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This paper offers a comparative study of two soils- Glauconite and Ottawa F65- utilizing X-ray micro-computed tomography (µCT) scan. The tendency of glauconite sand to transform from coarse to fine-grained material through particle crushing poses challenges in terms of stability and strength, particularly in foundation engineering and offshore site investigation. This paper investigates the particle size distribution and explores the subtleties of particle characteristics. Non-invasive µCT and 3D image analysis are used to measure and compare particle shape parameters: median aspect ratio (0.56 for Glauconite,0.54 for Ottawa F65), median convexity is 0.86 for both soils, and median sphericity (0.81 for Glauconite, 0.83 for Ottawa F65). By drawing comparisons between the statistical data of particle shape parameters from both soils, insights are gained into their morphological characteristics. Additionally, fitted Johnson distributions are provided for 3D Aspect ratio, sphericity, and convexity which may be useful for discrete element method modeling of these soils. 

Scientists use imaging to identify objects of interest and infer properties of these objects. The locations of these objects are often measured with error, which when ignored leads to biased parameter estimates and inflated variance. Current measurement error methods require an estimate or knowledge of the measurement error variance to correct these estimates, which may not be available. Instead, we create a spatial Bayesian hierarchical model that treats the locations as parameters, using the image itself to incorporate positional uncertainty. We lower the computational burden by approximating the likelihood using a noncontiguous block design around the object locations. We use this model to quantify the relationship between the intensity and displacement of hundreds of atom columns in crystal structures directly imaged via scanning transmission electron microscopy (STEM). Atomic displacements are related to important phenomena such as piezoelectricity, a property useful for engineering applications like ultrasound. Quantifying the sign and magnitude of this relationship will help materials scientists more precisely design materials with improved piezoelectricity. A simulation study confirms our method corrects bias in the estimate of the parameter of interest and drastically improves coverage in high noise scenarios compared to non-measurement error models.