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In the highest connectivity k-cluster algorithms, each undecided visited node checks all its undecided k-hop neighbors and chooses one with the largest connectivity to be its CH Chen and Stojmenovic consider the mobility of nodes and describe algorithms for modifying cluster structure in the presence of topological changes The ef ciency of the clustering algorithms is tested by measuring the average number of created clusters, the number of border nodes, and the cluster size in random unit graphs Basagni [23], also uses the IDSG approximation but generalizes the idea of using weight, such that any meaningful parameter can be used in order to best exploit the network properties..

7. For each equity investment vehicle, provide the top 10 holdings as of the last three calendar quarters. 8. For each balanced asset allocation and/or lifestyle investment option, provide the asset breakdown by cash, stocks, and bonds as of at least three calendar quarters. 9. For each international or global investment option, provide the dollar-weighted average of the investment s allocations in each country. 10. For stable value investments, describe how interest is credited, frequency of rate changes, asset type(s), credit quality, and breakdown of assets in percentage. 11. For each investment vehicle, articulate the investment strategy used by the manager to add value relative to the benchmark identified. 12. Please describe any additional information regarding the specific investment options you wish to include. 13. Please discuss your process for adding or replacing funds from your proprietary fund list, your alliance fund list, and any nonalliance funds ABC may request. F. Employee Communications and Investment Education (please provide samples) 1. Company ABC has special challenges in reaching and communicating to employees/participants who may be dispersed in many locations. As a result, communications strategies and education programs are very important. Please provide a detailed discussion on how you propose to effectively communicate plan information, enrollment strategies, increasing participation strategies, education materials, etc. to the ABC workforce. 2. How do you determine employee investment education and communication objectives 3. What preenrollment communications do you provide Can they be customized 4. Can you provide a customized video If so, approximately what would it cost What enrollment materials do you provide 5. Who will conduct the enrollment meetings What are their employee communications credentials Are they full-time employee communications specialists Are they on salary or commission

With these additions the test passes Unfortunately the color resets when we open or close a compilation unit in an editor To understand what is going on, let's enable the tracing option for Java element deltas To do so, we switch to the trace option tab of the launch configuration for our workbench and enable the javadelta tracing option, as shown inFigure 253

The fast distributed approximation algorithms proposed in reference 24 use a synchronous model of computation using a variant of DSG called the k-dominating set where every node is within a distance k of some server or directory copy in this case, of some CH The proposed algorithm is called Local Randomized Greedy Algorithm (LRG) and is similar to the greedy algorithm for Minimum Set Covering Computation presented in reference 25, but instead of only considering the span of the nodes, LRG considers how the nodes covered by an individual node are also covered by other nodes The algorithm creates the clusters in three steps First each node v calculates its span d(v); this is how many of the current adjacent uncovered nodes it will cover if it becomes a CH This span d(v) plus the node ID is sent to all nodes within a distance equal to 2 around u.

When this tracing option is set, all delta notifications are logged to the console In addition, you can see how much time each listener takes When opening the compilation unit FailTestjava, we get the following trace:

Each neighbor computes the maximum and forwards it one more step With this information, every node can determine whether it is a candidate or not In the third step, called the selection step, a node u calculates its support s(u), which is the number of candidates that cover u The node u sends its s(u) to the candidates in its N(u) Each node v adds itself to the dominating set lexicographically higher than that of any node within its 2-hop neighbors One drawback of this approach for WSN is that once a node and it neighbors are covered, the node does not participate further in the cluster formation process, which will cause a higher rate of energy consumption in the node if it is a CH, leading it to its death because of battery depletion 73.