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Each QSP instance in the environment is capable of receiving client (SQL) requests. Once the QSP receives a request, it connects directly to a GIS instance and performs the necessary veri cation and disambiguation activities. If there are any errors, the QSP instance returns an error message to the client (exactly like we did in the COOP environment), and if there are no errors, then the actual processing can begin. The QSP instance that received the initial client request will use the registration information to build a list of candidate QSP instances (exactly like the DDSP did for candidate LQSP instances in the COOP environment). The veri ed and unambiguous version of the request will then be sent to all the candidate (QSP) sites. Each (QSP) instance capable of providing part of the query processing will then build a rough plan and respond with its self-ranking metadata used to elect the actual providers. This is similar to the election process in the COOP environment, but it is not quite the same since we have no LQSP/DQSP separation. The QSP subsystem will use the same criteria that the DQSP used in the COOP environment to determine when an election is over and whether there is adequate coverage or insuf cient provider availability. When the query can be processed, the QSP will notify all the candidates and each candidate will use the information contained in the previously generated plan and the common query strategy information to determine which partial results are sent to which QSP instance. The result format is the same as used by the COOP. Ultimately, the results are returned to the client.

The Regional Congress of Psychology for Professionals in the Americas, cosponsored by the IAAP and IUPsyS, took place in Mexico City.

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4 Conditions should be matched on length, number, and frequency of sessions For dismantling and additive designs, a common factors component must be added to single-component conditions in order to equate them on length of sessions with the multicomponent condition 5 Protocol manuals should be used to operationalize the independent variable of the investigation They should include an outline of the rationale for treatment, theoretical foundations of the therapy, and session-by-session goals and techniques when possible 6 Integrity and adherence checks should be performed on 20 25% of randomly selected tapes from each condition of the study Raters blind to condition should categorize each therapist utterance against a checklist of allowed and not-allowed techniques and utterances Investigators should make a priori decisions regarding the number of protocol breaks allowed before a client s data will be excluded from the analyses 7.

Then the Voronoi polygon p with the biggest deviation DEVp* DEVmax corresponds to the sub-area of data with the worst information granulation. (B) In the Grow 2 algorithm, the Voronoi polygon with the biggest cumulative error CEp* CEmax corresponds to the sub-area with the worst information granulation. Step 3. Improve the granulation quality in the worst Voronoi polygon p by adding one new neuron (granule) to it: n n 1. Step 4. Perform any standard competitive unsupervised learning algorithm, such as in [4 6], in order to properly tune the locations of the only two neurons within this Voronoi polygon, namely the newly added neuron and the previous (old) one. For the unsupervised learning of the two neurons in the current Voronoi polygon, we use here the FSCL algorithm from [6], for reasons mentioned in Section 11.2.

Conditions should not differ in terms of clients expectancy to change or how credible they think the rationale for treatment is Thus, credibility-expectancy scales should be administered after the rationale for treatment has been provided (usually after the rst session) 8 Conditions should be equal in terms of how many clients have discontinued treatment while it is being administered, as well as how many clients have been lost during the follow-up period of assessment 9 Sources of participants (ie, referrals from private practitioners, self-referrals by clients, responses to media advertisements) should be equally distributed across conditions 10 Demographic characteristics (eg, age, marital status, level of education, ethnicity) should be equivalent across conditions 11 Inclusion and exclusion criteria should be speci ed before patient recruitment begins Comorbidity of disorders should be equivalent across conditions.

Exclusion of comorbid disorders presents a limit to the generalizability of ndings; however, a more homogeneous sample reduces the amount of error variance present 12 To assess the representativeness of the sample under investigation, investigators should track reasons for participant exclusion 13 Severity and duration of the problem should be assessed, reported, and balanced across conditions 14 Concurrent or past treatment must be balanced across conditions and reported..