Estimation of 3D shapes using active surface models

This paper addresses the estimation of surfaces from a set of 3D points using the unified framework described in [1]. This framework proposes the use of competitive learning for curve estimation, i.e., a set of points is defined on a deformable curve and they all compete to represent the available data. This paper extends the use of the unified framework to surface estimation. It o shown that competitive learning performes better than snakes, improving the model performance in the presence of concavities and allowing to desciminate close surfaces. The proposed model is evaluated in this paper using syntheticdata and medical images (MRI and ultrasound images).

Estimation of signal subspace on hyperspectral data

Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.

Estimation of rotational frequency response functions

As it is widely known, in structural dynamic applications, ranging from structural coupling to model updating, the incompatibility between measured and simulated data is inevitable, due to the problem of coordinate incompleteness. Usually, the experimental data from conventional vibration testing is collected at a few translational degrees of freedom (DOF) due to applied forces, using hammer or shaker exciters, over a limited frequency range. Hence, one can only measure a portion of the receptance matrix, few columns, related to the forced DOFs, and rows, related to the measured DOFs. In contrast, by finite element modeling, one can obtain a full data set, both in terms of DOFs and identified modes. Over the years, several model reduction techniques have been proposed, as well as data expansion ones. However, the latter are significantly fewer and the demand for efficient techniques is still an issue. In this work, one proposes a technique for expanding measured frequency response functions (FRF) over the entire set of DOFs. This technique is based upon a modified Kidder's method and the principle of reciprocity, and it avoids the need for modal identification, as it uses the measured FRFs directly. In order to illustrate the performance of the proposed technique, a set of simulated experimental translational FRFs is taken as reference to estimate rotational FRFs, including those that are due to applied moments.

Estimation of infection rate in epidemic models with multiple populations

Dissertação para obtenção do Grau de Mestre em Matemática e Aplicações Especialização em Actuariado, Estatística e Investigação Operacional

Recycling old screen-printed electrodes with newly designed plastic antibodies on the wall of carbon nanotubes as sensory element for in situ detection of bacterial toxins in water

Using low cost portable devices that enable a single analytical step for screening environmental contaminants is today a demanding issue. This concept is here tried out by recycling screen-printed electrodes that were to be disposed of and by choosing as sensory element a low cost material offering specific response for an environmental contaminant. Microcystins (MCs) were used as target analyte, for being dangerous toxins produced by cyanobacteria released into water bodies. The sensory element was a plastic antibody designed by surface imprinting with carefully selected monomers to ensure a specific response. These were designed on the wall of carbon nanotubes, taking advantage of their exceptional electrical properties. The stereochemical ability of the sensory material to detect MCs was checked by preparing blank materials where the imprinting stage was made without the template molecule. The novel sensory material for MCs was introduced in a polymeric matrix and evaluated against potentiometric measurements. Nernstian response was observed from 7.24 × 10−10 to 1.28 × 10−9 M in buffer solution (10 mM HEPES, 150 mM NaCl, pH 6.6), with average slopes of −62 mVdecade−1 and detection capabilities below 1 nM. The blank materials were unable to provide a linear response against log(concentration), showing only a slight potential change towards more positive potentials with increasing concentrations (while that ofthe plastic antibodies moved to more negative values), with a maximum rate of +33 mVdecade−1. The sensors presented good selectivity towards sulphate, iron and ammonium ions, and also chloroform and tetrachloroethylene (TCE) and fast response (<20 s). This concept was successfully tested on the analysis of spiked environmental water samples. The sensors were further applied onto recycled chips, comprehending one site for the reference electrode and two sites for different selective membranes, in a biparametric approach for “in situ” analysis.

An Extension of Estimation of Domain of Attraction for Fractional Order Linear System Subject to Saturation Control

This paper employs the Lyapunov direct method for the stability analysis of fractional order linear systems subject to input saturation. A new stability condition based on saturation function is adopted for estimating the domain of attraction via ellipsoid approach. To further improve this estimation, the auxiliary feedback is also supported by the concept of stability region. The advantages of the proposed method are twofold: (1) it is straightforward to handle the problem both in analysis and design because of using Lyapunov method, (2) the estimation leads to less conservative results. A numerical example illustrates the feasibility of the proposed method.

On the Estimation of the Short-Circuit Impedance of Power Transformers Using Fractional Order Calculus

This paper reports investigation on the estimation of the short circuit impedance of power transformers, using fractional order calculus to analytically study the influence of the diffusion phenomena in the windings. The aim is to better characterize the medium frequency range behavior of leakage inductances of power transformer models, which include terms to represent the magnetic field diffusion process in the windings. Comparisons between calculated and measured values are shown and discussed.

Cohesive law estimation of adhesive joints in mode II condition

The adhesive bonding technique enables both weight and complexity reduction in structures that require some joining technique to be used on account of fabrication/component shape issues. Because of this, adhesive bonding is also one of the main repair methods for metal and composite structures by the strap and scarf configurations. The availability of strength prediction techniques for adhesive joints is essential for their generalized application and it can rely on different approaches, such as mechanics of materials, conventional fracture mechanics or damage mechanics. These two last techniques depend on the measurement of the fracture toughness (GC) of materials. Within the framework of damage mechanics, a valid option is the use of Cohesive Zone Modelling (CZM) coupled with Finite Element (FE) analyses. In this work, CZM laws for adhesive joints considering three adhesives with varying ductility were estimated. The End-Notched Flexure (ENF) test geometry was selected based on overall test simplicity and results accuracy. The adhesives Araldite® AV138, Araldite® 2015 and Sikaforce® 7752 were studied between high-strength aluminium adherends. Estimation of the CZM laws was carried out by an inverse methodology based on a curve fitting procedure, which enabled a precise estimation of the adhesive joints’ behaviour. The work allowed to conclude that a unique set of shear fracture toughness (GIIC) and shear cohesive strength (ts0) exists for each specimen that accurately reproduces the adhesive layer’ behaviour. With this information, the accurate strength prediction of adhesive joints in shear is made possible by CZM.

Software development for estimation of optical clearing agent’s diffusion coefficients in biological tissues

The study of chemical diffusion in biological tissues is a research field of high importance and with application in many clinical, research and industrial areas. The evaluation of diffusion and viscosity properties of chemicals in tissues is necessary to characterize treatments or inclusion of preservatives in tissues or organs for low temperature conservation. Recently, we have demonstrated experimentally that the diffusion properties and dynamic viscosity of sugars and alcohols can be evaluated from optical measurements. Our studies were performed in skeletal muscle, but our results have revealed that the same methodology can be used with other tissues and different chemicals. Considering the significant number of studies that can be made with this method, it becomes necessary to turn data processing and calculation easier. With this objective, we have developed a software application that integrates all processing and calculations, turning the researcher work easier and faster. Using the same experimental data that previously was used to estimate the diffusion and viscosity of glucose in skeletal muscle, we have repeated the calculations with the new application. Comparing between the results obtained with the new application and with previous independent routines we have demonstrated great similarity and consequently validated the application. This new tool is now available to be used in similar research to obtain the diffusion properties of other chemicals in different tissues or organs.

Credit risk stress testing: The Portuguese environment, macroeconomic scenarios and the estimation of losses for corporate sectors

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Catalytic hydrogenation of carbon dioxide to form methanol and methane

Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

An estimation of the frequency of gestational toxoplasmosis in the Brazilian Federal District

Acute infections by the protozoan Toxoplasma gondii during pregnancy (gestational toxoplasmosis) are known to cause serious health problems to the fetus (congenital toxoplasmosis). In Brasília, there have been few studies on the incidence of toxoplasmosis. This report summarizes a retrospective study performed on 2,636 selected pregnant women attended by the public health system of Guará, a satellite-city of Brasília. In this survey, 17 cases of gestational toxoplasmosis were detected; 15 of which were primary maternal infection and the remaining 2 were consistent with secondary maternal infection. These results suggest an annual seroconversion rate of 0.64 percent (90 percent confidence interval: 0.38, 0.90).

Evaluation of uv spectrophotometry for estimation of nitrite and nitrate in nitrified urine

Water is a limited resource for which demand is growing. Contaminated water from inadequate wastewater treatment provides one of the greatest health challenges as it restricts development and increases poverty in emerging and developing countries. Therefore, the connection between wastewater and human health is linked to access to sanitation and to human waste disposal. Adequate sanitation is expected to create a barrier between disposed human excreta and sources of drinking water. Different approaches to wastewater management are required for different geographical regions and different stages of economic governance depending on the capacity to manage wastewater. Effective wastewater management can contribute to overcome the challenges of water scarcity. Separate collection of human urine at its source is one promising approach that strongly reduces the economic and load demands on wastewater treatment plants (WWTP). Treatment of source-separated urine appears as a sanitation system that is affordable, produces a valuable fertiliser, reduces pollution of water resources and promotes health. However, the technical realisation of urine separation still faces challenges. Biological hydrolysis of urea causes a strong increase of ammonia and pH. Under these conditions ammonia volatilises which can cause odour problems and significant nitrogen losses. The above problems can be avoided by urine stabilisation. Biological nitrification is a suitable process for stabilisation of urine. Urine is a highly concentrated nutrient solution which can lead to strong inhibition effects during bacterial nitrification. This can further lead to process instabilities. The major cause of instability is accumulation of the inhibitory intermediate compound nitrite, which could lead to process breakdown. Enhanced on-line nitrite monitoring can be applied in biological source-separated urine nitrification reactors as a sustainable and efficient way to improve the reactor performance, avoiding reactor failures and eventual loss of biological activity. Spectrophotometry appears as a promising candidate for the development and application of on-line nitrite monitoring. Spectroscopic methods together with chemometrics are presented in this work as a powerful tool for estimation of nitrite concentrations. Principal component regression (PCR) is applied for the estimation of nitrite concentrations using an immersible UV sensor and off-line spectra acquisition. The effect of particles and the effect of saturation, respectively, on the UV absorbance spectra are investigated. The analysis allows to conclude that (i) saturation has a substantial effect on nitrite estimation; (ii) particles appear to have less impact on nitrite estimation. In addition, improper mixing together with instabilities in the urine nitrification process appears to significantly reduce the performance of the estimation model.

Non-cell autonomous neuroprotective effect of carbon monoxide : microglia-to-neuron communication

RESUMO: A isquémia cerebral é uma das doenças mais predominantes a nivel mundial, sendo uma das principais causas de mortalidade e invalidez. Parte da propagação de dano no cérebro é causado por inflamação descontrolada, causada principalmente por disfunção da microglia. Desta forma, existe a necessidade de tentar desenvolver estratégias para melhor compreender e modular as acções destas células. O monóxido de carbono (CO), é uma molécula endógena com provas dadas como anti-neuroinflamatório em vários modelos. Assim, o principal objectivo do trabalho foi o estudo do CO como um modulador da acção da microglia, com principal foco dado à comunicação entre estas células e neurónios, tentando entender se existe um efeito neuroprotector por inibição da inflamação. Um protocolo de meio condicionado foi estabelecido usando as linhas celulares BV2 e SH-SY5Y, de microglia e neurónio. A molécula CORM-A1, que liberta expontaniamente CO, foi usada como método de entrega da molécula às celulas. Demonstrámos que o pre-tratamento de células BV2 com CORM-A1 gera neuroprotecção já que reduz a morte celular de neurónios SH-SY5Y quando são incubados com meio condicionado de microglia activada em conjunto com o pró-oxidante t-BHP (tert-butil hidroperóxido). Assim, considerámos que o CO promove neuroprotecção ao inibir as acções inflamatórias da microglia. O papel anti-inflamatório da molécula CORM-A1 foi confirmado quando se verificou que pré-tratamento desta molécula em microglia BV2 limita a secreção de TNF-α mas estimula a secreção de IL-10. Por último, a CORM-A1 induziu a expressão do receptor da microglia CD200R1, molécula que participa na comunicação neurónio-microglia e fundamental para a modulação das acções inflamatórias destas últimas. Em suma, o nosso trabalho reforçou as propriedades anti-neuroinflamatórias do CO e uma capacidade de modular viabilidade neuronal através do seu efeito a nível de comunicação célula-célula. ---------------------------- ABSTRACT: Brain ischemia is a widespread disease worldwide, being one of the main causes of mortality and permanent disability. A portion of the damage that ensues following the ischemic event is caused by unrestrained inflammation, which is mainly orchestrated by exacerbated microglial activity. Hence, developing strategies for modulating microglial inflammation is a major concern nowadays. The endogenous molecule carbon monoxide (CO) has been shown to possess anti-neuroinflammatory properties using in vitro and in vivo approaches. Thus, our objective was to study CO as modulator of microglial activity, in particular in what concerns their communication with neurons, by promoting neuronal viability and limiting inflammatory output of activated microglia. A conditioned media strategy was established with BV2 microglia and SH-SY5Y neurons as cell models. CO-releasing molecule A1 (CORM-A1), a compound that releases CO spontaneously, was used as method of CO delivery to cells. We found that CORM-A1 pre-treatment in BV2 cells yields neuroprotective results, as it limits cell death when SH-SY5Y neurons are challenged with conditioned media from LPS-activated microglia and the pro-oxidant t-BHP (tert-butyl-hydroperoxide). Thus, we assumed carbon monoxide promotes neuroprotection via inhibition of microglial inflammation, displaying a non-cell autonomous role. CORM-A1 pre-treatment limited inflammation by inhibiting BV2 secretion of TNF-α and stimulating IL-10 production. These results reinforce that CO’s anti-inflammatory role confers neuroprotection, as the alterations in these cytokines occur concurrently with the increase in SH-SY5Y viability. Finally, we showed for the first time that carbon monoxide promotes the expression of CD200R1, a microglial receptor involved in neuron-glia communication and modulation of microglia inflammation. Further studies are necessary to clarify this role. Altogether, other than just highlighting CO as an anti-inflammatory and neuroprotective molecule, this work set the foundation for disclosing its involvement in cell-to-cell communication.

Impact of carbon/nitrogen feeding strategy on polyhydroxyalkanoates production using mixed microbial cultures

Polyhydroxyalkanoates (PHA) production using mixed microbial cultures (MMC) requires a multi-stage process involving the microbial selection of PHA-storing microorganisms, typically operated in sequencing batch reactors (SBR), and an accumulation reactor. Since low-cost renewable feedstocks used as process feedstock are often nitrogen-deficient, nutrient supply in the selection stage is required to allow for microbial growth. In this context, the possibility to uncouple nitrogen supply from carbon feeding within the SBR cycle has been investigated in this study. Moreover, three different COD:N ratios (100:3.79, 100:3.03 and 100:2.43) were tested in three different runs which also allowed the study of COD:N ratio on the SBR performance. For each run, a synthetic mixture of acetic and propionic acids at an overall organic load rate of 8.5 gCOD L-1 d-1 was used as carbon feedstock, whereas ammonium sulfate was the nitrogen source in a lab-scale sequence batch reactor (SBR) with 1 L of working volume. Besides, a sludge retention time (SRT) of 1 d was used as well as a 6 h cycle length. The uncoupled feeding strategy significantly enhanced the selective pressure towards PHA-storing microorganisms, resulting in a two-fold increase in the PHA production (up to about 1.3 gCOD L-1). A high storage response was observed for the two runs with the COD:N ratios (gCOD:gN) of 100:3.79 and 100:3.03, whereas the lowest investigated nitrogen load resulted in very poor performance in terms of polymer production. In fact, strong nitrogen limitation caused fungi to grow and a very poor storage ability by microorganisms that thrived in those conditions. The COD:N ratio also affected the polymer composition, indeed the produced poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) showed a variable HV content (1-20 %, w/w) among the three runs, lessening as the COD:N increased. This clearly suggests the possibility to use the COD:N ratio as a tool for tuning polymer properties regardless the composition of the feedstock.