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er = (1 )e(r + s) or e s = (1 ) which is approximately (1 s) (1 ) implying = s. The implication of the preceding is that the risk-neutral probability of default is determined by the spread on a default risky bond, given our assumption that the spread is only compensating for default risk (and not, say, for the liquidity of the bond). To see how risk-neutral probabilities of default are actually obtained, consider the following relation: CORP1 = BENCH1 [(1 ) 1 + RR] CORP1 is the price today of a zero coupon bond issued by Company X that will mature in one period. This price can be interpreted as the present value (using risk-free discount rates) of the risk-adjusted expected cash ows on the bond. The discounting is accomplished with BENCH1, the price today of a zero coupon Treasury that also matures in one period. The expected value is calculated from the cash ows on Company X bonds and the risk-adjusted probability of default, . Thus, either Company X defaults, paying a known percentage of the face value, RR, with probability , or it does not default, and pays 100% of the face value with probability (1 ). Mathematically this pricing model is an equation with four variables: CORP1, BENCH1, , and RR. Given any three of the variables, we can solve for the fourth. Assuming the recovery rate is known (this can be relaxed later), and with market prices for CORP1 and BENCH1, we can solve for the market s assessment of the default probability. Expressing interest rates in continuously compounded form and assuming the risk-free interest rate is r, we can express the price today of the one-year, risk-free zero-coupon bond as BENCH1 = e rt

Some key features of TinyDB are as follows: TinyDB provides metadata management, provides a declarative query language, supports multiple query resolution on the same set of nodes and supports different levels of in-network aggregation It also includes a facility for simple triggers or queries that execute some command when a result is produced Cougar [132]: a query layer for sensor networks which accepts queries in a declarative language that are then optimized to generate ef cient query execution plans with in-network processing which can signi cantly reduce resource requirements Active query forwarding scheme (ACQUIRE) [104]: this aims at reducing the number of nodes involved in queries In this scheme, each node that forwards a query tries to resolve it If the node resolves the query, it does not forward it further but sends the result back.

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Nodes collaborate with their n hop neighbors, where n is referred to as the look ahead parameter If a node cannot resolve a query after collaborating with n hop neighbors, it forwards it to another neighbor When n equals 1, ACQUIRE carries out ooding in the worst case Some open issues The routing protocols for WSNs are generally designed for networks that have xed homogenous sensor nodes and are based on the assumption that all nodes try to convey data to a central node, often named the sink However, in COs networks there will be heterogenous nodes that can be mobile, and the sensed data will be needed by many nodes, ie multiple sinks Distributed data storage and search (DSS) solutions should have support for heterogenity.

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Additionally, open research issues on DSS can be summarized as follows: replication, concurrency and consistency control of distributed data, mobility support, general purpose solutions for declarative query languages, interoperability with open standards and query driven architectures with QoS support..

OUTSOURCING Training managers have been outsourcing parts of their programs for some time. In small organizations with scant staff, outside training programs and sometimes administration are essential to providing needed services. Some large organizations are now kicking the tires on what is known as business process outsourcing (BPO) for their training. Comprehensive BPO implies the transfer of responsibility and accountability for the entire training function to an outside vendor who will then deliver it back in a cheaper, better, faster form. Outsourced learning is gaining momentum among major companies. Indeed, recent research by industry analyst IDC shows that training tops the list of functions that corporate executives are considering for outsourcing, higher than sales and marketing, HR, finance, and accounting, says Michael Brennan, corporate learning program manager for IDC. Is Training a Good Bet for Outsourcing Edward Trolley, co-author of Running Training Like a Business1 and now VP of outsourcing for Knowledge Planet, believes that training shares the following characteristics with other successful BPO opportunities: The scope of training is easily defined, yet it is fragmented and dispersed in most companies. Training represents a large fixed cost for many companies. In most cases, training departments are unable to achieve economies of scale and leverage on these costs a great characteristic for BPO.

% Improvement Caused by 60% 15 5 20 100% Adjusted Improvement Caused by 48% 10.5 3 16

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