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IOMA and the IIA is reported to have found that 72 percent of respondents indicated their organizations did not even have fraud detection software in place!40 These observations are supported by data and analysis provided by the ACFE. In reflecting on its 1996 Report to the Nation occupational fraud survey, the ACFE reported that small organizations, those with fewer than 100 employees, are particularly susceptible to occupational fraud. In such organizations the median fraud loss was $120,000. The median loss per incident to occupational fraud goes down as organizations get bigger, up to 10,000 employees. Above the 10,000-employee level, fraud begins to go up again in terms of median size per incident. This is particularly troublesome since smaller organizations are usually less likely to be able to absorb such a loss. Several of those cited in the study went out of business because of their occupational fraud losses. The ACFE proposes two theories to explain why smaller organizations have such heavy losses. The first is that they have fewer divisions of responsibility. Separation of duties is normally one of the more basic controls used to mitigate financial risk. We shall discuss controls in more detail later in this book, but we have recently seen from examples like Allfirst Bank and Frank Gruttadauria of Lehman Brothers that even large organizations can apparently have control lapses that lead to serious problems. The second rationale proffered is that in small organizations there is a greater level of intimacy and, therefore, trust. The ACFE notes this presents a challenge for executives in smaller organizations:

7.8.5.1 DOP Calculations In the MATLAB implementation, the GDOP, PDOP, HDOP, VDOP, and TDOP, are de ned and plotted for the two different cases of satellite phasings. Case 1: Good Geometry The results from Case 1 (satellites 1, 2, 3 and 4) show an excellent GDOP ranging to less 3.2 as a function of time. Figure 7.10 shows the variation of GDOP in meters as a function of time. This is a reasonable GDOP. Figure 7.11 shows all of the DOPs in meters as a function of time. Case 2: Bad Geometry Case 2 satellite phasing results in the infamous GDOP ``chimney peak'' during that time when satellite geometry fails to provide observability of user position. Figure 7.12 shows the resulting GDOP plots. It shows that two satellites out of four are close to each other and thereby do not provide linearly independent equations. This combination of satellites cannot be used to nd the user position, clock drift, and biases. Figure 7.13 is a multiplot of all the DOPs. 7.8.5.2 Kalman Filter Implementation For the second part of the example, a covariance analysis of the GPS=Kalman lter system is used to evaluate the performance of the system, given initial position estimates and estimates of receiver R and system dynamic Q noise. This type of analysis is done if actual measurement

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A message-driven bean is an asynchronous message consumer. The EJB container invokes a message-driven bean as a result of the arrival of a JMS message. The container controls the life cycle for the message-driven bean. A message-driven bean has neither a home nor a remote interface. A message-driven bean instance is an instance of a message-driven bean class. To a client, a message-driven bean is a JMS message consumer running on the server that implements some business logic. A client accesses a message-driven bean through JMS by sending messages to the JMS Destination (Queue or Topic) for which the message-driven bean class is the message listener. Message-driven bean instances have no conversational state, which means that all bean instances, like stateless session beans, are equivalent when they are not involved in servicing a client message. In other words, messagedriven beans have no special identity to the client. Actually, message-driven beans are not even visible to the client. A bean instance has no state for a specific client. However, the instance variables of the message-driven bean instance can maintain state during the handling of client messages. Examples of state a message-driven bean might maintain include an open database connection or an object reference to an EJB object.

[1] Project Proposal: Rapid Intelligent Sensing and Control of Forest Fires RISCOFF, July 2003. [2] Jakob Bardram, Hospitals of the Future-Ubiquitous Computing Support for Medical Work in Hospitals, in Proceedings of UbiHealth 2003, The 2nd International Workshop on Ubiquitous Computing for Pervasive Healthcare Applications, 2003. [3] Hartmut Benz and Paul Havinga, Home Care SenseNet Proposal for a Sub-project within Smart Surroundings, 2005. [4] M.M. Chen, C. Majidi, D.M. Doolin, S. Glaser and N. Sitar, Design and Construction of a Wild Reinstrumentation System Using Networked Sensors, 2003. [5] CORTEX:, Analysis and Design of Application Scenarios, Deliverable D8, Lancaster University, May 2003. [6] D.M. Doolin, S.D. Glaser and N. Sitar, Software Architecture for GPS-Enabled Wild re Sensorboard, TinyOS Technology Exchange, University of California, Berkeley CA, February 2004. [7] D.M. Doolin and N. Sitar, Wireless Sensors for wild re Monitoring, in Proceedings of the SPIE-Smart Structures and Integrated Systems, vol. 5765, May 2005, pp. 477 484.

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Table 14.3 FIGO risk factor score for gestational trophoblastic neoplasia Scores Age Antecedent pregnancy Interval months from index pregnancy Pretreatment serum hCG (IU/L) Largest tumor size (including uterus) Site of metastases Number of metastases Previous failed chemotherapy 0 < 40 Mole <4 < 103 1 40 Abortion 4 <7 103 < 104 3 5 cm Spleen Kidney 1 4 2 Term 7 < 13 104 < 105 > 5 cm GI tract 5 8 Single agent 4 13 105 Liver Brain >8 2 drugs

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