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Assemble ow chamber WITHOUT cells by adding blank microscope slide to polycarbonate block and closing with stainless steel bracket. Connect blank ow chamber to ow circuit and start ow (0.5 ml/min), in order to preequilibrate O2 within lines and calibrate O2 probe. Maintain all components of the ow circuit except the syringe pump within an incubator set at 37 C. Allow an equilibration period of at least 15 min before continuing with the following steps. On a separate polycarbonate block, apply inert silicone lubricant to the outer edge of recessed section, outside the ow region. Invert slide with hepatocytes and lower onto this second ow deck. Overlay the stainless steel bracket and tighten with screws. The resultant ow eld dimensions are 28 mm (width) 55 mm (length) 100 m (height). Stop ow and remove ow chamber without cells. Quickly insert chamber with cultured hepatocytes into ow circuit as illustrated in Fig. 15.9, and restart ow (0.5 ml/min).

In [19], several threshold-based schemes are proposed to relieve the broadcast storm problem. These protocols are for unreliable broadcast. Reliable broadcasting protocols are proposed in [1, 20]. 17.4 MULTICASTING PROTOCOLS FOR MANET Previous sections have discussed unicast routing and broadcasting protocols. This section will introduce multicasting protocols. The multicasting protocols can be classified into two categories based on how multicast trees are constructed: source-based and core-based (or group-shared) [29, 34]. The source-based protocol tries to maintain a per-source multicast tree from each source host to every member in the multicast group. Thus, there may exist multiple multicast trees in the network. The core-based protocol, on the other hand, uses only one multicast tree rooted at a core host. The tree then spans from the core host to every member of the multicast group. Although multicasting can be achieved by using multiple unicast routing, the traffic might be too high and choke the network. Hence, many multicast protocols have been developed with applications adopting multicasting technologies. Video conferencing is one important example. Multicasting in MANET is much more complex than in wired networks because of host mobility, interference of wireless signals, and the broadcast nature of wireless communication. In the following, we review two such protocols. 17.4.1 ODMRP In the on-demand multicast routing protocol (ODMRP) [3, 14], the multicast tree is established by the source host s periodical JOIN packets. Consider the example in Figure 17.12(a). The source node S, desiring to send data packets to multicast members, will

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.57 .46 .44 .52 .42

20.5 CONCLUSIONS AND FUTURE DIRECTIONS In this chapter, we have proposed a simple and efficient distributed algorithm for calculating the connected dominating set in an ad hoc wireless network. When the transmission radius of a mobile host is not too large, the proposed algorithm generates a small connected dominating set. Our proposed algorithm calculates connected dominating sets in O( 2) time with distance-2 neighborhood information, where is the maximum node degree in

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The results in the Classi er output window show that naive Bayes achieves a very impressive 96.34% (658/683) classi cation accuracy. This obviously leaves little room for improvement. Do you suppose that all nine attributes are equally important to the task of classi cation Is there possibly a subset of the nine attributes, when selected as input to naive Bayes, which leads to improved classi cation accuracy Before determining the answers to these questions, let s review WEKA s approach to attribute selection. It s not unusual for real-word data sets to contain irrelevant, redundant, or noisy attributes, which ultimately contribute to degradation in classi cation accuracy. In contrast, removing nonrelevant attributes often leads to improved classi cation accuracy. WEKA s supervised attribute selection lter enables a combination of evaluation and search methods to be speci ed, where the objective is to determine a useful subset of attributes as input to a learning scheme.

0.69 1.84 0.05 1.28 0.78 0.76 1.79 0.18 1.90 0.152

Figure 28.2 A conceptual model showing the parallel relationships assumed to exist among situational, cognitive, physiological, and behavioral components of stress and burnout. Source: Toward a Cognitive-Affective Model of Athletic Burnout, by R. E. Smith, 1986, Journal of Sport Psychology, 8, pp. 36 50. Reprinted with permission.

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