The Effects of High Pressure on the Point of No Return in Simulated Penalty Kicks

We investigated the effects of high pressure on the point of no return or the minimum time required for a kicker to respond to the goalkeeper's dive in a simulated penalty kick task. The goalkeeper moved to one side with different times available for the participants to direct the ball to the opposite side in low-pressure (acoustically isolated laboratory) and high-pressure situations (with a participative audience). One group of participants showed a significant lengthening of the point of no return under high pressure. With less time available, performance was at chance level. Unexpectedly, in a second group of participants, high pressure caused a qualitative change in which for short times available participants were inclined to aim in the direction of the goalkeeper's move. The distinct effects of high pressure are discussed within attentional control theory to reflect a decreasing efficiency of the goal-driven attentional system, slowing down performance, and a decreasing effectiveness in inhibiting stimulus-driven behavior.

The mere presence of a goalkeeper affects the accuracy of penalty kicks

The keeper-independent strategy, in which a football penalty kicker selects a target location in advance and ignores the goalkeeper's actions during the run-up, has been suggested to be the preferable strategy for taking a penalty kick. The current in-field experiment investigated the question of whether the goalkeeper can indeed be ignored. Ten intermediate-level football players were instructed to adopt a goalkeeper-independent strategy and to perform penalty kicks directed at one of two targets located in the upper corners of the goal under three conditions: without a goalkeeper, in the presence of a goalkeeper (who tried to save the ball), and in the presence of a goalkeeper who was informed by the penalty kickers where they intended to direct the ball. The mere presence of a goalkeeper impaired shot accuracy. The shots were more centralised, that is, biased toward the goalkeeper. The effects were enhanced for the condition in which the penalty kicker knew the goalkeeper was knowledgeable about ball direction. The findings were consistent with the response activation model that holds that aiming at a target can be biased toward salient visual non-targets. The implications for adopting and practising goalkeeper-independent strategies are discussed.

Padrões de dominância manual, podálica e performance motora em gêmeos

O método de gêmeos envolve a análise diferencial de gêmeos monozigóticos (MZs) e dizigóticos (DZs) a fim de determinar a proporção de variância em um determinado traço atribuível a fatores genéticos e ambientais. Sendo geneticamente idênticos, os gêmeos MZs tendem a apresentar alto índice de concordância em vários atributos comportamentais, enquanto que, os DZs, compartilham 50 % da carga genética, manifestando um reduzido grau de concordância. Todavia, pesquisadores têm investigado possíveis mecanismos genéticos subjacentes do desenvolvimento cerebral para dominância manual e, não têm encontrado utilidade no método de gêmeos, pois, desde 1924, em todos os estudos de dominância manual em gêmeos, a taxa de discordância para o canhotismo e destrimanismo têm sido similares em MZs e DZs. Além disso, frequentemente gêmeos mostram taxa mais alta de prevalência para o canhotismo que os não gêmeos. O objetivo deste estudo foi investigar as possíveis diferenças entre gêmeos destros e canhotos (MZs e DZs combinados) e não gêmeos em variáveis fenótipicas específicas relacionadas à dominância manual, incluindo dominância podálica (chute de penalty), consistência de dominância manual, canhotismo familiar (CF+), postura manual e medidas de habilidades motoras em três testes : Batidas digitais, Tabuleiro de Annett e Pontilhar Pontinhos. Foram avaliados 286 gêmeos ( 255 destros, 34 canhotos) e, 251 não gêmeos (141 destros e 110 canhotos). A análise dos dados revelou o aumento de canhotismo no sexo masculino em três vezes mais do que no feminino. Além disto, destros e canhotos gêmeos foram mais mistos em suas dominâncias manuais e apontaram alta incidência de CF+ entre parentes de primeiro grau que não gêmeos canhotos e destros. Em concordância com estudos anteriores, a frequência de chute contralateral foi mais alta nos mistos do que nos que apresentaram consistência na dominância manual, especialmente entre os canhotos. No que diz respeito à habilidade manual, não foram encontradas diferenças significativas nos três testes de habilidade manual. Conforme as expectativas, destros e canhotos foram mais proficientes na manipulação do estímulo com suas mãos dominantes. Em todas as três medidas, em contraste com os destros, os canhotos (gêmeos e não gêmeos) apresentaram redução entre as assimetrias manuais. Por extensão, o aumento da prevalência de canhotismo entre gêmeos não poderia ser atribuída a influências patológicas, inversão de assimetria ou ordem de nascimento. É possível, levar em consideração, observando a diferença robusta em dominância manual mista que, o padrão de lateralização neuromotora, pode ser mais difusamente organizado nos hemisférios dos gêmeos que em não gêmeos, mas, esta sugestão requer evidência direta, pelo menos a partir de testes de neuro-imagem.

A STUDY OF PENALTY FORMULATIONS USED IN THE NUMERICAL APPROXIMATION OF A RADIALLY SYMMETRIC ELASTICITY PROBLEM

We consider a class of two-dimensional problems in classical linear elasticity for which material overlapping occurs in the absence of singularities. Of course, material overlapping is not physically realistic, and one possible way to prevent it uses a constrained minimization theory. In this theory, a minimization problem consists of minimizing the total potential energy of a linear elastic body subject to the constraint that the deformation field must be locally invertible. Here, we use an interior and an exterior penalty formulation of the minimization problem together with both a standard finite element method and classical nonlinear programming techniques to compute the minimizers. We compare both formulations by solving a plane problem numerically in the context of the constrained minimization theory. The problem has a closed-form solution, which is used to validate the numerical results. This solution is regular everywhere, including the boundary. In particular, we show numerical results which indicate that, for a fixed finite element mesh, the sequences of numerical solutions obtained with both the interior and the exterior penalty formulations converge to the same limit function as the penalization is enforced. This limit function yields an approximate deformation field to the plane problem that is locally invertible at all points in the domain. As the mesh is refined, this field converges to the exact solution of the plane problem.

Solving MPCC problem with the hyperbolic penalty function

The main goal of this work is to solve mathematical program with complementarity constraints (MPCC) using nonlinear programming techniques (NLP). An hyperbolic penalty function is used to solve MPCC problems by including the complementarity constraints in the penalty term. This penalty function [1] is twice continuously differentiable and combines features of both exterior and interior penalty methods. A set of AMPL problems from MacMPEC [2] are tested and a comparative study is performed.

Solving a signalized traffic intersection problem with an hyperbolic penalty function

Mathematical Program with Complementarity Constraints (MPCC) finds many applications in fields such as engineering design, economic equilibrium and mathematical programming theory itself. A queueing system model resulting from a single signalized intersection regulated by pre-timed control in traffic network is considered. The model is formulated as an MPCC problem. A MATLAB implementation based on an hyperbolic penalty function is used to solve this practical problem, computing the total average waiting time of the vehicles in all queues and the green split allocation. The problem was codified in AMPL.

Classification of some penalty methods

Optimization problems arise in science, engineering, economy, etc. and we need to find the best solutions for each reality. The methods used to solve these problems depend on several factors, including the amount and type of accessible information, the available algorithms for solving them, and, obviously, the intrinsic characteristics of the problem. There are many kinds of optimization problems and, consequently, many kinds of methods to solve them. When the involved functions are nonlinear and their derivatives are not known or are very difficult to calculate, these methods are more rare. These kinds of functions are frequently called black box functions. To solve such problems without constraints (unconstrained optimization), we can use direct search methods. These methods do not require any derivatives or approximations of them. But when the problem has constraints (nonlinear programming problems) and, additionally, the constraint functions are black box functions, it is much more difficult to find the most appropriate method. Penalty methods can then be used. They transform the original problem into a sequence of other problems, derived from the initial, all without constraints. Then this sequence of problems (without constraints) can be solved using the methods available for unconstrained optimization. In this chapter, we present a classification of some of the existing penalty methods and describe some of their assumptions and limitations. These methods allow the solving of optimization problems with continuous, discrete, and mixing constraints, without requiring continuity, differentiability, or convexity. Thus, penalty methods can be used as the first step in the resolution of constrained problems, by means of methods that typically are used by unconstrained problems. We also discuss a new class of penalty methods for nonlinear optimization, which adjust the penalty parameter dynamically.

Penalty fuzzy function for derivative-free optimization

Penalty and Barrier methods are normally used to solve Nonlinear Optimization Problems constrained problems. The problems appear in areas such as engineering and are often characterised by the fact that involved functions (objective and constraints) are non-smooth and/or their derivatives are not know. This means that optimization methods based on derivatives cannot net used. A Java based API was implemented, including only derivative-free optimizationmethods, to solve both constrained and unconstrained problems, which includes Penalty and Barriers methods. In this work a new penalty function, based on Fuzzy Logic, is presented. This function imposes a progressive penalization to solutions that violate the constraints. This means that the function imposes a low penalization when the violation of the constraints is low and a heavy penalisation when the violation is high. The value of the penalization is not known in beforehand, it is the outcome of a fuzzy inference engine. Numerical results comparing the proposed function with two of the classic penalty/barrier functions are presented. Regarding the presented results one can conclude that the prosed penalty function besides being very robust also exhibits a very good performance.

Direct-search penalty/barrier methods

In Nonlinear Optimization Penalty and Barrier Methods are normally used to solve Constrained Problems. There are several Penalty/Barrier Methods and they are used in several areas from Engineering to Economy, through Biology, Chemistry, Physics among others. In these areas it often appears Optimization Problems in which the involved functions (objective and constraints) are non-smooth and/or their derivatives are not know. In this work some Penalty/Barrier functions are tested and compared, using in the internal process, Derivative-free, namely Direct Search, methods. This work is a part of a bigger project involving the development of an Application Programming Interface, that implements several Optimization Methods, to be used in applications that need to solve constrained and/or unconstrained Nonlinear Optimization Problems. Besides the use of it in applied mathematics research it is also to be used in engineering software packages.

Adaptive Penalty and Barrier function based on Fuzzy Logic

Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twenty-eight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance.

A firefly dynamic penalty approach for solving engineering design problems

Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

The Part-time pay penalty in a segmented labor market

This paper is the first to examine the implications of switching to PT work for women's subsequent earnings trajectories, distinguishing by their type of contract: permanent or fixedterm. Using a rich longitudinal Spanish data set from Social Security records of over 76,000 prime-aged women strongly attached to the Spanish labor market, we find that PT work aggravates the segmentation of the labor market insofar there is a PT pay penalty and this penalty is larger and more persistent in the case of women with fixed-term contracts. The paper discusses problems arising in empirical estimation (including a problem not discussed in the literature up to now: the differential measurement error of the LHS variable by PT status), and how to address them. It concludes with policy implications relevant for Continental Europe and its dual structure of employment protection.

Alive 'n' Kicking

The Alive N Kicking (ANK) programme is a child weight management programme. Its primary aim is to support and empower parents and children from 2-19 years old to adopt healthy eating practices, increase physical activity levels, reduce sedentary behaviour and build self esteem.A secondary aim is to contribute towards children participating in the programme improving their long term health through achieving and maintaining a health body weight. The objectives of the programme are:1.To increase the level of healthy eating practices amongst participating families. 2.To increase the level of daily physical activity of participating children.3.To increase levels of self-esteem amongst children participating in the programme. 4.To contribute towards the long-term aim of achieving and maintaining a healthy body weight for children and parents participating in the programme.5.To deliver a safe, high-quality service.