977 resultados para Behavior problem


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To date, the published controlled trials on exposure to alcohol cues have had an abstinence treatment goal. A modification of cue exposure (CE) for moderation drinking, which incorporated priming doses of alcohol, could train participants to stop drinking after 2 to 3 drinks. This study examined the effects of modified CE within sessions, combined with directed homework practice. Nondependent problem drinkers who requested a moderation drinking goal were randomly allocated to modified CE or standard cognitive-behavior therapy (CBT) for alcohol abuse. Both interventions were delivered in 6 90-min group sessions. Eighty-one percent of eligible participants completed treatment and follow-up assessment. Over 6 months, CE produced significantly greater reductions than CBT in participants' reports of drinking frequency and consumption on each occasion. No pretreatment variables significantly predicted outcome, The modified CE procedure appears viable for nondependent drinkers who want to adopt a moderate drinking goal.

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In this study, the effectiveness of a group-based attention and problem solving (APS) treatment approach to executive impairments in patients with frontal lobe lesions was investigated. Thirty participants with lesions in the frontal lobes, 16 with left frontal (LF) and 14 with right frontal (RF) lesions, were allocated into three groups, each with 10 participants. The APS treatment was initially compared to two other control conditions, an information/education (IE) approach and treatment-as-usual or traditional rehabilitation (TR), with each of the control groups subsequently receiving the APS intervention in a crossover design. This design allowed for an evaluation of the treatment through assessment before and after treatment and on follow up, six months later. There was an improvement on some executive and functional measures after the implementation of the APS programme in the three groups. Size, and to a lesser extent laterality, of lesion affected baseline performance on measures of executive function, but there was no apparent relationship between size, laterality or site of lesion and level of benefit from the treatment intervention. The results were discussed in terms of models of executive functioning and the effectiveness of domain specific interventions in the rehabilitation of executive dysfunction.

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The industry of ergogenic supplements is increasing rapidly (Cole et al., 2003). Supplements may constitute an important aid for some vigourous exercise routines, but they may also be used as a hypothetical mean to achieve increases in muscular mass. This supposed effect of suplements can lead to its use by individuals who have high levels of drive for muscularity, a condition that is known to be associated with muscular dismorphy (or as it has been called recently vigorexia, Pope et al., 1997). Another psychological factor which can influence supplement consumption is exercise dependence, a borderline problem, as most studies present a prevalence of less than 10% in regular exercisers (Palmeira & Matos, 2006). Symptoms like tolerance or continuity could lead to the use of ergogenic aids to maintain the exercise levels. Also of interest for the understanding of the use of suplements are exercise frequency, volume and intensity, which could explain the ratesof consumption on a more physicological level.

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We address the problem of coordinating two non-holonomic mobile robots that move in formation while transporting a long payload. A competitive dynamics is introduced that gradually controls the activation and deactivation of individual behaviors. This process introduces (asymmetrical) hysteresis during behavioral switching. As a result behavioral oscillations, due to noisy information, are eliminated. Results in indoor environments show that if parameter values are chosen within reasonable ranges then, in spite of noise in the robots communi- cation and sensors, the overall robotic system works quite well even in cluttered environments. The robots overt behavior is stable and smooth.

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Engineering Education includes not only teaching theoretical fundamental concepts but also its verification during practical lessons in laboratories. The usual strategies to carry out this action are frequently based on Problem Based Learning, starting from a given state and proceeding forward to a target state. The possibility or the effectiveness of this procedure depends on previous states and if the present state was caused or resulted from earlier ones. This often happens in engineering education when the achieved results do not match the desired ones, e.g. when programming code is being developed or when the cause of the wrong behavior of an electronic circuit is being identified. It is thus important to also prepare students to proceed in the reverse way, i.e. given a start state generate the explanation or even the principles that underlie it. Later on, this sort of skills will be important. For instance, to a doctor making a patient?s story or to an engineer discovering the source of a malfunction. This learning methodology presents pedagogical advantages besides the enhanced preparation of students to their future work. The work presented on his document describes an automation project developed by a group of students in an engineering polytechnic school laboratory. The main objective was to improve the performance of a Braille machine. However, in a scenario of Reverse Problem-Based learning, students had first to discover and characterize the entire machine's function before being allowed (and being able) to propose a solution for the existing problem.

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We analyze the classical Bertrand model when consumers exhibit some strategic behavior in deciding from which seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers' behavior in uences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the process behavior. Second, we use nite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the rst approach and still obtain the same basic results. It is suggested that the limitations of the rst approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach.

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A family of nonempty closed convex sets is built by using the data of the Generalized Nash equilibrium problem (GNEP). The sets are selected iteratively such that the intersection of the selected sets contains solutions of the GNEP. The algorithm introduced by Iusem-Sosa (2003) is adapted to obtain solutions of the GNEP. Finally some numerical experiments are given to illustrate the numerical behavior of the algorithm.

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In this paper we propose a stabilized conforming finite volume element method for the Stokes equations. On stating the convergence of the method, optimal a priori error estimates in different norms are obtained by establishing the adequate connection between the finite volume and stabilized finite element formulations. A superconvergence result is also derived by using a postprocessing projection method. In particular, the stabilization of the continuous lowest equal order pair finite volume element discretization is achieved by enriching the velocity space with local functions that do not necessarily vanish on the element boundaries. Finally, some numerical experiments that confirm the predicted behavior of the method are provided.

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Recently, the surprising result that ab initio calculations on benzene and other planar arenes at correlated MP2, MP3, configuration interaction with singles and doubles (CISD), and coupled cluster with singles and doubles levels of theory using standard Pople’s basis sets yield nonplanar minima has been reported. The planar optimized structures turn out to be transition states presenting one or more large imaginary frequencies, whereas single-determinant-based methods lead to the expected planar minima and no imaginary frequencies. It has been suggested that such anomalous behavior can be originated by two-electron basis set incompleteness error. In this work, we show that the reported pitfalls can be interpreted in terms of intramolecular basis set superposition error (BSSE) effects, mostly between the C–H moieties constituting the arenes. We have carried out counterpoise-corrected optimizations and frequency calculations at the Hartree–Fock, B3LYP, MP2, and CISD levels of theory with several basis sets for a number of arenes. In all cases, correcting for intramolecular BSSE fixes the anomalous behavior of the correlated methods, whereas no significant differences are observed in the single-determinant case. Consequently, all systems studied are planar at all levels of theory. The effect of different intramolecular fragment definitions and the particular case of charged species, namely, cyclopentadienyl and indenyl anions, respectively, are also discussed

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According to the WHO (World Health Organization) and the European Union, suicide is considered to be a health problem of prime importance and to be one of the principal causes of unnatural death. In Spain, the number of suicides has increased 12% since 2005 . The Research Project “European Regions Enforcing Actions against Suicide (EUREGENAS), funded by the Health Program 2008-2013, has as main objective the description of an integrated model of Mental Health orientated to the prevention of suicide. The differences that allow distinguishing the meaning of prevention in suicide behavior are described and explained through a qualitative methodological strategy and through the creation of discussion groups formed by different groups of health professionals. The results highlight the existing differences between the diverse health professionals who come more in contact with this problem and it shows as well the coincidence of meaning that suicide has to be considered as a priority in the field of health.

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Elucidating the molecular and neural basis of complex social behaviors such as communal living, division of labor and warfare requires model organisms that exhibit these multi-faceted behavioral phenotypes. Social insects, such as ants, bees, wasps and termites, are attractive models to address this problem, with rich ecological and ethological foundations. However, their atypical systems of reproduction have hindered application of classical genetic approaches. In this review, we discuss how recent advances in social insect genomics, transcriptomics, and functional manipulations have enhanced our ability to observe and perturb gene expression, physiology and behavior in these species. Such developments begin to provide an integrated view of the molecular and cellular underpinnings of complex social behavior.

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This study describes a task that combines random searching with goal directed navigation. The testing was conducted on a circular elevated open field (80 cm in diameter), with an unmarked target area (20 cm in diameter) in the center of 1 of the 4 quadrants. Whenever the rat entered the target area, the computerized tracking system released a pellet to a random point on the open field. Rats were able to learn the task under light and in total darkness, and on a stable or a rotating arena. Visual information was important in light, but idiothetic information became crucial in darkness. Learning of a new position was quicker under light than in total darkness on a rotating arena. The place preference task should make it possible to study place cells (PCs) when the rats use an allothetic (room frame) or idiothetic (arena frame) representation of space and to compare the behavioral response with the PCs' activity.

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Customer choice behavior, such as 'buy-up' and 'buy-down', is an importantphe-nomenon in a wide range of industries. Yet there are few models ormethodologies available to exploit this phenomenon within yield managementsystems. We make some progress on filling this void. Specifically, wedevelop a model of yield management in which the buyers' behavior ismodeled explicitly using a multi-nomial logit model of demand. Thecontrol problem is to decide which subset of fare classes to offer ateach point in time. The set of open fare classes then affects the purchaseprobabilities for each class. We formulate a dynamic program todetermine the optimal control policy and show that it reduces to a dynamicnested allocation policy. Thus, the optimal choice-based policy caneasily be implemented in reservation systems that use nested allocationcontrols. We also develop an estimation procedure for our model based onthe expectation-maximization (EM) method that jointly estimates arrivalrates and choice model parameters when no-purchase outcomes areunobservable. Numerical results show that this combined optimization-estimation approach may significantly improve revenue performancerelative to traditional leg-based models that do not account for choicebehavior.

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We study the complexity of rationalizing choice behavior. We do so by analyzing two polar cases, and a number of intermediate ones. In our most structured case, that is where choice behavior is defined in universal choice domains and satisfies the "weak axiom of revealed preference," finding the complete preorder rationalizing choice behavior is a simple matter. In the polar case, where no restriction whatsoever is imposed, either on choice behavior or on choice domain, finding the complete preordersthat rationalize behavior turns out to be intractable. We show that the task of finding the rationalizing complete preorders is equivalent to a graph problem. This allows the search for existing algorithms in the graph theory literature, for the rationalization of choice.