Quebra de barreiras de comunicação para portadores de paralisia cerebral

Tese dout., Engenharia Electrónica e Computação, Universidade do Algarve, 2009Nos últimos anos, o estudo das tecnologias de acessibilidade tem adquirido relevância na àrea de interfaces pessoa-máquina. O desenvolvimento de dis- positivos, métodos, e aplicações especificamente desenhadas para superar as limitações dos utilizadores facilita a interação destes com o mundo exterior. A importância do estudo nesta àrea é enorme, visto facilitar a interação dos portadores de deficiências com os meios tecnológicos que, por sua vez, possibilitam-lhes uma melhor comunicação e interação com os outros, con- tribuindo significativamente para a sua integração na sociedade. Esta dissertação apresenta um sistema inovador, conhecido por Easy- Voice, que integra diversas tecnologias para permitir que uma pessoa com deficiências na fala possa efectuar chamadas telefónicas usando uma voz sintetizada. Subjacente a este sistema, desenhado com o objectivo de dis- ponibilizar um interface acessível até para utilizadores que possuam graves problemas de coordenação motora, está o conceito que é possível combinar tecnologias já existentes para criar mecanismos que facilitem a comunicação dos portadores de deficiências a longas distâncias, nomeadamente as tecno- logias de síntese de voz, voz sobre IP (VoIP) e métodos de interação para portadores de limitações motoras. Salienta-se que antes do desenvolvimento deste sistema essas pessoas encontravam-se limitadas pelas barreira físicas, apenas podendo utilizar sis- temas text-to-speech (TTS) para comunicarem com quem estava na sua pre- sença. O EasyVoice transformou esta situação ao possibilitar que portadores de problemas na fala comuniquem mais facilmente com quem está distante.Fundação para a Ciência e Tecnologia (FCT

Genetic Land - Modeling land use change using evolutionary algorithms

Future land use configurations provide valuable knowledge for policy makers and economic agents, especially under expected environmental changes such as decreasing rainfall or increasing temperatures, or scenarios of policy guidance such as carbon sequestration enforcement. In this paper, modelling land use change is designed as an optimization problem in which landscapes (land uses) are generated through the use of genetic algorithms (GA), according to an objective function (e.g. minimization of soil erosion, or maximization of carbon sequestration), and a set of local restrictions (e.g. soil depth, water availability, or landscape structure). GAs are search and optimization procedures based on the mechanics of natural selection and genetics. The GA starts with a population of random individuals, each corresponding to a particular candidate solution to the problem. The best solutions are propagated; they are mated with each other and originate “offspring solutions” which randomly combine the characteristics of each “parent”. The repeated application of these operations leads to a dynamic system that emulates the evolutionary mechanisms that occur in nature. The fittest individuals survive and propagate their traits to future generations, while unfit individuals have a tendency to die and become extinct (Goldberg, 1989). Applications of GA to land use planning have been experimented (Brookes, 2001, Ducheyne et al, 2001). However, long-term planning with a time-span component has not yet been addressed. GeneticLand, the GA for land use generation, works on a region represented by a bi-dimensional array of cells. For each cell, there is a number of possible land uses (U1, U2, ..., Un). The task of the GA is to search for an optimal assignment of these land uses to the cells, evolving the landscape patterns that are most suitable for satisfying the objective function, for a certain time period (e.g. 50 years in the future). GeneticLand develops under a multi-objective function: (i) Minimization of soil erosion – each solution is validated by applying the USLE, with the best solution being the one that minimizes the landscape soil erosion value; (ii) Maximization of carbon sequestration – each solution is validated by applying atmospheric CO2 carbon uptake estimates, with the best solution being the one that maximizes the landscape carbon uptake; and (iii) Maximization of the landscape economic value – each solution is validated by applying an economic value (derived from expert judgment), with the best solution being the one that maximizes the landscape economic value. As an optimization problem, not all possible land use assignments are feasible. GeneticLand considers two sets of restrictions that must be met: (i) physical constraints (soil type suitability, slope, rainfall-evapotranspiration ratio, and a soil wetness index) and (ii) landscape ecology restrictions at several levels (minimum patch area, land use adjacency index and landscape contagion index). The former assures physical feasibility and the latter the spatial coherence of the landscape. The physical and landscape restrictions were derived from the analysis of past events based on a time series of Landsat images (1985-2003), in order to identify the drivers of land use change and structure. Since the problem has multiple objectives, the GA integrates multi-objective extensions allowing it to evolve a set of non-dominated solutions. An evolutive type algorithm – Evolutive strategy (1+1) – is used, due to the need to accommodate the very large solution space. Current applications have about 1000 decision variables, while the problem analysed by GeneticLand has almost 111000, generated by a landscape with 333*333 discrete pixels. GeneticLand is developed and validated for a Mediterranean type landscape located in southern Portugal. Future climate triggers, such as the increase of intense rainfall episodes, is accommodated to simulate climate change . This paper presents: (1) the formulation of land use modelling as an optimization problem; (2) the formulation of the GA for the explicit spatial domain, (3) the land use constraints derived for a Mediterranean landscape, (4) the results illustrating conflicting objectives, and (5) limitations encountered.

Multi-objective evolutionary algorithm for land-use management problem

Due to increasing population, and human activities on land to meet various demands, land uses are being continuously changed without a clear and logical planning with any attention to their long term environmental impacts. Thus affecting the natural balance of the environment, in the form of global warming, soil degradation, loss of biodiversity, air and water pollution, and so on. Hence, it has become urgent need to manage land uses scientifically to safeguard the environment from being further destroyed. Owing to the difficulty of deploying field experiments for direct assessment, mechanistic models are needed to be developed for improving the understanding of the overall impact from various land uses. However, very little work has been done so far in this area. Hence, NSGA-II-LUM, a spatial-GIS based multi-objective evolutionary algorithm, has been developed for three objective functions: maximization of economic return, maximization of carbon sequestration and minimization of soil erosion, where the latter two are burning topics to today's researchers as the remedies to global warming and soil degradation. The success of NSGA-II-LUM has been presented through its application to a Mediterranean landscape from Southern Portugal

Genetic Land - Modeling land use change using evolutionary algorithms

Future land use configurations provide valuable knowledge for policy makers and economic agents, especially under expected environmental changes such as decreasing rainfall or increasing temperatures, or scenarios of policy guidance such as carbon sequestration enforcement. In this paper, modelling land use change is designed as an optimization problem in which landscapes (land uses) are generated through the use of genetic algorithms (GA), according to an objective function (e.g. minimization of soil erosion, or maximization of carbon sequestration), and a set of local restrictions (e.g. soil depth, water availability, or landscape structure). GAs are search and optimization procedures based on the mechanics of natural selection and genetics. The GA starts with a population of random individuals, each corresponding to a particular candidate solution to the problem. The best solutions are propagated; they are mated with each other and originate "offspring solutions” which randomly combine the characteristics of each "parent”. The repeated application of these operations leads to a dynamic system that emulates the evolutionary mechanisms that occur in nature. The fittest individuals survive and propagate their traits to future generations, while unfit individuals have a tendency to die and become extinct (Goldberg, 1989). Applications of GA to land use planning have been experimented (Brookes, 2001, Ducheyne et al, 2001). However, long-term planning with a time-span component has not yet been addressed. GeneticLand, the GA for land use generation, works on a region represented by a bi-dimensional array of cells. For each cell, there is a number of possible land uses (U1, U2, ..., Un). The task of the GA is to search for an optimal assignment of these land uses to the cells, evolving the landscape patterns that are most suitable for satisfying the objective function, for a certain time period (e.g. 50 years in the future). GeneticLand develops under a multi-objective function: (i) Minimization of soil erosion – each solution is validated by applying the USLE, with the best solution being the one that minimizes the landscape soil erosion value; (ii) Maximization of carbon sequestration – each solution is validated by applying atmospheric CO2 carbon uptake estimates, with the best solution being the one that maximizes the landscape carbon uptake; and (iii) Maximization of the landscape economic value – each solution is validated by applying an economic value (derived from expert judgment), with the best solution being the one that maximizes the landscape economic value. As an optimization problem, not all possible land use assignments are feasible. GeneticLand considers two sets of restrictions that must be met: (i) physical constraints (soil type suitability, slope, rainfall-evapotranspiration ratio, and a soil wetness index) and (ii) landscape ecology restrictions at several levels (minimum patch area, land use adjacency index and landscape contagion index). The former assures physical feasibility and the latter the spatial coherence of the landscape. The physical and landscape restrictions were derived from the analysis of past events based on a time series of Landsat images (1985-2003), in order to identify the drivers of land use change and structure. Since the problem has multiple objectives, the GA integrates multi-objective extensions allowing it to evolve a set of non-dominated solutions. An evolutive type algorithm – Evolutive strategy (1+1) – is used, due to the need to accommodate the very large solution space. Current applications have about 1000 decision variables, while the problem analysed by GeneticLand has almost 111000, generated by a landscape with 333*333 discrete pixels. GeneticLand is developed and validated for a Mediterranean type landscape located in southern Portugal. Future climate triggers, such as the increase of intense rainfall episodes, is accommodated to simulate climate change . This paper presents: (1) the formulation of land use modelling as an optimization problem; (2) the formulation of the GA for the explicit spatial domain, (3) the land use constraints derived for a Mediterranean landscape, (4) the results illustrating conflicting objectives, and (5) limitations encountered

Alterações morfométricas na retina, coroide e nervo ótico após infeção por SARS-CoV-2

O novo coronavírus responsável pela síndrome respiratória aguda grave (SARS-CoV- 2) surgiu associado à pandemia por COVID-19. A enzima conversora da angiotensina 2 (ACE-2), com aparente importância na COVID-19, pela interação com as proteínas na superfície do vírus, tem expressão em vários tecidos oculares e várias alterações como conjuntivite, uveíte, vasculite e neurite foram descritas inicialmente em modelos animais. Em estudos mais recentes, embora na maioria em doentes COVID-19 moderados/grave, tem sido descrito o comprometimento da superfície ocular anterior e do polo posterior reforçando a ideia de neurotropismo (pela facilidade de envolvimento do sistema nervoso central) que classicamente é descrito em outros coronavirus. Algumas alterações do polo posterior incluem o compromisso vascular/ isquémico tornando relevante também a observação da coroide. A SARS-CoV-2 tem sido associada à diminuição das camadas internas da retina e à presença de lesões hiperrefletivas, micro-hemorragias e manchas algodonosas. No entanto, o envolvimento da retina e a coróide em doentes previamente infetados com COVID-19 ainda não é totalmente compreendido. De forma a clarificar o envolvimento dos fatores de neuro-degeneração e vasculares, descritos em indivíduos recuperados de COVID-19 moderada/grave, é fundamental perceber que alterações existem ao nível da retina interna e da coroide, em indivíduos recuperados de COVID-19 ligeira. Questão de investigação: Existem alterações morfométricas da retina, coroide e nervo ótivo em indivíduos recuperados de COVID-19 ligeira?info:eu-repo/semantics/publishedVersio

Impacto subsequente a infeção por COVID-19: mudanças estruturais da retina, da coroide e no nervo ótico

A conjuntivite, a uveíte, a vasculite, a retinite e a neuropatia ótica têm sido documentadas em modelos animais como possíveis complicações oculares de doenças infeciosas com neurotropismo semelhante ao SARS COV. Considerando a retina uma extensão do SNS e o neurotropismo dos CoVs pode justificar se o uso de metodologias não invasivas como a OCT para caracterizar a retina, coroide e nervo ótico de pacientes infectados com COVID 19 dada a hipótese de uma possível neurodegeneração associada ao coronavírus. Assim, torna-se importante avaliar a espessura das camadas mais internas da retina com envolvimento descrito em outras doenças neurodegenerativas e metabólicas. Objetivo do estudo: Descrever as alterações que ocorrem ao nível da espessura da retina, complexo de células ganglionares fibras nervosas peri-papilares e coróde sub foveal em pacientes infetados por COVID 19 comparando as com um grupo controlo.info:eu-repo/semantics/publishedVersio