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Molecular/Cellular Processes and the Physiological Response to Pollution
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with the current path from the observer to the phenomenon moves, the path may fail. In this case, either the observer or the concerned sensor must take the initiative to rebuild a new path. During initial set-up, the observer can build multiple paths between itself and the phenomenon and cache them, choosing the one that is the most bene cial at that time as the current path. If the path fails, another of the cached paths can be used. If all the cached paths are invalid, then the observer must rebuild new paths. This observer-initiated approach is a reactive approach, where path recovery action is only taken after observing a broken path. Another model for rebuilding new paths from the observer to the phenomenon is a sensor-initiated approach. In a sensor-initiated path recovery procedure, path recovery is initiated by a sensor that is currently part of the logical path between the observer and the phenomenon, and is planning to move out of range. Dynamic sensor networks can be further classi ed by considering the motion of the components. This motion is important from the communications perspective since the degree and type of communication is dependent on network dynamics: - Mobile observer. In this case, the observer is mobile with respect to the sensors and phenomena. For example, a plane might y over a eld periodically to collect information from a sensor network. A model that is well-suited to this case is the Data MULES model [63]. The MULE architecture provides wide-area connectivity for a sparse sensor network by exploiting mobile agents such as people, animals or vehicles moving in the environment. The system architecture is comprised of a threetier layered abstraction.The top tier is composed of access points/central repositories, which can be set up at convenient locations where network connectivity and power are present. These devices communicate with a central data warehouse that allows them to synchronize the data that they collect, detect duplicates and return acknowledgments to the MULEs. The intermediate layer of mobile MULE nodes provides the system with scalability and exibility at a relatively low cost. The key traits of a MULE are large storage capacities (relative to sensors), renewable power and the ability to communicate with the sensors and networked access points. MULEs are assumed to be serendipitous agents whose movements cannot be predicted in advance. However, as a result of their motion, they collect and store data from the sensors, as well as deliver ACKs back to the sensor nodes. In addition, MULEs can communicate with each other to improve system performance. The bottom tier of the network consists of randomly distributed wireless sensors. Work performed by these sensor nodes should be minimized as they contain the most constrained resources of any of the tiers. Depending on the application and situation, a number of the tiers in our three-tier abstraction could be collapsed onto one device. As data MULEs perform the collection of information to and from the sensor nodes when they are in the sensor s radio range, sensor nodes can be very simple (they are only required to sense data and communicate them to the data MULE when in range). This, in turn, may reduce complexity, and thus the cost, of sensor nodes, enabling their adoption in very large scale systems. The price to pay in the case where a data MULE-like solution is adopted is an energy-latency trade-off. Complexity is shifted from the sensor nodes to the MULEs, and the energy consumption is reduced as nodes only have to communicate the data they generate,
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Until 2003, ILEC deployments had been limited to temporary and inconclusive, single-community demonstrations, in a pattern that has become very familiar to telco watchers over the years. However, in June, 2003, three of the four
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In 1995 Robert Jarrow and Stuart Turnbull introduced a model of credit risk that derives the probability of default from the spread on a rm s risky debt. The Jarrow Turnbull model and extensions are widely used for pricing credit risk; however, the probabilities derived are not typically used for risk management (except for marking-to-market) because they contain a liquidity premium. Sources of Probabilities of Default Implied from Market Credit Spread Data To get this discussion started, let s look at some probabilities of default that were implied from credit spreads observed in the debt market. Savvysoft, a vendor of derivative software, sponsors a site called freederivatives.com. Exhibit 3.16 reproduces a page from that website. Note the matrix where the columns are banks, nance, industrial companies, and utilities and the rows are Moody s ratings. Suppose you were to click on the BAA2 industrial cell. What do you expect to see You probably would expect to see a table of default probabilities the one-year probability, the two-year probability, and so on. However, as illustrated in Exhibit 3.17, instead of getting one table, you would actually get 10 tables. The differences in the 10 tables are the recovery rate; each of the tables relates to a different recovery rate. For example, the one-year probability of default for a recovery rate of 10% is 1.65%, while the one-year probability of default for a recovery rate of 70% is 4.96%. I hope you see from the preceding that, if you are going to imply probabilities of default from credit spreads, the recovery rate will be crucial. Mechanics of Implying Probability of Default from Credit Spreads Two credit spreads are illustrated in Exhibit 3.18. One of the spreads is the spread over U.S. Treasuries and the other is the spread over the London Interbank Offer Rate (LIBOR).
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