O novo Código florestal e a regularização do passivo ambiental dos imóveis rurais : o caso de Querência

Dissertação (mestrado)—Universidade de Brasília, Centro de Desenvolvimento Sustentável, Programa de Pós-Graduação em Desenvolvimento Sustentável, 2016.

Do acesso clássico à comercialização: especificidades do mercado imobiliário informal na área especial de interesse social de Mãe Luiza, Natal/ RN.

This study presents research regarding affordable housing and their effects on the spatial reconfiguration of Natal/ RN, aiming to identify the specificities of the informality of urban land. This study aims to understand how informal housing market operates housing provision for the population located in popular informal settlements, through buying and selling market and rental market of residential properties irregular / illegal. This understanding will be through the neighborhood of Mãe Luisa, Special Area of Social Interest (SASI), located between neighborhoods with a population of high purchasing power and inserted into the tourist shaft of seaside of town. The characterization of informal housing market in Mãe Luiza, from buyers, sellers and renters, will help to understand how these informal transactions operate on SASI and housing provision for public policy development and implementation of housing programs and land regularization for low-income population, adequate to dynamic and reality of housing of informal areas

O descompasso de uma experiência: avaliação do Programa Habitar Brasil na Comunidade África-Natal/RN

This research deals with the evaluation of the Programa do Governo Federal para Urbanização de Favelas Habitar Brasil(1993) carried out in the Africa slum - Redinha neighbourhood in Natal-Rn. This study carried out in period from 2005 to 2006 searches to identify the effects of the actions proposed by Program in 1993-1994 about the current urbanistic configuration of the Africa community. It analyzes the effectiveness in the process of achievement of the considered objectives to habitation, communitity equipments, infrastructure and agrarian regularization. On the evaluation process, it has been as reference the works developed by Adauto Cardoso (2004), Blaine Worthen (2004), Ronaldo Garcia (2001) and Rosângela Paz (2006). About the Habitational Policy with approach to the Urbanistic Right and the right to the housing, the reflections by Raquel Rolnik, Nabil Bonduki, Ermínia Maricato, Saule Júnior, Betânia de Moraes Alfonsin and Edésio Fernandes are main references. To gauge the execution of the objectives proposed by Habitar Brasil in 1993, it has searched in the documentary data of the time and in information gotten in interviews with technicians that had participated of the program, consistent references on what was considered, what was executed and the process of the intervention of Habitar Brasil in the Africa community. The area analysis in 2005-2006 has developed on the base of the urbanistic survey of the current situation from the four performance lines of the Program: habitation, infrastructure, community equipments and agrarian regularization, with a current urbanistic evaluation of Africa considering the intervention carried out in 1993 and 1994. The study points out the context of Brazilian Habitational Policy where the Programa Habitar Brasil was launched, explaining the main principles of the Program. In terms of local, it empahsizes the administrative-political factors that had contributed so that Natal-Rn city has been pioneering in the resources captation of Habitar Brazil (1993). Considering Habitar Brazil in Africa, the work argues and presents the intervention diagnosis and the proposal, developed by Program in 1993 evidencing the local problem of the time. After that, it makes a current reading of the area, identifying in 2006 representative elements of Habitar Brasil (1993-1994) for the Africa community. It identifies significant advances in the constitution of the institucional apparatus of the plaining system of Habitation of Social Interest for the city of Natal and points the fragilities in the implementation of the urban infrastructure actions and above all in the achievement of the objectives of the agrarian regularization

Habitabilidade na cidade sobre as águas: desafios da implantação de infra-estrutura de saneamento nas palafitas do Igarapé do Quarenta bairro Japiim Manaus/AM

The present research if inserts in the subject of the habitation of social interest and its relation with the sanitation infra-structure questions (sewer, water, draining and garbage). Having as study universe the narrow river of the Forty , situated one in the city of Manaus, capital of Amazon, approaches questions that if present between the necessities of housing and the especificidades of the natural environment, whose characteristics evidence limits for the implantation of adequate habitations. The objective is to analyze the possibilities and the limits of the urbanística regularization in the palafitas of the narrow rivers of Manaus, in view of the factors of habitability and ambient protection, expresses for the sanitation system - sanitary exhaustion, water supply, urban draining and garbage collection. The work approaches initially relative the conceptual aspects to the subject of social habitation in the country and its relation with the habitability factors, also focusing the question of the housing and the processes of urban informality in the city of Manaus. It deals with the process of constitution of the palafitas in the space of the city and its relation with the habitacionais politics, presenting the analysis of the implantation of the palafitas in relation to the sanitation infra-structure conditions (sewer, water, draining and garbage). As conclusion, it identifies to the possibilities and limits of urbanística regularization of the palafitas implanted to the long one of the narrow river of the Forty , taking in consideration the systems of the sanitation infrastructure

Colonialidade do poder no direito e povos indígenas na América Latina: as faces da subordinação/dominação jurídica frente ao direito de retorno às terras ancestrais dos povos indígenas Kaiowá do Tekohá Laranjeira Ñande‘Rú no Brasil e Mapuche do Lof Temucuicui no Chile

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Sociais, Centro de Pesquisa e Pós-Graduação sobre as Américas, Programa de Pós-Graduação em Estudos Comparados sobre as Américas, 2016.

Regolarizzazione di Tikhonov multiparametro

Il vantaggio di una regolarizzazione multiparametro sta nel fatto che scegliendo un parametro diverso per ogni punto della soluzione si ricostruiscono con più accuratezza i picchi e le regioni piatte. In una prima fase di sperimentazione si è applicato l'algoritmo UPEN ad alcuni problemi test del Regularization Tools e si è osservato che l'algoritmo produce ottimi risultati inquesto ambito. In una seconda fase di sperimentazione si sono confrontati i dati ottenuti da UPEN con quelli derivanti da altri criteri di scelta noti in letteratura. Si è anche condotta un'analisi su dati simulati di risonanza magnetica nucleare e su problemi di deblurring di un'immagine.

Jointly event extraction and visualization on Twitter via probabilistic modelling

Event extraction from texts aims to detect structured information such as what has happened, to whom, where and when. Event extraction and visualization are typically considered as two different tasks. In this paper, we propose a novel approach based on probabilistic modelling to jointly extract and visualize events from tweets where both tasks benefit from each other. We model each event as a joint distribution over named entities, a date, a location and event-related keywords. Moreover, both tweets and event instances are associated with coordinates in the visualization space. The manifold assumption that the intrinsic geometry of tweets is a low-rank, non-linear manifold within the high-dimensional space is incorporated into the learning framework using a regularization. Experimental results show that the proposed approach can effectively deal with both event extraction and visualization and performs remarkably better than both the state-of-the-art event extraction method and a pipeline approach for event extraction and visualization.

Phase-field analysis of finite-strain plates and shells including element subdivision

With the theme of fracture of finite-strain plates and shells based on a phase-field model of crack regularization, we introduce a new staggered algorithm for elastic and elasto-plastic materials. To account for correct fracture behavior in bending, two independent phase-fields are used, corresponding to the lower and upper faces of the shell. This is shown to provide a realistic behavior in bending-dominated problems, here illustrated in classical beam and plate problems. Finite strain behavior for both elastic and elasto-plastic constitutive laws is made compatible with the phase-field model by use of a consistent updated-Lagrangian algorithm. To guarantee sufficient resolution in the definition of the crack paths, a local remeshing algorithm based on the phase- field values at the lower and upper shell faces is introduced. In this local remeshing algorithm, two stages are used: edge-based element subdivision and node repositioning. Five representative numerical examples are shown, consisting of a bi-clamped beam, two versions of a square plate, the Keesecker pressurized cylinder problem, the Hexcan problem and the Muscat-Fenech and Atkins plate. All problems were successfully solved and the proposed solution was found to be robust and efficient.

Damage and fracture algorithm using the screened Poisson equation and local remeshing

We propose a crack propagation algorithm which is independent of particular constitutive laws and specific element technology. It consists of a localization limiter in the form of the screened Poisson equation with local mesh refinement. This combination allows the cap- turing of strain localization with good resolution, even in the absence of a sufficiently fine initial mesh. In addition, crack paths are implicitly defined from the localized region, cir- cumventing the need for a specific direction criterion. Observed phenomena such as mul- tiple crack growth and shielding emerge naturally from the algorithm. In contrast with alternative regularization algorithms, curved cracks are correctly represented. A staggered scheme for standard equilibrium and screened equations is used. Element subdivision is based on edge split operations using a given constitutive quantity (either damage or void fraction). To assess the robustness and accuracy of this algorithm, we use both quasi-brittle benchmarks and ductile tests.

Predictions of J integral and tensile strength of clay/epoxy nanocomposites material using phase field model

We predict macroscopic fracture related material parameters of fully exfoliated clay/epoxy nano- composites based on their fine scale features. Fracture is modeled by a phase field approach which is implemented as user subroutines UEL and UMAT in the commercial finite element software Abaqus. The phase field model replaces the sharp discontinuities with a scalar damage field representing the diffuse crack topology through controlling the amount of diffusion by a regularization parameter. Two different constitutive models for the matrix and the clay platelets are used; the nonlinear coupled system con- sisting of the equilibrium equation and a diffusion-type equation governing the phase field evolution are solved via a NewtoneRaphson approach. In order to predict the tensile strength and fracture toughness of the clay/epoxy composites we evaluated the J integral for different specimens with varying cracks. The effect of different geometry and material parameters, such as the clay weight ratio (wt.%) and the aspect ratio of clay platelets are studied.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Autonomous Pricing using Policy-Gradient Reinforcement Learning

Nella letteratura economica e di teoria dei giochi vi è un dibattito aperto sulla possibilità di emergenza di comportamenti anticompetitivi da parte di algoritmi di determinazione automatica dei prezzi di mercato. L'obiettivo di questa tesi è sviluppare un modello di reinforcement learning di tipo actor-critic con entropy regularization per impostare i prezzi in un gioco dinamico di competizione oligopolistica con prezzi continui. Il modello che propongo esibisce in modo coerente comportamenti cooperativi supportati da meccanismi di punizione che scoraggiano la deviazione dall'equilibrio raggiunto a convergenza. Il comportamento di questo modello durante l'apprendimento e a convergenza avvenuta aiuta inoltre a interpretare le azioni compiute da Q-learning tabellare e altri algoritmi di prezzo in condizioni simili. I risultati sono robusti alla variazione del numero di agenti in competizione e al tipo di deviazione dall'equilibrio ottenuto a convergenza, punendo anche deviazioni a prezzi più alti.

Numerical prediction of the lateral displacement capacity of flexural reinforced concrete shear walls

Previous earthquakes showed that shear wall damage could lead to catastrophic failures of the reinforced concrete building. The lateral load capacity of shear walls needs to be estimated to minimize associated losses during catastrophic events; hence it is necessary to develop and validate reliable and stable numerical methods able to converge to reasonable estimations with minimum computational effort. The beam-column 1-D line element with fiber-type cross-section model is a practical option that yields results in agreement with experimental data. However, shortcomings of using this model to predict the local damage response may come from the fact that the model requires fine calibration of material properties to overcome regularization and size effects. To reduce the mesh-dependency of the numerical model, a regularization method based on the concept of post-yield energy is applied in this work to both the concrete and the steel material constitutive laws to predict the nonlinear cyclic response and failure mechanism of concrete shear walls. Different categories of wall specimens known to produce a different response under in plane cyclic loading for their varied geometric and detailing characteristics are considered in this study, namely: 1) scaled wall specimens designed according to the European seismic design code and 2) unique full-scale wall specimens detailed according to the U.S. design code to develop a ductile behavior under cyclic loading. To test the boundaries of application of the proposed method, two full-scale walls with a mixed shear-flexure response and different values of applied axial load are also considered. The results of this study show that the use of regularized constitutive models considerably enhances the response predictions capabilities of the model with regards to global force-drift response and failure mode. The simulations presented in this thesis demonstrate the proposed model to be a valuable tool for researchers and engineers.

A deep learning model for controlling the galvanizing process in a production line

In the industry of steelmaking, the process of galvanizing is a treatment which is applied to protect the steel from corrosion. The air knife effect (AKE) occurs when nozzles emit a steam of air on the surfaces of a steel strip to remove excess zinc from it. In our work we formalized the problem to control the AKE and we implemented, with the R&D dept.of MarcegagliaSPA, a DL model able to drive the AKE. We call it controller. It takes as input the tuple : a tuple of the physical conditions of the process line (t,h,s) with the target value of the zinc coating (c); and generates the expected tuple of (pres and dist) to drive the mechanical nozzles towards the (c). According to the requirements we designed the structure of the network. We collected and explored the data set of the historical data of the smart factory. Finally, we designed the loss function as sum of three components: the minimization between the coating addressed by the network and the target value we want to reach; and two weighted minimization components for both pressure and distance. In our solution we construct a second module, named coating net, to predict the coating of zinc resulting from the AKE when the conditions are applied to the prod. line. Its structure is made by a linear and a deep nonlinear “residual” component learned by empirical observations. The predictions made by the coating nets are used as ground truth in the loss function of the controller. By tuning the weights of the different components of the loss function, it is possible to train models with slightly different optimization purposes. In the tests we compared the regularization of different strategies with the standard one in condition of optimal estimation for both; the overall accuracy is ± 3 g/m^2 dal target for all of them. Lastly, we analyze how the controller modeled the current solutions with the new logic: the sub-optimal values of pres and dist can be optimize of 50% and 20%.