Identificação de modelos polinomiais narx utilizando algoritmos combinados de detecção de estrutura e estimação de parâmetros com aplicações práticas

A modelagem de processos industriais tem auxiliado na produção e minimização de custos, permitindo a previsão dos comportamentos futuros do sistema, supervisão de processos e projeto de controladores. Ao observar os benefícios proporcionados pela modelagem, objetiva-se primeiramente, nesta dissertação, apresentar uma metodologia de identificação de modelos não-lineares com estrutura NARX, a partir da implementação de algoritmos combinados de detecção de estrutura e estimação de parâmetros. Inicialmente, será ressaltada a importância da identificação de sistemas na otimização de processos industriais, especificamente a escolha do modelo para representar adequadamente as dinâmicas do sistema. Em seguida, será apresentada uma breve revisão das etapas que compõem a identificação de sistemas. Na sequência, serão apresentados os métodos fundamentais para detecção de estrutura (Modificado Gram- Schmidt) e estimação de parâmetros (Método dos Mínimos Quadrados e Método dos Mínimos Quadrados Estendido) de modelos. No trabalho será também realizada, através dos algoritmos implementados, a identificação de dois processos industriais distintos representados por uma planta de nível didática, que possibilita o controle de nível e vazão, e uma planta de processamento primário de petróleo simulada, que tem como objetivo representar um tratamento primário do petróleo que ocorre em plataformas petrolíferas. A dissertação é finalizada com uma avaliação dos desempenhos dos modelos obtidos, quando comparados com o sistema. A partir desta avaliação, será possível observar se os modelos identificados são capazes de representar as características estáticas e dinâmicas dos sistemas apresentados nesta dissertação

On the multicomponent polynomial solution models

The present study addresses the problem of predicting the properties of multicomponent systems from those of corresponding binary systems. Two types of multicomponent polynomial models have been analysed. A probabilistic interpretation of the parameters of the Polynomial model, which explicitly relates them with the Gibbs free energies of the generalised quasichemical reactions, is proposed. The presented treatment provides a theoretical justification for such parameters. A methodology of estimating the ternary interaction parameter from the binary ones is presented. The methodology provides a way in which the power series multicomponent models, where no projection is required, could be incorporated into the Calphad approach.

The Role of Plant Primary and Secondary Metabolites in Root-Herbivore Behaviour, Nutrition and Physiology

Many insect herbivores feed on belowground plant tissues. In this chapter, we discuss how they have adapted to deal with root primary and secondary metabolites. It is becoming evident that root herbivores can use root volatiles and exudates for host location and foraging. Their complex sensory apparatus suggests a sophisticated recognition and signal transduction system. Furthermore, endogenous metabolites trigger attractive or repellent responses in root feeders, indicating that they may specifically fine-tune food uptake to meet their dietary needs. Little evidence for direct toxic effects of root secondary metabolites has accumulated so far, indicating high prevalence of tolerance mechanisms. Root herbivores furthermore facilitate the entry of soil microbes into the roots, which may influence root nutritional quality. Investigating the role of plant metabolites in an ecologically and physiologically relevant context will be crucial to refine our current models on root-herbivore physiology and behaviour in the future.

Identificação e comparação entre controle preditivo com modelo não linear e PI sintonizados com PSO em sistema de separação gravitacional de águia-óleo

The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Identification of the essential histidine residue for high-affinity binding of AlbA protein to albicidin antibiotics

The albA gene from Klebsiella oxytoca encodes a protein that binds albicidin phytotoxins and antibiotics with high affinity. Previously, it has been shown that shifting pH from 6 to 4 reduces binding activity of AlbA by about 30%, indicating that histidine residues might be involved in substrate binding. In this study, molecular analysis of the albA coding region revealed sequence discrepancies with the albA sequence reported previously, which were probably due to sequencing errors. The albA gene was subsequently cloned from K oxytoca ATCC 13182(T) to establish the revised sequence. Biochemical and molecular approaches were used to determine the functional role of four histidine residues (His(78), HiS(125), HiS(141) and His(189)) in the corrected sequence for AlbA. Treatment of AlbA with diethyl pyrocarbonate (DEPC), a histidine-specific alkylating reagent, reduced binding activity by about 95%. DEPC treatment increased absorbance at 240-244 nm by an amount indicating conversion to N-carbethoxyhistidine of a single histidine residue per AlbA molecule. Pretreatment with albicidin protected AlbA against modification by DEPC, with a 1 : 1 molar ratio of albicidin to the protected histidine residues. Based on protein secondary structure and amino acid surface probability indices, it is predicted that HiS125 might be the residue required for albicidin binding. Mutation of HiS125 to either alanine or leucine resulted in about 32% loss of binding activity, and deletion of HiS125 totally abolished binding activity. Mutation of HiS125 to arginine and tyrosine had no effect. These results indicate that HiS125 plays a key role either in an electrostatic interaction between AlbA and albicidin or in the conformational dynamics of the albicidin-binding site.

Identification of Sclerotinia species

A variety of morphological and molecular characters were compared for their ability to separate the three plant pathogenic species that comprise the genus Sclerotinia: Sclerotinia sclerotiorum, Sclerotinia minor and Sclerotinia trifoliorum. Restriction fragment length polymorphism ( RFLP) probes generated from cloned genomic DNA fragments of S. sclerotiorum were used for accurate species designation and to compare against other markers, before further use in population genetics and breeding studies. Other characters used for comparison included host species, sclerotial diameters, ascospore morphism and breeding type. Several RFLP probes, either singly or in combination, enabled clear separation of the Sclerotinia species. Sclerotial diameters remain a good criterion for separating S. minor from S. sclerotiorum and S. trifoliorum, but the host species criterion was inadequate for accurately differentiating the 3 species of Sclerotinia.

Um processo para utilizar a tecnologia de impressão 3D na construção de instrumentos didáticos para o ensino de Ciências

Pós-graduação em Educação para a Ciência - FC

Location-aided distributed primary user identification in a cognitive radio scenario

We address a cognitive radio scenario, where a number of secondary users performs identification of which primary user, if any, is trans- mitting, in a distributed way and using limited location information. We propose two fully distributed algorithms: the first is a direct iden- tification scheme, and in the other a distributed sub-optimal detection based on a simplified Neyman-Pearson energy detector precedes the identification scheme. Both algorithms are studied analytically in a realistic transmission scenario, and the advantage obtained by detec- tion pre-processing is also verified via simulation. Finally, we give details of their fully distributed implementation via consensus aver- aging algorithms.

Using the canonical modelling approach to simplify the simulation of function in functional-structural plant models

Functional-structural plant models that include detailed mechanistic representation of underlying physiological processes can be expensive to construct and the resulting models can also be extremely complicated. On the other hand, purely empirical models are not able to simulate plant adaptability and response to different conditions. In this paper, we present an intermediate approach to modelling plant function that can simulate plant response without requiring detailed knowledge of underlying physiology. Plant function is modelled using a 'canonical' modelling approach, which uses compartment models with flux functions of a standard mathematical form, while plant structure is modelled using L-systems. Two modelling examples are used to demonstrate that canonical modelling can be used in conjunction with L-systems to create functional-structural plant models where function is represented either in an accurate and descriptive way, or in a more mechanistic and explanatory way. We conclude that canonical modelling provides a useful, flexible and relatively simple approach to modelling plant function at an intermediate level of abstraction.

The genome of Laccaria bicolor provides insights into mycorrhizal symbiosis

Mycorrhizal symbioses--the union of roots and soil fungi--are universal in terrestrial ecosystems and may have been fundamental to land colonization by plants. Boreal, temperate and montane forests all depend on ectomycorrhizae. Identification of the primary factors that regulate symbiotic development and metabolic activity will therefore open the door to understanding the role of ectomycorrhizae in plant development and physiology, allowing the full ecological significance of this symbiosis to be explored. Here we report the genome sequence of the ectomycorrhizal basidiomycete Laccaria bicolor (Fig. 1) and highlight gene sets involved in rhizosphere colonization and symbiosis. This 65-megabase genome assembly contains approximately 20,000 predicted protein-encoding genes and a very large number of transposons and repeated sequences. We detected unexpected genomic features, most notably a battery of effector-type small secreted proteins (SSPs) with unknown function, several of which are only expressed in symbiotic tissues. The most highly expressed SSP accumulates in the proliferating hyphae colonizing the host root. The ectomycorrhizae-specific SSPs probably have a decisive role in the establishment of the symbiosis. The unexpected observation that the genome of L. bicolor lacks carbohydrate-active enzymes involved in degradation of plant cell walls, but maintains the ability to degrade non-plant cell wall polysaccharides, reveals the dual saprotrophic and biotrophic lifestyle of the mycorrhizal fungus that enables it to grow within both soil and living plant roots. The predicted gene inventory of the L. bicolor genome, therefore, points to previously unknown mechanisms of symbiosis operating in biotrophic mycorrhizal fungi. The availability of this genome provides an unparalleled opportunity to develop a deeper understanding of the processes by which symbionts interact with plants within their ecosystem to perform vital functions in the carbon and nitrogen cycles that are fundamental to sustainable plant productivity.

Bias related to body mass index in pediatric echocardiographic z scores.

In pediatric echocardiography, cardiac dimensions are often normalized for weight, height, or body surface area (BSA). The combined influence of height and weight on cardiac size is complex and likely varies with age. We hypothesized that increasing weight for height, as represented by body mass index (BMI) adjusted for age, is poorly accounted for in Z scores normalized for weight, height, or BSA. We aimed to evaluate whether a bias related to BMI was introduced when proximal aorta diameter Z scores are derived from bivariate models (only one normalizing variable), and whether such a bias was reduced when multivariable models are used. We analyzed 1,422 echocardiograms read as normal in children ≤18 years. We computed Z scores of the proximal aorta using allometric, polynomial, and multivariable models with four body size variables. We then assessed the level of residual association of Z scores and BMI adjusted for age and sex. In children ≥6 years, we found a significant residual linear association with BMI-for-age and Z scores for most regression models. Only a multivariable model including weight and height as independent predictors produced a Z score free of linear association with BMI. We concluded that a bias related to BMI was present in Z scores of proximal aorta diameter when normalization was done using bivariate models, regardless of the regression model or the normalizing variable. The use of multivariable models with weight and height as independent predictors should be explored to reduce this potential pitfall when pediatric echocardiography reference values are evaluated.

Plant species distributions along environmental gradients: do belowground interactions with fungi matter?

The distribution of plants along environmental gradients is constrained by abiotic and biotic factors. Cumulative evidence attests of the impact of biotic factors on plant distributions, but only few studies discuss the role of belowground communities. Soil fungi, in particular, are thought to play an important role in how plant species assemble locally into communities. We first review existing evidence, and then test the effect of the number of soil fungal operational taxonomic units (OTUs) on plant species distributions using a recently collected dataset of plant and metagenomic information on soil fungi in the Western Swiss Alps. Using species distribution models (SDMs), we investigated whether the distribution of individual plant species is correlated to the number of OTUs of two important soil fungal classes known to interact with plants: the Glomeromycetes, that are obligatory symbionts of plants, and the Agaricomycetes, that may be facultative plant symbionts, pathogens, or wood decayers. We show that including the fungal richness information in the models of plant species distributions improves predictive accuracy. Number of fungal OTUs is especially correlated to the distribution of high elevation plant species. We suggest that high elevation soil show greater variation in fungal assemblages that may in turn impact plant turnover among communities. We finally discuss how to move beyond correlative analyses, through the design of field experiments manipulating plant and fungal communities along environmental gradients.

Primääri-sekundäärivuodon tunnistaminen aktiivisuusmittauksin Loviisan voimalaitoksella

Diplomityössä tutkittiin Loviisan voimalaitoksen primääri- ja sekundääripiirin aktiivisuusmittausten kykyä tunnistaa pienet primääri-sekundäärivuodot. Tarkasteltavat primääri-sekundäärivuotojen suuruudet valittiin laitoksen hätätilanne- ja häiriönselvitysohjeiden mukaisesti. Vuodon vaikutuksia arvioitiin erilaisilla primäärijäähdytteen ominaisaktiivisuuksilla. Ominaisaktiivisuudet primääripiirissä määritettiin nuklidikohtaisesti erilaisille polttoainevuototapauksille. Työssä huomioitiin myös transienteissa mahdollisesti esiintyvä primääripiirin aktiivisuustasoa nostava spiking-ilmiö. Vuodon tarkempaa tunnistamista varten työssä laskettiin tarkasteltaville mittareille kalibrointikertoimet. Primääri-sekundäärivuoto mallinnettiin APROS-simulointiohjelmalla laitoksen eri käyttötiloissa ja kahdella eri vuotokoolla. Varsinainen aktiivisuuslaskenta suoritettiin SEKUN-ohjelmalla. Työssä tätä aktiivisuus- ja päästölaskentaohjelmaa muokattiin ohjelmoimalla siihen tarkasteltavat aktiivisuusmittaukset sekä primääripiirin puhdistus ja ulospuhallus. Laskelmien tuloksena saatiin arviot kunkin tarkasteltavana olleen aktiivisuusmittauksen soveltuvuudesta primääri-sekundäärivuodon tunnistamiseen erilaisissa polttoainevuototapauksissa ja reaktorin eri tehotasoilla. Häiriönselvitysohje I3:n käyttöönottoa edellyttävät vuotokoot määritettiin aktiivisuusmittausten havaitseman perusteella. Erityisesti kuumavalmiustilassa tapauksissa, joissa reaktorisydämessä oletetaan olevan tiiveytensä menettäneitä polttoainesauvoja, spikingin vaikutus jäähdytteiden aktiivisuuspitoisuuksiin ja mittaustuloksiin oli merkittävä. Niiltä osin, kuin tulokset käsittelevät ohjeissa vuodon tunnistamiseen käytettyjä aktiivisuusrajoja, tulokset osoittivat aktiivisuusrajat oikeiksi. Kuumavalmiudessa aktiivisuusmittausten mittausalueet saattavat joissakin tapauksissa rajoittaa primääri-sekundäärivuodon tunnistamista.

Tree cover at fine and coarse spatial grains interacts with shade tolerance to shape plant species distributions across the Alps

The role of competition for light among plants has long been recognized at local scales, but its potential importance for plant species' distribution at larger spatial scales has largely been ignored. Tree cover acts as a modulator of local abiotic conditions, notably by reducing light availability below the canopy and thus the performance of species that are not adapted to low-light conditions. However, this local effect may propagate to coarser spatial grains. Using 6,935 vegetation plots located across the European Alps, we fit Generalized Linear Models (GLM) for the distribution of 960 herbs and shrubs species to assess the effect of tree cover at both plot and landscape grain sizes (~ 10-m and 1-km, respectively). We ran four models with different combinations of variables (climate, soil and tree cover) for each species at both spatial grains. We used partial regressions to evaluate the independent effects of plot- and landscape-scale tree cover on plant communities. Finally, the effects on species' elevational range limits were assessed by simulating a removal experiment comparing the species' distribution under high and low tree cover. Accounting for tree cover improved model performance, with shade-tolerant species increasing their probability of presence at high tree cover whereas shade-intolerant species showed the opposite pattern. The tree cover effect occurred consistently at both plot and landscape spatial grains, albeit strongest at the former. Importantly, tree cover at the two grain sizes had partially independent effects on plot-scale plant communities, suggesting that the effects may be transmitted to coarser grains through meta-community dynamics. At high tree cover, shade-intolerant species exhibited elevational range contractions, especially at their upper limit, whereas shade-tolerant species showed elevational range expansions at both limits. Our findings suggest that the range shifts for herb and shrub species may be modulated by tree cover dynamics.