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ment striving What gives meaning to achievement striving In sport and physical activity, we need to address these questions and expand our conceptual understanding of motivational processes and achievement behaviors so that we can intervene effectively to enhance motivation and make the sport and physical activity context enjoyable and satisfying for all.

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weight is decreased It is possible to show from this learning mechanism that the weight ij converges to the probability of the user using application j in context i It is shown in [35] how this mechanism can be used with the K-SCM to generate a context-sensitive shortcut menu for a mobile device 4324 Communicating Context Information In unsupervised learning, the interpretation of context is derived from the user and the environment, but without any global framework as a reference Essentially, the problem is that the learning for different users is independent and isolated and thus there is no common language in which to communicate context information A paradigm described in detail in [37] is based on short-range, wireless communication that allows a set of users devices to arrive at a common labelling scheme for different contexts.

Internalization Body Dissatisfaction Societal Pressures Moderators: - Self-esteem - Self-concept/Id. - Locus of control - Gender nondisc. Moderators: - Perfectionism - Neuroticism - Physical selfconcept (body/ appearance focus) - Self-esteem Restrained Eating

From now on, each user s context-aware device is referred to as an agent This paradigm is based on a simple learning process and is independent of the manner in which each agent recognises contexts Consider a set of mobile agents that are regularly in close physical proximity Assume each agent has a short-range wireless ability and agents can transfer data between each other Furthermore, each agent has some means of recognising contexts Each agent maintains a list of names for each context and associated with each name is a weight in the range [0,1] The names can be ordered with respect to the values of the weights The name an agent uses for a context is the name with the highest weight In a recognised context, each agent communicates its name for the context to each of the other agents.

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The third generation of Roomba cleaners includes a great many more improvements than were made in the first to second generation jump. In addition to a dirt sensor, these models include a home base dock for self-charging, a remote control, a scheduling capability, and, most importantly for hackers, a serial port. This generation introduced ROI functionality as a firmware upgrade in October 2005. This is the current line of Roombas: Roomba Red: This model, shown in Figure 1-4, was released in 2004 and improved in 2005. It is the least expensive member of the current Roomba family. It comes with a seven-hour charger instead of a three-hour one and a single dirt sensor. It doesn t have a remote control or a self-charging home base, which are standard with the Discovery model.

If a majority of the agents have the same name for a context, then each agent increases the weight associated with that name If an agent does not have that name in its list, then the name and a weight is added to the list All the weights associated with names which are not a majority name are decreased in each of the agent s lists In the case where there is no majority name, the weight each agent has associated with their name is used to decide on a majority name This type of update of the names and the weights means that the current majority name is more likely to be the name used by the agents to label this context The agents are free to move between different contexts and communicate with different agents in different contexts.