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.

Traceability support in software product lines

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

Gender differences in social support and leisure-time physical activity

OBJECTIVE To identify gender differences in social support dimensions’ effect on adults’ leisure-time physical activity maintenance, type, and time.METHODS Longitudinal study of 1,278 non-faculty public employees at a university in Rio de Janeiro, RJ, Southeastern Brazil. Physical activity was evaluated using a dichotomous question with a two-week reference period, and further questions concerning leisure-time physical activity type (individual or group) and time spent on the activity. Social support was measured with the Medical Outcomes Study Social Support Scale. For the analysis, logistic regression models were adjusted separately by gender.RESULTS A multinomial logistic regression showed an association between material support and individual activities among women (OR = 2.76; 95%CI 1.2;6.5). Affective support was associated with time spent on leisure-time physical activity only among men (OR = 1.80; 95%CI 1.1;3.2).CONCLUSIONS All dimensions of social support that were examined influenced either the type of, or the time spent on, leisure-time physical activity. In some social support dimensions, the associations detected varied by gender. Future studies should attempt to elucidate the mechanisms involved in these gender differences.

Dpws middleware to support agent-based manufacturing control and simulation

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

ENCOURAGE architecture: support for heterogeneous smart grids

Our society relies on energy for most of its activities. One application domain inciding heavily on the energy budget regards the energy consumption in residential and non-residential buildings. The ever increasing needs for energy, resulting from the industrialization of developing countries and from the limited scalability of the traditional technologies for energy production, raises both problems and opportunities. The problems are related to the devastating effects of the greenhouse gases produced by the burning of oil and gas for energy production, and from the dependence of whole countries on companies providing gas and oil. The opportunities are mostly technological, since novel markets are opening for both energy production via renewable sources, and for innovations that can rationalize energy usage. An enticing research effort can be the mixing of these two aspects, by leveraging on ICT technologies to rationalize energy production, acquisition, and consumption. The ENCOURAGE project aims to develop embedded intelligence and integration technologies that will directly optimize energy use in buildings and enable active participation in the future smart grid environment.The primary application domains targeted by the ENCOURAGE project are non-residential buildings (e.g.: campuses) and residential buildings (e.g.: neighborhoods). The goal of the project is to achieve 20% of energy savings through the improved interoperability between various types of energy generation, consumption and storage devices; interbuilding energy exchange; and systematic performance monitoring.

A high level e-maintenance architecture to support on-site teams

Emergent architectures and paradigms targeting reconfigurable manufacturing systems increasingly rely on intelligent modules to maximize the robustness and responsiveness of modern installations. Although intelligent behaviour significantly minimizes the occurrence of faults and breakdowns it does not exclude them nor can prevent equipment’s normal wear. Adequate maintenance is fundamental to extend equipments’ life cycle. It is of major importance the ability of each intelligent device to take an active role in maintenance support. Further this paradigm shift towards “embedded intelligence”, supported by cross platform technologies, induces relevant organizational and functional changes on local maintenance teams. On the one hand, the possibility of outsourcing maintenance activities, with the warranty of a timely response, through the use of pervasive networking technologies and, on the other hand, the optimization of local maintenance staff are some examples of how IT is changing the scenario in maintenance. The concept of e-maintenance is, in this context, emerging as a new discipline with defined socio-economic challenges. This paper proposes a high level maintenance architecture supporting maintenance teams’ management and offering contextualized operational support. All the functionalities hosted by the architecture are offered to the remaining system as network services. Any intelligent module, implementing the services’ interface, can report diagnostic, prognostic and maintenance recommendations that enable the core of the platform to decide on the best course of action.

Risk of dependence associated with health, social support, and lifestyle

OBJECTIVE To analyze the prevalence of individuals at risk of dependence and its associated factors.METHODS The study was based on data from the Catalan Health Survey, Spain conducted in 2010 and 2011. Logistic regression models from a random sample of 3,842 individuals aged ≥ 15 years were used to classify individuals according to the state of their personal autonomy. Predictive models were proposed to identify indicators that helped distinguish dependent individuals from those at risk of dependence. Variables on health status, social support, and lifestyles were considered.RESULTS We found that 18.6% of the population presented a risk of dependence, especially after age 65. Compared with this group, individuals who reported dependence (11.0%) had difficulties performing activities of daily living and had to receive support to perform them. Habits such as smoking, excessive alcohol consumption, and being sedentary were associated with a higher probability of dependence, particularly for women.CONCLUSIONS Difficulties in carrying out activities of daily living precede the onset of dependence. Preserving personal autonomy and function without receiving support appear to be a preventive factor. Adopting an active and healthy lifestyle helps reduce the risk of dependence.

Catalytic behavior of alkali-treated Pt/HMOR n-hexane hydroisomerization

Bifunctional Pt-HMOR catalysts were prepared by incipient wetness impregnation of various desilicated MOR obtained by alkaline treatment using NaOH concentrations ranging from 0.1 to 0.5 M. The zeolite structural changes upon modification were investigated by several techniques including powder X-ray diffraction,Al-27 and Si-29 MAS-NMR spectroscopy, N-2 adsorption, pyridine adsorption followed by infrared spectroscopy and the catalytic model reaction of m-xylene transformation. For low alkaline concentration the zeolite acidity is preserved, along with a slight increase of the volume correspondent to the larger micropores due to the removal of extra-framework debris already existent at the parent zeolite. At higher NaOH concentrations there is a significant loss of crystalinity and acidity as well as the formation of mesoporosity. The characterization of the metal function shows similar patterns for Pt-HMOR and Pt-M/0.1 samples, with Pt particles located mainly inside the inner porosity. In contrast, large Pt particles become visible at the intercrystalline mesoporosity of MOR crystals developed during the desilication treatments at severe alkaline conditions. The catalytic results obtained for n-hexane hydroisomerization showed an improved selectivity for dibranched over monobranched isomers for Pt-M/0.1 sample, likely due to the preservation of the support acidity and the slight enlargement of the micropores. This work is a new example in which the mesoporous development does not improve the catalytic efficiency of the zeolites, whereas mild alkaline desilication might be considered as an effective solution to produce customized catalysts with enhanced performance for a given application. (C) 2014 Elsevier B.V. All rights reserved.

Self-organization and complexity theory to support evolvable production systems

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

The effect of metal transfer modes and shielding gas composition on the emission of ultraafine particles in MAG steel welding

The present study aims to characterize ultrafine particles emitted during gas metal arc welding of mild steel and stainless steel, using different shielding gas mixtures, and to evaluate the effect of metal transfer modes, controlled by both processing parameters and shielding gas composition, on the quantity and morphology of the ultrafine particles. It was found that the amount of emitted ultrafine particles (measured by particle number and alveolar deposited surface area) are clearly dependent from the main welding parameters, namely the current intensity and the heat input of the Welding process. The emission of airborne ultrafine particles increases with the current intensity as fume formation rate does. When comparing the shielding gas mixtures, higher emissions were observed for more oxidizing mixtures, that is, with higher CO2 content, which means that these mixtures originate higher concentrations of ultrafine particles (as measured by number of particles. by cubic centimeter of air) and higher values of alveolar deposited surface area of particles, thus resulting in a more hazardous condition regarding welders exposure.

Institutional and matrix support and its relationship with primary healthcare

OBJECTIVE To analyze whether the level of institutional and matrix support is associated with better certification of primary healthcare teams.METHODS In this cross-sectional study, we evaluated two kinds of primary healthcare support – 14,489 teams received institutional support and 14,306 teams received matrix support. Logistic regression models were applied. In the institutional support model, the independent variable was “level of support” (as calculated by the sum of supporting activities for both modalities). In the matrix support model, in turn, the independent variables were the supporting activities. The multivariate analysis has considered variables with p < 0.20. The model was adjusted by the Hosmer-Lemeshow test.RESULTS The teams had institutional and matrix supporting activities (84.0% and 85.0%), respectively, with 55.0% of them performing between six and eight activities. For the institutional support, we have observed 1.96 and 3.77 chances for teams who had medium and high levels of support to have very good or good certification, respectively. For the matrix support, the chances of their having very good or good certification were 1.79 and 3.29, respectively. Regarding to the association between institutional support activities and the certification, the very good or good certification was positively associated with self-assessment (OR = 1.95), permanent education (OR = 1.43), shared evaluation (OR = 1.40), and supervision and evaluation of indicators (OR = 1.37). In regards to the matrix support, the very good or good certification was positively associated with permanent education (OR = 1.50), interventions in the territory (OR = 1.30), and discussion in the work processes (OR = 1.23).CONCLUSIONS In Brazil, supporting activities are being incorporated in primary healthcare, and there is an association between the level of support, both matrix and institutional, and the certification result.

Mono(Eta(5)-cyclopentadienyl)metal(II) complexes with thienyl acetylide chromophores: synthesis, electrochemical studies, and first hyperpolarizabilities

A series of mono(eta(5)-cyclopentadienyl)metal-(II) complexes with nitro-substituted thienyl acetylide ligands of general formula [M(eta(5)-C5H5)(L)(C C{C4H2S}(n)NO2)] (M = Fe, L = kappa(2)-DPPE, n = 1,2; M = Ru, L = kappa(2)-DPPE, 2 PPh3, n = 1, 2; M = Ni, L = PPh3, n = 1, 2) has been synthesized and fully characterized by NMR, FT-IR, and UV-Vis spectroscopy. The electrochemical behavior of the complexes was explored by cyclic voltammetry. Quadratic hyperpolarizabilities (beta) of the complexes have been determined by hyper-Rayleigh scattering (HRS) measurements at 1500 nm. The effect of donor abilities of different organometallic fragments on the quadratic hyperpolarizabilities was studied and correlated with spectroscopic and electrochemical data. Density functional theory (DFT) and time-dependent DFT (TDDFT) calculations were employed to get a better understanding of the second-order nonlinear optical properties in these complexes. In this series, the complexity of the push pull systems is revealed; even so, several trends in the second-order hyperpolarizability can still be recognized. In particular, the overall data seem to indicate that the existence of other electronic transitions in addition to the main MLCT clearly controls the effectiveness of the organometallic donor ability on the second-order NLO properties of these push pull systems.

An embedded 1149.4 extension to support mixed-signal debugging

Debugging electronic circuits is traditionally done with bench equipment directly connected to the circuit under debug. In the digital domain, the difficulties associated with the direct physical access to circuit nodes led to the inclusion of resources providing support to that activity, first at the printed circuit level, and then at the integrated circuit level. The experience acquired with those solutions led to the emergence of dedicated infrastructures for debugging cores at the system-on-chip level. However, all these developments had a small impact in the analog and mixed-signal domain, where debugging still depends, to a large extent, on direct physical access to circuit nodes. As a consequence, when analog and mixed-signal circuits are integrated as cores inside a system-on-chip, the difficulties associated with debugging increase, which cause the time-to-market and the prototype verification costs to also increase. The present work considers the IEEE1149.4 infrastructure as a means to support the debugging of mixed-signal circuits, namely to access the circuit nodes and also an embedded debug mechanism named mixed-signal condition detector, necessary for watch-/breakpoints and real-time analysis operations. One of the main advantages associated with the proposed solution is the seamless migration to the system-on-chip level, as the access is done through electronic means, thus easing debugging operations at different hierarchical levels.

The effect of metal transfer stability (spattering) on fume generation, morphology and composition in short-circuit MAG welding

The main objective of this work was to evaluate the hypothesis that the greater transfer stability leads also to less volume of fumes. Using an Ar + 25%CO2 blend as shielding gas and maintaining constant the average current, wire feed speed and welding speed, bead-on-plate welds were carried out with plain carbon steel solid wire. The welding voltage was scanned to progressively vary the transfer stability. Using two conditions of low stability and one with high stability, fume generation was evaluated by means of the AWS F1.2:2006 standard. The influence of these conditions on fume morphology and composition was also verified. A condition with greater transfer stability does not generate less fume quantity, despite the fact that this condition produces fewer spatters. Other factors such as short-circuit current, arcing time, droplet diameters and arc length are the likely governing factors, but in an interrelated way. Metal transfer stability does not influence either the composition or the size/morphology of fume particulates. (c) 2014 Elsevier B.V. All rights reserved.

Trits(Pyrazol-1-YL)methane metal complexes for catalytic mild oxidative functionalizations of alkanes, alkanes and ketones

This work concerns recent advances (since 2005) in the oxidative functionalization of alkanes, alkenes and ketones, under mild conditions, catalyzed by homoscorpionate tris(pyrazol-1-yl)methane metal complexes. The main types of such homogeneous or supported catalysts are classified, and the critical analysis of the most efficient catalytic systems in the different reactions is presented. These reactions include the mild oxidation of alkanes (typically cyclohexane as a model substrate) with hydrogen peroxide (into alkyl hydroperoxides, alcohols, and ketones), the hydrocarboxylation of gaseous alkanes (with carbon monoxide and potassium peroxodisulfate) into the corresponding Cn+1 carboxylic acids, as well as the epoxidation of alkenes and the Baeyer-Villiger oxidation of linear and cyclic ketones with hydrogen peroxide into the corresponding esters and lactones. Effects of various reaction parameters are highlighted and the preferable requirements for a prospective homogeneous or supported C-scorpionate-M-based catalyst in oxidative transformations of those substrates are identified. (C) 2014 Elsevier B.V. All rights reserved.