Search results for: Aquiles A. Barros

Authors: C. P. Parente, M. J. Reis Lima, E. Teixeira-Lemos, M. M. Moreira, Aquiles A. Barros, Luís F. Guido

Abstract:

Broccoli has been widely recognized as a wealthy vegetable which contains multiple nutrients with potent anti-cancer properties. Lamb’s lettuce has been used as food for many centuries but only recently became commercially available and literature is therefore exiguous concerning these vegetables. The aim of this work was to evaluate the influence of the extraction conditions on the yield of phenolic compounds and the corresponding antioxidant capacity of broccoli and lamb’s lettuce. The results indicate that lamb’s lettuce, compared to broccoli, contains simultaneously a large amount of total polyphenols as well as high antioxidant activity. It is clearly demonstrated that extraction solvent significantly influences the antioxidant activity. Methanol is the solvent that can globally maximize the antioxidant extraction yield. The results presented herein prove lamb’s lettuce as a very interesting source of polyphenols, and thus a potential health-promoting food.

Keywords: Broccoli, lamb’s lettuce, extraction, antioxidant activity, phenolic compounds.

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Authors: Jorge Barros Mendes

Abstract:

The Portuguese Constitution, in article 22, instituted the general principle of tort liability of the State and other public law entities. Consequently, ordinary legislation established the tort liability of the State into the Portuguese Legal Order, by means of Decree-Law 48051, of 1967. This decree, which was criticised extensively, was amended by virtue of Law 67/2007, of 31st December, establishing the regime for tort liability arising from losses caused by third parties, due to the acts of public management in relation to all the functions of the State, i.e. i) administrative, ii) legislative, and iii) jurisdictional.

Keywords: Portuguese courts, tort liability of the state.

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Authors: Simone C. F. Neves, Lúcio F. A. Campos, Ewaldo Santana, Ginalber L. O. Serra, Allan K. Barros

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We propose a method for discrimination and classification of ovarian with benign, malignant and normal tissue using independent component analysis and neural networks. The method was tested for a proteomic patters set from A database, and radial basis functions neural networks. The best performance was obtained with probabilistic neural networks, resulting I 99% success rate, with 98% of specificity e 100% of sensitivity.

Keywords: Cancer ovarian, Proteomic patterns in serum, independent component analysis and neural networks.

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Authors: R. C. Ferreira, H. H. C. De Lima, A. A. Cândido, O. M. Couto Junior, P. A. Arroyo, K. Q De Carvalho, G. F. Gauze, M. A. S. D. Barros

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Removal of the widespread used drug paracetamol from water was investigated using activated carbon originated from dende coconut mesocarp and babassu coconut mesocarp. Kinetic and equilibrium data were obtained at different values of pH. Both activated carbons showed high efficiency when pH ≤ pHPZC as the carbonil group of paracetamol molecule are adsorbed due to positively charged carbon surface. Microporosity also played an important role in such process. Pseudo-second order model was better adjusted to the kinetic results. Equilibrium data may be represented by Langmuir equation.

Keywords: Adsorption, activated carbon, babassu, dende.

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Authors: D. Pinto, L. Castro, M.L. Cruzat, S. Barros, J. Gironás, C. Oberli, M. Torres, C. Escauriaza, A. Cipriano

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Flash Floods, together with landslides, are a common natural threat for people living in mountainous regions and foothills. One way to deal with this constant menace is the use of Early Warning Systems, which have become a very important mitigation strategy for natural disasters. In this work we present our proposal for a pilot Flash Flood Early Warning System for Santiago, Chile, the first stage of a more ambitious project that in a future stage shall also include early warning of landslides. To give a context for our approach, we first analyze three existing Flash Flood Early Warning Systems, focusing on their general architectures. We then present our proposed system, with main focus on the decision support system, a system that integrates empirical models and fuzzy expert systems to achieve reliable risk estimations.

Keywords: Decision Support System, Early Warning Systems, Flash Flood, Natural Hazard.

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Authors: Mona Lisa M. Oliveira, Nara A. Policarpo, Ana Luiza B. P. Barros, Carla A. Silva

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Selective catalytic reduction systems for nitrogen oxides reduction by ammonia has been the chosen technology by most of diesel vehicle (i.e. bus and truck) manufacturers in Brazil, as also in Europe. Furthermore, at some conditions, over-stoichiometric ammonia availability is also needed that increases the NH₃ slips even more. Ammonia (NH₃) by this vehicle exhaust aftertreatment system provides a maximum efficiency of NOx removal if a significant amount of NH₃ is stored on its catalyst surface. In the other words, the practice shows that slightly less than 100% of the NOx conversion is usually targeted, so that the aqueous urea solution hydrolyzes to NH₃ via other species formation, under relatively low temperatures. This paper presents a model based on neural networks integrated with a road vehicle simulator that allows to estimate NH₃-slip emission factors for different driving conditions and patterns. The proposed model generates high NH₃slips which are not also limited in Brazil, but more efforts needed to be made to elucidate the contribution of vehicle-emitted NH₃ to the urban atmosphere.

Keywords: Ammonia slip, neural-network, vehicles emissions, SCR-NOx.

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Authors: Raquel M. de Sousa, Sofiane Labidi, Allan Kardec D. Barros, Alex O. Barradas Filho, Aldalea L. B. Marques

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Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of back propagation of back propagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this caseiodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm.

Keywords: Artificial Neural Networks, Biodiesel, Iodine Value, Prediction.

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Authors: André C. Silva, Mariana R. Barros, Elenice M. S. Silva, Douglas. Y. Marinho, Diego F. Lopes, Débora N. Sousa, Raphael S. Tomáz

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Modern agriculture requires productivity, efficiency and quality. Therefore, there is need for agricultural limestone implementation that provides adequate amounts of calcium and magnesium carbonates in order to correct soil acidity. During the limestone process, fine particles (with average size under 400#) are generated. These particles do not have economic value in agricultural and metallurgical sectors due their size. When limestone is used for agriculture purposes, these fine particles can be easily transported by wind generated air pollution. Therefore, briquetting, a mineral processing technique, was used to mitigate this problem resulting in an agglomerated product suitable for agriculture use. Briquetting uses compressive pressure to agglomerate fine particles. It can be aided by agglutination agents, allowing adjustments in shape, size and mechanical parameters of the mass. Briquettes can generate extra profits for mineral industry, presenting as a distinct product for agriculture, and can reduce the environmental liabilities of the fine particles storage or disposition. The produced limestone briquettes were subjected to shatter and water action resistance tests. The results show that after six minutes completely submerged in water, the briquettes where fully diluted, a highly favorable result considering its use for soil acidity correction.

Keywords: Agglomeration, briquetting, limestone, agriculture.

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner, David Gabriel F. de Barros

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Review researches are useful in terms of analysis of research problems. Between the types of review documents, we commonly find bibliometric studies. This type of application often helps the global visualization of a research problem and helps academics worldwide to understand the context of a research area better. In this document, a bibliometric view surrounding clustering techniques and sustainability problems is presented. The authors aimed at which issues mostly use clustering techniques and even which sustainability issue would be more impactful on today’s moment of research. During the bibliometric analysis, we found 10 different groups of research in clustering applications for sustainability issues: Energy; Environmental; Non-urban Planning; Sustainable Development; Sustainable Supply Chain; Transport; Urban Planning; Water; Waste Disposal; and, Others. Moreover, by analyzing the citations of each group, it was discovered that the Environmental group could be classified as the most impactful research cluster in the area mentioned. After the content analysis of each paper classified in the environmental group, it was found that the k-means technique is preferred for solving sustainability problems with clustering methods since it appeared the most amongst the documents. The authors finally conclude that a bibliometric assessment could help indicate a gap of researches on waste disposal – which was the group with the least amount of publications – and the most impactful research on environmental problems.

Keywords: Bibliometric assessment, clustering, sustainability, territorial partitioning.

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Authors: Eduardo N. Velloso, Fernando M. N. Dantas, Luciano S. Barros

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A common complaint from power system analysis students lies in the overly complex tools they need to learn and use just to simulate very basic systems or just to check the answers to power system calculations. The most basic power system studies are power-flow solutions and short-circuit calculations. This paper presents a simple tool with an intuitive interface to perform both these studies and assess its performance in comparison with existent commercial solutions. With this in mind, Elegant is a pure Python software tool for learning power system analysis developed for undergraduate and graduate students. It solves the power-flow problem by iterative numerical methods and calculates bolted short-circuit fault currents by modeling the network in the domain of symmetrical components. Elegant can be used with a user-friendly Graphical User Interface (GUI) and automatically generates human-readable reports of the simulation results. The tool is exemplified using a typical Brazilian regional system with 18 buses. This study performs a comparative experiment with 1 undergraduate and 4 graduate students who attempted the same problem using both Elegant and a commercial tool. It was found that Elegant significantly reduces the time and labor involved in basic power system simulations while still providing some insights into real power system designs.

Keywords: Free- and open-source software, power-flow, power system analysis, Python, short-circuit.

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Authors: C. A. Barros, Ana P. Barroso

Abstract:

Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: Automotive industry, Industry 4.0, internet of things, IATF 16949:2016, measurement system analysis.

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