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When learning models online, data are collected and processed continuously. New data may con rm and reinforce the current model if data are compatible with existing knowledge. Otherwise, new data may suggest changes and a need to review the current model. This is the case in recurrent models and dynamic systems where operating conditions modify, fault occurs, or parameters of the processes change. In evolving systems, a key question is how to modify the current model structure using the newest data sample. Evolving systems use incoming information to continuously develop their structure and functionality through online self-organization. The rule-based models whose rules are endowed with local models forming their consequents are commonly referred to as fuzzy functional models. The Takagi-Sugeno (TS) is a typical example of a fuzzy functional model. A particularly important case is whenthe rule consequentsare linear functions. The evolvingTakagi-Sugeno(eTS) models and their variations [3] assume rule-based models whose fuzzy rules are as follows: Ri : IF x is Gi THEN yi g i0 i 1; . . . ; ck where Ri ith fuzzy rule ck number of fuzzy rules at k; k 1,. . . x 2 0; 1 p input data yi output of the ith rule Gi vector of antecedent fuzzy rules g ij parameters of the consequent The collection of the i 1; . . . ; ck rules assembles a model as a combination of local linear models. The contribution of a local linear model to the overall output is proportional to the degree of ring of each rule. eTS uses antecedent fuzzy sets with Gaussian membership functions: m e rjjx

Explanations for the Stress-Buffering Effect of Exercise Along with cognitive functioning and antidepressive effects, BDNF has also been implicated in improving the stress response Exposure to stress is known to reduce BDNF levels in the hippocampus of laboratory animals These decreases in BDNF levels following a stressful event (ie, a forced swim test) have been shown to be counteracted when animals have been given 1 week of exercise or 1 week of an antidepressant (Russo-Neustadt et al, 2001) Like BDNF, NE levels also markedly decrease when animals are exposed to a severe stress (see Dishman, 1997, for a review) For example, stress associated with chronic, unavoidable foot shocks produces substantially lower levels of NE and a deficit or delay in escaping from this shock when tested on subsequent days However, these effects can be attenuated by chronic voluntary wheel running and treadmill.

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(2005) failed to find the expected relationship between performance climate and sportspersonship. This negative result may have been an artifact of a sample that had a very low average perception of performance climate. Interactions among Motivational Variables Though helpful as quick summaries, the pictures portrayed in Tables 30.1 and 30.2 are less complex than the reality they seek to depict. For one thing, both the task and ego dispositional orientations and the perception of performance and mastery motivational climates are orthogonal constructs. It is possible, therefore, for a person to be high on both task and ego, high on one and low on the other, or low on both; the same is true, at least in theory, of perceived motivational climate. This allows for the investigation of a number of interesting and complex interactions. Moreover, the possible effects of both dispositional goal orientations and perceived motivational climate on moral functioning may be mediated by other variables. In one of the first investigations to include a more complex model of dispositional goal orientations, Dunn and Dunn (1999) divided their sample of elite adolescent ice hockey players into four goal orientation profile groups based on a mean-split protocol. The groups were then used to predict aggression legitimacy judgments and sportspersonship, as assessed by a modified, hockey-specific version of the MSOS. There were no significant findings for legitimacy judgments, but a multivariate effect was found

Instructions for transforming variables in four software packages are given in Table 5.2. Notice that a constant is added if the distribution contains zero or negative numbers. The constant (to bring the smallest value to at least 1) is added to each score to avoid taking the log, square root, or inverse of zero. This section on transformations merely scratches the surface of the topic about which a great deal more is known. The

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