Pontifical Catholic University of Parana

Curitiba, Brazil

The Pontifical Catholic University of Paraná is a private, not-for-profit Catholic university. The main campus is located in Curitiba, the capital city of the State of Paraná, Brazil. Four additional campuses are located in the cities of Londrina, Maringá, São José dos Pinhais and Toledo. It is maintained by APC , an organization run by Marist Brothers. The Catholic Archbishop of the city of Curitiba is the ceremonial chancellor of the University.The Curitiba campus was the first to be established and houses five academic units: the Center for Biological and Health science, the Center for Exact science and Technology, the Center for Juridical and Social science, the Center for Humanities and Theology, and the Business School. The main buildings of the campus are the central library, which manages the integrated library system , research labs, classrooms and lecture halls, a 570-seat theater, a pilot plant, and a sports complex. The Museum of Zoology, with a collection of over 6,000 specimens and an Herbarium with approximately 7,000 preserved plant specimens are located on the Curitiba Campus.There are more than 27,000 students in 60 undergraduate and over 150 postgraduate courses. Over 80% of the faculty possess a master's or doctoral degree. There are 22 graduate courses, at master's and doctoral levels: Health science, Law, Animal Science, Urban Management, Philosophy, Theology, Business Administration, Mechanical Engineering, Dental Health, Production Engineering, Education, Informatics and Health Technology. Wikipedia.

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Bauman A.E.,University of Sydney | Reis R.S.,Pontifical Catholic University of Parana | Reis R.S.,Federal University of Paraná | Sallis J.F.,University of California at San Diego | And 4 more authors.
The Lancet | Year: 2012

Physical inactivity is an important contributor to non-communicable diseases in countries of high income, and increasingly so in those of low and middle income. Understanding why people are physically active or inactive contributes to evidence-based planning of public health interventions, because effective programmes will target factors known to cause inactivity. Research into correlates (factors associated with activity) or determinants (those with a causal relationship) has burgeoned in the past two decades, but has mostly focused on individual-level factors in high-income countries. It has shown that age, sex, health status, self-efficacy, and motivation are associated with physical activity. Ecological models take a broad view of health behaviour causation, with the social and physical environment included as contributors to physical inactivity, particularly those outside the health sector, such as urban planning, transportation systems, and parks and trails. New areas of determinants research have identified genetic factors contributing to the propensity to be physically active, and evolutionary factors and obesity that might predispose to inactivity, and have explored the longitudinal tracking of physical activity throughout life. An understanding of correlates and determinants, especially in countries of low and middle income, could reduce the effect of future epidemics of inactivity and contribute to effective global prevention of non-communicable diseases.

Coelho L.D.S.,Pontifical Catholic University of Parana | Santos A.A.P.,Charles III University of Madrid
Electric Power Systems Research | Year: 2011

In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose an estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide multi-step-ahead point and direction-of-change forecasts of the Spanish electricity pool prices. © 2010 Elsevier B.V.

Coelho L.D.S.,Pontifical Catholic University of Parana | Mariani V.C.,Pontifical Catholic University of Parana
Energy Conversion and Management | Year: 2010

Recently, a new class of stochastic optimization algorithm called SOMA (self-organizing migrating algorithm) was proposed in the literature. SOMA works on a population of potential solutions called specimen and it is based on the self-organizing behavior of groups of individuals in a "social environment". This paper proposes a SOMA approach combined with a cultural algorithm (CSOMA) technique based on normative knowledge as an alternative method to solving the economic load dispatch problem of thermal generators with the valve-point effect. The classical SOMA and CSOMA approaches are validated for two test systems consisting of 13 and 40 thermal generators whose non-smooth fuel cost function takes into account the valve-point loading effects. Numerical results indicate that performance of the CSOMA present best results when compared with results of others optimization methods found in the literature in solving load dispatch problems with the valve-point effect. © 2010 Elsevier Ltd. All rights reserved.

Coelho L.d.S.,Pontifical Catholic University of Parana
Expert Systems with Applications | Year: 2010

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents novel quantum-behaved PSO (QPSO) approaches using mutation operator with Gaussian probability distribution. The application of Gaussian mutation operator instead of random sequences in QPSO is a powerful strategy to improve the QPSO performance in preventing premature convergence to local optima. In this paper, new combinations of QPSO and Gaussian probability distribution are employed in well-studied continuous optimization problems of engineering design. Two case studies are described and evaluated in this work. Our results indicate that Gaussian QPSO approaches handle such problems efficiently in terms of precision and convergence and, in most cases, they outperform the results presented in the literature. © 2009 Elsevier Ltd. All rights reserved.

Pecoits-Filho R.,Pontifical Catholic University of Parana
Rheumatology International | Year: 2010

Ankylosing spondylitis (AS) is a systemic inflammatory rheumatic disease characterized primarily by axial joint involvement, sacroiliitis and various extra-articular manifestations. High cardiovascular mortality in AS has led many researchers to investigate possible risk factors involved with cardiovascular disease in these patients. This review summarizes published data concerning endothelial dysfunction and atherosclerosis in patients with AS. The author discusses current limitations and problems related to a better assessment of these two possible changes in AS. © 2010 Springer-Verlag.

Askarzadeh A.,Kerman Graduate University of Technology | Dos Santos Coelho L.,Pontifical Catholic University of Parana
Energy Conversion and Management | Year: 2015

The main goal of this paper is to provide a framework to accurately estimate the electrical equivalent circuit parameters of photovoltaic arrays by use of an efficient heuristic technique. Owing to the non-linearity of the current vs. voltage (I-V) characteristics of PV modules, using a superior optimization technique helps to effectively find the real electrical parameters. Inspired by the mating process of different bird species, bird mating optimizer (BMO) is a new invented search technique which has shown superior performance for solving complex optimization problems. In this paper, the original BMO algorithm is simplified and used to estimate the electrical parameters of the module model for an amorphous silicon PV system at different operating conditions. The simplified BMO (SBMO) eliminates tedious efforts of parameter setting in original BMO and also modifies some rules. The usefulness of the proposed algorithm is investigated by comparing the obtained results with those found by two particle swarm optimization (PSO) variants, two harmony search (HS) variants as well as seeker optimization algorithm (SOA). Based on the investigated situations of this paper, SBMO yields more accurate results than the other studied methods. © 2014 Elsevier Ltd. All rights reserved.

Silva A.C.,Pontifical Catholic University of Parana
Transplantation proceedings | Year: 2012

Mesenchymal stem cells (MSCs) from human adipose tissue have a great potential for use in cell therapy due to their ease of isolation, expansion, and differentiation, besides the relative acceptance from the ethical point of view. Our intention was to isolate and promote in vitro expansion and differentiation of MSCs from human adipose tissue into cells with a pancreatic endocrine phenotype. Human adipose tissue obtained from patients undergoing abdominal dermolipectomy was digested with type I collagenase. MSCs isolated by plastic adherence and characterized by cytochemistry and FACS were expanded in vitro. MSC differentiation into an endocrine phenotype was induced over 2 to 4 months with high glucose (25 mmol/L) media containing nicotinamide, exendin-4, and 2-mercaptoethanol. Insulin and glucagon expressions were analyzed by immunofluorescence. Cells isolated from human adipose tissue and expanded in vitro expressed MSC markers as confirmed by FACS and cytochemistry. Insulin but not glucagon production by differentiated cells was demonstrated by immunofluorescence. MSCs isolated from human adipose tissue were induced to differentiate in vitro into an endocrine phenotype that expressed insulin. Copyright © 2012 Elsevier Inc. All rights reserved.

Hultmann Ayala H.V.,LACTEC Institute of Technology for Development | Dos Santos Coelho L.,Pontifical Catholic University of Parana
Expert Systems with Applications | Year: 2012

Most controllers optimization and design problems are multiobjective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Instead of aiming at finding a single solution, the multiobjective optimization methods try to produce a set of good trade-off solutions from which the decision maker may select one. Several methods have been devised for solving multiobjective optimization problems in control systems field. Traditionally, classical optimization algorithms based on nonlinear programming or optimal control theories are applied to obtain the solution of such problems. The presence of multiple objectives in a problem usually gives rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Recently, Multiobjective Evolutionary Algorithms (MOEAs) have been applied to control systems problems. Compared with mathematical programming, MOEAs are very suitable to solve multiobjective optimization problems, because they deal simultaneously with a set of solutions and find a number of Pareto optimal solutions in a single run of algorithm. Starting from a set of initial solutions, MOEAs use iteratively improving optimization techniques to find the optimal solutions. In every iterative progress, MOEAs favor population-based Pareto dominance as a measure of fitness. In the MOEAs context, the Non-dominated Sorting Genetic Algorithm (NSGA-II) has been successfully applied to solving many multiobjective problems. This paper presents the design and the tuning of two PID (Proportional-Integral-Derivative) controllers through the NSGA-II approach. Simulation numerical results of multivariable PID control and convergence of the NSGA-II is presented and discussed with application in a robotic manipulator of two-degree-of-freedom. The proposed optimization method based on NSGA-II offers an effective way to implement simple but robust solutions providing a good reference tracking performance in closed loop. © 2012 Elsevier Ltd. All rights reserved.

Dirschnabel A.J.,Pontifical Catholic University of Parana
Quintessence international (Berlin, Germany : 1985) | Year: 2011

Oral lesions secondary to chronic renal failure or related to immunosuppressive therapy after transplant are reported in the literature, but their prevalence is still obscure. The aim of this study was to investigate oral clinical findings in patients undergoing renal dialysis and renal transplant recipients. Forty-six patients treated with dialysis (DL), 33 kidney-transplant (KT) patients, and 37 control (C) patients were examined intraorally. Oral clinical findings were diagnosed and treated. The results showed that 95.6% (44/46) of the DL group, 93.9% (31/33) of KT patients, and 56.7% (21/37) of the control group presented at least one pathological entity in the oral mucosa. A high prevalence of oral lesions, such as saburral tongue and xerostomia, was found in the DL and KT groups. Certain oral lesions demonstrated a predisposition toward one type of group, such as a higher prevalence of metallic taste in the DL group and gingival overgrowth in the KT group. The prevalence of oral lesions was significantly higher in renal patients (DL and KT groups). The most prevalent oral clinical findings were saburral tongue and xerostomia for both groups. Metallic taste was more prevalent in the DL group. Although geographic tongue was more frequent in KT patients and melanin pigmentation in the control group, the number of lesions was low for all groups. In addition, gingival overgrowth was more prevalent in the KT group; however, the difference was not significant (P = .06).

Bazzi J.Z.,Pontifical Catholic University of Parana
Journal of the American Dental Association (1939) | Year: 2012

The authors conducted a study to evaluate the stain removal ability of tooth bleaching and simulated toothbrushing after coffee and cigarette smoke staining and to determine the enamel susceptibility to restaining. The authors used a colorimeter to determine the baseline color of 40 bovine labial enamel surfaces according to the Commission Internationale de l'Eclairage L*a*b* coordinates. They immersed one-half of the specimens in coffee and exposed one-half to cigarette smoke in a smoking machine. They took color measurements again and determined the color change from baseline (ΔE1) for each group. The authors divided each group into two subgroups and subjected the specimens to at-home bleaching (one hour per day for 21 days) or simulated toothbrushing (120 cycles per day for 21 days), followed by another color measurement (ΔE2). The authors repeated both staining procedures (that is, cigarette smoke and coffee) and followed them with a third color measurement (ΔE3). They analyzed the data by using a two-way analysis of variance and the Tukey test (α = 5 percent). Both staining procedures resulted in similar values for ΔE1. The specimens stained with coffee and cigarette smoke exhibited a significant reduction in color change after bleaching (P < .05). However, toothbrushing resulted in a significantly reduced color change only for cigarette smoke-stained specimens (P < .001). The discoloration in coffee-stained specimens increased after restaining, irrespective of the stain removal method (P < .05). The study results show that at-home bleaching removed both coffee and cigarette smoke staining. The restaining potential was greater for specimens stained with coffee than for those stained with cigarette smoke, regardless of the removal method used. Six percent hydrogen peroxide at-home bleaching was effective in removing stains caused by coffee or cigarette smoke. However, continued frequent consumption of coffee can increase the staining susceptibility of enamel.

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