Radiation portal monitors are starting to be deployed at overseas ports to prevent nuclear weapons from entering the U.S. in a shipping container. Current designs have containers on trucks passing through a portal monitor at approximately 10 mph, before being routed to one of several lanes at the port's front gate for a driver identification check. For a fixed cost of testing, which consists of the costs of radiation portal monitors plus offsite x-ray and possibly manual testing of containers generating a false radiation alarm that cannot be resolved by gamma-ray imaging, the neutron detection limits of the current design are compared with those of two other designs that do not affect truck congestion at the front gate. For a wide range of budgets, it is optimal to have six monitors in each lane that simultaneously test a truck while it is being processed at the front gate. This design is robust against the location (within the container) of the weapon and reduces the detection limit (relative to the current design) by approximately a factor of three (although the accuracy of this value is limited by the lack of publicly available data) for practical budgets, which is enough to offset some shielding for a plutonium weapon, but insufficient to detect an uranium weapon.
We formulate and analyze an optimal stopping problem concerning a terrorist who is attempting to drive a nuclear or radiological weapon toward a target in a city center. In our model, the terrorist needs to travel through a two-dimensional lattice containing imperfect radiation sensors at some of the nodes, and decides at each node whether to detonate the bomb or proceed. We consider five different scenarios containing various informational structures and two different sensor array topologies: the sensors are placed randomly or they form an outer wall around the periphery of the city. We find that sensors can act as a deterrent in some cases, and that the government prefers the outer wall topology unless the sensors have a very low detection probability and the budget is tight (so that they are sparsely deployed).
The complete genome of severe acute respiratory syndrome coronavirus (SARS-CoV) reveals the existence of putative proteins unique to SARS-CoV. Identification of their function facilitates a mechanistic understanding of SARS infection and drug development for its treatment. The sequence of the majority of these putative proteins has no significant similarity to those of known proteins, which complicates the task of using sequence analysis tools to probe their function. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to SARS-CoV proteins. Testing results indicate that SVM is able to predict the functional class of 73% of the known SARS-CoV proteins with available sequences and 67% of 18 other novel viral proteins. A combination of the sequence comparison method BLAST and SVMProt can further improve the prediction accuracy of SMVProt such that the functional class of two additional SARS-CoV proteins is correctly predicted. Our study suggests that the SARS-CoV genome possibly contains a putative voltage-gated ion channel, structural proteins, a carbon-oxygen lyase, oxidoreductases acting on the CH-OH group of donors, and an ATP-binding cassette transporter. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi .
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