5,335 research outputs found

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Swift: A modern highly-parallel gravity and smoothed particle hydrodynamics solver for astrophysical and cosmological applications

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    Numerical simulations have become one of the key tools used by theorists in all the fields of astrophysics and cosmology. The development of modern tools that target the largest existing computing systems and exploit state-of-the-art numerical methods and algorithms is thus crucial. In this paper, we introduce the fully open-source highly-parallel, versatile, and modular coupled hydrodynamics, gravity, cosmology, and galaxy-formation code Swift. The software package exploits hybrid task-based parallelism, asynchronous communications, and domain-decomposition algorithms based on balancing the workload, rather than the data, to efficiently exploit modern high-performance computing cluster architectures. Gravity is solved for using a fast-multipole-method, optionally coupled to a particle mesh solver in Fourier space to handle periodic volumes. For gas evolution, multiple modern flavours of Smoothed Particle Hydrodynamics are implemented. Swift also evolves neutrinos using a state-of-the-art particle-based method. Two complementary networks of sub-grid models for galaxy formation as well as extensions to simulate planetary physics are also released as part of the code. An extensive set of output options, including snapshots, light-cones, power spectra, and a coupling to structure finders are also included. We describe the overall code architecture, summarize the consistency and accuracy tests that were performed, and demonstrate the excellent weak-scaling performance of the code using a representative cosmological hydrodynamical problem with \approx300300 billion particles. The code is released to the community alongside extensive documentation for both users and developers, a large selection of example test problems, and a suite of tools to aid in the analysis of large simulations run with Swift.Comment: 39 pages, 18 figures, submitted to MNRAS. Code, documentation, and examples available at www.swiftsim.co

    Characterising the neck motor system of the blowfly

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    Flying insects use visual, mechanosensory, and proprioceptive information to control their movements, both when on the ground and when airborne. Exploiting visual information for motor control is significantly simplified if the eyes remain aligned with the external horizon. In fast flying insects, head rotations relative to the body enable gaze stabilisation during highspeed manoeuvres or externally caused attitude changes due to turbulent air. Previous behavioural studies into gaze stabilisation suffered from the dynamic properties of the supplying sensor systems and those of the neck motor system being convolved. Specifically, stabilisation of the head in Dipteran flies responding to induced thorax roll involves feed forward information from the mechanosensory halteres, as well as feedback information from the visual systems. To fully understand the functional design of the blowfly gaze stabilisation system as a whole, the neck motor system needs to be investigated independently. Through X-ray micro-computed tomography (μCT), high resolution 3D data has become available, and using staining techniques developed in collaboration with the Natural History Museum London, detailed anatomical data can be extracted. This resulted in a full 3- dimensional anatomical representation of the 21 neck muscle pairs and neighbouring cuticula structures which comprise the blowfly neck motor system. Currently, on the work presented in my PhD thesis, μCT data are being used to infer function from structure by creating a biomechanical model of the neck motor system. This effort aims to determine the specific function of each muscle individually, and is likely to inform the design of artificial gaze stabilisation systems. Any such design would incorporate both sensory and motor systems as well as the control architecture converting sensor signals into motor commands under the given physical constraints of the system as a whole.Open Acces

    Introduction to Riemannian Geometry and Geometric Statistics: from basic theory to implementation with Geomstats

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    International audienceAs data is a predominant resource in applications, Riemannian geometry is a natural framework to model and unify complex nonlinear sources of data.However, the development of computational tools from the basic theory of Riemannian geometry is laborious.The work presented here forms one of the main contributions to the open-source project geomstats, that consists in a Python package providing efficient implementations of the concepts of Riemannian geometry and geometric statistics, both for mathematicians and for applied scientists for whom most of the difficulties are hidden under high-level functions. The goal of this monograph is two-fold. First, we aim at giving a self-contained exposition of the basic concepts of Riemannian geometry, providing illustrations and examples at each step and adopting a computational point of view. The second goal is to demonstrate how these concepts are implemented in Geomstats, explaining the choices that were made and the conventions chosen. The general concepts are exposed and specific examples are detailed along the text.The culmination of this implementation is to be able to perform statistics and machine learning on manifolds, with as few lines of codes as in the wide-spread machine learning tool scikit-learn. We exemplify this with an introduction to geometric statistics

    Geometric Data Analysis: Advancements of the Statistical Methodology and Applications

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    Data analysis has become fundamental to our society and comes in multiple facets and approaches. Nevertheless, in research and applications, the focus was primarily on data from Euclidean vector spaces. Consequently, the majority of methods that are applied today are not suited for more general data types. Driven by needs from fields like image processing, (medical) shape analysis, and network analysis, more and more attention has recently been given to data from non-Euclidean spaces–particularly (curved) manifolds. It has led to the field of geometric data analysis whose methods explicitly take the structure (for example, the topology and geometry) of the underlying space into account. This thesis contributes to the methodology of geometric data analysis by generalizing several fundamental notions from multivariate statistics to manifolds. We thereby focus on two different viewpoints. First, we use Riemannian structures to derive a novel regression scheme for general manifolds that relies on splines of generalized Bézier curves. It can accurately model non-geodesic relationships, for example, time-dependent trends with saturation effects or cyclic trends. Since Bézier curves can be evaluated with the constructive de Casteljau algorithm, working with data from manifolds of high dimensions (for example, a hundred thousand or more) is feasible. Relying on the regression, we further develop a hierarchical statistical model for an adequate analysis of longitudinal data in manifolds, and a method to control for confounding variables. We secondly focus on data that is not only manifold- but even Lie group-valued, which is frequently the case in applications. We can only achieve this by endowing the group with an affine connection structure that is generally not Riemannian. Utilizing it, we derive generalizations of several well-known dissimilarity measures between data distributions that can be used for various tasks, including hypothesis testing. Invariance under data translations is proven, and a connection to continuous distributions is given for one measure. A further central contribution of this thesis is that it shows use cases for all notions in real-world applications, particularly in problems from shape analysis in medical imaging and archaeology. We can replicate or further quantify several known findings for shape changes of the femur and the right hippocampus under osteoarthritis and Alzheimer's, respectively. Furthermore, in an archaeological application, we obtain new insights into the construction principles of ancient sundials. Last but not least, we use the geometric structure underlying human brain connectomes to predict cognitive scores. Utilizing a sample selection procedure, we obtain state-of-the-art results

    Hybrid BEM-FEM for 2D and 3D dynamic soil-structure interaction considering arbitrary layered half-space and nonlinearities

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    Experiences and studies have shown that soil-structure interaction (SSI) effect has a vital role in the dynamic behaviour of a soil-structure system. Despite this, analyses involving dynamic SSI are still challenging for practicing engineers due to their complexity and accessibility. In this thesis, the hybrid BEM-FEM implementation is aimed at practicality by combining commercial software and an in-house code. The pre-processing task can be performed under one graphical environment, and it is enhanced with the capability to compute different types of dynamic sources and other improvements to increase its efficiency, accuracy, and modeling flexibility. Further, the underlying soil is commonly a layered profile with arbitrary geometries. Most existing solutions solve the problem through simplification of the geometry and pattern. One of the main contributions in this thesis is the development of layer-wise condensation method to solve these cases using hybrid BEM-FEM. The method significantly reduces the computational memory requirement. Another challenge in the dynamic SSI addressed in this work is the consideration of secondary nonlinearities. Existing solutions using the time domain BEM and iterative hybrid method are computationally costly, and implementation of such a hybrid method on commercial software is tedious. The solution to address this case using a sequential frequency-time domain procedure is presented. The relatively simple approach makes it possible to consider the nonlinearities in the simulation without using the time domain BEM and without requiring additional iterations. Case studies demonstrating the application of the enhanced hybrid method are presented including cases of bridges, containment structures, and a 3D multi-storey structure under point source and double-couple sources. These case studies illustrate the role of following critical factors such as the site effect, inhomogeneity, and nonlinearities

    Data-driven deep-learning methods for the accelerated simulation of Eulerian fluid dynamics

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    Deep-learning (DL) methods for the fast inference of the temporal evolution of fluid-dynamics systems, based on the previous recognition of features underlying large sets of fluid-dynamics data, have been studied. Specifically, models based on convolution neural networks (CNNs) and graph neural networks (GNNs) were proposed and discussed. A U-Net, a popular fully-convolutional architecture, was trained to infer wave dynamics on liquid surfaces surrounded by walls, given as input the system state at previous time-points. A term for penalising the error of the spatial derivatives was added to the loss function, which resulted in a suppression of spurious oscillations and a more accurate location and length of the predicted wavefronts. This model proved to accurately generalise to complex wall geometries not seen during training. As opposed to the image data-structures processed by CNNs, graphs offer higher freedom on how data is organised and processed. This motivated the use of graphs to represent the state of fluid-dynamic systems discretised by unstructured sets of nodes, and GNNs to process such graphs. Graphs have enabled more accurate representations of curvilinear geometries and higher resolution placement exclusively in areas where physics is more challenging to resolve. Two novel GNN architectures were designed for fluid-dynamics inference: the MuS-GNN, a multi-scale GNN, and the REMuS-GNN, a rotation-equivariant multi-scale GNN. Both architectures work by repeatedly passing messages from each node to its nearest nodes in the graph. Additionally, lower-resolutions graphs, with a reduced number of nodes, are defined from the original graph, and messages are also passed from finer to coarser graphs and vice-versa. The low-resolution graphs allowed for efficiently capturing physics encompassing a range of lengthscales. Advection and fluid flow, modelled by the incompressible Navier-Stokes equations, were the two types of problems used to assess the proposed GNNs. Whereas a single-scale GNN was sufficient to achieve high generalisation accuracy in advection simulations, flow simulation highly benefited from an increasing number of low-resolution graphs. The generalisation and long-term accuracy of these simulations were further improved by the REMuS-GNN architecture, which processes the system state independently of the orientation of the coordinate system thanks to a rotation-invariant representation and carefully designed components. To the best of the author’s knowledge, the REMuS-GNN architecture was the first rotation-equivariant and multi-scale GNN. The simulations were accelerated between one (in a CPU) and three (in a GPU) orders of magnitude with respect to a CPU-based numerical solver. Additionally, the parallelisation of multi-scale GNNs resulted in a close-to-linear speedup with the number of CPU cores or GPUs.Open Acces

    Selected problems of materials science. Vol. 2. Nano-dielectrics metals in electronics. Mеtamaterials. Multiferroics. Nano-magnetics

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    The textbook examines physical foundations and practical application of current electronics materials. Modern theories are presented, more important experimental data and specifications of basic materials necessary for practical application are given. Contemporary research in the field of microelectronics and nanophysics is taken into account, while special attention is paid to the influence of the internal structure on the physical properties of materials and the prospects for their use. English-language lectures and other classes on the subject of the book are held at Igor Sikorsky Kyiv Polytechnic Institute at the departments of “Applied Physics” and “Microelectronics” on the subject of materials science, which is necessary for students of higher educational institutions when performing scientific works. For master’s degree applicants in specialty 105 “Applied physics and nanomaterials”.Розглянуто фізичні основи та практичне застосування актуальних матеріалів електроніки. Подано сучасні теорії, наведено найважливіші експериментальні дані та специфікації основних матеріалів, які потрібні для практичного застосування. Враховано сучасні дослідження у галузі мікроелектроніки та нанофізики, при цьому особливу увагу приділено впливу внутрішньої структури на фізичні властивості матеріалів і на перспективи їх використання. Англомовні лекції та інші види занять за тематикою книги проводяться в КПІ ім. Ігоря Сікорського на кафедрах «Прикладна фізика» та «Мікро-електроніка» за напрямом матеріалознавство, що необхідно студентам вищих навчальних закладів при виконанні наукових робіт. Для здобувачів магістратури за спеціальністю 105 «Прикладна фізика та наноматеріали»

    Surface-Based tools for Characterizing the Human Brain Cortical Morphology

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    Tesis por compendio de publicacionesThe cortex of the human brain is highly convoluted. These characteristic convolutions present advantages over lissencephalic brains. For instance, gyrification allows an expansion of cortical surface area without significantly increasing the cranial volume, thus facilitating the pass of the head through the birth channel. Studying the human brain’s cortical morphology and the processes leading to the cortical folds has been critical for an increased understanding of the pathological processes driving psychiatric disorders such as schizophrenia, bipolar disorders, autism, or major depression. Furthermore, charting the normal developmental changes in cortical morphology during adolescence or aging can be of great importance for detecting deviances that may be precursors for pathology. However, the exact mechanisms that push cortical folding remain largely unknown. The accurate characterization of the neurodevelopment processes is challenging. Multiple mechanisms co-occur at a molecular or cellular level and can only be studied through the analysis of ex-vivo samples, usually of animal models. Magnetic Resonance Imaging can partially fill the breach, allowing the portrayal of the macroscopic processes surfacing on in-vivo samples. Different metrics have been defined to measure cortical structure to describe the brain’s morphological changes and infer the associated microstructural events. Metrics such as cortical thickness, surface area, or cortical volume help establish a relation between the measured voxels on a magnetic resonance image and the underlying biological processes. However, the existing methods present limitations or room for improvement. Methods extracting the lines representing the gyral and sulcal morphology tend to over- or underestimate the total length. These lines can provide important information about how sulcal and gyral regions function differently due to their distinctive ontogenesis. Nevertheless, some methods label every small fold on the cortical surface as a sulcal fundus, thus losing the perspective of lines that travel through the deeper zones of a sulcal basin. On the other hand, some methods are too restrictive, labeling sulcal fundi only for a bunch of primary folds. To overcome this issue, we have proposed a Laplacian-collapse-based algorithm that can delineate the lines traversing the top regions of the gyri and the fundi of the sulci avoiding anastomotic sulci. For this, the cortex, represented as a 3D surface, is segmented into gyral and sulcal surfaces attending to the curvature and depth at every point of the mesh. Each resulting surface is spatially filtered, smoothing the boundaries. Then, a Laplacian-collapse-based algorithm is applied to obtain a thinned representation of the morphology of each structure. These thin curves are processed to detect where the extremities or endpoints lie. Finally, sulcal fundi and gyral crown lines are obtained by eroding the surfaces while preserving the structure topology and connectivity between the endpoints. The assessment of the presented algorithm showed that the labeled sulcal lines were close to the proposed ground truth length values while crossing through the deeper (and more curved) regions. The tool also obtained reproducibility scores better or similar to those of previous algorithms. A second limitation of the existing metrics concerns the measurement of sulcal width. This metric, understood as the physical distance between the points on opposite sulcal banks, can come in handy in detecting cortical flattening or complementing the information provided by cortical thickness, gyrification index, or such features. Nevertheless, existing methods only provided averaged measurements for different predefined sulcal regions, greatly restricting the possibilities of sulcal width and ignoring the intra-region variability. Regarding this, we developed a method that estimates the distance from each sulcal point in the cortex to its corresponding opposite, thus providing a per-vertex map of the physical sulcal distances. For this, the cortical surface is sampled at different depth levels, detecting the points where the sulcal banks change. The points corresponding to each sulcal wall are matched with the closest point on a different one. The distance between those points is the sulcal width. The algorithm was validated against a simulated sulcus that resembles a simple fold. Then the tool was used on a real dataset and compared against two widely-used sulcal width estimation methods, averaging the proposed algorithm’s values into the same region definition those reference tools use. The resulting values were similar for the proposed and the reference methods, thus demonstrating the algorithm’s accuracy. Finally, both algorithms were tested on a real aging population dataset to prove the methods’ potential in a use-case scenario. The main idea was to elucidate fine-grained morphological changes in the human cortex with aging by conducting three analyses: a comparison of the age-dependencies of cortical thickness in gyral and sulcal lines, an analysis of how the sulcal and gyral length changes with age, and a vertex-wise study of sulcal width and cortical thickness. These analyses showed a general flattening of the cortex with aging, with interesting findings such as a differential age-dependency of thickness thinning in the sulcal and gyral regions. By demonstrating that our method can detect this difference, our results can pave the way for future in vivo studies focusing on macro- and microscopic changes specific to gyri or sulci. Our method can generate new brain-based biomarkers specific to sulci and gyri, and these can be used on large samples to establish normative models to which patients can be compared. In parallel, the vertex-wise analyses show that sulcal width is very sensitive to changes during aging, independent of cortical thickness. This corroborates the concept of sulcal width as a metric that explains, in the least, the unique variance of morphology not fully captured by existing metrics. Our method allows for sulcal width vertex-wise analyses that were not possible previously, potentially changing our understanding of how changes in sulcal width shape cortical morphology. In conclusion, this thesis presents two new tools, open source and publicly available, for estimating cortical surface-based morphometrics. The methods have been validated and assessed against existing algorithms. They have also been tested on a real dataset, providing new, exciting insights into cortical morphology and showing their potential for defining innovative biomarkers.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidente: Juan Domingo Gispert López.- Secretario: Norberto Malpica González de Vega.- Vocal: Gemma Cristina Monté Rubi

    Drift-diffusion models for innovative semiconductor devices and their numerical solution

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    We present charge transport models for novel semiconductor devices which may include ionic species as well as their thermodynamically consistent finite volume discretization
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