9 resultados para density surface modelling
em Universidade do Minho
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Biofilm adhesion to metals (copper, aluminium and brass) was studied at two different velocities and pH values of 7 and 9. Both bacteria and metals showed negative surface charges at those values of pH, which tends to slow down adhesion. Film densities increased with the fluid velocity and were also affected by the pH and by the growth rate of the bacteria. Long duration tests based on heat transfer measurements were run at five different fluid velocities and at pH = 7, showing in general an asymptotic behaviour and a control of deposition by adhesion and growth phenomena.
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Purpose: To determine the relationship of goblet cell density (GCD) with tear function and ocular surface physiology. Methods: This was a cross-sectional study conducted in 35 asymptomatic subjects with mean age 23.8±3.6 years. Tear film assessment, conjunctiva and cornea examination were done in each subject. Conjunctival impression cytology was performed by applying Nitrocellulose Millipore MFTM-Membrane filter over the superior bulbar conjunctiva. The filter paper was than fixed with 96% ethanol and stained with Periodic Acid Schiff, Hematoxylin and Eosin. GCD was determined by optical microscopy. Relation between GCD and Schirmer score, tear break-up time (TBUT), bulbar redness, limbal redness and corneal staining was determined. Results: The mean GCD was 151±122 cells/mm2. GCD was found higher in eyes with higher Schirmer score but it was not significant (p = 0.75). There was a significant relationship ofGCDwith TBUT (p = 0.042). GCD was not correlated with bulbar redness (p = 0.126), and limbal redness (p = 0.054) as well as corneal staining (p = 0.079). No relationship of GCD with age and gender of the subjects (p > 0.05) was observed. Conclusion: GCD was found correlated with TBUT but no significant correlation was found with the aqueous portion of the tear, limbal as well as bulbar redness and corneal staining.
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The success of synthetic bone implants requires good interface between the material and the host tissue. To study the biological relevance of fi bronectin (FN) density on the osteogenic commitment of human bone marrow mesenchymal stem cells (hBMMSCs), human FN was adsorbed in a linear density gradient on the surface of PCL. The evolution of the osteogenic markers alkaline phosphatase and collagen 1 alpha 1 was monitored by immunohistochemistry, and the cytoskeletal organization and the cell-derived FN were assessed. The functional analysis of the gradient revealed that the lower FN-density elicited stronger osteogenic expression and higher cytoskeleton spreading, hallmarks of the stem cell commitment to the osteoblastic lineage. The identifi cation of the optimal FN density regime for the osteogenic commitment of hBM-MSCs presents a simple and versatile strategy to signifi cantly enhance the surface properties of polycaprolactone as a paradigm for other synthetic polymers intended for bone-related applications.
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The performance of parts produced by Free Form Extrusion (FFE), an increasingly popular additive manufacturing technique, depends mainly on their dimensional accuracy, surface quality and mechanical performance. These attributes are strongly influenced by the evolution of the filament temperature and deformation during deposition and solidification. Consequently, the availability of adequate process modelling software would offer a powerful tool to support efficient process set-up and optimisation. This work examines the contribution to the overall heat transfer of various thermal phenomena developing during the manufacturing sequence, including convection and radiation with the environment, conduction with support and between adjacent filaments, radiation between adjacent filaments and convection with entrapped air. The magnitude of the mechanical deformation is also studied. Once this exercise is completed, it is possible to select the material properties, process variables and thermal phenomena that should be taken in for effective numerical modelling of FFE.
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Tese de Doutoramento em Ciência e Engenharia de Polímeros e Compósitos
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Tese de Doutoramento em Engenharia de Tecidos, Medicina Regenerativa e Células Estaminais.
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The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and uneven- ness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.
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Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica)
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In recent decades, an increased interest has been evidenced in the research on multi-scale hierarchical modelling in the field of mechanics, and also in the field of wood products and timber engineering. One of the main motivations for hierar-chical modelling is to understand how properties, composition and structure at lower scale levels may influence and be used to predict the material properties on a macroscopic and structural engineering scale. This chapter presents the applicability of statistic and probabilistic methods, such as the Maximum Likelihood method and Bayesian methods, in the representation of timber’s mechanical properties and its inference accounting to prior information obtained in different importance scales. These methods allow to analyse distinct timber’s reference properties, such as density, bending stiffness and strength, and hierarchically consider information obtained through different non, semi or destructive tests. The basis and fundaments of the methods are described and also recommendations and limitations are discussed. The methods may be used in several contexts, however require an expert’s knowledge to assess the correct statistic fitting and define the correlation arrangement between properties.