Stroking Parameters during Continuous and Intermittent Exercise in Regional-Level Competitive Swimmers

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Industrial Robotics Applied to Education

This work describes a ludic proposal for programming learning of industrial robots to be developed by groups of engineering students. Two projects are presented: Tic-tac-toe Opponent Robot and Environmentalist Robot. The first project use competitive search techniques of the Artificial Intelligence, computational vision, electronic and pneumatic concepts for ability decision making for a robotic agent on the tic-tae-toe game. The second project consists of a game that contains a questions and answers database about environmental themes. An algorithm selects the group of questions to be answered by the player, analyses the answers and sends the result to a industrial robot through serial port. According with the player performance, the robot makes congratulation movements and giving a gift to the winner player. Otherwise, the robot makes movements, disapproving the player performance.

Evidence of zinc deficiency in competitive swimmers

Objective: The aim of this study was to assess the nutritional zinc (Zn) status of elite swimmers during different training periods.Methods: A longitudinal paired study was performed at the University of São Paulo in eight male swimmers 18 to 25 y old who had been swimming competitively at the state and national levels for at least 5 y. The swimmers were evaluated over a total period of 14 wk: before the basic and specific preparatory period (BSPP-baseline), at the end of the basic and specific preparatory period (post-BSPP), and at the end of the polishing period (PP). Levels of Zn were determined in the plasma, erythrocyte, urine, and saliva by atomic absorption spectrophotometry. Anthropometric measurements and a 3-d food record were also evaluated.Results: The median plasma Zn concentration was below the reference value in all training periods (BSPP-baseline 59 mu g/dL, post-BSPP 55.9 mu g/dL, after PP 58.8 mu g/dL, P > 0.05), as were threshold values for erythrocytes (BSPP-baseline 36.5 mu g of Zn/g of hemoglobin, post-BSPP 42 mu g of Zn/g of hemoglobin, after PP 40.7 mu g of Zn/g of hemoglobin, P > 0.05), urinary Zn (BSPP-baseline 280 mu g/24 h, post-BSPP 337 mu g/24 h, after PP 284 mu g/24 h, P > 0.05), and salivary Zn (BSPP-baseline 66.1 mu g/L, post-BSPP 54.1 mu g/L, after PP 79.7 mu g/L, > 0.05). Salivary Zn did not correlate with plasma and erythrocyte Zn levels.Conclusion: The results suggest that the elite swimmers studied presented a possible Zn deficiency and that salivary Zn was not adequate to evaluate the Zn nutritional status. (C) 2012 Elsevier B.V. All rights reserved.

Fuzzy distributed artificial intelligence systems

In this paper, we introduce a DAI approach called hereinafter Fuzzy Distributed Artificial Intelligence (FDAI). Through the use of fuzzy logic, we have been able to develop mechanisms that we feel may effectively improve current DAI systems, giving much more flexibility and providing the subsidies which a formal theory can bring. The appropriateness of the FDAI approach is explored in an important application, a fuzzy distributed traffic-light control system, where we have been able to aggregate and study several issues concerned with fuzzy and distributed artificial intelligence. We also present a number of current research directions necessary to develop the FDAI approach more fully.

Detection of infectious bronchitis virus in experimentally infected chickens by an antigen-competitive ELISA

An antigen-competitive enzyme-linked immunosorbent assay (Ag-C-ELISA) was developed for the detection of infectious bronchitis virus (IBV) antigens, M41 strain, in tissues from experimentally infected chickens, or in allantoic fluid harvested from inoculated embryonated eggs. The detection limit of IBV in the Ag-C-ELISA was 104.1 median embryo infective doses (EID50)/well. Tracheal and lung samples from chickens vaccinated with 102.5 EID50 of live attenuated infectious bronchitis (H120) vaccine were negative in the direct detection Ag-C-ELISA. The results indicate that the Ag-C-ELISA has the potential to detect IBV, either directly in tissue samples or when combined with the passage of material in embryonated eggs, thereby constituting an alternative method for the diagnosis of IBV.

Using artificial intelligence for the control of medium voltage substations

This paper introduces a method for the supervision and control of devices in electric substations using fuzzy logic and artificial neural networks. An automatic knowledge acquisition process is included which allows the on-line processing of operator actions and the extraction of control rules to replace gradually the human operator. Some experimental results obtained by the application of the implemented software in a simulated environment with random signal generators are presented.

Intelligence functions disorders in patients with complex partial epilepsy

Objective: To compare the performance of patients with complex partial epilepsy with the normal controls in the subtests of an instrument used to assess intelligence function. Method: Fifty epileptic patients, whose ages ranged from 19 to 49 years and 20 normal controls without any neuropsychiatric disorders. The Wechsler-Bellevue adult intelligence test was applied in groups, epileptic patients and control subjects. This test is composed of several subtests that assess specific cognitive functions. A statistical analysis was performed using non-parametric tests. Results: All the Wechsler-Bellevue subtests revealed that the intelligence functions of the patients were significantly inferior to that of the controls (p<0.05). This performance was supported by the patient's complaints in relation to their cognitive performance. Conclusion: Patients with complex partial epilepsy presented poorer results in the intelligence test when compared with individuals without neuropsychiatric disorders.

Information and mechanical models of intelligence: What can we learn from cognitive science?

The impact of new advanced technology on issues that concern meaningful information and its relation to studies of intelligence constitutes the main topic of the present paper. The advantages, disadvantages and implications of the synthetic methodology developed by cognitive scientists, according to which mechanical models of the mind, such as computer simulations or self-organizing robots, may provide good explanatory tools to investigate cognition, are discussed. A difficulty with this methodology is pointed out, namely the use of meaningless information to explain intelligent behavior that incorporates meaningful information. In this context, it is inquired what are the contributions of cognitive science to contemporary studies of intelligent behavior and how technology may play a role in the analysis of the relationships established by organisms in their natural and social environments. © John Benjamins Publishing Company.

Optimal contract pricing of distributed generation under a competitive framework

A bilevel programming approach for the optimal contract pricing of distributed generation (DG) in distribution networks is presented. The outer optimization problem corresponds to the owner of the DG who must decide the contract price that would maximize his profits. The inner optimization problem corresponds to the distribution company (DisCo), which procures the minimization of the payments incurred in attending the expected demand while satisfying network constraints. The meet the expected demand the DisCo can purchase energy either form the transmission network through the substations or form the DG units within its network. The inner optimization problem is substituted by its Karush- Kuhn-Tucker optimality conditions, turning the bilevel programming problem into an equivalent single-level nonlinear programming problem which is solved using commercially available software. © 2010 IEEE.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

The competitive challenge for Brazilian industry

Includes bibliography

Foreign investment and competitive development in Latin America and the Caribbean

Includes bibliography

Developing competitive advantages: successful export SMEs in Argentina, Chile and Colombia

Includes bibliography