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x2 = 138.80, p = .22, df = 127, RMSEA = .020, 90% UL = .039, AIC = 115.20 German Sample Trust Straightforwardness Altruism Compliance Modesty Tender-mindedness .38 .09 .72 .41 .00 .23 .00 .32 .15 .20 1.00 .19 .24 .00 .25 .06 .04 .17 .03 .29 .14 .22 .29 .03 .62 .59 .13 .39 .00 .58 .73 .71 .61 .73 .67 .80

ers and athletes who overtrain may experience decreased quality of life at various times. Exercise Dependence There may be a link between staleness and exercise dependence, and some exercisers and athletes who overtrain may be exercise-dependent (Adams & Kirkby, 2001). As noted by Morgan (1979, p. 58), excessive physical activity may be indicative of a pathological condition:

Reagents and Materials Sterile or aseptically prepared hMSCs, prepared according to Protocol 13.5 Osteogenic medium Screw cap microcentrifuge tubes, 2 ml Nonsterile Incubator at 37 C, 5% CO2 Protocol (a) Prepare cells according to Protocol 13.5. (b) Dilute cells to 2 105 cells/ml in osteogenic medium. (c) Pipette 1 ml of the cell suspension (200,000 cells) into each screw cap tube. Make sure cells are well resuspended, to get the same number of cells per tube. (d) Centrifuge at 300 g for 10 min at 4 C. (e) Unscrew the cap to allow ventilation and prevent contamination. (f) Incubate cells at 37 C, 5% CO2 . (g) Change medium 3 times per week by carefully aspirating 0.5 ml medium with a pipette and adding 0.5 ml osteogenic medium double concentration (See Section 3.4). Note: For analysis of the osteogenic differentiation of the cell pellet use Protocols 13.10, 13.11, 13.12, 13.14, 13.15, 13.16 and 13.17.

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Typical sensor network scenarios assume that sensor nodes are densely deployed in the monitored area Greedy routing algorithms applied on uniformly distributed and densely deployed network nodes perform close to the shortest-path algorithm and are thus the rst choice for such a scenario However, even for densely deployed network nodes a recovery strategy may still be necessary, since greedy forwarding might get stuck at convex network boundaries or at network voids resulting from an inhomogeneous node distribution Such an inhomogenity may be due to the physical properties of the monitored surface (eg, a lake inside the monitored area where sensor nodes cannot be deployed) Frey and Gorgen [23] observed that such an inhomogeneous node distribution can have a signi cant impact on the recovery strategy being applied.

Simulation experiments show that performance of face routing and the internal nodes concept may even degrade when node density is increased in a network scenario with an inhomogeneous node distribution On the other hand, face recovery in combination with the shortcut procedure is almost unaffected by such an inhomogenity However, the shortcut procedure has an increased message complexity compared to face routing or the internal nodes concept, since information about all 2-hop neighbors is required Frey and Gorgen [23] described the geographic cluster routing (GCR) algorithm, which is based on the concept of GFG and assumes that the network is modeled as a unit-disk graph in the 2D Euclidean space as well Routing in GCR is not performed on a per-node basis, but packets are forwarded along the edges of adjacent geographical clusters.

In order to de ne geographical clusters, the plane is partitioned by an in nite mesh of regular hexagons (see Fig 125), while each hexagon de nes one cluster Two geographical clusters C1 and C2 are denoted as adjacent, if there are at least two connected nodes with one located in C1 and the other located in C2 The graph resulting from adjacent clusters is not necessarily planar, thus, before face routing can be applied, a planar subgraph has to be extracted in advance This is obtained by a variant of the localized planar graph construction applied by GFG However, the method used by GCR may produce a disconnected subgraph even when the original graph is connected Thus, in contrast with GFG and its variants, GCR cannot guarantee delivery, even if there is a path from source to destination.

However, simulation results reveal that the delivery rate quickly tends to 100% when network degree is increased In particular, in densely deployed networks.

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