Modelagem multiescala de fenômenos eletrocinéticos em meios porosos carregados eletricamente: aplicação a meios porosos argilosos

In this master thesis, we propose a multiscale mathematical and computational model for electrokinetic phenomena in porous media electrically charged. We consider a porous medium rigid and incompressible saturated by an electrolyte solution containing four monovalent ionic solutes completely diluted in the aqueous solvent. Initially we developed the modeling electrical double layer how objective to compute the electrical potential, surface density of electrical charges and considering two chemical reactions, we propose a 2-pK model for calculating the chemical adsorption occurring in the domain of electrical double layer. Having the nanoscopic model, we deduce a model in the microscale, where the electrochemical adsorption of ions, protonation/ deprotonation reactions and zeta potential obtained in the nanoscale, are incorporated through the conditions of interface uid/solid of the Stokes problem and transportation of ions, modeled by equations of Nernst-Planck. Using the homogenization technique of periodic structures, we develop a model in macroscopic scale with respective cells problems for the e ective macroscopic parameters of equations. Finally, we propose several numerical simulations of the multiscale model for uid ow and transport of reactive ionic solute in a saturated aqueous solution of kaolinite. Using nanoscopic model we propose some numerical simulations of electrochemical adsorption phenomena in the electrical double layer. Making use of the nite element method discretize the macroscopic model and propose some numerical simulations in basic and acid system aiming to quantify the transport of ionic solutes in porous media electrically charged.

Modelagem multiescala de fenômenos eletrocinéticos em meios porosos carregados eletricamente: aplicação a meios porosos argilosos

In this master thesis, we propose a multiscale mathematical and computational model for electrokinetic phenomena in porous media electrically charged. We consider a porous medium rigid and incompressible saturated by an electrolyte solution containing four monovalent ionic solutes completely diluted in the aqueous solvent. Initially we developed the modeling electrical double layer how objective to compute the electrical potential, surface density of electrical charges and considering two chemical reactions, we propose a 2-pK model for calculating the chemical adsorption occurring in the domain of electrical double layer. Having the nanoscopic model, we deduce a model in the microscale, where the electrochemical adsorption of ions, protonation/ deprotonation reactions and zeta potential obtained in the nanoscale, are incorporated through the conditions of interface uid/solid of the Stokes problem and transportation of ions, modeled by equations of Nernst-Planck. Using the homogenization technique of periodic structures, we develop a model in macroscopic scale with respective cells problems for the e ective macroscopic parameters of equations. Finally, we propose several numerical simulations of the multiscale model for uid ow and transport of reactive ionic solute in a saturated aqueous solution of kaolinite. Using nanoscopic model we propose some numerical simulations of electrochemical adsorption phenomena in the electrical double layer. Making use of the nite element method discretize the macroscopic model and propose some numerical simulations in basic and acid system aiming to quantify the transport of ionic solutes in porous media electrically charged.

Temporal Coding of Volumetric Imagery

'Image volumes' refer to realizations of images in other dimensions such as time, spectrum, and focus. Recent advances in scientific, medical, and consumer applications demand improvements in image volume capture. Though image volume acquisition continues to advance, it maintains the same sampling mechanisms that have been used for decades; every voxel must be scanned and is presumed independent of its neighbors. Under these conditions, improving performance comes at the cost of increased system complexity, data rates, and power consumption.

This dissertation explores systems and methods capable of efficiently improving sensitivity and performance for image volume cameras, and specifically proposes several sampling strategies that utilize temporal coding to improve imaging system performance and enhance our awareness for a variety of dynamic applications.

Video cameras and camcorders sample the video volume (x,y,t) at fixed intervals to gain understanding of the volume's temporal evolution. Conventionally, one must reduce the spatial resolution to increase the framerate of such cameras. Using temporal coding via physical translation of an optical element known as a coded aperture, the compressive temporal imaging (CACTI) camera emonstrates a method which which to embed the temporal dimension of the video volume into spatial (x,y) measurements, thereby greatly improving temporal resolution with minimal loss of spatial resolution. This technique, which is among a family of compressive sampling strategies developed at Duke University, temporally codes the exposure readout functions at the pixel level.

Since video cameras nominally integrate the remaining image volume dimensions (e.g. spectrum and focus) at capture time, spectral (x,y,t,\lambda) and focal (x,y,t,z) image volumes are traditionally captured via sequential changes to the spectral and focal state of the system, respectively. The CACTI camera's ability to embed video volumes into images leads to exploration of other information within that video; namely, focal and spectral information. The next part of the thesis demonstrates derivative works of CACTI: compressive extended depth of field and compressive spectral-temporal imaging. These works successfully show the technique's extension of temporal coding to improve sensing performance in these other dimensions.

Geometrical optics-related tradeoffs, such as the classic challenges of wide-field-of-view and high resolution photography, have motivated the development of mulitscale camera arrays. The advent of such designs less than a decade ago heralds a new era of research- and engineering-related challenges. One significant challenge is that of managing the focal volume (x,y,z) over wide fields of view and resolutions. The fourth chapter shows advances on focus and image quality assessment for a class of multiscale gigapixel cameras developed at Duke.

Along the same line of work, we have explored methods for dynamic and adaptive addressing of focus via point spread function engineering. We demonstrate another form of temporal coding in the form of physical translation of the image plane from its nominal focal position. We demonstrate this technique's capability to generate arbitrary point spread functions.

Learning from Geometry

Subspaces and manifolds are two powerful models for high dimensional signals. Subspaces model linear correlation and are a good fit to signals generated by physical systems, such as frontal images of human faces and multiple sources impinging at an antenna array. Manifolds model sources that are not linearly correlated, but where signals are determined by a small number of parameters. Examples are images of human faces under different poses or expressions, and handwritten digits with varying styles. However, there will always be some degree of model mismatch between the subspace or manifold model and the true statistics of the source. This dissertation exploits subspace and manifold models as prior information in various signal processing and machine learning tasks.

A near-low-rank Gaussian mixture model measures proximity to a union of linear or affine subspaces. This simple model can effectively capture the signal distribution when each class is near a subspace. This dissertation studies how the pairwise geometry between these subspaces affects classification performance. When model mismatch is vanishingly small, the probability of misclassification is determined by the product of the sines of the principal angles between subspaces. When the model mismatch is more significant, the probability of misclassification is determined by the sum of the squares of the sines of the principal angles. Reliability of classification is derived in terms of the distribution of signal energy across principal vectors. Larger principal angles lead to smaller classification error, motivating a linear transform that optimizes principal angles. This linear transformation, termed TRAIT, also preserves some specific features in each class, being complementary to a recently developed Low Rank Transform (LRT). Moreover, when the model mismatch is more significant, TRAIT shows superior performance compared to LRT.

The manifold model enforces a constraint on the freedom of data variation. Learning features that are robust to data variation is very important, especially when the size of the training set is small. A learning machine with large numbers of parameters, e.g., deep neural network, can well describe a very complicated data distribution. However, it is also more likely to be sensitive to small perturbations of the data, and to suffer from suffer from degraded performance when generalizing to unseen (test) data.

From the perspective of complexity of function classes, such a learning machine has a huge capacity (complexity), which tends to overfit. The manifold model provides us with a way of regularizing the learning machine, so as to reduce the generalization error, therefore mitigate overfiting. Two different overfiting-preventing approaches are proposed, one from the perspective of data variation, the other from capacity/complexity control. In the first approach, the learning machine is encouraged to make decisions that vary smoothly for data points in local neighborhoods on the manifold. In the second approach, a graph adjacency matrix is derived for the manifold, and the learned features are encouraged to be aligned with the principal components of this adjacency matrix. Experimental results on benchmark datasets are demonstrated, showing an obvious advantage of the proposed approaches when the training set is small.

Stochastic optimization makes it possible to track a slowly varying subspace underlying streaming data. By approximating local neighborhoods using affine subspaces, a slowly varying manifold can be efficiently tracked as well, even with corrupted and noisy data. The more the local neighborhoods, the better the approximation, but the higher the computational complexity. A multiscale approximation scheme is proposed, where the local approximating subspaces are organized in a tree structure. Splitting and merging of the tree nodes then allows efficient control of the number of neighbourhoods. Deviation (of each datum) from the learned model is estimated, yielding a series of statistics for anomaly detection. This framework extends the classical {\em changepoint detection} technique, which only works for one dimensional signals. Simulations and experiments highlight the robustness and efficacy of the proposed approach in detecting an abrupt change in an otherwise slowly varying low-dimensional manifold.

Perspectives on gender, migration and transnational work: joint work of social reproduction and care in Southern Europe

This paper presents the "state of the art" and some of the main issues discussed in relation to the topic of transnational migration and reproductive work in southern Europe. We start doing a genealogy of the complex theoretical development leading to the consolidation of the research program, linking consideration of gender with transnational migration and transformation of work and ways of survival, thus making the production aspects as reproductive, in a context of globalization. The analysis of the process of multiscale reconfiguration of social reproduction and care, with particular attention to its present global dimension is presented, pointing to the turning point of this line of research that would have taken place with the beginning of this century, with the rise notions such as "global care chains" (Hochschild, 2001), or "care drain" (Ehrenreich and Hochschild, 2013). Also, the role of this new agency, now composed in many cases women who migrate to other countries or continents, precisely to address these reproductive activities, is recognized. Finally, reference is made to some of the new conceptual and theoretical developments in this area.

On the Nature of Temporal Variability of the Gulf Stream Path from 75° to 55°W

The response of the Gulf Stream (GS) system to atmospheric forcing is generally linked either to the basin-scale winds on the subtropical gyre or to the buoyancy forcing from the Labrador Sea. This study presents a multiscale synergistic perspective to describe the low-frequency response of the GS system. The authors identify dominant temporal variability in the North Atlantic Oscillation (NAO), in known indices of the GS path, and in the observed GS latitudes along its path derived from sea surface height (SSH) contours over the period 1993-2013. The analysis suggests that the signature of interannual variability changes along the stream's path from 75 degrees to 55 degrees W. From its separation at Cape Hatteras to the west of 65 degrees W, the variability of the GS is mainly in the near-decadal (7-10 years) band, which is missing to the east of 60 degrees W, where a new interannual (4-5 years) band peaks. The latter peak (4-5 years) was missing to the west of 65 degrees W. The region between 65 degrees and 60 degrees W seems to be a transition region. A 2-3-yr secondary peak was pervasive in all time series, including that for the NAO. This multiscale response of the GS system is supported by results from a basin-scale North Atlantic model. The near-decadal response can be attributed to similar forcing periods in the NAO signal; however, the interannual variability of 4-5 years in the eastern segment of the GS path is as yet unexplained. More numerical and observational studies are warranted to understand such causality.

On the Nature of Temporal Variability of the Gulf Stream Path from 75° to 55°W

The response of the Gulf Stream (GS) system to atmospheric forcing is generally linked either to the basin-scale winds on the subtropical gyre or to the buoyancy forcing from the Labrador Sea. This study presents a multiscale synergistic perspective to describe the low-frequency response of the GS system. The authors identify dominant temporal variability in the North Atlantic Oscillation (NAO), in known indices of the GS path, and in the observed GS latitudes along its path derived from sea surface height (SSH) contours over the period 1993-2013. The analysis suggests that the signature of interannual variability changes along the stream's path from 75 degrees to 55 degrees W. From its separation at Cape Hatteras to the west of 65 degrees W, the variability of the GS is mainly in the near-decadal (7-10 years) band, which is missing to the east of 60 degrees W, where a new interannual (4-5 years) band peaks. The latter peak (4-5 years) was missing to the west of 65 degrees W. The region between 65 degrees and 60 degrees W seems to be a transition region. A 2-3-yr secondary peak was pervasive in all time series, including that for the NAO. This multiscale response of the GS system is supported by results from a basin-scale North Atlantic model. The near-decadal response can be attributed to similar forcing periods in the NAO signal; however, the interannual variability of 4-5 years in the eastern segment of the GS path is as yet unexplained. More numerical and observational studies are warranted to understand such causality.

Freak waves and modulational dynamics in plasmas with negative ions

The nonlinear dynamics of modulated electrostatic wavepackets propagating in negativeion plasmas is investigated from first principles. A nonlinear Schrödinger equation is derived by adopting a multiscale technique. The stability of breather- like (bright envelope soliton) structures, considered as a precursor to freak wave (rogue wave) formation, is investigated and then tested via numerical simulations.

Multi-scale colorectal tumour segmentation using a novel coarse to fine strategy

This paper addresses the problem of colorectal tumour segmentation in complex real world imagery. For efficient segmentation, a multi-scale strategy is developed for extracting the potentially cancerous region of interest (ROI) based on colour histograms while searching for the best texture resolution. To achieve better segmentation accuracy, we apply a novel bag-of-visual-words method based on rotation invariant raw statistical features and random projection based l2-norm sparse representation to classify tumour areas in histopathology images. Experimental results on 20 real world digital slides demonstrate that the proposed algorithm results in better recognition accuracy than several state of the art segmentation techniques.

Nanoindentation Relaxation Study and Micromechanics of Cement-Based Materials

Ce travail évalue le comportement mécanique des matériaux cimentaires à différentes échelles de distance. Premièrement, les propriétés mécaniques du béton produit avec un bioplastifiant à base de microorganismes efficaces (EM) sont etudiées par nanoindentation statistique, et comparées aux propriétés mécaniques du béton produit avec un superplastifiant ordinaire (SP). Il est trouvé que l’ajout de bioplastifiant à base de produit EM améliore la résistance des C–S–H en augmentant la cohésion et la friction des nanograins solides. L’analyse statistique des résultats d’indentation suggère que le bioplastifiant à base de produit EM inhibe la précipitation des C–S–H avec une plus grande fraction volumique solide. Deuxièmement, un modèle multi-échelles à base micromécanique est dérivé pour le comportement poroélastique de la pâte de ciment au jeune age. L’approche proposée permet d’obtenir les propriétés poroélastiques requises pour la modélisation du comportoment mécanique partiellement saturé des pâtes de ciment viellissantes. Il est montré que ce modèle prédit le seuil de percolation et le module de Young non drainé de façon conforme aux données expérimentales. Un metamodèle stochastique est construit sur la base du chaos polynomial pour propager l’incertitude des paramètres du modèle à travers plusieurs échelles de distance. Une analyse de sensibilité est conduite par post-traitement du metamodèle pour des pâtes de ciment avec ratios d’eau sur ciment entre 0.35 et 0.70. Il est trouvé que l’incertitude sous-jacente des propriétés poroélastiques équivalentes est principalement due à l’énergie d’activation des aluminates de calcium au jeune age et, plus tard, au module élastique des silicates de calcium hydratés de basse densité.

Key insights for the future of urban ecosystem services research

Understanding the dynamics of urban ecosystem services is a necessary requirement for adequate planning, management, and governance of urban green infrastructure. Through the three-year Urban Biodiversity and Ecosystem Services (URBES) research project, we conducted case study and comparative research on urban biodiversity and ecosystem services across seven cities in Europe and the United States. Reviewing > 50 peer-reviewed publications from the project, we present and discuss seven key insights that reflect cumulative findings from the project as well as the state-of-the-art knowledge in urban ecosystem services research. The insights from our review indicate that cross-sectoral, multiscale, interdisciplinary research is beginning to provide a solid scientific foundation for applying the ecosystem services framework in urban areas and land management. Our review offers a foundation for seeking novel, nature-based solutions to emerging urban challenges such as wicked environmental change issues.

Predators on private land: broad-scale socioeconomic interactions influence large predator management

The proliferation of private land conservation areas (PLCAs) is placing increasing pressure on conservation authorities to effectively regulate their ecological management. Many PLCAs depend on tourism for income, and charismatic large mammal species are considered important for attracting international visitors. Broad-scale socioeconomic factors therefore have the potential to drive fine-scale ecological management, creating a systemic scale mismatch that can reduce long-term sustainability in cases where economic and conservation objectives are not perfectly aligned. We assessed the socioeconomic drivers and outcomes of large predator management on 71 PLCAs in South Africa. Owners of PLCAs that are stocking free-roaming large predators identified revenue generation as influencing most or all of their management decisions, and rated profit generation as a more important objective than did the owners of PLCAs that did not stock large predators. Ecotourism revenue increased with increasing lion (Panthera leo) density, which created a potential economic incentive for stocking lion at high densities. Despite this potential mismatch between economic and ecological objectives, lion densities were sustainable relative to available prey. Regional-scale policy guidelines for free-roaming lion management were ecologically sound. By contrast, policy guidelines underestimated the area required to sustain cheetah (Acinonyx jubatus), which occurred at unsustainable densities relative to available prey. Evidence of predator overstocking included predator diet supplementation and frequent reintroduction of game. We conclude that effective facilitation of conservation on private land requires consideration of the strong and not necessarily beneficial multiscale socioeconomic factors that influence private land management.

Classification multi-échelle d'images à très haute résolution spatiale basée sur une nouvelle approche texturale

Résumé : Face à l’accroissement de la résolution spatiale des capteurs optiques satellitaires, de nouvelles stratégies doivent être développées pour classifier les images de télédétection. En effet, l’abondance de détails dans ces images diminue fortement l’efficacité des classifications spectrales; de nombreuses méthodes de classification texturale, notamment les approches statistiques, ne sont plus adaptées. À l’inverse, les approches structurelles offrent une ouverture intéressante : ces approches orientées objet consistent à étudier la structure de l’image pour en interpréter le sens. Un algorithme de ce type est proposé dans la première partie de cette thèse. Reposant sur la détection et l’analyse de points-clés (KPC : KeyPoint-based Classification), il offre une solution efficace au problème de la classification d’images à très haute résolution spatiale. Les classifications effectuées sur les données montrent en particulier sa capacité à différencier des textures visuellement similaires. Par ailleurs, il a été montré dans la littérature que la fusion évidentielle, reposant sur la théorie de Dempster-Shafer, est tout à fait adaptée aux images de télédétection en raison de son aptitude à intégrer des concepts tels que l’ambiguïté et l’incertitude. Peu d’études ont en revanche été menées sur l’application de cette théorie à des données texturales complexes telles que celles issues de classifications structurelles. La seconde partie de cette thèse vise à combler ce manque, en s’intéressant à la fusion de classifications KPC multi-échelle par la théorie de Dempster-Shafer. Les tests menés montrent que cette approche multi-échelle permet d’améliorer la classification finale dans le cas où l’image initiale est de faible qualité. De plus, l’étude effectuée met en évidence le potentiel d’amélioration apporté par l’estimation de la fiabilité des classifications intermédiaires, et fournit des pistes pour mener ces estimations.

Three-dimensional, in-silico breast phantom for multimodality image simulations

We report on the construction of anatomically realistic three-dimensional in-silico breast phantoms with adjustable sizes, shapes and morphologic features. The concept of multiscale spatial resolution is implemented for generating breast tissue images from multiple modalities. Breast epidermal boundary and subcutaneous fat layer is generated by fitting an ellipsoid and 2nd degree polynomials to reconstructive surgical data and ultrasound imaging data. Intraglandular fat is simulated by randomly distributing and orienting adipose ellipsoids within a fibrous region immediately within the dermal layer. Cooper’s ligaments are simulated as fibrous ellipsoidal shells distributed within the subcutaneous fat layer. Individual ductal lobes are simulated following a random binary tree model which is generated based upon probabilistic branching conditions described by ramification matrices, as originally proposed by Bakic et al [3, 4]. The complete ductal structure of the breast is simulated from multiple lobes that extend from the base of the nipple and branch towards the chest wall. As lobe branching progresses, branches are reduced in height and radius and terminal branches are capped with spherical lobular clusters. Biophysical parameters are mapped onto the complete anatomical model and synthetic multimodal images (Mammography, Ultrasound, CT) are generated for phantoms of different adipose percentages (40%, 50%, 60%, and 70%) and are analytically compared with clinical examples. Results demonstrate that the in-silico breast phantom has applications in imaging performance evaluation and, specifically, great utility for solving image registration issues in multimodality imaging.

Combining Leaf Shape and Texture for Costa Rican Plant Species Identification

In the last decade, research in Computer Vision has developed several algorithms to help botanists and non-experts to classify plants based on images of their leaves. LeafSnap is a mobile application that uses a multiscale curvature model of the leaf margin to classify leaf images into species. It has achieved high levels of accuracy on 184 tree species from Northeast US. We extend the research that led to the development of LeafSnap along two lines. First, LeafSnap’s underlying algorithms are applied to a set of 66 tree species from Costa Rica. Then, texture is used as an additional criterion to measure the level of improvement achieved in the automatic identification of Costa Rica tree species. A 25.6% improvement was achieved for a Costa Rican clean image dataset and 42.5% for a Costa Rican noisy image dataset. In both cases, our results show this increment as statistically significant. Further statistical analysis of visual noise impact, best algorithm combinations per species, and best value of , the minimal cardinality of the set of candidate species that the tested algorithms render as best matches is also presented in this research