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This paper proposes a precise and general multiple linear regression (MLR) model to predict the unconfined compressive strength (UCS) of various soil types. The study used a wide range of data sets, including 952 data points considering 39 soil types with varying grain sizes. The model inputs were soil physical properties, grain size, age, mixture proportion, and chemical composition of binder materials. An innovative and novel approach was developed to enhance the accuracy of the MLR model, a randomized exploratory algorithm. The model demonstrated significant accuracy with a 0.921 R2 in the testing data set. The Bayesian model averaging (BMA) method was employed for feature reduction, focusing on important variables. Alternative models were also developed based on the significant variables highlighted by the BMA approach, all showing high accuracy in predicting the UCS. The proposed models demonstrated superiority over traditional approaches based on the data set size and statistical metrics. The paper provides instances of predicting soil UCS and determining the mix design corresponding to the target UCS. [ABSTRACT FROM AUTHOR]

In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that occurred a long time ago–a phenomenon known as "memory decay". Previous studies either predefined a short-run and long-run memory or fixed a parametric exponential memory decay using a predefined half-life period. In real-life relational event networks, however, it is generally unknown how the influence of past events fades as time goes by. For this reason, it is not recommendable to fix memory decay in an ad-hoc manner, but instead we should learn the shape of memory decay from the observed data. In this paper, a novel semi-parametric approach based on Bayesian Model Averaging is proposed for learning the shape of the memory decay without requiring any parametric assumptions. The method is applied to relational event history data among socio-political actors in India and a comparison with other relational event models based on predefined memory decays is provided. [ABSTRACT FROM AUTHOR]

As a fundamental factor driving economic growth, natural capital plays a key role in sustainable development. This paper incorporates various economic growth proxy variables into the MRW model and uses the BMA method to tackle the uncertainty issue of the model. Based on China's provincial-level panel data, this paper examines the impact of renewable natural capital and non-renewable natural capital on the country's economic growth and then carries out multiple mechanism analysis with the CART algorithm. The main findings are: 1 Natural capital has a significant deep impact on China's economic growth in models incorporating proximate and fundamental theories, and the direction of natural capital's impact varies with proxy indicators, which shows a significant 'Curse' effect of dependence and a 'Blessing' effect of abundance. This panoramic perspective can reconcile the serious disagreement on 'Resource Curse' among the literature. 2 The effects of renewable natural capital are in line with the 'Resource neutral' hypothesis, while nonrenewable natural capital has a stronger impact on economic growth in terms of both the 'Curse' effect of dependence and the 'Blessing' effect of abundance, and the overall effect on economic growth depends on patterns of resource use. 3 Under high physical capital accumulation, natural capital has a more fundamental impact on economic growth, and the 'Blessing' effect of abundance has been significantly enhanced. Based on their resource endowments and economic development levels, all provinces should reinvest natural resource rents in national wealth to mitigate the 'Resource Curse' through effective protection of natural capital and compensation from reinvestment. This is a reasonable way to embark on a win-win sustainable development path of economic development and ecological civilization. [ABSTRACT FROM AUTHOR]

Numerous analytical models have been developed to predict ground deformations induced by tunneling, which is a critical issue in tunnel engineering. However, the accuracy of these predictions is often limited by errors and uncertainties resulting from model selection and parameter fittings, given the paucity of monitoring data in field settings. This paper proposes a novel approach to estimate tunnelling-induced ground deformations by applying Bayesian model averaging to several representative prediction models. By accounting for both model and parameter uncertainties, this approach enables more realistic predictions of ground deformations than individual models. Specifically, our results indicate that the Gonzalez-Sagaseta model outperforms other models in predicting ground surface settlements, while the Loganathan-Poulos model is most suitable for predicting subsurface vertical and horizontal deformations. Importantly, our analysis reveals that when monitoring data are sparse, model uncertainties may contribute up to 78.7% of the total uncertainties. Thus, obtaining sufficient data for parameter fitting is crucial for accurate predictions. The proposed method in this study offers a more realistic and efficient prediction of tunnelling-induced ground deformations. [ABSTRACT FROM AUTHOR]

Selecting a safe and clinically beneficial dose can be difficult in drug development. Dose justification often relies on dose-response modeling where parametric assumptions are made in advance which may not adequately fit the data. This is especially problematic in longitudinal dose-response models, where additional parametric assumptions must be made. This paper proposes a class of longitudinal dose-response models to be used in the Bayesian model averaging paradigm which improve trial operating characteristics while maintaining flexibility a priori. A new longitudinal model for non-monotonic longitudinal profiles is proposed. The benefits and trade-offs of the proposed approach are demonstrated through a case study and simulation. [ABSTRACT FROM AUTHOR]

This paper studies the long-run persistence of inequality in the U.S. With this aim, both income and wealth inequality measures covering the period from 1870 to 2019 have been analyzed. The persistent character of inequality has been assessed using unit root and structural break test statistics for time series. Furthermore, the determinants of inequality persistence have been explored by implementing Bayesian model averaging techniques in a generalized linear model framework. Our results suggest that the wealth-to-income ratio displays a non-stationary behavior throughout the whole sample period. On the contrary, the Gini index of disposable income and the top 10% income share alternate between I(0) and I(1) regimes. We also find that, while a higher level of globalization increases the persistence of income inequality, it is inversely related to the levels of educational attainment and trade union membership. [ABSTRACT FROM AUTHOR]

When long‐term follow up is required for a primary endpoint in a randomized clinical trial, a valid surrogate marker can help to estimate the treatment effect and accelerate the decision process. Several model‐based methods have been developed to evaluate the proportion of the treatment effect that is explained by the treatment effect on the surrogate marker. More recently, a nonparametric approach has been proposed allowing for more flexibility by avoiding the restrictive parametric model assumptions required in the model‐based methods. While the model‐based approaches suffer from potential mis‐specification of the models, the nonparametric method fails to give desirable estimates when the sample size is small, or when the range of the data does not follow certain conditions. In this paper, we propose a Bayesian model averaging approach to estimate the proportion of treatment effect explained by the surrogate marker. Our procedure offers a compromise between the model‐based approach and the nonparametric approach by introducing model flexibility via averaging over several candidate models and maintains the strength of parametric models with respect to inference. We compare our approach with previous model‐based methods and the nonparametric method. Simulation studies demonstrate the advantage of our method when surrogate supports are inconsistent and sample sizes are small. We illustrate our method using data from the Diabetes Prevention Program study to examine hemoglobin A1c as a surrogate marker for fasting glucose. [ABSTRACT FROM AUTHOR]

• Constructing data-physics-model based fatigue reliability assessment framework. • Providing a physical-based HI for degradation modeling and reliability evaluation. • Achieving application in steam turbine rotor with the proposed methodology. This paper puts forward a data-physics-model based fatigue reliability assessment methodology, integrating monitoring data, physics of failure and stochastic process models, for high-temperature components. In detail, the monitored parameters are mapped into load spectrum assisted by the constructed surrogate, a physics-based health index is constructed through the damage accumulations considering uncertainties. Bayesian model averaging is utilized to combine three stochastic process models to evaluate the fatigue reliability. Furthermore, this methodology oriented to engineering application is thereafter implemented into steam turbine rotor, and the remaining useful life evaluation is presented to demonstrate the superiority of proposed method over the existing engineering method. It shows that an over conservative estimation can be avoided in application because the condition information is integrated into the fatigue modeling framework, which provides a reference to condition-based maintenance of high-temperature components. [ABSTRACT FROM AUTHOR]