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5.4.1.1 Centralized 2PL Centralized 2PL is an implementation of the two-phase locking approach in a distributed system where one site is designated as the central lock manager (CLM). All the sites in the environment know where the CLM is. Each site is directed to obtain locks, according to 2PL rules, from the CLM. To request a lock, a TM sends a lock request to the CLM instead of its local lock manager. If the data item to be locked is available, the CLM locks the item on behalf of the transaction and sends a lock granted message to the requesting TM, thereby granting the lock. If the data item cannot be locked, because it is locked by one or more con icting transactions, the lock is not granted and the transaction is added to the list of transactions that are waiting for this item. Upon receiving a lock granted message, the requesting TM can continue to run. Note that the data item that is locked on behalf of the transaction may be local to the site where the transaction is or it may be located elsewhere. It is the responsibility of the TM to access the data item wherever it is. If the data item is replicated, then the

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This technique builds on other techniques in this section and shows you how the stego creation process works. You create a new technique, you use it, you find weaknesses in it, and then you figure out ways to fix those weaknesses to build a stronger technique.

(C1.1)

winterbournes of the Chalk downland in the countryside surrounding London (UK), which ow only in late winter and early spring when the water table is at its highest. Complicated patterns of stream aquifer exchange can arise where a previously gaining stream reach is suddenly subjected to ood ows due to surface runoff generated upstream. The possible sequence of events is illustrated in Figure 5.5.

American clients (Murase, 1977; S. Sue et al., 1994). Furthermore, researchers and clinicians are more sensitive to the notion that tremendous diversity in perspectives, needs, and issues exists within each ethnic or minority group (Bernal & Castro, 1994; Fowers & Richardson, 1996; Landrine, 1992; S. Sue et al., 1994). Therefore, generalizing assessment, treatment, or research strategies to all ethnic or minority groups is inappropriate. Acculturation and assimilation factors as well as unique individual differences must be taken into consideration when working with members of diverse groups (APA, 2003b; Lopez et al., 1989; Roysircar & Singh, 2003). More minority representation is needed within the field of psychology to provide better service to minority group members (APA, 2003b; Bernal & Castro, 1994; C. Hall, 1997; Landrine, 1992; Lopez et al., 1989). Although the United States has become a highly diverse nation, the vast majority of clinical psychologists are Caucasian. For example, fewer than 5% of the clinical psychology division of the APA (Division 12) is Asian, Hispanic, or African American. Of all the psychology doctorates awarded in 2001, fewer than 10% were awarded to ethnic minority group members (APA, 2003b). The APA has thus encouraged graduate training programs to increase minority group representation in graduate and postgraduate (e.g., internship and postdoctoral fellowship) admissions. As a result of these efforts, about 20% of incoming doctoral students in psychology are now ethnic minority group members (APA, 2000b; Norcross, Sayette, et al., 2002).

ADVANCED TOPICS IN BINARY LOGISTIC REGRESSION Modeling Interaction Effects Interaction effects occur when the impact of one variable depends on the values of another variable or variables. The other variables are said to interact with, moderate, or condition the impact of the rst variable. In this section I discuss the modeling of rst-order interaction effects the case in which the focus variable interacts with only one other variable. These are the most commonly modeled types of interaction effects. I also discuss the case in which the model as a whole might differ depending on group membership that is, the effects of possibly all model predictors depend on levels of a grouping variable. Interaction effects in the log odds are modeled using cross-product terms just as in linear regression. The logistic regression model for two predictors, X and Z , with interaction between the predictors is therefore log 1 =

[4,0] [1,3]

Note: When you increase the size of a number of items, those near the edges of the bounding box might be moved off of the Stage. If this occurs, choose View > Work Area to see the elements that are beyond the edges of the Stage.

where r is between 150 MHz and 1500 MHz; ht is between 30 m and 300 m; and d is the path distance and is between 1 km and 20 km. A(hr ) is the correction factor for mobile antenna height and is computed as follows: For a small- or medium-size city, A(hr ) (1.1 log f 0.7)hr (1.56 log f 0.8), (dB) (16.2a)