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The transition towards flexible work arrangements, notably work-from-home (WFH) practices, has prompted significant discourse on their potential to reshape urban landscapes. While existing urban growth models (UGM) offer insights into environmental and economic impacts, There is a need to study the urban phenomena from the bottom-up style, considering the essential influence of individuals' behavior and decision-making process at disaggregate and local levels (Brail, 2008, p. 89). Addressing this gap, this study aims to comprehensively understand how evolving work modalities influence the urban form and land use patterns by focusing on socioeconomic and environmental factors. This research employs an Agent-Based Model (ABM) and Artificial Neural Network (ANN), integrated with GIS technologies, to predict the future Land Use and Land Cover (LULC) changes within Roanoke, Virginia. The study uniquely explores the dynamic interplay between macro-level policies and micro-level individual behaviors—categorized by employment types, social activities, and residential choices—shedding light on their collective impact on urban morphology. Contrary to conventional expectations, findings reveal that the current low rate in WFH practices has not significantly redirected urban development trends towards sprawl but rather has emphasized urban densification, largely influenced by on-site work modalities. This observation is corroborated by WFH ratios not exceeding 10% in any analyzed census tract. Regarding model performance, the integration of micro-agents into the model substantially improved its accuracy from 86% to 89.78%, enabling a systematic analysis of residential preferences between WFH and on-site working (WrOS) agents. Furthermore, logistic regression analysis and decision score maps delineate the distinct spatial preferences of these agent groups, highlighting a pronounced suburban and rural preference among WFH agents, in contrast to the urban-centric inclination of WrOS agents. Utilizing ABM and ANN integrated with GIS technologies, this research advances the precision and complexity of urban growth predictions. The findings contribute valuable insights for urban planners and policymakers and underline the intricate relationships between work modalities and urban structure, challenging existing paradigms and setting a precedent for future urban planning methodologies. / Doctor of Philosophy / As more people start working from home, cities might change unexpectedly. This study in Roanoke, Virginia, explores how work-from-home (WFH) practices affect urban development. Traditional city growth models look at big-picture trends, but this study dives into the details of workers' individual behaviors and their residential choices. Using advanced computer models such as machine learning and geographic information systems (GIS), predictions are made on how different work arrangements influence where workers live and how cities expand. Surprisingly, fewer people work from home than expected. This hasn't caused cities to spread out more. Instead, Roanoke is expected to become denser in the next ten years because on-site workers tend to live in urban centers, while those who work from home prefer suburban and rural areas and, sometimes, urban. Different work arrangements lead to distinct residential preferences. By including the workers' individual behaviors in the models, the model's accuracy increased from 86% to 89.78%. Logistic regression analysis highlights the factors influencing land use changes, such as proximity to roads, slopes, home values, and wages. This research helps city planners and policymakers understand working arrangement trends and create better policies to manage urban development. It shows the complex relationship between work practices and city structures, providing valuable insights for future city planning.

The main objective of this study is to develop a modeling methodology that facilitates incorporating the plant pathway into environmental contamination models recognizing the fact that plants are dynamic entities that regulate their life cycle according to natural and anthropogenic environmental conditions. A modeling framework that incorporates the plant pathway into an integrated water flow and contaminant transport model in terrestrial systems is developed. The modeling framework is aimed to provide a tool to analyze the plant pathway of exposure to contaminants. The model developed using this framework describes the temporal and spatial variation of the contaminant concentration within the plant as it is interacting with the soil and the atmosphere. The first part of the study focuses on the integration of the dynamics of water and contaminant distribution and plant related processes within the vadose zone. A soil-plant system model is developed by coupling soil-water flow, contaminant transport, plant life-cycle, and plant pathway models. The outcome unifies single media continuous models with multimedia compartmental models in a flexible framework. The coupling of the models was established at multiple interfaces and at different levels of solution steps (i.e. model development phase vs. numerical solution phase). In the second part of the study, the soil-plant system model is extended to cover large spatial areas by describing the environmental system as a collection of soil-plant systems connected through overland flow and transport processes on the ground surface and through lateral interactions in the subsurface. An overland flow model is integrated with the previously coupled model of unsaturated zone soil-water flow and plant life-cycle by solving the flow model equations simultaneously within a single global matrix structure. An overland / subsurface interaction algorithm is developed to handle the ground surface conditions. The simultaneous solution, single-matrix approach is also adopted when integrating the overland transport model with the previously coupled models of vadose zone transport and plant pathway. The model developed is applied to various environmental contamination scenarios where the effect of the presence of plants on the contaminant migration within environmental systems is investigated.

L'objective de la thèse est de comprendre s'il est envisageable d'utiliser les modèles qui décrivent la croissance tumorale (systèmes d'EDP) pour des applications médicales. En particulier, les modèles paramétriques sont calibrés en utilisant les données d'imagerie médicale d'un patient. Une fois calibré, le modèle donne une représentation de la croissance tumorale. Des techniques différentes sont proposées. Un approche classique basé sur la sensibilité est comparé à un approche réduit basé sur la Proper Orthogonal Decomposition. Des cas réalistes concernants l'étude des métastases dans les poumons ont été mis à point en collaboration avec l'Institut Bergonié. Des exigence pratique de traitement de l'image ont motivé l'étude des méthodes de recalage non-rigide des images et parmi ceux là, le transport optimale. Un étude de la numérique du problème de Monge-Kantorovich est décrit, avec des cas test numérique. Des applications concernants l'application de la distance de Wasserstein à la réduction de modèle sont envisagées. / The main purpose of this work was to understand if and wether PDE based modeling of tumor growth may be used in realistic applications. Models proposed in the literature are parametric. The goal is to identify parameters in such a way that the pathology evolution of a given patient is recovered. The identification is performed by means of inverse problems, taking medical images as data.Different techniques were tested: a classical Sensitivity approach is compared to a reduced one, based on Proper Orthogonal Decomposition. Realistic cases were set up in collaboration with Institut Bergonié, concerning lung metastasis evolution.Practical needs when dealing with medical images pushed us to interest to Optimal transport theory and Monge-Kantorovich problem. A numerical study was carried out and a family of lagrangian methods proposed. A perspective on the using of Wasserstein distance in model reduction concludes this work.

Le cancer du sein est le cancer le plus fréquent chez les femmes. Il y a une multitude de solutions proposées concernant une éventuelle intervention médicale pour le cancer du sein ‐ une en particulier est la chirurgie mammaire conservatrice (tumoréctomie). Le but de la tumoréctomie est de parvenir à un contrôle local du cancer, ainsi que de préserver une forme du sein qui satisfait les besoins esthétiques de la femme. Bien que ces objectifs sont généralement atteint, il reste encore parfois des résultats inattendus,tels qu'une tumeur récurrence locale, ou des résultats cosmétiques insuffisants.L'objectif de cette thèse est de proposer une plateforme de calcul, qui contribue à la tumoréctomie. Cela comprend:1) Une étude de la dynamique de croissance des tumeurs du sein.2) Une étude sur la prédiction du contour du sein grâce a la chirurgie virtuelle.3) Un modèle de calcul de la forme finale du sein après cicatrisation. / Breast cancer is the most common cancer among women in the developed as well as the developing countries. There are a plethora of proposed solutions regarding possible medical interventions for breast cancer–one in particular is Breast Conserving Therapy (BCT). BCT comprises of complete surgical excision of the tumor (partialmastectomy), and post-operative radiotherapy for the remaining breast tissue. This is a feasible treatment for most women with breast cancer. The goal of BCT is toachieve local control of the cancer, as well as to preserve breast shape that appeases awoman’s cosmetic concerns. Although these goals are usually achieved, there are still occasional unexpected results, such as reexcision of the tumor due to a positive margin assessment, tumor local recurrence, unsatisfactory cosmetic results, and breastpain. Other than surgical experience and judgment, there are currently no toolswhich can predict the outcome of partial mastectomy on the contour and deformity of the treated breast. The objective of this dissertation is to propose computational framework, which contributes to BCT operations, this was achieve by exploring two areas.On the one hand we developed a multiscale model adapted for breast cancer tumor growth, ductal carcinoma in situ (DCIS). The model features included: nutrients growth limitation, wall degradation enzyme and HER2 chemical expression tumor phenotype. Our model successfully simulate some pattern of DCIS carcinoma.Among the interesting result we showed that the enzyme contributed to a greater tumor size and that when HER2 was over expressed, the growth limiting factor wasthe EGFR. On the other hand, we developed a virtual surgery box to simulate BCT surgery. The box will input MRI patient data and will output cosmetic and functional indicator to rate the impact of the surgery. It appears that stiffness of the tissue, resection radius as well as the lump quadrant location are the most sensitive parameters to the indicators. A healing model was also embedded to simulate the wound closure after resection, this model was stress dependent and illustrate anasymmetric wound closure progression.The tools developed in this research allows a new type of field convergence between the surgery and computation field. At the local level it will allow surgeons and patient to be able to communicate on the pertinence and necessity of performing alumpectomy surgery, enabling to anticipate the possible outcome of the operation.On the global aspect this type of tool gives birth to a new type of field: computational surgery, where computer scientist and surgeons work hand in hand to provide the best and the most reliable service to the patients.

Geospatial analysis involves application of statistical methods, algorithms and information retrieval techniques to geospatial data. It incorporates time into spatial databases and facilitates investigation of land cover (LC) dynamics through data, model, and analytics. LC dynamics induced by human and natural processes play a major role in global as well as regional scale patterns, which in turn influence weather and climate. Hence, understanding LC dynamics at the local / regional as well as at global levels is essential to evolve appropriate management strategies to mitigate the impacts of LC changes. This can be captured through the multi-resolution remote sensing (RS) data. However, with the advancements in sensor technologies, suitable algorithms and techniques are required for optimal integration of information from multi-resolution sensors which are cost effective while overcoming the possible data and methodological constraints. In this work, several per-pixel traditional and advanced classification techniques have been evaluated with the multi-resolution data along with the role of ancillary geographical data on the performance of classifiers. Techniques for linear and non-linear un-mixing, endmember variability and determination of spatial distribution of class components within a pixel have been applied and validated on multi-resolution data. Endmember estimation method is proposed and its performance is compared with manual, semi-automatic and fully automatic methods of endmember extraction. A novel technique - Hybrid Bayesian Classifier is developed for per pixel classification where the class prior probabilities are determined by un-mixing a low spatial-high spectral resolution multi-spectral data while posterior probabilities are determined from the training data obtained from ground, that are assigned to every pixel in a high spatial-low spectral resolution multi-spectral data in Bayesian classification. These techniques have been validated with multi-resolution data for various landscapes with varying altitudes. As a case study, spatial metrics and cellular automata based models applied for rapidly urbanising landscape with moderate altitude has been carried out.

Die vorliegende Arbeit gliedert sich in zwei Hauptteile. Im ersten Teil werden verschiedene geometrische Konkurrenzindizes (CIs) auf ihre Eignung zur Abbildung der Konkurrenzverhältnisse in zweischichtigen Waldumbaubeständen aus Fichte (Picea abies (L.) Karst.) und Buche (Fagus sylvatica L.) hin untersucht. Die dazu angelegten Versuchsflächen befinden sich im Sächsischen Forstamt Heinzebank und umfassen verschiedene Altersstadien des Voranbaus von Buche unter Fichte. Der zweite Teil der Arbeit beschäftigt sich mit der Modellierung des Einzelbaumwachstums und der Mortalität in mehrschichtigen Beständen der oben genannten Baumarten. Dazu wird als Ausgangsbasis der an der Niedersächsischen Forstlichen Versuchsanstalt entwickelte Waldwachstumssimulator BWINPro verwendet. / The presented thesis is divided into two parts. In the first part competition effects on height and basal area increment in mixed two-storey stands of Norway spruce (Picea abies L.) and European beech (Fagus sylvatica L.) are investigated using different types of geometrical competition indices. For this purpose a series of three trial plots was established in 2001 in the forest district of Heinzebank which is located in the central Erzgebirge Mountains, East Germany. The plots were set up in a "chronosequential" order. They approximately represent the three age stages of 10, 20, and 40 years in the second layer of beech under canopies of Norway spruce aged 50, 100, and 130 years, respectively. The second part of the thesis deals with the modeling of growth and mortality in stands of the above described type. The single-tree oriented simulator BWINPro originally developed for growth and yield analyses and prognoses in Lower Saxony, Germany, was chosen as a starting point to develop a modified tool for Saxony.