Characterization of airborne particles generated from metal active gas welding process

This study is focused on the characterization of particles emitted in the metal active gas welding of carbon steel using mixture of Ar + CO2, and intends to analyze which are the main process parameters that influence the emission itself. It was found that the amount of emitted particles (measured by particle number and alveolar deposited surface area) are clearly dependent on the distance to the welding front and also on the main welding parameters, namely the current intensity and heat input in the welding process. The emission of airborne fine particles seems to increase with the current intensity as fume-formation rate does. When comparing the tested gas mixtures, higher emissions are observed for more oxidant mixtures, that is, mixtures with higher CO2 content, which result in higher arc stability. These mixtures originate higher concentrations of fine particles (as measured by number of particles by cm 3 of air) and higher values of alveolar deposited surface area of particles, thus resulting in a more severe worker's exposure.

Biomonitorization in hospital settings with cytostatics occupational exposure

Exposure in a hospital setting is normally due to the use of several antineoplastic drugs simultaneously. Nevertheless, the effects of such mixtures at the cell level and on human health in general are unpredictable and unique due to differences in practice of hospital oncology departments, in the number of patients, protection devices available, and the experience and safety procedures of medical staff. Health care workers who prepare or administer hazardous drugs or who work in areas where these drugs are used may be exposed to these agents in the air, on work surfaces, contaminated clothing, medical equipment, patient excreta, and other surfaces. These workers include specially pharmacists, pharmacy technicians, and nursing personnel. Exposures may occur through inhalation resulting from aerosolization of powder or liquid during reconstitution and spillage taking place while preparing or administering to patients, through Cytokinesis-block micronucleus test (CBMN) is extensively used in biomonitoring, since it determines several biomarkers of genotoxicity, such as micronuclei (MN), which are biomarkers of chromosomes breakage or loss, nucleoplasmic bridges (NPB), common biomarkers of chromosome rearrangement, poor repair and/or telomeres fusion, and nuclear buds (NBUD), biomarkers of elimination of amplified DNA.

Polycyclic aromatic hydrocarbons in gas and particulate phases of indoor environments influenced by tobacco smoke: levels, phase distributions, and health risks

As polycyclic aromatic hydrocarbons (PAHs) have a negative impact on human health due to their mutagenic and/or carcinogenic properties, the objective of this work was to study the influence of tobacco smoke on levels and phase distribution of PAHs and to evaluate the associated health risks. The air samples were collected at two homes; 18 PAHs (the 16 PAHs considered by U.S. EPA as priority pollutants, dibenzo[a,l]pyrene and benzo[j]fluoranthene) were determined in gas phase and associated with thoracic (PM10) and respirable (PM2.5) particles. At home influenced by tobacco smoke the total concentrations of 18 PAHs in air ranged from 28.3 to 106 ngm 3 (mean of 66.7 25.4 ngm 3),∑PAHs being 95% higher than at the non-smoking one where the values ranged from 17.9 to 62.0 ngm 3 (mean of 34.5 16.5 ngm 3). On average 74% and 78% of ∑PAHs were present in gas phase at the smoking and non-smoking homes, respectively, demonstrating that adequate assessment of PAHs in air requires evaluation of PAHs in both gas and particulate phases. When influenced by tobacco smoke the health risks values were 3.5e3.6 times higher due to the exposure of PM10. The values of lifetime lung cancer risks were 4.1 10 3 and 1.7 10 3 for the smoking and nonsmoking homes, considerably exceeding the health-based guideline level at both homes also due to the contribution of outdoor traffic emissions. The results showed that evaluation of benzo[a]pyrene alone would probably underestimate the carcinogenic potential of the studied PAH mixtures; in total ten carcinogenic PAHs represented 36% and 32% of the gaseous ∑PAHs and in particulate phase they accounted for 75% and 71% of ∑PAHs at the smoking and non-smoking homes, respectively.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Clustering and selecting categorical features

In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness.

Impact of vehicular traffic emissions on particulate-bound PAHs: levels and associated health risks

Considering vehicular transport as one of the most health‐relevant emission sources of urban air, and with aim to further understand its negative impact on human health, the objective of this work was to study its influence on levels of particulate‐bound PAHs and to evaluate associated health risks. The 16 PAHs considered by USEPA as priority pollutants, and dibenzo[a, l]pyrene associated with fine (PM2.5) and coarse (PM2.5–10) particles were determined. The samples were collected at one urban site, as well as at a reference place for comparison. The results showed that the air of the urban site was more seriously polluted than at the reference one, with total concentrations of 17 PAHs being 2240% and 640% higher for PM2.5 and PM2.5–10, respectively; vehicular traffic was the major emission source at the urban site. PAHs were predominantly associated with PM2.5 (83% to 94% of ΣPAHs at urban and reference site, respectively) with 5 rings PAHs being the most abundant groups of compounds at both sites. The risks associated with exposure to particulate PAHs were evaluated using the TEF approach. The estimated value of lifetime lung cancer risks exceeded the health‐based guideline levels, thus demonstrating that exposure to PM2.5‐bound PAHs at levels found at urban site might cause potential health risks. Furthermore, the results showed that evaluation of benzo[a] pyrene (regarded as a marker of the genotoxic and carcinogenic PAHs) alone would probably underestimate the carcinogenic potential of the studied PAH mixtures.

Plano de negócios para a construção de uma unidade fabril de misturas complementares

A Cooperativa Agrícola de Vila do Conde desenvolve um negócio de fabrico e comercialização de misturas complementares para alimentação bovina, sobretudo para vacas leiteiras. Há alguns anos a esta parte, esta Cooperativa sabe que terá que deslocalizar a unidade fabril existente devido a imposições da Direção Geral de Alimentação e Veterinária, relacionadas com questões de natureza ambiental. A necessidade de ser realizado um novo investimento, para garantir a sustentabilidade do negócio mais rentável gerido por esta Cooperativa, levou a pensar-se na possibilidade de construção de uma nova unidade fabril, de dimensão superior, capaz de servir outras cooperativas, visando o desejado entendimento das cooperativas em torno de um objetivo comum, logrando a obtenção de economias de escala, de extrema importância para a sobrevivência do setor leiteiro na região do Entre Douro e Minho. Para o efeito será constituída uma nova sociedade por quotas, designada por AGRIVIL XXI, Lda., de capital exclusivamente cooperativo, possibilitando que, em cada momento, se possa aferir a situação económica e financeira do negócio de forma mais rigorosa e autónoma. Esta realidade foi conducente à elaboração do presente Plano de Negócios que se espera profícuo para definição dos objetivos e metas a atingir num futuro próximo pela Cooperativa Agrícola de Vila do Conde. As análises de viabilidade e do risco do projeto demonstraram estarem criadas as condições de aceitação do mesmo, sendo expectável um VAL de 1.371.764 euros, uma TIR de 12,04% e um pay-back period próximo dos 11 anos. No entanto é notório a existência de um risco inerente ao investimento na medida em que o montante dos fluxos gerados tende a aproximar-se dos fluxos investidos, não gerando um excedente de riqueza significativo.

Biodegradação enzimática de aminas aromáticas tóxicas

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Química e Biológica

Ibuprofen-loaded poly(trimethylene carbonate-co-ϵ- caprolactone) electrospun fibres for nerve regeneration

The development of scaffolds that combine the delivery of drugs with the physical support provided by electrospun fibres holds great potential in the field of nerve regeneration. Here it is proposed the incorporation of ibuprofen, a well-known non-steroidal anti-inflammatory drug, in electrospun fibres of the statistical copolymer poly(trimethylene carbonate-co-ε-caprolactone) [P(TMC-CL)] to serve as a drug delivery system to enhance axonal regeneration in the context of a spinal cord lesion, by limiting the inflammatory response. P(TMC-CL) fibres were electrospun from mixtures of dichloromethane (DCM) and dimethylformamide (DMF). The solvent mixture applied influenced fibre morphology, as well as mean fibre diameter, which decreased as the DMF content in solution increased. Ibuprofen-loaded fibres were prepared from P(TMC-CL) solutions containing 5% ibuprofen (w/w of polymer). Increasing drug content to 10% led to jet instability, resulting in the formation of a less homogeneous fibrous mesh. Under the optimized conditions, drug-loading efficiency was above 80%. Confocal Raman mapping showed no preferential distribution of ibuprofen in P(TMC-CL) fibres. Under physiological conditions ibuprofen was released in 24h. The release process being diffusion-dependent for fibres prepared from DCM solutions, in contrast to fibres prepared from DCM-DMF mixtures where burst release occurred. The biological activity of the drug released was demonstrated using human-derived macrophages. The release of prostaglandin E2 to the cell culture medium was reduced when cells were incubated with ibuprofen-loaded P(TMC-CL) fibres, confirming the biological significance of the drug delivery strategy presented. Overall, this study constitutes an important contribution to the design of a P(TMC-CL)-based nerve conduit with anti-inflammatory properties.

Caracterização laboratorial de misturas betuminosas com incorporação de agregado siderúrgico inerte para construção (ASIC)

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Vias de Comunicação e Transportes

Estudo da compactabilidade em laboratório de misturas betuminosas com resíduos plásticos

Dissertação para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização em Vias de Comunicação e Transportes

Occupational co-exposure to several mycotoxins in the waste management setting

Contrary to fungi, exposure to mycotoxins is not usually identified as a risk factor present in occupational settings. This is probably due to the inexistence of limits regarding concentration of airborne mycotoxins, and also due to the fact that these compounds are rarely monitored in occupational environments. Despite the optimal conditions for fungal growth and, consequently, for mycotoxins production in all the waste management chain, only a few articles were dedicated to study occupational exposure to mycotoxins in this occupational setting. Aim of study: A study was developed in Portugal aiming to assess occupational co-exposure to mycotoxins in the waste management setting.

Fungal contamination in feed production in Portugal: what to expect regarding mycotoxins contamination?

Fungi on crops produce mycotoxins in the field, during handling, and in storage. Exposure of animals and humans are usually through consumption of contaminated feedstuffs or foods. Molds can grow and mycotoxins can be produced either pre-harvest or post-harvest, during storage, transport, processing, or feeding. Worldwide, approximately 25% of crops are affected by mycotoxins annually. Because of this is possible to concluded that mycotoxins occur frequently in a variety of feedstuffs that are given to animals causing several effects: subclinical losses in performance, increases the incidence of disease and reduced reproductive performance. Aim of study: A study was developed intending to know environmental fungal contamination in a Portuguese feed production unit. Corn, wheat and soybeans were the most common cereals used in the feed production.

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.

Mechanical characterization of polyhydroxyalkanoate and poly (lactic acid) blends

In this work, the mechanical behavior of polyhyroxyalkanoate (PHA)/poly(lactic acid) (PLA) blends is investigated in a wide range of compositions. The mechanical properties can be optimized by varying the PHA contents of the blend. The flexural and tensile properties were estimated by different models: the rule of mixtures, Kerner–Uemura–Takayanagi (KUT) model, Nicolai–Narkis model and Béla–Pukánsky model. This study was aimed at investigating the adhesion between the two material phases. The results anticipate a good adhesion between both phases. Nevertheless, for low levels of incorporation of PHA (up to 30%), where PLA is expectantly the matrix, the experimental data seem to deviate from the perfect adhesion models, suggesting a decrease in the adhesion between both polymeric phases when PHA is the disperse phase. For the tensile modulus, a linear relationship is found, following the rules of mixtures (or a KUT model with perfect adhesion between phases) denoting a good adhesion between the phases over the composition range. The incorporation of PHA in the blend leads to a decrease in the flexural modulus but, at the same time, increases the tensile modulus. The impact energy of the blends varies more than 157% over the entire composition. For blends with PHA weight fraction lower than 50%, the impact strength of the blend is higher than the pure base polymers. The highest synergetic effect is found when the PLA is the matrix and the PHA is the disperse phase for the blend PHA/PLA of 30/70. The second maximum is found for the inverse composition of 70/30. PLA has a heat-deflection temperature (HDT) substantially lower than PHA. For the blends, the HDT increases with the increment in the percentage of the incorporation of PHA. With up to 50% PHA (PLA as matrix), the HDT is practically constant and equal to PLA value. Above this point (PHA matrix), the HDT of the polymer blends increases linearly with the percentage of addition of PHA.