Enhancing physical activity coaching through personalized motivational strategies and self-adaptive goal-setting: development of self-adaptive processes in a monitoring and coaching smartphone application

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Characterization tests for insulation boards made from corn cob and natural glues

In recent years there has been a growing interest in developing news solutions for more ecologic and efficient construction, including natural, renewable and local materials, thus contributing in the search for more efficient, economic and environmentally friendly construction. Several authors have assessed the possibility of using various agricultural sub products or wastes, as part of the effort of the scientific community to find alternative and more ecologic construction materials. Corn cob is an agricultural waste from a very important worldwide crop. Natural glues are made from natural materials, non-mineral, that can be used as such or after some modifications to achieve the behaviour and performance required. Two examples of these natural glues are casein and wheat flour-based glues that were used in the present study. Boards with different compositions were manufactured, having as variables the type of glue, the dimension of the corn cob particles and the features of the pressing process. The tests boards were characterized with physical and mechanical tests, such as thermal conductivity (λ) with a ISOMET 2104 and 60 mm diameter contact probe, density (ρ) based on EN 1602:2013, surface hardness (SH) with a PCE Shore A durometer, surface resistance (SR) with a PROCEQ PT pendular sclerometer, bending behaviour (σ) based on EN 12089:2013, compression behaviour (σ10) based on EN 826:2013 and resilience (R) based on EN 1094-1:2008, with a Zwick Rowell bending equipment with 2 kN and 50 kN load cells (Fig. 1), dynamic modulus of elasticity (Ed) with a Zeus Resonance Meter equipment (Fig. 5) based on NP EN 14146:2006 and water vapour permeability (δ) based on EN 12086:2013. The various boards produced were characterized according to the tests and the ones with the best results were C8_c8 (casein glue, grain size 2,38-4,76 mm, cold pressing for 8 hours), C8_c4 (casein glue, grain size 2,38-4,76 mm, cold pressing for 4 hours), F8_h0.5 (wheat flour glue, grain size 2,38-4,76 mm, hot pressing for 0,5 hours), FEV8_h0.5 (wheat flour, egg white and vinegar glue, grain size 2,38-4,76 mm, hot pressing for 0,5 hours) and FEVH68_c4 (wheat flour, egg white, vinegar and 6 g of sodium hydroxide glue, grain size 2,38-4,76 mm, cold pressing for 4 hours). Taking into account the various boards produced and respective test results the type of glue and the pressure and pressing time are very important factors which strongly influence the final product. The results obtained confirmed the initial hypotheses that these boards have potential as a thermal and, eventually, acoustic insulation material, to use as coating or intermediate layer on walls, floors or false ceilings. This type of board has a high mechanical resistance when compared with traditional insulating materials.The integrity of these boards seems to be maintained even in higher humidity environments. However, due to biological susceptibility and sensitivity to water, they would be more adequate for application in dry interior conditions.

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.

Earth-based repair mortars: experimental analysis with different binders and natural fibers

RESTAPIA 2012 - Int. Conf. on Rammed Earth Conservation, Valencia, 21-23 June 2012

Unstabilised rammed earth: characterization of material collected from old constructions in South Portugal and comparison to normative requirements

International Journal of Architectural Heritage, 8: 185–212, 2014

Performance assessment of waste fibre-reinforced mortar

Materials Science Forum Vols. 730-732 (2013) pp 617-622

Characterization of earth-based mortars for rammed earth repair

All over the world, many earth buildings are deteriorating due to lack of maintenance and repair. Repairs on rammed earth walls are mainly done with mortars, by rendering application; however, often the repair is inadequate, resorting to the use of incompatible materials, including cement-based mortars. It has been observed that such interventions, in walls that until that day only had presented natural ageing issues, created new problems, much more dangerous for the building than the previous ones, causing serious deficiencies in this type of construction. One of the problems is that the detachment of the new cement-based mortar rendering only occurs after some time but, until that occurrence, degradations develop in the wall itself. When the render detaches, instead of needing only a new render, the surface has to be repaired in depth, with a repair mortar. Consequently, it has been stablished that the renders, and particularly repair mortars, should have physical, mechanical and chemical properties similar to those of the rammed earth walls. This article intends to contribute to a better knowledge of earth-based mortars used to repair the surface of rammed earth walls. The studied mortars are based on four types of earth: three of them were collected from non-deteriorated parts of walls of unstabilized rammed earth buildings located in Alentejo region, south of Portugal; the fourth is a commercial earth, consisting mainly of clay. Other components were also used, particularly: sand to control shrinkage; binders stabilizers such as dry hydrated air-lime, natural hydraulic lime, Portland cement and natural cement; as well as natural vegetal fibers (hemp fibers). The experimental analysis of the mortars in the fresh state consisted in determining the consistency by flow table and the bulk density. In the hardened state, the tests made it possible to evaluate the following properties: linear and volumetric shrinkage; capillary water absorption; drying capacity; dynamic modulus of elasticity; flexural and compressive strength.