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The current leader adopts the sequential Bayesian ltering technique to estimate the current location of the target A sensor, which is estimated to hold the maximum information, is then chosen to be the next leader This process is termed the information-driven sensor query (IDSQ) 1542 System Architectures of Data Fusion As mentioned earlier, the data-fusion mechanisms can also be categorized based on where to place the data aggregators Data funneling [43] is intrinsically an energy-ef cient routing protocol [45] integrated with data aggregation and compression techniques The basic idea of data funneling is to build a cost eld with the funnel shape to pull the data from sources to the sink The sink initials directional ooding to send an interest packet toward a target region, as shown in Figure 1511(a).

Papez (1937) and P D MacLean (1949).

act on a request from a much younger transaction until the system has processed requests from a large enough number of the older transactions. Therefore, any potential con ict of a younger transaction with older transactions will be detected before the system commits a much younger transaction. For instance, in the above example, the concurrency control algorithm will delay responding to the write request from T14

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During the process of forwarding interest packets, a forwarder computes its (energy) cost for communicating back to the sink and updates the cost eld in the interest packet When a node within the target region receives an interest packet from the nodes outside the region, it designates itself a border node The cost required to reach the sink (ie, the cost in the interest packet) is recorded, and a eld that is used to keep track of the cost to reach the border node is also included Since there could be multiple entries (border nodes) to the target region, a node within the target region might receive multiple interest packets from border nodes Instead of requesting all the nodes to send individual reports back to the sink, one of the border nodes is responsible for the task of collecting and aggregating all reports in the region and.

Simmers 2002) depending on the temporal resolution of the required estimates. For the purposes of estimating localized recharge, one of the most appropriate techniques (e.g. Healy and Cook 2002) is to examine water table rise in response to individual recharge events (Box 2.1). To estimate total recharge from water table rise, it is necessary to have independently derived estimates of the speci c yield of the aquifer under investigation. These are best obtained by analysis of test pumping data (see Section 3.4); where such data are not available, approximate estimates can be obtained using geologically based inference techniques (e.g. Younger 1993). Once we have a credible value of speci c yield, we simply multiply this by the water table rise over the period of interest to obtain the equivalent depth ii of water added to the saturated zone in that time (Box 2.1). Having obtained an estimate for total recharge in this manner, it is possible to estimate the

Although no factor analysis is ever completely exploratory there is always an underlying theoretical model by which the data are collected some factor analyses are primarily exploratory with no hypotheses, and others are primarily con rmatory, speci cally testing hypotheses. Both types of factor analysis are examined in this chapter. The purpose of this chapter is to provide a basic but comprehensive treatment of factor analysis. The intention is to give the reader the background to read, appreciate, and critique research from a factor analytic perspective, whether it be an article using factor analysis, an article using factor analysis inappropriately, or an article that could be strengthened if factor analysis were used. While no particular statistical package is assumed, this chapter also provides material needed to select the options for a factor analysis that are most appropriate to the purpose of the study. The chapter starts with the basic equations and de nitions of factor analysis. This section introduces the terms needed to understand factor analytic models and variations in the models. The second section of the chapter presents factor models, including component analysis (CA) and common factor analysis (CFA). CFA includes both exploratory (ECFA) and con rmatory (CCFA) factor analysis. In addition, all of these variants can be used with correlated or uncorrelated factor models. Presented with each model is the essential theoretical information to understand the model and the essential practical information to use the model. Rather than reviewing all the possible procedures that could apply to each model, each section includes the

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