Earthen plasters based on illitic soils from Barrocal region of Algarve: contributions for building performance and sustainability

Clayish earth-based mortars can be considered eco-efficient products for indoor plastering since they can contribute to improve important aspects of building performance and sustainability. Apart from being products with low embodied energy when compared to other types of mortars used for interior plastering, mainly due to the use raw clay as natural binder, earth-based plasters may give a significant contribution for health and comfort of inhabitants. Due to high hygroscopicity of clay minerals, earth-based mortars present a high adsorption and desorption capacity, particularly when compared to other type of mortars for interior plastering. This capacity allows earth-based plasters to act as a moisture buffer, balancing the relative humidity of the indoor environment and, simultaneously, acting as a passive removal material, improving air quality. Therefore, earth-based plasters may also passively promote the energy efficiency of buildings, since they may contribute to decreasing the needs of mechanical ventilation and air conditioning. This study is part of an ongoing research regarding earth-based plasters and focuses on mortars specifically formulated with soils extracted from Portuguese ‘Barrocal’ region, in Algarve sedimentary basin. This region presents high potential for interior plastering due to regional geomorphology, that promote the occurrence of illitic soils characterized by a high adsorption capacity and low expansibility. More specifically, this study aims to assess how clayish earth and sand ratio of mortars formulation can influence the physical and mechanical properties of plasters. For this assessment four mortars were formulated with different volumetric proportions of clayish earth and siliceous sand. The results from the physical and mechanical characterization confirmed the significantly low linear shrinkage of all the four mortars, as well as their extraordinary adsorption-desorption capacity. These results presented a positive correlation with mortars´ clayish earth content and are consistent with the mineralogical analysis, that confirmed illite as the prevalent clay mineral in the clayish earth used for this study. Regarding mechanical resistance, although the promising results of the adhesion test, the flexural and compressive strength results suggest that the mechanical resistance of these mortars should be slightly improved. Considering the present results the mortars mechanical resistance improvement may be achieved through the formulation of mortars with higher clayish earth content, or alternatively, through the addition of natural fibers to mortars formulation, very common in this type of mortars. Both those options will be investigated in future research.

Modelling of the kinetics of municipal solid waste composting in full-scale mechanical-biological treatment plants

A thesis submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy in Sanitary Engineering in the Faculty of Sciences and Technology of the New University of Lisbon

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

A engenharia de requisitos como processo inovador nas organizações

Trabalho realizado sob orientação do Prof. António Brandão Moniz para a disciplina “Factores Sociais da Inovação” do Mestrado Engenharia Informática realizado na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa

Effect of fungal colonization on mechanical performance of cork

The industrialization of traditional processes relies on the scientific ability to understand the empirical evidence associated with traditional knowledge. Cork manufacturing includes one operation known as stabilization, where humid cork slabs are extensively colonized by fungi. The implications of fungal growth on the chemical quality of cork through the analysis of putative fungal metabolites have already been investigated. However, the effect of fungal growth on the mechanical properties of cork remains unexplored. This study investigated the effect of cork colonization on the integrity of the cork cell walls and their mechanical performance. Fungal colonization of cork by Chrysonilia sitophila, Mucor plumbeus Penicillium glabrum, P. olsonii, and Trichoderma longibrachiatum was investigated by microscopy. Growth occurred primarily on the surface of the cork pieces, but mycelium extended deeper into the cork layers, mostly via lenticular channels and by hyphal penetration of the cork cell wall. In this first report on cork decay in which specific correlation between fungal colonization and mechanical proprieties of the cork has been investigated, all colonizing fungi except C. sitophila, reduced cork strength, markedly altering its viscoelastic behaviour and reducing its Young’s modulus.

The strategic deployment of quality-improvement innovations

Journal of Business, Vol. 78 Issue 3, p1049-1072

Preliminary study on the production of functionally graded materials by friction stir processing

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Mecânica

Vibration control with shape-memory alloys in civil engineering structures

Dissertação apresentada à Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Doutor em Engenharia Civil

Probabilistic models for mechanical properties of prestressing strands

This study focus on the probabilistic modelling of mechanical properties of prestressing strands based on data collected from tensile tests carried out in Laboratório Nacional de Engenharia Civil (LNEC), Portugal, for certification purposes, and covers a period of about 9 years of production. The strands studied were produced by six manufacturers from four countries, namely Portugal, Spain, Italy and Thailand. Variability of the most important mechanicalproperties is examined and the results are compared with the recommendations of the ProbabilisticModel Code, as well as the Eurocodes and earlier studies. The obtained results show a very low variability which, of course, benefits structural safety. Based on those results, probabilistic modelsfor the most important mechanical properties of prestressing strands are proposed.

Correlation between the mechanical cycling behavior and microstructure in laser welded joints using NiTi memory shape alloys

Dissertação para obtenção do Grau de Mestre em Engenharia de Materiais

Novel transcription factors regulating the expression of the rice gene OsDREB1B

Dissertation presented to obtain the Ph.D degree in Biology

The value relevance of investment property fair value

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

Towards cryogenic liquid –vapor energy storage units for space applications

Dissertation to obtain the Doctoral degree in Physics Engineering

Influence of lime type and curing conditions on lime and lime-metakaolin mortars

Durability of Building Materials and Components (Vasco Peixoto de de Freitas, J.M.P.Q. Delgado, eds.), Building Pathology and Rehabilitation, vol. 3, VIII, 105-126. ISBN: 978-3-642-37474-6 (Print) 978-3-642-37475-3 (Online). Springer-Verlag Berlin Heidelberg. DOI: 10.1007/978-3-642-37475-3_5

Influence of curing conditions in lime-metakaolin blended mortars – A mineralogical and mechanical study

3rd Historic Mortars Conference, 11-14 September 2013, Glasgow, Scotland