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Swelling of clay–sulfate rocks is a serious and devastating geo-hazard, often causing damage to geotechnical structures. Therefore, understanding underlying swelling processes is crucial for the safe design, construction, and maintenance of infrastructure. Planning appropriate countermeasures to the swelling problem requires a thorough understanding of the processes involved. We developed a coupled hydro-mechanical (HM) model to reproduce the observed heave in the historic city of Staufen in south-west Germany, which was caused by water inflow into the clay–sulfate bearing Triassic Grabfeld Formation (formerly Gipskeuper = “Gypsum Keuper”) after geothermal drilling. Richards’ equation coupled to a deformation process with linear kinematics was used to describe the hydro-mechanical behavior of clay–sulfate rocks. The mathematical model is implemented into the scientific open-source framework OpenGeoSys. We compared the model calculations with the measured long-term heave records at the study site. We then designed a sensitivity analysis to achieve a deeper insight into the swelling phenomena. The synthetic database obtained from the sensitivity analysis was used to develop a machine learning (ML) model, namely least-squares boosting ensemble (LSBoost) model coupled with a Bayesian optimization algorithm to rank the importance of parameters controlling the swelling. The HM model reproduced the heave observed at Staufen with sufficient accuracy, from a practical point of view. The ML model showed that the maximum swelling pressure is the most important parameter controlling the swelling. The other influential parameters rank as Young’s modulus, Poisson’s ratio, overburden thickness, and the initial volumetric water content of the swelling layer.

Landslides are more widespread than any other geological events on Earth. Due to their steep topography and contrasted weather conditions, volcanic regions are especially prone to water-triggered landslides. Indeed, examples of volcanic slope failures abound. Regions endowed with volcanic soils are often densely populated and landslides are causing devastating impacts including loss of human lives, damage to critical infrastructure and disruption to livelihoods. There is growing concern that the intensifying effects of climate change on the hydrological cycle – changing the amount and frequency of rainfall and meltwater input – will exacerbate shallow-seated landslide susceptibility. Soil is the weakest material involved in landslide-related disasters, and soil properties are pivotal in determining the susceptibility of a slope to mass movement. Volcanic soils have unique, but hitherto poorly constrained, hydraulic and mechanical properties. Depending on the volcanic parent materials and weathering conditions, these soils can display clay fractions of different mineralogical composition which likely influence their hydraulic and mechanical properties.Our study aims to advance understanding of the relationships between the hydraulic and mechanical properties of volcanic soils. We sampled undisturbed volcanic soils in Tenerife (Spain) and Ecuador characterized by different mineralogies in order to measure their microstructural and hydraulic properties (particle size distribution ; pore size distribution ; hydraulic conductivity ; water retention curve). We also determined the mechanical properties (shear strength) of these soils and quantified the effect of soil water content on these properties conducting triaxial tests at different moisture level. The mineralogical analysis performed reveal clay fractions either enriched in allophanes or halloysites for the different sampled sites. The allophanic soils display large porosities and water retention values, whereas halloysites-rich soils are less efficient to retain water but seem to conduct it faster. Halloysites-rich soils also show higher, but more water content dependent shear strengths. Indeed, maximum shear stresses reached during triaxial tests are largely increased with drying while allophanic soils’ shear strength are less impacted by a decreased water content. This could be explained by the aggregation of allophane particles in clumps during drying, causing a reduction of shear strength offsetting the classic increasing shear strength due to the increased capillary effects.

We connect two random graph models, the Popularity Adjusted Block Model (PABM) and the Generalized Random Dot Product Graph (GRDPG), by demonstrating that the PABM is a special case of the GRDPG in which communities correspond to mutually orthogonal subspaces of latent vectors. This insight allows us to construct new algorithms for community detection and parameter estimation for the PABM, as well as improve an existing algorithm that relies on Sparse Subspace Clustering. Using established asymptotic properties of Adjacency Spectral Embedding for the GRDPG, we derive asymptotic properties of these algorithms. In particular, we demonstrate that the absolute number of community detection errors tends to zero as the number of graph vertices tends to infinity. Simulation experiments illustrate these properties., Comment: 36 pages, 9 figures

The development of non-platinum metal catalysts used in anion exchange membrane fuel cells (AEMFCs) has been considered as a key to bring the fuel cell devices to practical applications. This paper presents the achievement in developing non-platinum metal catalysts including bimetallic palladium-nickel nanoparticles attached on Vulcan carbon particles (PdNi/C) and silver nanoparticles supported on multi-walled carbon nanotubes (Ag/MWCNT) employed in the anode and cathode electrodes of AEMFCs, respectively. These catalysts were synthesized by a wet impregnation method. The SEM analysis showed that the nanoparticle sizes of Pd, Ni, and Ag were less than 50 nm. The electrochemical characterizations of the synthesized PdNi/C and Ag/MWCNT were explored by cyclic voltammetry measurements. In addition, the prepared catalyst performance was evaluated using a single anion exchange membrane fuel cell (AEMFC). The measured results showed that the cell performance of a single H2/O2 AEMFC with PdNi/C and Ag/MWCNT in anode and cathode electrodes, respectively, exhibited a peak power density of about 463 mW·cm-1, which was lower about 24.1% than that of the single-cell containing Pt/C (~610 mW·cm-1) at both anode and cathode sides, indicating that the PdNi/C và Ag/MWCNT catalysts can be used to substitute Pt/C in AEMFCs for cost reduction.

We derive the limiting distribution for the outlier eigenvalues of the adjacency matrix for random graphs with independent edges whose edge probability matrices have low-rank structure. We show that when the number of vertices tends to infinity, the leading eigenvalues in magnitude are jointly multivariate Gaussian with bounded covariances. As a special case, this implies a limiting normal distribution for the outlier eigenvalues of stochastic blockmodel graphs and their degree-corrected or mixed-membership variants. Our result extends the classical result of Furedi and Komlos on the fluctuation of the largest eigenvalue for Erdős–Renyi graphs.