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Recently, there has been interest in determining the viscoelastic properties of polymeric liquids and other complex fluids by means of Diffusing Wave Spectroscopy (DWS). In this technique, light-scattering spectroscopy is applied to highly turbid fluids containing optical probe particles. The DWS spectrum is used to infer the time-dependent mean-square displacement and time-dependent diffusion coefficient D of the probes. From D, values for the storage modulus G ′ (ω) and the loss modulus G ′ ′ (ω) are obtained. This paper is primarily concerned with the inference of the mean-square displacement from a DWS spectrum. However, in much of the literature, central to the inference that is said to yield D is an invocation g (1) (t) = exp (− 2 q 2 X (t) 2 ¯) of the Gaussian Approximation for the field correlation function g (1) (t) of the scattered light in terms of the mean-square displacement X (t) 2 ¯ of a probe particle during time t. Experiment and simulation both show that the Gaussian approximation is invalid for probes in polymeric liquids and other complex fluids. In this paper, we obtain corrections to the Gaussian approximation that will assist in interpreting DWS spectra of probes in polymeric liquids. The corrections reveal that these DWS spectra receive contributions from higher moments X (t) 2 n ¯ , n > 1 , of the probe displacement distribution function. [ABSTRACT FROM AUTHOR]

Summer precipitation prediction in China is important to society and economic development, but still a challenging issue in current meteorological studies, due to the uncertainty of the climate system. This paper developed a weighted integration method founded on dynamic-statistic prediction methods. The advantages and disadvantages of station-based and grid-based integration on summer precipitation prediction, along with the underlying reasons, are respectively analyzed by calculating the anomaly correlation coefficients (ACC), prediction score (PS), and root-mean-square errors (RMSE). The main manifestations indicated that 1) The weighted integration method can provide better skill of summer rainfall prediction in China than the single dynamic-statistic combined prediction method; 2) For the station-based integration, the 10-year ACC mean of summer precipitation prediction is 0.098–0.106, passing the 90% significance level, which is also obviously higher than that of the grid-based integration. The station-based integration has a higher symbol consistency ratio (SCR) than the grid-based integration, and the probability distribution of anomaly percentiles of station-based integration is closer to the observation, which causes the corresponding PS score to be 69.2–70.0 and higher than the grid-based integration. The independent sample validation of 2020 and 2021 further confirmed that station-based integration had a higher ACC than grid-based integration. It is indicated that station-based integration may have better performance in improving the accuracy of the summer precipitation prediction in China, which needs to be deeply considered in the scientific study and real prediction issues. [ABSTRACT FROM AUTHOR]

To consider the failure correlation among key subsystems, based on the reliability allocation method of the series system, a wind turbine reliability allocation calculation method based on the vine copula correlation model is proposed. The reliability modeling method of the series system is adopted to model the reliability of each subsystem, combined with the copula function to model the correlation between the variables, and then, the multidimensional variable problem is converted into the two‐dimensional variable problem through the vine copula function. The reliability calculation model of the copula function model, under the influence of factors such as the complexity, importance, and failure hazard of each subsystem, allocates the reliability of the key component systems of the wind turbine generator set. Finally, through three allocation methods based on the independent, multivariate copula function and vine copula function and other distribution methods, the calculation and analysis of the wind turbine generator set are performed. The results show that the allocated control system has the lowest reliability, and the tower has the highest reliability; compared with the assumption of the independent allocation method, the allocation result of this paper's method is lower; compared with the allocation method of the multidimensional copula function, the allocated failure rate is increased by more than 20%. This paper verifies that the proposed method is not only effective and reasonable but also more consistent with the actual situation. [ABSTRACT FROM AUTHOR]

In this paper we continued our research of the uniform electron gas in a warm dense matter regime, focusing on the momentum distribution functions and pair correlation functions. We use the single–momentum path integral Monte Carlo method, based on the Wigner formulation of quantum statistics to calculate both momentum- and coordinate-depending distributions and average values of quantum operators for many-fermion Coulomb systems. We discovered that the single-particle momentum distribution function deviates from the ideal Fermi distribution and forms the so-called "quantum tails" at high momenta, if non-ideality is strong enough in both degenerate and non-degenerate cases. This effect is always followed by the appearance of the short-range order on pair correlation functions and can be explained by the tunneling through the effective potential wells surrounding the electrons. Furthermore, we calculated the average kinetic and potential energies in the wide range of states, expanding our previous results significantly. [ABSTRACT FROM AUTHOR]

Trend identification procedures are used to identify systematic monotonic trendlines in a given hydro-meteorological time series recording to represent time-dependent variations as increases or decreases. Different methodologies have been proposed for such descriptions, but most of them require restrictive assumptions such as normal (Gaussian) probability distribution function (PDF), serial independence, and long sample sizes. In particular, pre-whitening and over-whitening are recommended to meet the need for serial independence, but they cannot transform a serially dependent series into a completely independent one. In this paper, a new trend methodology is proposed based on the characteristics of crossings along any given straight line within the given time series, and the sought-after trend component is the one with the maximum number of crossings. This approach does not require any restrictive assumptions. Unlike previous trend algorithms, the proposed cross-empirical trend analysis (CETA) does not give a single trend, but a series of trends at different levels within the variation range of hydro-meteorological time series records. For the sake of brevity, only three levels are considered in this article, at 10%, 50%, and 90% risk levels. The comparison of the CETA approach is presented with the classical and frequently used Mann–Kendall (MK) trend determination procedure method based on Sen's slope calculation. For very small series correlation coefficients and normal PDF function cases, CETA and the classical technique give almost the same trendline within the ± 5% error band. The application of this methodology is presented for monthly and annual discharge records of the Danube River and annual precipitation records from seven geographical regions of Turkey. [ABSTRACT FROM AUTHOR]

In a masonry veneer wall system, tie strengths and stiffnesses vary randomly and so are not consistent for all ties throughout the wall. To ensure an economical and safe design, this paper uses tie calibration experimental approach in accordance with the standard AS2699.1 to investigate the tie failure load under compression and tension loading. Probabilistic wall tie characterisations are accomplished by estimating the mean, coefficient of variation and characteristic axial compressive and tensile strength from 50 specimens. The displacement across the cavity is recorded, which resulted the complete load versus displacement response. Using the maximum likelihood method, a range of probability distributions are fitted to tie strengths at different displacement histogram data sets, and a best-fitted probability distribution is selected for each case. The inverse cumulative distribution function plots are also used along with the Anderson-Darling test to infer a goodness-of-fit for the probabilistic models. An extensive statistical correlation analysis is also conducted to check the correlation between different tie strengths and associated displacement for both compression and tension loading. Based on the findings, a wall tie constitutive law is proposed to define probabilistic tie behaviour in numerical modelling. [ABSTRACT FROM AUTHOR]

Integrated computational materials engineering (ICME) has significantly enhanced the systemic analysis of the relationship between microstructure and material properties, paving the way for developing high-performance materials. However, analyzing microstructure-sensitive material behavior remains challenging due to the scarcity of three-dimensional (3D) microstructure datasets. Moreover, this challenge is intensified if the microstructure is anisotropic, as this also results in anisotropic material properties. This paper presents a framework for reconstructing anisotropic microstructures solely based on two-dimensional (2D) micrographs using conditional diffusion-based generative models (DGMs). The proposed framework involves the spatial connection of multiple 2D conditional DGMs, each trained to generate 2D microstructure samples for three orthogonal planes. The connected multiple reverse diffusion processes then enable effective modeling of a Markov chain for transforming noise into a 3D microstructure sample. Furthermore, a modified harmonized sampling enhances the sample quality while preserving the spatial connection between the slices of anisotropic microstructure samples in 3D space. The 2D-to-3D reconstructed anisotropic microstructure samples are evaluated regarding the spatial correlation function and the physical material behavior to validate the proposed framework. The results demonstrate that the framework can reproduce the statistical distribution of material phases and properties in 3D space. It highlights the potential application of the proposed 2D-to-3D reconstruction framework in establishing microstructure-property linkages, which could aid high-throughput material design for future studies. [ABSTRACT FROM AUTHOR]

This paper presents the results of the characterization of alumina ball size distribution in the mill of a crushing and grinding plant. The mill was unloaded and the balls were sieved. Then, the results were adjusted to three kinds of models; two of them are the traditional models of Rosin-Rammler and Gates-Gaudin-Schumann. The third model applied was the Swebrec distribution function, which has been successfully used on fragmented rock size distribution. The best adjustment for the ball size distribution was obtained by the Swebrec function, with a correlation coefficient of 0.9984. [ABSTRACT FROM AUTHOR]

A graph signal is a random vector with a partially known statistical description. The observations are usually sufficient to determine marginal distributions of graph node variables and their pairwise correlations representing the graph edges. However, the curse of dimensionality often prevents estimating a full joint distribution of all variables from the available observations. This paper introduces a computationally effective generative model to sample from arbitrary but known marginal distributions with defined pairwise correlations. Numerical experiments show that the proposed generative model is generally accurate for correlation coefficients with magnitudes up to about 0.3, whilst larger correlations can be obtained at the cost of distribution approximation accuracy. The generative models of graph signals can also be used to sample multivariate distributions for which closed-form mathematical expressions are not known or are too complex. [ABSTRACT FROM AUTHOR]

Mastering the lodging risk of planting environment is of great significance to the optimal layout of maize varieties and the breeding of lodging resistant varieties. However, the existing lodging risk models are still at the stage of single or multi-factors independent analysis, and lack of assessment for different lodging types. To address this issue, based on the mechanism of different lodging types, the Archimedean copula function was used to describe the joint probability distribution of wind speed and precipitation, and the lodging risk assessment model of maize was established. By comparing the goodness of fit, when the rank correlation coefficient of these two is positive and negative, the corresponding optimal joint probability distribution functions are the Gumbel copula and Frank copula. According to the spatial distribution of lodging risk, the area from Liaodong Bay northward to Tongyu, Jilin province in the Northeast and the North China Plain has a high frequency of lodging, in which the probability of stalk lodging is two to four times that of root lodging. Finally, we discussed how to apply the lodging risk distribution results to optimize the maize variety test sites to improve the efficiency and reliability of the existing test system. The method proposed in this paper comprehensively considers the synergistic effect of multiple factors and can provide technical support for other risk assessment. [ABSTRACT FROM AUTHOR]