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toward each other to avoid creating sensing hole Finally, nodal sensing ranges touch each other without overlapping (Fig 105c); hence, no virtual force is generated, and nodes do not move Existing virtual-force-based sensor self-deployment algorithms extend this basic idea by adding different terminating mechanisms or additional constraints For example, in Howard et al (2002a), virtual friction force, which is proportional to nodal velocity and always against nodal moving direction, is used to stop nodal movement so that a static equilibrium can be eventually reached In Wang et al (2004a), Voronoi diagram is used to judge nodes coverage effectiveness and help them decide to stop moving In Heo and Varshney (2005), node density is brought into consideration during virtual force computation for energy saving In Ma and Yang (2007), each node receives virtual force from at most six neighbors such that the resulting network has a TT layout.

therapy in large numbers. In the United States, state and national statistics concerning how many have sought and successfully completed treatment are disturbing, suggesting that many of these high-risk groups may be grossly underserved (also see s 4 and 5). It is unclear exactly what is preventing successful treatment seeking and completion by these high-risk populations, but the National Institutes for Health (NIH) is encouraging research to investigate these disparities in mental health services. To give you a sense of who is being served, Table 1.5 shows the recent figures, by age and racial-ethnic group, for how many of those who need treatment are actually receiving it.

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It is likely to play out in several arenas, including the public mental health system, court systems, funding agency policies, treatment guidelines, EBP toolkits, and the relevant professional literatures As noted, several studies have shown that the majority of people seeking mental health treatment are receiving therapeutic approaches of unknown efficacy or of efficacy known to be inferior to EBPs (Addis & Krasnow, 2000; Goisman et al, 1999; Plante et al, 1999; Sanderson et al, 2001; Taylor et al, 1989) Use of EBPs for those clients is likely to improve care, but EBPs are not universally effective with the disorders for which they were developed Even experienced EBP providers know that some client problems are of a complexity or severity to be unamenable to an unmodified EBP protocol.

Two most recent variants of this approach are described in detail below Poduri et al (2009) studied control of positions of nodes for desired levels of network connectivity and sensing coverage The problem is to determine positions of nodes such that the sensing coverage is maximized while satisfying the connectivity constraint It is assumed that mobile nodes (robots) are densely deployed in the ROI The authors proposed the neighbor-every-theta (NET) graph where each node has at least one neighbor in every angle sector of its communication range That is, the angle between any two neighbors in sorted order (measured from given node) is NET graphs are proven guaranteed to have, when < , an edge-connectivity of at least 2 even with the assumption of irregular communication range (Poduri et al, 2009).

The nature of these limits to the generalizability of EBPs will be better known as they are subjected to effectiveness studies over the next decade Advocates and critics agree that EBPs still requires using clinical expertise and sensitivity to the client s needs and wishes This chapter highlighted the risk that exclusive use of EBPs may prevent potentially effective, field-developed, or otherwise innovative treatments from being recognized Science and practice are likely to move toward integration if a means can be found to bring the underrecognized voice of seasoned practitioners into the effort to define and shape best practices Although there are several means to this end, one option highlighted in this chapter is the growing effort to collect, evaluate, and disseminate outcome data collected on community-based interventions Work has begun in this regard, and its continued development may be fruitful.

A graph is said to be k-(edge) connected iff there are at least k node-disjoint paths between any two nodes in the graph The graphs can achieve coverage-connectivity trade-offs based on a single parameter If the communication range equals to the sensing range, then sensing coverage is maximized when k 3 nodes are placed at the edges of k disjoint 2 sectors of boundary of communication range (Poduri et al, 2009) In the proposed deployment algorithm (Poduri et al, 2009), repelling and attracting forces between mobile nodes are used These forces have inverse square.

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