Fabricação e caracterização metalográfica e mecânica de tiras de ligas metálicas fundidas e tixolaminadas no estado semi-sólido de diferentes intervalos de solidificação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Produção de biossurfactantes pelos fungos Aspergillus ochraceus e Penicillium expansum em fermentação semi-sólida utilizando resíduos agroindustriais como substrato

Pós-graduação em Microbiologia - IBILCE

Orientação semi-automática de uma sequência de pares de imagens frontais por fototriangulação a partir de fotocoordenadas extraídas pelo SIFT

Pós-graduação em Ciências Cartográficas - FCT

A unified solution to the small scale problems of the Lambda CDM model II: introducing parent-satellite interaction

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

On abstract differential equations with nonlocal conditions involving the temporal derivative of the solution

In this paper we discuss the existence of solutions for a class of abstract differential equations with nonlocal conditions for which the nonlocal term involves the temporal derivative of the solution. Some concrete applications to parabolic differential equations with nonlocal conditions are considered. (C) 2012 Royal Dutch Mathematical Society (KWG). Published by Elsevier B.V. All rights reserved.

An analytic method to compute the stress dependence on the dimensions and its influence in the characteristics of triple gate devices

Triple-gate devices are considered a promising solution for sub-20 nm era. Strain engineering has also been recognized as an alternative due to the increase in the carriers mobility it propitiates. The simulation of strained devices has the major drawback of the stress non-uniformity, which cannot be easily considered in a device TCAD simulation without the coupled process simulation that is time consuming and cumbersome task. However, it is mandatory to have accurate device simulation, with good correlation with experimental results of strained devices, allowing for in-depth physical insight as well as prediction on the stress impact on the device electrical characteristics. This work proposes the use of an analytic function, based on the literature, to describe accurately the strain dependence on both channel length and fin width in order to simulate adequately strained triple-gate devices. The maximum transconductance and the threshold voltage are used as the key parameters to compare simulated and experimental data. The results show the agreement of the proposed analytic function with the experimental results. Also, an analysis on the threshold voltage variation is carried out, showing that the stress affects the dependence of the threshold voltage on the temperature. (C) 2011 Elsevier Ltd. All rights reserved.

Influence of spatial constraints in non-crystalline regions on the polymer dynamics in semi-crystalline polyethylene: a solid state NMR study

A broad variety of solid state NMR techniques were used to investigate the chain dynamics in several polyethylene (PE) samples, including ultrahigh molecular weight PEs (UHMW-PEs) and low molecular weight PEs (LMW-PEs). Via changing the processing history, i.e. melt/solution crystallization and drawing processes, these samples gain different morphologies, leading to different molecular dynamics. Due to the long chain nature, the molecular dynamics of polyethylene can be distinguished in local fluctuation and long range motion. With the help of NMR these different kinds of molecular dynamics can be monitored separately. In this work the local chain dynamics in non-crystalline regions of polyethylene samples was investigated via measuring 1H-13C heteronuclear dipolar coupling and 13C chemical shift anisotropy (CSA). By analyzing the motionally averaged 1H-13C heteronuclear dipolar coupling and 13C CSA, the information about the local anisotropy and geometry of motion was obtained. Taking advantage of the big difference of the 13C T1 relaxation time in crystalline and non-crystalline regions of PEs, the 1D 13C MAS exchange experiment was used to investigate the cooperative chain motion between these regions. The different chain organizations in non-crystalline regions were used to explain the relationship between the local fluctuation and the long range motion of the samples. In a simple manner the cooperative chain motion between crystalline and non-crystalline regions of PE results in the experimentally observed diffusive behavior of PE chain. The morphological influences on the diffusion motion have been discussed. The morphological factors include lamellar thickness, chain organization in non-crystalline regions and chain entanglements. Thermodynamics of the diffusion motion in melt and solution crystallized UHMW-PEs is discussed, revealing entropy-controlled features of the chain diffusion in PE. This thermodynamic consideration explains the counterintuitive relationship between the local fluctuation and the long range motion of the samples. Using the chain diffusion coefficient, the rates of jump motion in crystals of the melt crystallized PE have been calculated. A concept of "effective" jump motion has been proposed to explain the difference between the values derived from the chain diffusion coefficients and those in literatures. The observations of this thesis give a clear demonstration of the strong relationship between the sample morphology and chain dynamics. The sample morphologies governed by the processing history lead to different spatial constraints for the molecular chains, leading to different features of the local and long range chain dynamics. The knowledge of the morphological influence on the microscopic chain motion has many implications in our understanding of the alpha-relaxation process in PE and the related phenomena such as crystal thickening, drawability of PE, the easy creep of PE fiber, etc.

Inorganic-organic hybrid polymers: Solution-processible coating materials for defined surface functionalization

A new class of inorganic-organic hybrid polymers could successfully been prepared by the combination of different polymerization techniques. The access to a broad range of organic polymers incorporated into the hybrid polymer was realized using two independent approaches.rnIn the first approach a functional poly(silsesquioxane) (PSSQ) network was pre-formed, which was capable to initiate a controlled radical polymerization to graft organic vinyl-type monomers from the PSSQ precursor. As controlled radical polymerization techniques atom transfer radical polymerization (ATRP), as well as reversible addition fragmentation chain transfer (RAFT) polymerization could be used after defined tuning of the PSSQ precursor either toward a PSSQ macro-initiator or to a PSSQ macro-chain-transfer-agent. The polymerization pathway, consisting of polycondensation of trialkoxy-silanes followed by grafting-from polymerization of different monomers, allowed synthesis of various functional hybrid polymers. A controlled synthesis of the PSSQ precursors could successfully be performed using a microreactor setup; the molecular weight could be adjusted easily while the polydispersity index could be decreased well below 2.rnThe second approach aimed to incorporate differently derived organic polymers. As examples, polycarbonate and poly(ethylene glycol) were end-group-modified using trialkoxysilanes. After end-group-functionalization these organic polymers could be incorporated into a PSSQ network.rnThese different hybrid polymers showed extraordinary coating abilities. All polymers could be processed from solution by spin-coating or dip-coating. The high amount of reactive silanol moieties in the PSSQ part could be cross-linked after application by annealing at 130° for 1h. Not only cross-linking of the whole film was achieved, which resulted in mechanical interlocking with the substrate, also chemical bonds to metal or metal oxide surfaces were formed. All coating materials showed high stability and adhesion onto various underlying materials, reaching from metals (like steel or gold) and metal oxides (like glass) to plastics (like polycarbonate or polytetrafluoroethylene).rnAs the material and the synthetic pathway were very tolerant toward different functionalities, various functional monomers could be incorporated in the final coating material. The incorporation of N-isopropylacrylamide yielded in temperature-responsive surface coatings, whereas the incorporation of redox-active monomers allowed the preparation of semi-conductive coatings, capable to produce smooth hole-injection layers on transparent conductive electrodes used in optoelectronic devices.rnThe range of possible applications could be increased tremendously by incorporation of reactive monomers, capable to undergo fast and quantitative conversions by polymer-analogous reactions. For example, grafting active esters from a PSSQ precursor yielded a reactive surface coating after application onto numerous substrates. Just by dipping the coated substrate into a solution of a functionalized amine, the desired function could be immobilized at the interface as well as throughout the whole film. The obtained reactive surface coatings could be used as basis for different functional coatings for various applications. The conversion with specifically tuned amines yielded in surfaces with adjustable wetting behaviors, switchable wetting behaviors or as recognition element for surface-oriented bio-analytical devices. The combination of hybrid materials with orthogonal reactivities allowed for the first time the preparation of multi-reactive surfaces which could be functionalized sequentially with defined fractions of different groups at the interface. rnThe introduced concept to synthesis functional hybrid polymers unifies the main requirements on an ideal coating material. Strong adhesion on a wide range of underlying materials was achieved by secondary condensation of the PSSQ part, whereas the organic part allowed incorporation of various functionalities. Thus, a flexible platform to create functional and reactive surface coatings was achieved, which could be applied to different substrates. rn

Structure and properties of nanostructures from polyelectrolytes and porphyrins: Self-Assembly in aqueous solution

In this work self-assembling model systems in aqueous solution were studied. The systems contained charged polymers, polyelectrolytes, that were combined with oppositely charged counterions to build up supramolecular structures. With imaging, scattering and spectroscopic techniques it was investigated how the structure of building units influences the structure of their assemblies. Polyelectrolytes with different chemical structure, molecular weight and morphology were investigated. In addition to linear polyelectrolytes, semi-flexible cylindrical bottle-brush polymers that possess a defined cross-section and a relatively high persistence along the backbone were studied. The polyelectrolytes were combined with structural organic counterions having charge numbers one to four. Especially the self-assembly of polyelectrolytes with different tetravalent water-soluble porphyrins was studied. Porphyrins have a rigid aromatic structure that has a structural effect on their self-assembly behavior and through which porphyrins are capable of self-aggregation via π-π interaction. The main focus of the thesis is the self-assembly of cylindrical bottle-brush polyelectrolytes with tetravalent porphyrins. It was shown that the addition of porphyrins to oppositely charged brush molecules induces a hierarchical formation of stable nanoscale brush-porphyrin networks. The networks can be disconnected by addition of salt and single porphyrin-decoratedrncylindrical brush polymers are obtained. These two new morphologies, brush-porphyrin networks and porphyrin-decorated brush polymers, may have potential as functional materials with interesting mechanical and optical properties.

A Closed-Form, Consistent and Robust Solution to Uncalibrated Photometric Stereo Via Local Diffuse Reflectance Maxima

Images of an object under different illumination are known to provide strong cues about the object surface. A mathematical formalization of how to recover the normal map of such a surface leads to the so-called uncalibrated photometric stereo problem. In the simplest instance, this problem can be reduced to the task of identifying only three parameters: the so-called generalized bas-relief (GBR) ambiguity. The challenge is to find additional general assumptions about the object, that identify these parameters uniquely. Current approaches are not consistent, i.e., they provide different solutions when run multiple times on the same data. To address this limitation, we propose exploiting local diffuse reflectance (LDR) maxima, i.e., points in the scene where the normal vector is parallel to the illumination direction (see Fig. 1). We demonstrate several noteworthy properties of these maxima: a closed-form solution, computational efficiency and GBR consistency. An LDR maximum yields a simple closed-form solution corresponding to a semi-circle in the GBR parameters space (see Fig. 2); because as few as two diffuse maxima in different images identify a unique solution, the identification of the GBR parameters can be achieved very efficiently; finally, the algorithm is consistent as it always returns the same solution given the same data. Our algorithm is also remarkably robust: It can obtain an accurate estimate of the GBR parameters even with extremely high levels of outliers in the detected maxima (up to 80 % of the observations). The method is validated on real data and achieves state-of-the-art results.

Numerical solution of optimal control problems with constant control delays

We investigate a class of optimal control problems that exhibit constant exogenously given delays in the control in the equation of motion of the differential states. Therefore, we formulate an exemplary optimal control problem with one stock and one control variable and review some analytic properties of an optimal solution. However, analytical considerations are quite limited in case of delayed optimal control problems. In order to overcome these limits, we reformulate the problem and apply direct numerical methods to calculate approximate solutions that give a better understanding of this class of optimization problems. In particular, we present two possibilities to reformulate the delayed optimal control problem into an instantaneous optimal control problem and show how these can be solved numerically with a stateof- the-art direct method by applying Bock’s direct multiple shooting algorithm. We further demonstrate the strength of our approach by two economic examples.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

Perfect imaging of three object points with only two analytic lens surfaces in two dimensions

In this work, a new two-dimensional analytic optics design method is presented that enables the coupling of three ray sets with two lens profiles. This method is particularly promising for optical systems designed for wide field of view and with clearly separated optical surfaces. However, this coupling can only be achieved if different ray sets will use different portions of the second lens profile. Based on a very basic example of a single thick lens, the Simultaneous Multiple Surfaces design method in two dimensions (SMS2D) will help to provide a better understanding of the practical implications on the design process by an increased lens thickness and a wider field of view. Fermat?s principle is used to deduce a set of functional differential equations fully describing the entire optical system. The transformation of these functional differential equations into an algebraic linear system of equations allows the successive calculation of the Taylor series coefficients up to an arbitrary order. The evaluation of the solution space reveals the wide range of possible lens configurations covered by this analytic design method. Ray tracing analysis for calculated 20th order Taylor polynomials demonstrate excellent performance and the versatility of this new analytical optics design concept.

Analytic free-form lens design for imaging applications with high aspect ratio

A new three-dimensional analytic optics design method is presented that enables the coupling of three ray sets with only two free-form lens surfaces. Closely related to the Simultaneous Multiple Surface method in three dimensions (SMS3D), it is derived directly from Fermat?s principle, leading to multiple sets of functional differential equations. The general solution of these equations makes it possible to calculate more than 80 coefficients for each implicit surface function. Ray tracing simulations of these free-form lenses demonstrate superior imaging performance for applications with high aspect ratio, compared to conventional rotational symmetric systems.

A Recommendation Framework Based on the Analytic Network Process and its Application in the Semantic Technology Domain

When users face a certain problem needing a product, service, or action to solve it, selecting the best alternative among them can be a dicult task due to the uncertainty of their quality. This is especially the case in the domains where users do not have an expertise, like for example in Software Engineering. Multiple criteria decision making (MCDM) methods are methods that help making better decisions when facing the complex problem of selecting the best solution among a group of alternatives that can be compared according to different conflicting criteria. In MCDM problems, alternatives represent concrete products, services or actions that will help in achieving a goal, while criteria represent the characteristics of these alternatives that are important for making a decision.