An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Resampling approaches for the quantitative analysis of spatially distributed cells

Image segmentation is a crucial step in various image analysis pipelines and constitutes one of the cutting-edge areas of digital pathology.The advent of quantitative analysis has enabled the evaluation of millions of individual cells in tissues,allowing for the combined assessment of morphological features,biomarker expression,and spatial context.The recorded cells can be described as a point pattern process.However,the classical statistical approaches to point pattern processes prove unreliable in this context due to the presence of multiple irregularly-shaped interstitial cell-devoid spaces in the domain,which correspond to anatomical features(e.g.vessels,lipid vacuoles,glandular lumina)or tissue artefacts(e.g.tissue fractures),and whose coordinates are unknown.These interstitial spaces impede the accurate calculation of the domain area,resulting in biased clustering measurements.Moreover,the mistaken inclusion of empty regions of the domain can directly impact the results of hypothesis testing.The literature currently lacks any introduced bias correction method to address interstitial cell-devoid spaces.To address this gap,we propose novel resampling methods for testing spatial randomness and evaluating relationships among different cell populations.Our methods obviate the need for domain area estimation and provide non-biased clustering measurements.We created the SpaceR software(https://github.com/GBertolazzi/SpaceR)to enhance the accessibility of our methodologies.

Spatio-temporal point pattern analysis on Wenchuan strong earthquake

For exploring the aftershock occurrence process of the 2008 Wenchuan strong earthquake, the spatio-temporal point pattern analysis method is employed to study the sequences of aflershocks with magnitude M≥4.0, M≥4.5, and M≥5.0. It is found that these data exhibit the spatio-temporal clustering on a certain distance scale and on a certain time scale. In particular, the space-time interaction obviously strengthens when the distance is less than 60 km and the time is less than 260 h for the first two aftershock sequences; however, it becomes strong when the distance scale is less than 80 km and the time scale is less than 150 h for the last aftershock sequence. The completely spatial randomness analysis on the data regardless of time component shows that the spatial clustering of the aftershocks gradually strengthens on the condition that the distance is less than 60 km. The results are valuable for exploring the occurrence rules of the Wenchuan strong earthquake and for predicting the aftershocks.

Bayesian spatial modeling to incorporate unmeasured information at road segment levels with the INLA approach:A methodological advancement of estimating crash modification factors

Estimating safety effectiveness of roadway improvements and countermeasures,using cross-sectional models,generally requires large amounts of data such as road geometric and traffic-related characteristics at road segment levels.These models do not consider all confounding crash contributory factors such as driving culture and environmental conditions at the segment level due to a lack of readily available data.This may result in inaccurate models representing actual conditions at road segment levels,followed by erroneous estimations of safety effectiveness.To minimize the effect of not including such variables,this study develops a new methodology to estimate safety effectiveness of roadway countermeasures,based on generalized linear mixed models,assuming zeroinflated Poisson distribution for the response,and adjusting for spatial autocorrelation using the spatial random effect.The Bayesian approach,with Integrated Nested Laplace Approximation,was used to make inference on this model with computational efficiency.Results showed that incorporating a spatial random effect into the models provided better model fit than non-spatial models;hence,estimated safety effectiveness based on such models is more accurate.The proposed approach is a methodological advancement in traffic safety,which allows evaluation of safety effectiveness or roadway improvements when data are not readily available.