating high‐ and low‐frequency ground motions enables its direct and independent application to broadband ground motion simulations. Moreover, the first validation of this consistent accuracy in both near‐ and far‐field scenarios further enhances its application in earthquake engineering practices.

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• The effect of phase ratio of DSS was investigated using a stochastic method. • The pitting potential at different sweep velocities obeyed a normal distribution. • DSS showed better corrosion resistance when the phase ratio was 1:1. • The phase ratio affected the passive current density more significantly. The effect of the phase ratio of austenite and ferrite on the pitting potential (E pit) and passive current density of ASTM A890 grade 3A duplex stainless steel (DSS) was investigated using a stochastic method. The cumulative probability of the E pit value of DSS at different potential sweep velocities was linear, indicating that the pitting potential obeyed a normal distribution. In addition, the relationship between the pitting potential and the potential sweep velocity at various phase ratios was fitted by E pit ∝ log(v). The increase in ferrite promoted the sensitivity of the DSS to the potential sweep velocity. When the phase ratio of austenite and ferrite was 1:1, it showed better corrosion resistance. The phase ratio affected the passive current density more significantly than pitting potential. [ABSTRACT FROM AUTHOR]