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, 1994) have lead to more recent research addressing the true prevalence of CFS in community samples, which is not biased by help-seeking behaviors or by access to the health care system (Jason et al 1995; Jason, Richman, et al, 1999) However, a broadened interpretation of CFS appears to have complicated estimates of CFS prevalence by confounding the diagnosis of CFS with diagnoses of other illnesses, which have overlapping symptoms but which may not constitute CFS at all, or with conditions that are often comorbid with CFS Moreover, beyond the need to obtain accurate epidemiologic estimates of CFS prevalence for public health policy formulation, reliable and valid diagnoses are crucial for determinations of CFS treatment ef cacy.

Table 5-1

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It is necessary that the underpinning processes governing the impacts that aerosol particles have on the surrounding environment are understood to permit development of appropriate prognostic tools. Crucially, this includes our ability to interrelate the chemical and physical characteristics of atmospheric aerosols, through both analytical and theoretical methods, the two ideally working in tandem. Particulate matter in the atmosphere may exist as a completely solid, completely liquid or a combined solid/liquid system and predicting the phase state in relation to the associated chemical composition requires the use of thermodynamic models. However, this broad classi cation masks a hierarchy of models and techniques with greatly varying complexity and range of applicability. This can be dictated, for example, by the need for computational ef ciency or simply a lack of experimental data from which important model parameters can be derived. To cover all of the branching areas of thermodynamics in detail is far beyond the scope of this chapter. Indeed, for an in-depth discussion of generalised thermodynamics, both macroscopic and microscopic, the reader is referred to sources in the literature [1, 2]. Rather, the following body of text is aimed at providing a relatively robust overview of ideas pertinent to analysing the phase characteristics of atmospheric aerosol whilst focusing speci cally on the aerosol water vapour interactions, thus aqueous systems, and equilibrium thermodynamics.

MDS MAP is a centralized algorithm due to Shang et al [10] Instead of using semide nite programming, however, MDS MAP uses a technique from mathematical psychology called multidimensional scaling (MDS) The intuition behind MDS is simple Suppose there are n points, suspended in a volume We do not know the positions of the points, but we do know the distance between each pair of points MDS is an O(n3) algorithm that uses the law of cosines and linear algebra to reconstruct the relative positions of the points based on the pairwise distances The mathematical details of MDS are in Appendix C of this chapter MDS MAP is almost a direct application of the simplest kind of MDS: classic metric MDS The algorithm has four stages, which are as follows: Step 1.

We believe that it is crucial for CFS research to move beyond fuzzy recapitulations of the neurasthenia concept and clearly delineate precise criteria for diagnosing pure CFS versus CFS that is comorbid with psychiatric disorders It is also necessary to better differentiate CFS from other disorders that share some CFS symptoms but are not true CFS cases Approaches to treatment must be comprehensive, addressing a variety of care needs Much attention of researchers has focused on the potential bene ts of cognitive behavioral interventions As mentioned, the long-term outcomes of this type of intervention are still unclear, but interventions that challenge basic patient illness beliefs might solidify alreadynegative attitudes of medical personnel toward people with this syndrome.

Gather ranging data from the network, and form a sparse matrix R, where rij is the range between nodes i and j, or zero if no range was collected (for instance, if i and j are physically too far apart) Step 2 Run a standard all-pairs shortest-path algorithm (Dijkstra s, Floyd s) on R to produce a complete matrix of internode distances D Step 3 Run classic metric MDS on D to nd estimated node positions X, as described in Appendix C Step 4 Transform the solution X into global coordinates using some number of xed anchor nodes using a coordinate system registration routine B MDS MAP performs well on RSSI data alone, getting performance on the order of half the radio range when the neighborhood size nlocal is higher than 12 As expected, MDS MAP estimates improve as ranging improves.

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