Applying the Principal Component Analysis for a deeper understanding of the groundwater system: case study of the Bacchiglione Basin (Veneto, Italy)
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Interactions between the tide and sloping sea boundary make watertable fluctuations in coastal unconfined aquifers complicated. Based on a perturbation method, we derived a new analytical solution to predict watertable fluctuations for coastal unconfined a ...
This study compares three imputation methods applied to the field observations of hydraulic head in subsurface hydrology. Hydrogeological studies that analyze the timeseries of groundwater elevations often face issues with missing data that may mislead bot ...
In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...
There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...
This contribution presents a model order reduction framework for real-time efficient solution of trimmed, multi-patch isogeometric Kirchhoff-Love shells. In several scenarios, such as design and shape optimization, multiple simulations need to be performed ...
The note presents selected results of an experimental campaign conducted with the aim to investigate the hydro-mechanical behaviour of a shale recovered at a depth of about 900 m below the ground. High-pressure oedometric tests were performed to investigat ...
Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster ...
Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...
We investigate the role of dense environments in suppressing star formation by studying log(10)(M- * / M-?) > 9 . 7 star-forming galaxies in nine clusters from the Local Cluster Survey (0.0137 < z < 0.0433) and a large comparison field sample drawn from th ...
We present a new method to simultaneously and self-consistently model the mass distribution of galaxy clusters that combines constraints from strong lensing features, X-ray emission, and galaxy kinematics measurements. We are able to successfully decompose ...