Unidades de conservação possuem o objetivo de preservar a natureza, evitando o desmatamento e promovendo a sustentabilidade do meio ambiente. Contudo, para que estas atendam aos propósitos para os quais foram criadas, sem acarretar prejuízos sociais ou conflitos com as populações locais, estudos aplicados interdisciplinares são essenciais, agregando conhecimento útil à gestão e ao planejamento das unidades de conservação. Sob a ótica da ciência da complexidade, o objetivo principal deste trabalho foi desenvolver modelos que auxiliassem na compreensão das mudanças no uso e cobertura da terra, realizassem simulações de cenários futuros, e permitissem observar os efeitos da implantação de políticas de preservação sobre a paisagem. Construímos modelos dinâmicos baseados em cadeias de Markov e autômatos celulares, aliados a técnicas de geoprocessamento. Os modelos foram aplicados a um estudo de caso, o Parque estadual do Itinguçu, ao longo de uma série temporal de materiais aerofotográficos de quase 50 anos (1962-2010). Os resultados dos modelos mostraram que a implantação da unidade de conservação foi essencial para barrar o desmatamento, mas que as práticas tradicionais de agricultura itinerante não estavam diretamente relacionadas à conversão da área de floresta, indicando que a incompatibilidade entre preservação e presença humana, muitas vezes usada como justificativa para a implantação de unidades de proteção integral, deve ser reavaliada sob outra perspectiva. Os resultados também apontaram para um desempenho satisfatório do modelo de Markov em projetar tendências, apesar de possuir certa aleatoriedade na alocação dos elementos no espaço. O incremento do autômato celular diminuiu tal aleatoriedade, mas não foi tão eficiente em reproduzir as tendências observadas nas matrizes de transição quanto o modelo de Markov. Concluímos que a metodologia aplicada no presente trabalho foi útil para compreendermos as mudanças na paisagem da área de estudo, e que a escolha do modelo (Markov ou Markov com autômato celular) deve ser feita com base em uma análise criteriosa caso a caso, em conformidade com as prioridades do estudo a ser realizado. Espera-se que esta pesquisa possa fomentar a discussão sobre o uso desta metodologia como uma ferramenta para planejamento e análise de políticas de conservação da natureza e gestão do território / Conservation units have the purpose to preserve the nature, avoiding the deforestation and promoting the environment sustainability. However, for these to be effective in that purpose, without causing social injuries or conflicts with the local population, interdisciplinary applied studies are essential and must be made by different areas of science, adding useful knowledge to the management of protected areas. Under the vision of the Complexity Science, the main goal of this research was to develop models that help in understanding the land use and cover changes, perform simulations of future scenarios, and allow observing the effects of the implementation of conservation policies on the landscape. We built Markov and cellular automata models, allied to the geoprocessing techniques. The models were applied to a case study, the Parque Estadual do Itinguçu, over a time series of aero photographic materials of almost 50 years (1962-2010). The results of the models showed that the implementation of the conservation unit was essential to stop the deforestation, but the traditional practices of shifting cultivation were not directly related to the conversion of forest area, indicating that the incompatibility between conservation and human presence, often used as justification for the implementation of some strict protection units, should be reviewed from a different perspective. The results also pointed to a satisfactory performance of the Markov model to project trends, despite having certain randomness in the allocation of elements in space. Add cellular automata to model decreased this randomness, but was not so effective in reproducing the observed trends in transition matrices than the Markov model. We concluded that the methodology applied in this study was useful for understanding the changes in the landscape of the study area, and that the choice of model (Markov or Markov with cellular automata) should be based on a careful analysis in accordance with the priorities of the study to be applied. We hope that this research can encourage the discussion of this methodology as a tool for analysis of conservation policies of nature and land management

Els paradigmes cel·lulars, com les xarxes neuronals cel·lulars (CNN, en anglès) i els autòmats cel·lulars (CA, en anglès), són una eina excel·lent de càlcul, al ser equivalents a una màquina universal de Turing. La introducció de la màquina universal CNN (CNN-UM, en anglès) ha permès desenvolupar hardware, el nucli computacional del qual funciona segons la filosofia cel·lular; aquest hardware ha trobat aplicació en diversos camps al llarg de la darrera dècada. Malgrat això, encara hi ha moltes preguntes a obertes sobre com definir els algoritmes d'una CNN-UM i com estudiar la dinàmica dels autòmats cel·lulars. En aquesta tesis es tracten els dos problemes: primer, es demostra que es possible acotar l'espai dels algoritmes per a la CNN-UM i explorar-lo gràcies a les tècniques genètiques; i segon, s'expliquen els fonaments de l'estudi dels CA per mitjà de la dinàmica no lineal (segons la definició de Chua) i s'il·lustra com aquesta tècnica ha permès trobar resultats innovadors. / Los paradigmas celulares, como las redes neuronales celulares (CNN, eninglés) y los autómatas celulares (CA, en inglés), son una excelenteherramienta de cálculo, al ser equivalentes a una maquina universal deTuring. La introducción de la maquina universal CNN (CNN-UM, eninglés) ha permitido desarrollar hardware cuyo núcleo computacionalfunciona según la filosofía celular; dicho hardware ha encontradoaplicación en varios campos a lo largo de la ultima década. Sinembargo, hay aun muchas preguntas abiertas sobre como definir losalgoritmos de una CNN-UM y como estudiar la dinámica de los autómatascelular. En esta tesis se tratan ambos problemas: primero se demuestraque es posible acotar el espacio de los algoritmos para la CNN-UM yexplorarlo gracias a técnicas genéticas; segundo, se explican losfundamentos del estudio de los CA por medio de la dinámica no lineal(según la definición de Chua) y se ilustra como esta técnica hapermitido encontrar resultados novedosos. / Cellular paradigms, like Cellular Neural Networks (CNNs) and Cellular Automata (CA) are an excellent tool to perform computation, since they are equivalent to a Universal Turing machine. The introduction of the Cellular Neural Network - Universal Machine (CNN-UM) allowed us to develop hardware whose computational core works according to the principles of cellular paradigms; such a hardware has found application in a number of fields throughout the last decade. Nevertheless, there are still many open questions about how to define algorithms for a CNN-UM, and how to study the dynamics of Cellular Automata. In this dissertation both problems are tackled: first, we prove that it is possible to bound the space of all algorithms of CNN-UM and explore it through genetic techniques; second, we explain the fundamentals of the nonlinear perspective of CA (according to Chua's definition), and we illustrate how this technique has allowed us to find novel results.

Economic changes such as rising açaí prices and the availability of off-farm employment are transforming the landscape of the Amazonian várzea, subject to decision-making at the farming household level. Land use change results from complex human-environment interactions which can be addressed by an agent-based model. An agent-based model is a simulation model composed of autonomous interacting entities known as agents, built from the bottom-up. Coupled with cellular automata, which forms the agents’ environment, agent-based models are becoming an important tool of land use science, complementing traditional methods of induction and deduction. The decision-making methods employed by agent-based models in recent years have included optimization, imitation, heuristics, classifier systems and genetic algorithms, among others, but multiple methods have rarely been comparatively analyzed. A modular agent-based model is designed to allow the researcher to substitute alternative decision-making methods. For a smallholder farming community in Marajó Island near Ponta de Pedras, Pará, Brazil, 21 households are simulated over a 40-year period. In three major scenarios of increasing complexity, these households first face an environment where goods sell at a constant price throughout the simulated period and there are no outside employment opportunities. This is followed by a scenario of variable prices based on empirical data. The third scenario combines variable prices with limited employment opportunities, creating multi-sited households as members emigrate. In each scenario, populations of optimizing agents and heuristic agents are analyzed in parallel. While optimizing agents allocate land cells to maximize revenue using linear programming, fast and frugal heuristic agents use decision trees to quickly pare down feasible solutions and probabilistically select between alternatives weighted by expected revenue. Using distributed computing, the model is run through several parameter sweeps and results are recorded to a cenral database. Land use trajectories and sensitivity analyses highlight the relative biases of each decision-making method and illustrate cases where alternative methods lead to significantly divergent outcomes. A hybrid approach is recommended, employing alternative decision-making methods in parallel to illustrate inefficiencies exogenous and endogenous to the decision-maker, or allowing agents to select among multiple methods to mitigate bias and best represent their real-world analogues.

Economic changes such as rising açaí prices and the availability of off-farm employment are transforming the landscape of the Amazonian várzea, subject to decision-making at the farming household level. Land use change results from complex human-environment interactions which can be addressed by an agent-based model. An agent-based model is a simulation model composed of autonomous interacting entities known as agents, built from the bottom-up. Coupled with cellular automata, which forms the agents’ environment, agent-based models are becoming an important tool of land use science, complementing traditional methods of induction and deduction. The decision-making methods employed by agent-based models in recent years have included optimization, imitation, heuristics, classifier systems and genetic algorithms, among others, but multiple methods have rarely been comparatively analyzed. A modular agent-based model is designed to allow the researcher to substitute alternative decision-making methods. For a smallholder farming community in Marajó Island near Ponta de Pedras, Pará, Brazil, 21 households are simulated over a 40-year period. In three major scenarios of increasing complexity, these households first face an environment where goods sell at a constant price throughout the simulated period and there are no outside employment opportunities. This is followed by a scenario of variable prices based on empirical data. The third scenario combines variable prices with limited employment opportunities, creating multi-sited households as members emigrate. In each scenario, populations of optimizing agents and heuristic agents are analyzed in parallel. While optimizing agents allocate land cells to maximize revenue using linear programming, fast and frugal heuristic agents use decision trees to quickly pare down feasible solutions and probabilistically select between alternatives weighted by expected revenue. Using distributed computing, the model is run through several parameter sweeps and results are recorded to a cenral database. Land use trajectories and sensitivity analyses highlight the relative biases of each decision-making method and illustrate cases where alternative methods lead to significantly divergent outcomes. A hybrid approach is recommended, employing alternative decision-making methods in parallel to illustrate inefficiencies exogenous and endogenous to the decision-maker, or allowing agents to select among multiple methods to mitigate bias and best represent their real-world analogues.

The impact of spatial characteristics on the dynamics of urban development is a topic of great interest in urban studies. The interaction between the residents and the spatial characteristics is of particular interest in the context of urban models where some of the most famous urban models have been based on the process of individual settlements in some specific parts of cities.This research investigates the dynamism of urban development modeled by Cellular Automata and Multi-Agent System. The urban development, in this study embraces urban renewal and residential mobility. It corresponds to the residential mobility of households, attracted by residential and centrality comfort; these comforts are crystallized in some areas and residences of Strasbourg. The diversity and quality of these comforts become criteria for residential choice in a way that the households seek for proximity to these comforts.The Cellular Automata in this study, models the spatial characteristics of urban spatial units and they are identified by some inherent attributes that are equal to the comfort in residences and urban areas. The Multi- Agent System represent a system in which the population of the city interact between them and between them and the city; the agents delegate the socio-professional classes of households. During the spatiotemporal change, the aspiration of households forms the socio-spatio-temporal development of the city.

Thesis (MSc (Mathematical Sciences))--Stellenbosch University, 2008. / In this thesis we consider the modelling of a particular layout optimisation problem, namely, the LEGO construction problem. The LEGO construction problem, in short, concerns the optimal layout of a set of LEGO bricks to represent a given object. Our goal is to develop a software package which LEGO enthusiasts can use to construct LEGO sculptures for any real-world object. We therefore not only consider the layout optimisation problem, but also the generation of the input data required by the LEGO construction problem. We show that by using 3D geometric models to represent the real-world object, our implemented voxelisation technique delivers accurate input data for the LEGO construction problem. The LEGO construction problem has previously been solved with optimisation techniques based on simulated annealing, evolutionary algorithms, and a beam search approach. These techniques all indicate that it is possible to generate LEGO building instructions for real-world objects, albeit not necessarily in reasonable time. We show that the LEGO construction problem can be modelled easily with cellular automata, provided that cells are considered as clusters which can merge or split during each time step of the evolution of the cellular automaton. We show that the use of cellular automata gives comparable layout results in general, and improves the results in many respects. The cellular automata method requires substantially less memory and generally uses fewer LEGO bricks to construct the LEGO sculpture when using comparable execution times.

Many diffusion processes in nature and society were found to be anomalous, in the sense of being fundamentally different from conventional Brownian motion. An important example is the migration of biological cells, which exhibits non-trivial temporal decay of velocity autocorrelation functions. This means that the corresponding dynamics is characterized by memory effects that slowly decay in time. Motivated by this we construct non-Markovian lattice-gas cellular automata models for moving agents with memory. For this purpose the reorientation probabilities are derived from velocity autocorrelation functions that are given a priori; in that respect our approach is “data-driven”. Particular examples we consider are velocity correlations that decay exponentially or as power laws, where the latter functions generate anomalous diffusion. The computational efficiency of cellular automata combined with our analytical results paves the way to explore the relevance of memory and anomalous diffusion for the dynamics of interacting cell populations, like confluent cell monolayers and cell clustering.

No description available.

Uma questão que ainda instiga discussões na literatura ecológica é como explicar a coocorrência dinâmica e milenar de formações florestais e campestres sob um mesmo regime climático que tende a favorecer as primeiras, como ocorre atualmente com mosaicos florestacampo no sul do Brasil. A partir de meados do século XX, têm-se evidenciado um fenômeno mundial de avanço de elementos lenhosos sobre áreas abertas. Neste sentido, a modelagem dos processos ecológicos envolvidos na manutenção de ambas as formações numa escala de paisagem permite o esclarecimento dos mecanismos que atuam na manutenção dessa coexistência até o presente e permite prever estados futuros diante dos prognósticos de drásticas alterações climáticas globais já nas próximas décadas. Para tanto, desenvolveu-se um modelo espacialmente explícito (2D-aDGVM) que agrega um Modelo Adaptativo Global de Dinâmica de Vegetação (aDGVM) e ainda inclui heterogeneidades topográficas, propagação do fogo e dispersão de sementes. Este modelo busca satisfazer a necessidade de modelagem mais realista de processos biofísicos, fisiológicos e demográficos na escala de indivíduos e relacionados de forma adaptativa às variações ambientais e aos regimes de distúrbios, ao mesmo tempo que agrega importantes processos ecológicos espaciais, até então pouco ou nada abordados por esse grupo de modelos numa escala de paisagem. Com este modelo, avaliaram-se os efeitos das variações topográficas da radiação solar incidente e destas nos mecanismos de interação (feedbacks) positiva e negativa que surgem daqueles processos na escala de indivíduos e que definem localmente os limites da coexistência entre elementos arbóreos e herbáceos. Ainda, foram analisados os efeitos do aumento da temperatura, precipitação e CO2 atmosférico, desde o período pré-industrial até projeções futuras para as próximas décadas, na performance das diferentes fisiologias envolvidas, bem como no balanço daquelas interações entre as mesmas e, finalmente, na sensibilidade da dinâmica dos mosaicos floresta-campo. Os resultados evidenciaram que, sob o regime climático vigente, uma coexistência relativamente estável entre floresta e campo numa mesma paisagem é mantida por uma alta freqüência de distúrbios, que por sua vez, resulta do forte feedback positivo do acúmulo de biomassa inflamável da vegetação campestre na intensidade do fogo, proporcionado pela condição altamente produtiva do atual clima mesotérmico. Por outro lado, intensificadas pela declividade do terreno, as heterogeneidades espaciais afetaram o balanço dessas interações, interferindo nos padrões espaço-temporais relacionados ao comportamento do fogo e dependentes da densidade de elementos arbóreos. Ainda, tanto esses efeitos observados na escala das manchas de vegetação, como o arranjo espacial inicial das mesmas na paisagem, afetaram as taxas de expansão florestal. Em outras palavras, a manutenção da coexistência de duas formações vegetais constituídas por elementos de inerente assimetria competitiva é possível pela manutenção de uma maior conectividade daquela que propicia o distúrbio, superando a vantagem da outra, que por sua vez é dependente da densidade dos indivíduos. Numa escala de paisagem, isto causa a manutenção de uma baixa conectividade entre as manchas florestais, propiciando sua relativa estabilidade num contexto de dispersão predominante a curtas distâncias. Contudo, embora ambos os sistemas tenham apresentado incremento no crescimento, produtividade e fecundidade, observou-se uma sensibilidade maior no sentido de aumento das taxas de avanço florestal em resposta às projeções climáticas futuras, principalmente nos próximos 90 anos, mesmo na presença do fogo. Isto seria proporcionado pela vantagem fotossintética das árvores-C3 sobre gramíneas-C4 na presença do fogo sob altas concentrações de CO2 atmosférico. Por fim, uma abordagem mais sistêmica dos mosaicos como estados alternativos mostrou ser adequada para o entendimento dos mecanismos que propiciam essa coexistência dinâmica na paisagem. / A longstanding problem in ecology is how to explain the coexistence over thousands of years of forests and natural grasslands under the same climatic regime, which favors the first, such as in forest-grasslands mosaics in South Brazil. Since the middle of the 20th century, a worldwide bush encroachment phenomenon of woody invasion in open vegetation has been threatening this relatively stable coexistence. In this sense, modelling ecological processes that arbitrate the maintenance of both vegetation formations at the landscape scale allows a better understanding of the mechanisms behind the maintenance of this coexistence, as well as predictions of future states under projections of drastic climate change over the next decades. For this, we developed a bidimensional spatial explicit model (2D-aDGVM) that aggregates an adaptive Global Vegetation Model (aDGVM), which includes topographic heterogeneity, fire spread and seed dispersal. The model aims at fulfilling the need for a more realistic representation of biophysical, physiological and demographical processes using an individualbased approach as it adapts these processes to environmental variations and disturbance regimes. In addition, the model includes important spatial ecological processes that have gained less attention by such models adopting a landscape-scale approach. Therefore, we evaluated the effect of topographic variations in incoming solar radiation on positive and on negative feedbacks that rise from those individual-based processes, and which in turns define the limiting thresholds upon which woody and grassy forms coexist. Additionally, the effects of increasing temperature, rainfall and atmospheric CO2 levels on the performance of distinct physiologies (C3-tree and C4-grass) were analyzed, as well as the sensitivity of forestgrassland mosaics to changes in climate from the preindustrial period to the next decades. Results showed that a relatively stable coexistence of forests and grasslands in the same landscape was observed with more frequent fires under the present climatic conditions. This was due to strong positive feedbacks of the huge accumulation of flammable grass biomass on fire intensity promoted by the high productivity of the present mesic conditions. On the other hand, spatio-temporal density dependent processes linked to fire and enhanced by slope at the patch scale, as well as the initial spatial arrangement of vegetation patches affected the rate of forest expansion at the landscape scale. The persistence of coexisting vegetation formations with an inherent asymmetry of competitive interactions was possible when the higher connectivity of the fire-prone patches (grassland) affected negatively the performance of the entire fire-sensitive system (forest). This was possible by overcoming its local densitydependent advantage, or by maintaining it with a low connectivity, which is expected to reduce the rate of coalescence of forest patches in a scenario of predominantly short distance dispersal. Despite the increments in biomass production, stem growth and fecundity that were observed in both grassland and forest, climate change increased the rates of forest expansion over grasslands even in presence of fire, and mainly over the next 90 years. This was attributed to a high photosynthetic advantage of C3-trees over C4-grasses in presence of fire under higher atmospheric CO2 levels. Finally, in the face of the general observed tendency of forest expansion over grasslands, the ancient grasslands have persisted as alternative ecosystem states in forest-grassland mosaics. In this sense, exploring this dynamic coexistence under the concept of alternative stable states have showed to be the most appropriate approach, and the outcomes of this novel perspective may highlight the understanding of the mechanisms behind the long-term coexistence.

No description available.