5 resultados para Self-other Discrimination
em Universidade do Minho
Resumo:
The future of the construction industry will require changes at many levels. One is the ability of companies to adapt to new challenges, converting needs to opportunities and simultaneously contributing to the solving of social and environmental problems. In the coming decades we will see a change in attitude in the industry, with a strong tendency to adopt natural and recycled materials, as well as bet on green technology and social innovation oriented to emerging countries. On the other hand, emerging countries have a high demand for housing construction on a large scale, but the current techniques in the developed countries for building requires a large amount of natural resources and skilled labor. This contextualization brings sustainability problems for the construction sector in emerging countries, often with scarce natural resources and with the construction sector underdeveloped. Through a cooperative action between the construction company Mota-Engil Engineering and the University of Minho in Portugal, a construction technology was developed based on the use of Compressed Earth Blocks as part of a social concept for innovative small houses, favoring the adoption of local and natural materials and with the main premise of being dedicated to self-construction. The HiLoTec project - Development of a Sustainable Self-Construction System for Developing Countries was based on this idea. One of the several results of this project is this construction manual. To Mota-Engil the project was a platform for incubation of knowledge about earth construction and to obtain a constructive solution validated technically and scientifically, suitable to be implemented in the markets where it operates. For the University of Minho the project was an opportunity to strengthen skills in research, laboratory and scientific development, through the development of engineering studies, architecture and sustainability, as well as supporting the doctoral scholarships and dissemination of scientific publications. May the knowledge of this project be of benefit, in the future, for the welfare of those who build a HiLoTec house.
Resumo:
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.
Resumo:
Degree of Doctor of Philosophy of Structural/Civil Engineering
Resumo:
The focus of this paper is given to investigate the effect of different fibers on the pore pressure of fiber reinforced self-consolidating concrete under fire. The investigation on the pore pressure-time and temperature relationships at different depths of fiber reinforced self-consolidating concrete beams was carried out. The results indicated that micro PP fiber is more effective in mitigating the pore pressure than macro PP fiber and steel fiber. The composed use of steel fiber, micro PP fiber and macro PP fiber showed clear positive hybrid effect on the pore pressure reduction near the beam bottom subjected to fire. Compared to the effect of macro PP fiber with high dosages, the effect of micro PP fiber with low fiber contents on the pore pressure reduction is much stronger. The significant factor for reduction of pore pressure depends mainly on the number of PP fibers and not only on the fiber content. An empirical formula was proposed to predict the relative maximum pore pressure of fiber reinforced self-consolidating concrete exposed to fire by considering the moisture content, compressive strength and various fibers. The suggested model corresponds well with the experimental results of other research and tends to prove that the micro PP fiber can be the vital component for reduction in pore pressure, temperature as well spalling of concrete.
Resumo:
"Published online: 07 Nov 2015"