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Objective: To report the experience of using realistic simulation as a teaching strategy for Basic Life Support for high school students. Method: I report the experience of extension activities, linked to the extension project “Realistic Simulation in Urgency and Emergency” of Universidade da Amazonia (UNAMA), held from April 5 to June 29, 2019, with middle school students from public and private schools in the metropolitan region of Belem, State of Para, Brazil. Results: In total, ten educational interventions were carried out, using as a teaching strategy, the realistic simulation of the Basic Life Support maneuvers. 600 students from the 1st to the 3rd year of high school participated in the educational activities. These were taught to perform cardiopulmonary resuscitation maneuvers, with an emphasis on Basic Life Support for lay people according to the assumptions of resuscitation science proposed by the American Heart Association 2015. It was found that the participants expressed satisfaction and interest in the proposed and applied activity and methodology. Conclusion: It is concluded that there was significant interest on the part of the students, and the proposed and applied active methodology is attributed such interest. In view of the results of this experience, it is recommended that activities of this nature should be encouraged more vigorously in the various contexts of society, as it is believed that it is an educational action with significant potential to make individuals capable of providing care at the first level response to Cardiopulmonary Arrest, reduce mortality and preventable sequelae.

Objective: Evaluate the perception of nursing students from a public university regarding the Objective Structured Clinical Examination (OSCE) on the topic of tuberculosis diagnosis. Method: Quanti-qualitative research, carried out in the Nursing Undergraduate Course of a public university, in june 2019, with the participation of 30 nursing students, who answered a self-administered questionnaire. It was used percentage and Pearson's Chi-square for statistical analysis. For the analysis of qualitative data, the free word evocation technique was used. Results: The study shows that 73.3% of academics reported that the scenario developed in the OSCE exam contributed to the exercise of nursing in the face of tuberculosis diagnosis, 76.7% considered it necessary that Nursing Courses should modify the preparation of academics in relation to tuberculosis and 76.7% recommend developing greater skill for nursing consultations and home visits to patients. Conclusion: It is concluded that the exam's potentialities were highlighted, discussing its benefits in relation to learning needs.

SummaryThis paper presents a modified genetic algorithm (GA) using a new crossover operator (ADX) and a novel statistic correlation mutation algorithm (CAM). Both ADX and CAM work with population information to improve existing individuals of the GA and increase the exploration potential via the correlation mutation. Solution-based methods offer better local improvement of already known solutions while lacking at exploring the whole search space; in contrast, evolutionary algorithms provide better global search in exchange of exploitation power. Hybrid methods are widely used for constrained optimization problems due to increased global and local search capabilities. The modified GA improves results of constrained problems by balancing the exploitation and exploration potential of the algorithm. The conducted tests present average performance for various CEC’2015 benchmark problems, while offering better reliability and superior results on path planning problem for redundant manipulator and most of the constrained engineering design problems tested compared with current works in the literature and classic optimization algorithms.

Evolutionary Computation (EC) is inspired from by evolution that explores the solution space by gene inheritance, mutation, and selection of the fittest candidate solutions. Since their inception in the 1960s, Evolutionary Computation has been used in various hard and complex optimization problems in search and optimization such as: combinatorial optimization, functions optimization with and without constraints, engineering problems and others (Adeyemo, 2011). This success is in part due to the unbiased nature of their operations, which can still perform well in situations with little or no domain knowledge (Reynolds, 1999). The basic EC framework consists of fairly simple steps like definition of encoding scheme, population generation method, objective function, selection strategy, crossover and mutation (Ahmed & Younas, 2011). In addition, the same procedures utilized by EC can be applied to diverse problems with relatively little reprogramming.

This paper has the purpose to present a new hybrid nature inspired metaheuristic developed based on three fundamentals pillars extremely well known: Genetic Algorithms, Game Theory and Fuzzy Systems. This new approach tries to mimic a little bit more closer how a population of individuals evolves along time, like human social evolution emphasizing the social interaction between individuals and the non-binary behavior of human decision making against the classical cooperate-defect behavior present in the Prisoner's Dilemma (PD), for example. In this way it is presented the Social Interaction Genetic Algorithm (SIGA), to establish the necessary basis for the application of fuzzy concepts to get the F-SIGA Algorithm. Besides that, it is also presented the structure of an individual more complex with a genotype composed of two chromosomes, one for the solution of the problem and the other representing its behavior's strategy, which could be binary or fuzzy. At least the F-SIGA approach is presented in details, including all its steps. And finally some results are presented to an instance of the Traveling Salesman Problem.

This work has the purpose to present a new hybrid metaheuristic developed based on three fundamentals pillars extremely well known: Genetic Algorithms, Game Theory and Fuzzy Systems. This new approach tries to mimic a little bit more closer how a population of individuals evolves along time, like human social evolution emphasizing the social interaction between individuals and the non-binary behavior of human decision making against the classical cooperate-defect behavior present in the Prisoner's Dilemma. In this way it is also presented the SIGA Algorithm [9], the approach of an individual more complex with a genotype composed of two chromosomes, one for the solution of the problem and the other representing its strategy, a binary or fuzzy. Finally some results are presented to an instance of the Traveling Salesman Problem.

Genetic Algorithms have been successful in several practical appliances and obtained great prominence among the techniques of Evolutionary Computation. A large portion of methods and parameters adopted in its use are heuristic or random choices, which remain unchanged throughout the execution once they are set. This can lead to a certain lack of exploration of the search surface and also compromise the variability of the population. For that matter, this work introduces the implementation of an intelligent agent based on fuzzy logic, enabling dynamic monitoring and regulation of six GA parameters. The results obtained surpass the GA traditional implementation in many aspects, and open a wide new space for research and study.