Use of Coarse-resolution models of species' distributions to guide local conservation inferences

Distribution models are used increasingly for species conservation assessments over extensive areas, but the spatial resolution of the modeled data and, consequently, of the predictions generated directly from these models are usually too coarse for local conservation applications. Comprehensive distribution data at finer spatial resolution, however, require a level of sampling that is impractical for most species and regions. Models can be downscaled to predict distribution at finer resolutions, but this increases uncertainty because the predictive ability of models is not necessarily consistent beyond their original scale. We analyzed the performance of downscaled, previously published models of environmental favorability (a generalized linear modeling technique) for a restricted endemic insectivore, the Iberian desman (Galemys pyrenaicus), and a more widespread carnivore, the Eurasian otter ( Lutra lutra), in the Iberian Peninsula. The models, built from presence–absence data at 10 × 10 km resolution, were extrapolated to a resolution 100 times finer (1 × 1 km). We compared downscaled predictions of environmental quality for the two species with published data on local observations and on important conservation sites proposed by experts. Predictions were significantly related to observed presence or absence of species and to expert selection of sampling sites and important conservation sites. Our results suggest the potential usefulness of downscaled projections of environmental quality as a proxy for expensive and time-consuming field studies when the field studies are not feasible. This method may be valid for other similar species if coarse-resolution distribution data are available to define high-quality areas at a scale that is practical for the application of concrete conservation measures

Numerical simulations of the spine for patient-specific models of severe scoliosis

In silico methods, such as musculoskeletal modelling, may aid the selection of the optimal surgical treatment for highly complex pathologies such as scoliosis. Many musculoskeletal models use a generic, simplified representation of the intervertebral joints, which are fundamental to the flexibility of the spine. Therefore, to model and simulate the spine, a suitable representation of the intervertebral joint is crucial. The aim of this PhD was to characterise specimen-specific models of the intervertebral joint for multi-body models from experimental datasets. First, the project investigated the characterisation of a specimen-specific lumped parameter model of the intervertebral joint from an experimental dataset of a four-vertebra lumbar spine segment. Specimen-specific stiffnesses were determined with an optimisation method. The sensitivity of the parameters to the joint pose was investigate. Results showed the stiffnesses and predicted motions were highly depended on both the joint pose. Following the first study, the method was reapplied to another dataset that included six complete lumbar spine segments under three different loading conditions. Specimen-specific uniform stiffnesses across joint levels and level-dependent stiffnesses were calculated by optimisation. Specimen-specific stiffness show high inter-specimen variability and were also specific to the loading condition. Level-dependent stiffnesses are necessary for accurate kinematic predictions and should be determined independently of one another. Secondly, a framework to create subject-specific musculoskeletal models of individuals with severe scoliosis was developed. This resulted in a robust codified pipeline for creating subject-specific, severely scoliotic spine models from CT data. In conclusion, this thesis showed that specimen-specific intervertebral joint stiffnesses were highly sensitive to joint pose definition and the importance of level-dependent optimisation. Further, an open-source codified pipeline to create patient-specific scoliotic spine models from CT data was released. These studies and this pipeline can facilitate the specimen-specific characterisation of the scoliotic intervertebral joint and its incorporation into scoliotic musculoskeletal spine models.

Data driven regularization models of non-linear ill-posed inverse problems in imaging

Imaging technologies are widely used in application fields such as natural sciences, engineering, medicine, and life sciences. A broad class of imaging problems reduces to solve ill-posed inverse problems (IPs). Traditional strategies to solve these ill-posed IPs rely on variational regularization methods, which are based on minimization of suitable energies, and make use of knowledge about the image formation model (forward operator) and prior knowledge on the solution, but lack in incorporating knowledge directly from data. On the other hand, the more recent learned approaches can easily learn the intricate statistics of images depending on a large set of data, but do not have a systematic method for incorporating prior knowledge about the image formation model. The main purpose of this thesis is to discuss data-driven image reconstruction methods which combine the benefits of these two different reconstruction strategies for the solution of highly nonlinear ill-posed inverse problems. Mathematical formulation and numerical approaches for image IPs, including linear as well as strongly nonlinear problems are described. More specifically we address the Electrical impedance Tomography (EIT) reconstruction problem by unrolling the regularized Gauss-Newton method and integrating the regularization learned by a data-adaptive neural network. Furthermore we investigate the solution of non-linear ill-posed IPs introducing a deep-PnP framework that integrates the graph convolutional denoiser into the proximal Gauss-Newton method with a practical application to the EIT, a recently introduced promising imaging technique. Efficient algorithms are then applied to the solution of the limited electrods problem in EIT, combining compressive sensing techniques and deep learning strategies. Finally, a transformer-based neural network architecture is adapted to restore the noisy solution of the Computed Tomography problem recovered using the filtered back-projection method.

Analytic models of self-gravitating rotating gaseous tori with central black hole

In this thesis project, I present stationary models of rotating fluids with toroidal distributions that can be used to represent the active galactic nuclei (AGN) central obscurers, i.e. molecular tori (Combes et al., 2019), as well as geometrically thick accretion discs, like ADAF discs (Narayan and Yi, 1995) or Polish doughnuts (Abramowicz, 2005). In particular, I study stationary rotating systems with a more general baroclinic distribution (with a vertical gradient of the angular velocity), which are often more realistic and less studied, due to their complexity, than the barotropic ones (with cylindrical rotation), which are easier to construct. In the thesis, I compute analytically the main intrinsic and projected properties of the power-law tori based on the potential-density pairs of Ciotti and Bertin (2005). I study the density distribution and the resulting gravitational potential for different values of α, in the range 2 < α < 5. For the same models, I compute the surface density of the systems when seen face-on and edge-on. I then apply the stationary Euler equations to obtain rotational velocity and temperature distributions of the self-gravitating models in the absence of an external gravitational potential. In the thesis I also consider the power-law tori with the presence of a central black hole in addition to the gas self-gravity, and solving analytically the stationary Euler equations, I compute how the properties of the system are modified by the black hole and how they vary as a function of the black hole mass. Finally, applying the Solberg-Høiland criterion, I show that these baroclinic stationary models are linearly stable in the absence of the black hole. In the presence of the black hole I derive the analytical condition for stability, which depends on α and on the black hole mass. I also study the stability of the tori in the hypothesis that they are weakly magnetized, finding that they are always unstable to this instability.

Computational Analysis Based on Parametric Models of Historic Masonry Vaulted Structures Strengthened by Fiber-Reinforced Composite Materials

Historic vaulted masonry structures often need strengthening interventions that can effectively improve their structural performance, especially during seismic events, and at the same time respect the existing setting and the modern conservation requirements. In this context, the use of innovative materials such as fiber-reinforced composite materials has been shown as an effective solution that can satisfy both aspects. This work aims to provide insight into the computational modeling of a full-scale masonry vault strengthened by fiber-reinforced composite materials and analyze the influence of the arrangement of the reinforcement on the efficiency of the intervention. At first, a parametric model of a cross vault focusing on a realistic representation of its micro-geometry is proposed. Then numerical modeling, simulating the pushover analyses, of several barrel vaults reinforced with different reinforcement configurations is performed. Finally, the results are collected and discussed in terms of force-displacement curves obtained for each proposed configuration.

Estudio del “plan fronteras para la prosperidad” en el departamento de Norte de Santander (2010-2013)

El interés de este estudio de caso es examinar la política pública “Plan Fronteras para la Prosperidad en el departamento de Norte de Santander” por medio de la realización de un análisis de política pública enfocado en la fase de la implementación de la misma. Se analiza y explica cómo es implementada la política pública en la sociedad beneficiaria de la misma y se identifican las herramientas dedicadas a cumplir tal fin. Posteriormente se evalúa la pertinencia de las mismas a través de un análisis de actores y una relación costo-efectividad comparada con otras de entidades del gobierno nacional, para así concluir cómo se desarrolló el proceso de implementación del PFP en el mencionado departamento.

Solvable models of Bose-Einstein condensates: A new algebraic Bethe ansatz scheme

A new algebraic Bethe ansatz scheme is proposed to diagonalize classes of integrable models relevant to the description of Bose-Einstein condensation in dilute alkali gases. This is achieved by introducing the notion of Z-graded representations of the Yang-Baxter algebra. (C) 2003 American Institute of Physics.

Mathematical models of influenza A virus infection : from intracellular replication to virus growth in cell populations

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2015

Analysis of animal models of neurodegenerative diseases with protein deposits

Otto-von-Guericke-Universität Magdeburg, Fakultät für Naturwissenschaften, Univ., Dissertation, 2016

Static diffusion models of the upper atmosphere with empirical temperature profiles.

v. 8 no. 9

The convenient calculation of some test statistics in models of discrete choice

The paper considers the use of artificial regression in calculating different types of score test when the log