Estudo da influência de nanotubos de carbono funcionalizados nas propriedades de revestimentos de poliuretano

Dissertação de mestrado em Propriedades e Tecnologias de Polímeros

A new empirical system for rock slope stability analysis in exploitation stage

An empirical system was developed to obtain a quality index for rock slopes in road infrastructures, named Slope Quality Index (SQI), and it was applied to a set of real slopes.The SQI is supported in nine factors affecting slope stability that contemplate the evaluation of different parameters. Consequently, each factor is classified by the degree of importance and influence by assigned weights. These weights were established through a statistical analysis of replies to a survey that was distributed to several experienced professionals in the field. The proposed SQI varies between1 and 5, corresponding to slopes in very good and very bad condition state, respectively. Besides the advantage linked to a quantitative and qualitative evaluation of slopes, theSQI also allows identifying the most critical factors on the slope stability, which is a fundamental issue for an efficient management of the slope network in the road infrastructure, namely in the planning of conservation and maintenance operations.

Development of a new 3D OpenFOAM® solver to model the cooling stage in profile extrusion

[Excerpt] The advantages resulting from the use of numerical modelling tools to support the design of processing equipment are almost consensual. The design of calibration systems in profile extrusion is not an exception . H owever , the complex geome tries and heat exchange phenomena involved in this process require the use of numerical solvers able to model the heat exchange in more than one domain ( calibrator and polymer), the compatibilization of the heat transfer at the profile - calibrator interface and with the ability to deal with complex geometries. The combination of all these features is usually hard to find in commercial software. Moreover , the dimension of the meshes required to ob tain accurate results, result in computational times prohibitive for industrial application. (...)

UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz)

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in -carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis--carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (redfleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Assessing the application of polypropylene multifilament yarns in brain phantoms

The currently available clinical imaging methods do not provide highly detailed information about location and severity of axonal injury or the expected recovery time of patients with traumatic brain injury [1]. High-Definition Fiber Tractography (HDFT) is a novel imaging modality that allows visualizing and quantifying, directly, the degree of axons damage, predicting functional deficits due to traumatic axonal injury and loss of cortical projections. This imaging modality is based on diffusion technology [2]. The inexistence of a phantom able to mimic properly the human brain hinders the possibility of testing, calibrating and validating these medical imaging techniques. Most research done in this area fails in key points, such as the size limit reproduced of the brain fibers and the quick and easy reproducibility of phantoms [3]. For that reason, it is necessary to develop similar structures matching the micron scale of axon tubes. Flexible textiles can play an important role since they allow producing controlled packing densities and crossing structures that match closely the human crossing patterns of the brain. To build a brain phantom, several parameters must be taken into account in what concerns to the materials selection, like hydrophobicity, density and fiber diameter, since these factors influence directly the values of fractional anisotropy. Fiber cross-section shape is other important parameter. Earlier studies showed that synthetic fibrous materials are a good choice for building a brain phantom [4]. The present work is integrated in a broader project that aims to develop a brain phantom made by fibrous materials to validate and calibrate HDFT. Due to the similarity between thousands of hollow multifilaments in a fibrous arrangement, like a yarn, and the axons, low twist polypropylene multifilament yarns were selected for this development. In this sense, extruded hollow filaments were analysed in scanning electron microscope to characterize their main dimensions and shape. In order to approximate the dimensional scale to human axons, five types of polypropylene yarns with different linear density (denier) were used, aiming to understand the effect of linear density on the filament inner and outer areas. Moreover, in order to achieve the required dimensions, the polypropylene filaments cross-section was diminished in a drawing stage of a filament extrusion line. Subsequently, tensile tests were performed to characterize the mechanical behaviour of hollow filaments and to evaluate the differences between stretched and non-stretched filaments. In general, an increase of the linear density causes the increase in the size of the filament cross section. With the increase of structure orientation of filaments, induced by stretching, breaking tenacity increases and elongation at break decreases. The production of hollow fibers, with the required characteristics, is one of the key steps to create a brain phantom that properly mimics the human brain that may be used for the validation and calibration of HDFT, an imaging approach that is expected to contribute significantly to the areas of brain related research.

Comparing different methods to measure biofilm thickness: the techniques are: optical coherence tomography, confocal laser scanning microscopy and low load compression test

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Engenharia Clínica)

Desenvolvimento de microcápsulas ativadas por radiação solar para aplicação em painéis cerâmicos exteriores

Dissertação de mestrado integrado em Engenharia de Materiais

Assessment of prognosis in patients with stage II colon cancer

Assessment of prognosis of patients with stage II colon cancer.

Stage at presentation of breast cancer in Luanda, Angola - a retrospective study

Background: It is expected that, by 2020, 15 million new cases of cancer will occur every year in the world, one million of them in Africa. Knowledge of cancer trends in African countries is far from adequate, and improvements in cancer prevention efforts are urgently needed. The aim of this study was to characterize breast cancer clinically and pathologically at presentation in Luanda, Angola; we additionally provide quality information that will be useful for breast cancer care planning in the country. Methods: Data on breast cancer cases were retrieved from the Angolan Institute of Cancer Control, from 2006 to 2014. For women diagnosed in 2009 (5-years of follow-up), demographic, clinical and pathological information, at presentation, was collected, namely age at diagnosis, parity, methods used for pathological diagnoses, tumor pathological characteristics, stage of disease and treatment. Descriptive statistics were performed. Results: The median age of women diagnosed with breast cancer in 2009 was 47 years old (range 25–89). The most frequent clinical presentation was breast swelling with axillary lymph nodes metastasis (44.9 %), followed by a mass larger than 5 cm (14.2 %) and lump (12.9 %). Invasive ductal carcinoma was the main histologic type (81.8 %). Only 10.1 % of cancer cases had a well differentiated histological grade. Cancers were diagnosed mostly at advanced stages (66.7 % in stage III and 11.1 % in stage IV). Discussion: In this study, breast cancer was diagnosed at a very advanced stage. Although it reports data from a single cancer center in Luanda, Angola it reinforces the need for early diagnosis and increasing awareness. According to the main challenges related to breast cancer diagnosis and treatment herein presented, we propose a realistic framework that would allow for the implementation of a breast cancer care program, built under a strong network based on cooperation, teaching, audit, good practices and the organization of health services. Conclusion: Angola needs urgently a program for early diagnosis of breast cancer.