Considerações sobre o papel da química bioinorgânica na saúde

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências FarmacêuticasUma das características dos metais que os torna tão importantes como componentes dos seres vivos é o facto de serem solúveis nos fluidos biológicos, o que promove a sua interação com as moléculas biológicas, explicando, assim, a razão pela qual muitos dos processos vitais exigirem a presença de iões metálicos. Respiração, fixação de azoto, contração muscular e muitas reações metabólicas, designadamente de desenvolvimento, crescimento, transdução de sinal e proteção face a agentes mutagénicos, são alguns exemplos desses processos. Deste modo, a utilização de metais como forma de tratamento é de extrema utilidade e, por isso, ao longo das últimas décadas, a Química Bioinorgânica tem sido responsável por grandes contribuições para a saúde humana e Ciência Médica. A caracterização das principais atividades dos metais para uso terapêutico e a sua importância são temas pertinentes nos dias de hoje, uma vez que a incorporação de metais em moléculas orgânicas está em crescimento exponencial, sendo de grande interesse conhecer o papel dos iões metálicos nas patogenias, as interações metal-fármaco e os metais presentes em fármacos. Estes metalofármacos podem ser usados, entre outros exemplos, como antineoplásicos, antibacterianos, antiartríticos ou antidepressivos. Os metais, apesar de serem farmacologicamente muito úteis, podem exibir, também, elevados níveis de toxicidade quando se encontram em excesso no organismo. A toxicidade pode ser responsável por doenças graves e, em casos extremos, a morte. A quelatoterapia é um método utilizado para a remoção de metais presentes no organismo, sendo há muito tempo utilizado em intoxicações por metais. One of the characteristics that make metals such important components of the living beings is the fact that they are soluble in biological fluids, which promotes their interaction with biological molecules, therefore explaining why many of the vital processes demand the presence of metal ions. Breathing, nitrogen fixation, muscular contraction and many metabolic reactions including development, growth, signalling transduction and protection against mutagenic agents, are some of the examples of these processes. Thus, the use of metals as a form of treatment is extremely useful and therefore, for the past decades, Bioinorganic Chemistry has been responsible for major contributions to the Human Health and Medical Science. The characterization of the main activities of metals for therapeutic use and their importance are pertinent topics today, because the incorporation of metals in organic molecules is in exponential growth, being extremely essential to understand the role of metal ions in pathogenesis, the metal-drug interactions and the metals that are present in drugs. These metallodrugs can be used, among other examples, as antineoplastic, antibacterial, antiarrhythmic or antidepressants. Metals, although pharmacologically very useful, can also exhibit high levels of toxicity when in excess in the organism. Toxicity may be responsible for severe illnesses and, in extreme cases, death. Chelation therapy is used to remove metals present in the organism, a method that has been used for a long time in metal intoxications

A Predictive Maintenance Model based on Multivariate Analysis with Artificial Intelligence

The maintenance of physical assets is increasingly assuming a leading role in the success of companies, whether industrial or services. The pressure of budgets, combined with a strict maintenance policy, gives companies competitive advantages in an increasingly demanding market. This PhD thesis emerged with the aim of solving some Predictive Maintenance problems. The research also aims to respond to gaps identified in the state of the art as the prediction and classification of the long-term state of equipment. The present work describes novel contributions to the state of the art of lubricating oils. The results show that it is possible to create good models using Artificial Neural Networks (ANN) to classify oils considering all variables. Models can even possibly rank lubricants with a small error. Using Principal Component Analysis (PCA), the relevance of each variable for oil analysis was determined, thus providing a better insight into the importance of each parameter under analysis. The results also show that a neural model does not need to use all variables. Principal Component Analysis also allowed the creation of an algorithm that calculates the percentage of degradation of a lubricating oil, from the manufacturer's standard references so, this algorithm works for any industrial lubricant. It is noteworthy that lubricant classifiers (PCA, RNA and Human Experts) were compared with each other, having converged in more than 90%, which confirms the reliability of the classification. The developed algorithms can support industries, in a general way, since they provide information that is easy to interpret, and helps them to make decisions about the most appropriate time to replace oil in the assets. Through exhaustive research of the state of the art in prediction and industrial forecast, it was concluded that, to date, there is no published model for predicting failures with such a long-time span, which demonstrates the innovation and contribution of the present research for science and for the competitiveness of the industry. It should be noted that the developed algorithms have already been tested and applied, showing in general a prediction error below 10%. The current project has a short-term prediction model and another long-term prediction model, both using neural networks. The long-term forecasting model can predict asset failures 90 days in advance, which allows industries to make scheduled stops on their assets, thus avoiding losses resulting from unscheduled stops. Adequate feature input vectors in Artificial Neural Networks using sliding windows along time series greatly improved the training, leading to the conclusion that overlapping windows allow the network to learn in less iterations. Larger windows make it easier to capture peak values, but the optimal window size needs to be determined experimentally. Regarding short-term forecasting, it has been shown that data resampling can make the forecasting process faster, as it considerably reduces the input data set in the network. An algorithm was also developed to determine the expected equipment state through classification of the predicted sensor values. This way, the algorithm will be able to classify the probable state of the assets in the future in normal operation, alert or malfunction. This PhD thesis is very important for the industrial area, especially in the areas of maintenance, safety, quality, sustainability and efficiency, as it will maximize the availability of assets, contributing to the success of the Quality Management Systems and Maintenance Management Systems. Such positive increments in several sectors will have as main consequence the reduction of costs, increase of equipment availability and improvement of quality, what will become a competitive differentiator for the industries, because they will be able to approach the market with more competitive prices and quality. Part of the work was published in scientific articles and presented at several congresses and received the distinction of best presentation award in TEPEN 2021 & IncoME-VI congress in China. It also received the 2nd Young Engineer Innovation Award- PIJE 2021, from Ordem dos Engenheiros, Portugal.A manutenção e a gestão de ativos têm um papel preponderante no sucesso de qualquer indústria. O desenvolvimento da sensorização e o aumento da capacidade de armazenamento e processamento de dados, aliados à Inteligência Artificial, vieram permitir uma melhoria significativa nas técnicas de manutenção e gestão do ciclo de vida dos ativos, contribuindo para uma maior disponibilidade e eficiência dos mesmos com menores custos de manutenção. O projeto doutoral proposto descreve modelos de manutenção preditiva baseados em Inteligência Artificial. A fiabilidade e o desempenho dos motores Diesel dependem bastante da qualidade e condição dos seus óleos lubrificantes. A presente tese descreve modelos para classificar automaticamente a condição do óleo, utilizando Redes Neuronais Artificiais e Análise de Componentes Principais. Os resultados dos modelos classificadores de lubrificantes foram comparados com as classificações de peritos humanos. A comparação mostra que os modelos de classificação desenvolvidos são credíveis. A presente tese de doutoramento apresenta modelos de previsão de valores de sensores a curto, médio e longo prazo, ambos usando redes neuronais. O modelo de previsão de longo prazo é capaz de prever o valor de sensores até 90 dias de antecedência. Usaram-se métodos de aprendizagem supervisionados e não supervisionados para criar modelos de classificação do estado de uma máquina industrial. O principal objetivo era determinar quando o ativo se encontrava no seu estado de funcionamento normal ou fora desta zona, correndo assim o risco de falha. Os resultados mostraram que é possível classificar e prever o estado de máquinas industriais utilizando redes neuronais artificiais. Os modelos propostos apoiam a monitorização e manutenção de ativos, sendo que as principais implicações são a melhoria da disponibilidade operacional, incremento de qualidade, menor impacto ambiental, mais segurança e racionalização de custos

Structural changes and external vulnerabilities in the Brazilian economy within the period 1995-2009

From the 1990s, the Brazilian development model went from protector of the industrial sector to the trade liberalization process, which brought the need for extensive restructuring of the productive sectors of the economy must, henceforth to confront openly the global competitors within the prevailing international conditions. Thus, during the 90s, the intensification of trade liberalization was combined with the privatization of key sectors of the economy (like the electricity and telecommunications sectors), resulting in structural changes, with the liberalization of capital flows and with a stabilization program (Plano Real), based on an exchange rate anchor, with important effects on the entire economy. Within this context, this article aims is to assess the evolution of external dependence and changes in terms of generating value added sectors of the Brazilian economy in the period 1995-2009, divided into three periods, namely, 1995-2000, 2000 - 2005 and 2005-2009. The database are the input-output Brazil annual matrices for the period 1995-2009, structured into 42 sectors, from which traditional Hirschman-Rasmussen indicators are calculated. The evolution of these indices is, then, combined with the coefficients of value added and imported inputs over time, allowing for a new treatment of intersectoral production multipliers. Preliminary results indicated a very satisfactory behavior of the national economy over time, with a predominance of positive gains in the ability to generate value added and lower imports of intermediate inputs by industries of medium and high technology, and less losses resulting growth the change in value added sector and greater external dependence for the minority of other sectors, particularly in recent years. It is hoped, with the development of the study to evaluate in detail the behavior of the 42 sectors in which Brazil's economy is structured in the study period

Remote M2M healthcare : applications and algorithms

Tese de mestrado. Mestrado Integrado em Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

10-year portuguese football strategic plan: sustainable growth

Analysis of main pillars in the 10-year strategic plan for Portuguese football in partnership with FPF and Deloitte. Fan engagement and how to improve their experience. Women’s football current development and how to incentivize the youth to practice. How to increase the numbers of participants, as most athletes drop the sport during university years. Which broadcasting rights model, decentralized and centralized, should be implemented in Portugal. However ,the choice remains arguable. International exposure and developing countries as an opportunity to increase viewership. This paper presents a comprehensive assessment of the strategy being created by FPF and Deloitte and makes recommendations

Effects of demand shocks in the Brazilian economy : new production and value added multipliers

Assuming that the traditional input-output multipliers lead to a misinterpretation of macroeconomic concepts for multisectoral analysis of a given economy, the aim of this article is to calculate the new multipliers of variation in the final demand on the production and value added by the sectors of the Brazilian economy in the period between 1995 and 2009. Using the Euclidean distance method proposed in Amaral et al (2012) and data from input-output tables, the main results showed that: a) the structural change effect was more important than the scale effect, both for the production and for the value added, although it was less important for the latter; b) Brazil is still a major exporter of unprocessed products which will generate jobs, income and taxes abroad, depriving the country of this important benefit; c) the main key sectors were Agricultural (1), Mining (2), Steel industry (4), Chemistry (11), Food industry (16) and some service sectors, such as Public Utilities (18), Trade, (20) Transportation (21), Communication (22), Public administration (24) and Other services (25), revealing the gain in importance of these activities in the national economy.This paper was prepared during a post doc program of Antonio Carlos Moretto and Rossana Lott Rodrigues in UECE, a research center supported by FCT (Fundação para a Ciência e a Tecnologia). These authors acknowledge the financial support of the State University of Londrina, Brazil, during the post doc

Structural changes and external vulnerabilities in the brazilian economy : 1995-2009

From the 1990s, the reorientation of the development model, which switched from protective of industrial sector to intensifier of the trade liberalization process, brought the need for reorganization of large productive sectors of the Brazilian economy, were openly forced to face global competitors, within the established international conditions. Thus, during the 1990s, the Brazilian economy experienced a period of fast and deep changes, combining the process of intensification of trade liberalization with a view of industrial and technological policy which combines sectoral and systemic dimension, with privatization of important sectors of the economy (such as electricity and telecommunications sectors), and a stabilization program (Real Plan), based on a fixed exchange rate, with important effects on the whole economy. This article aims to assess the evolution of external vulnerability and structural changes in terms of generating value added in the Brazilian economy in the period 1995-2009, subdivided into three sub-periods, namely 1995-2000, 2000-2005 and 2005 -2009, by means of a novel treatment of the inter-sectoral output multipliers. The data base was the annual input-output matrices from Brazil for 1995, 2000, 2005 and 2009, structured into 42 sectors. The main results indicated a very satisfactory behavior of the national economy over time, with a predominance of positive gains in the ability to generate value added and lower imports of intermediate inputs by the most technologically advanced industries and by services, and fewer losses and greater external dependence for the minority of sectors, particularly in recent years

Water and energy savings in micro irrigation systems design using optimization models

The main disadvantage of trickle irrigation systems is its comparatively high initial cost, which depends on the layout, design, and management of its hydraulic network. Designing the sub-main and lateral lines aiming the emitter uniformity maximization can reduce the microirrigation system costs. This research aimed to compare linear and nonlinear programming models and maximization versus minimization criteria to optimize the crop net benefit, considering the water and energy savings. Two versions of LP and NLP models were developed: the first minimized the equivalent annual cost of the irrigation system considering the pipeline cost and the energy cost; the second maximized the yearly increment in the net benefit (Bn) of the irrigated crop. In both cases, uncertainty about the crop price was considered. The models were applied in a 40 ha citrus orchard in São Paulo State, Brazil. The highest net benefit was found using the NLP model with the maximization criterion. The worst result was obtained with the LP model and the minimization of the total annual cost. The layout and management previously established by the designer are subjective and rarely results in the best solution, although the linear programming model always gets the global optimum. The NLP models get local optimal, but they defined the layout, design, and management of the systems, with more chance to obtain a higher net benefit. The NLP model for maximization showed to be an adequate option for designing microsprinkler irrigation systems, defining the hydraulic network and the operational conditions that maximize Bn and WUE, with the lowest water consumption and lowest energy cost