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obtain system parameters t0, t, Psleep, a, b, g, and Qc. We have varied the key exchange threshold between 20 and 100 packets, while the requested event sensing reliability per cluster was kept at R=10 packets per second. Source cluster size was varied between 20 and 100 nodes. We assumed that each node is powered with two AA batteries that supply a voltage between 2.1 and 3.6 V and 500 mA-h as required by tmote_sky [5] operating conditions with total energy b=10, 260 J. We have also assumed that the network operates in the ISM band at 2.45 GHz, with a raw data rate of 250 kbps. The superframe size was controlled with SO, BO=0. The packet size has been xed at Gp 12 backoff periods, while the device buffer had a xed size of L=2 packets. The packet size includes message authentication code and all physical layer and medium access control protocol sublayer headers and is expressed as the multiple of the backoff period [1]. We also assume that the physical layer header has 6 bytes, and that the medium access control sublayer header and frame check sequence elds have a total of 9 bytes. Other parameters from the medium access control layer were kept at default values. In Fig. 16.9 we present access probabilities in bottom, middle, and top cluster, respectively, and in Fig. 16.10 we present transmission success probabilities. Figure 16.11 shows a number of nodes in middle and sink cluster with population in source cluster indicated as nbot and period of key exchange indicated as nk. We notice signi cant increase of populations as we move

becomes more complex when the form of relationship has not been hypothesized or the form may not have been correctly speci ed. On one hand, using the lowess smooth provides a powerful method of exploring the data and describing the form of the relationship. On the other hand, powerful graphical methods like lowess can detect chance relationships that only exist in the particular sample at hand. Unpredicted relationships detected through exploratory analyses should be replicated using a new sample to rule out the possibility that the results re ect relationships that are unique to the current sample. In the present example, sampling participants so as to include a larger number of cases with high scores on worry would greatly improve the ability of the regression model to distinguish between the linear and quadratic effects of worry on intention (see Pitts & West, 2001). When a new sample cannot be collected, a variety of resampling procedures can be used to probe the stability of the ndings across permutations of the sample. Diaconis (1985) presents an excellent discussion of statistical inference in the context of exploratory data analysis.

conscious aspects of the brain s ability to absorb subtle clues from our environment and to process them through what both nature and nurture provide gives us our ways of acting in and reacting to the world. It is the most complex biological network we know. Academics and scientists, it turns out, have begun to marry advances in their ability to see the brain at work with behavioral research in economics, politics, and other sociological activities, to reveal an inherent, biological human predilection for certain behaviors that increase our ability to be effective and prosperous.3 The networked brain and the networked world of business have more in common than we ever thought possible. First, we ll examine some of this groundbreaking research, and then try to draw some conclusions. Though it may seem at rst counterintuitive to launch into a deeper discussion of HOW in an internetworked world by discussing neuroscience and evolutionary anthropology, the workings of the brain can provide us with some keystone understandings about how we think and act. Since most people concede that you get better results from energy invested in improving on what you already do well as opposed to energy spent improving on your weaknesses, understanding the brain s biological proclivities can give us new perspective on where best to concentrate our efforts. Good science points to the fact that getting your HOWs right may, in fact, mean playing to your strengths.

One observation that Hanrahan et al made.

The owner of a proposed new motel has received a feasibility study from a consultant that shows that, at best, the operation would be only marginally profitable. The owner knows that this report will not convince possible investors to advance the funds for this proposed project, so the owner changes the feasibility study figures to improve the profitability of the operation. Discuss the ethics of this situation.