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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]

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]

Motivated by the magnitude of discrepancies that are typically encountered in blind analysis contests between numerical model predictions and test data, a methodology is presented to incorporate modeling uncertainties in the assessment of capacity-designed steel moment-resisting frames (MRFs) under earthquake loading. Sources of modeling uncertainties are identified in order to define a set of variables that control the seismic response of steel MRFs. For each variable, statistical distributions that rely on experimental databases are deduced. Special attention is paid to the truncation limits to enable the generation of individual parameter samples that have an actual physical meaning. Besides strength modification factors for various steel grades, distributions are offered for the parameters that are used to model wide-flange (composite) beams, steel hollow structural section columns and damping. Both intracomponent and intercomponent interdependencies are explicitly discussed in an attempt to propose correlation coefficients that comply with the current design philosophy and construction sequence. Although the focus is strictly on collapse of capacity-designed steel MRFs, the proposed methodology can be utilized to examine less severe limit states and/or existing structures where capacity-design principles do not necessarily apply. A 4-story steel MRF, tested full-scale at the E-Defense facility to collapse, is employed as a case study to demonstrate the applicability of the proposed methodology. It is shown that although the steel MRF examined is insensitive to modeling uncertainties regarding both collapse and story mechanism prediction, local response parameters can vary considerably versus the ones observed during the test. Through a parametric investigation on the strong-column-weak-beam (SCWB) ratio, the parameters that have an impact on the associated predictions are identified. Implications for capacity design, collapse capacity and residual drift are also discussed, highlighting the benefits that higher SCWB ratios can have on seismic response. [ABSTRACT FROM AUTHOR]

We analyze time-averaged experimental data from in vitro activities of neuronal networks. Through a Pairwise Maximum-Entropy method, we identify through an inverse binary Ising-like model the local fields and interaction couplings which best reproduce the average activities of each neuron as well as the statistical correlations between the activities of each pair of neurons in the system. The specific information about the type of neurons is mainly stored in the local fields, while a symmetric distribution of interaction constants seems generic. Our findings demonstrate that, despite not being directly incorporated into the inference approach, the experimentally observed correlations among groups of three neurons are accurately captured by the derived Ising-like model. Within the context of the thermodynamic analogy inherent to the Ising-like models developed in this study, our findings additionally indicate that these models demonstrate characteristics of second-order phase transitions between ferromagnetic and paramagnetic states at temperatures above, but close to, unity. Considering that the operating temperature utilized in the Maximum-Entropy method is T o = 1 , this observation further expands the thermodynamic conceptual parallelism postulated in this work for the manifestation of criticality in neuronal network behavior. [ABSTRACT FROM AUTHOR]

Introduction: In cities, the intensity of integrated development, both above and below ground, has been continuously increasing. Unfortunately, associated events such as surface cracking and ground collapse are common, and research into the susceptibility of urban ground collapse (UGC) has consequently, also been increasing. Methods: In this study, we calculated the spatial probability distribution of historical UGC events in Hangzhou, China as a case study. To avoid the influence of the subjectivity of expert opinions on the evaluation results, and to distinguish it from the first version of the UGC sensitivity map in 2021, collinearity and correlation analyses based on geo-detector were conducted, and the 11 most representative factors from 22 candidates were identified. Then, a GIS-based susceptibility assessment system was established and applied to eastern Hangzhou, China. Results: The assessment results were divided into three grades. High-susceptibility areas accounted for 6.08% of the total area and were mainly distributed in the central area on both sides of the Qiantang River. Medium-susceptibility areas accounted for 23.92% of the total area and showed an expanding trend around the central area. Low-susceptibility areas accounted for 12.42% of the total area and were mainly distributed within the bedrock mountainous areas on the west side. Discussion: The assessment model take more urban construction activies into consideration, which can fits better with the trigger factors of UGC itself, and has good applicability for other cities in Zhejiang Province to assess the susceptibility of UGC events. [ABSTRACT FROM AUTHOR]

In this research, we introduce a truncation of the half-logistic distribution function as a multiplicative Archimedean generator. The corresponding Archimedean copula is obtained, namely the half-logistic family. The dependency structure of this copula is distinct from other well-known ones. Kendall's tau correlation coefficient is obtained in exact form and found to cover the entire positive dependence range (i.e., [0, 1]). We have proven that this copula is positively ordered and has no tail dependencies. The density of this copula is shown to be totally positive of order two. An extension of this copula is also introduced by adding a second parameter. This extension allows for a negative correlation and connects the famous Frank copula to the half-logistic copula. Two datasets were used to compare the half-logistic copula with some other known copula models. [ABSTRACT FROM AUTHOR]

This study presents experimental results on capacitance fluctuations in several devices located within an interleaving structure. Specifically, it examines the behavior of the capacitance between the anode and cathode of a photomultiplier, comparing it with the characteristics of the ultra-stable capacitor analyzed in via measurements inside and outside a modified Faraday cage. The results cover spectral and correlation analyses both inside and outside the box, confirming differences in the spectrum using the periodograms. In particular, the confidence intervals for the mean capacitance values show significant changes between the two scenarios, from the inside to the outside of the enclosure. In the case of the ultra-stable capacitor, there is an increase from 0.004 to 0.008 nF. On the other hand, a symmetry analysis is conducted for all measurements taken both outside and inside the modified Faraday cage. It is observed that in all cases, there is clear non-symmetric behavior in the data. [ABSTRACT FROM AUTHOR]