Targets and methods for the detection of processed animal proteins in animal feedstuffs

Bovine spongiform encephalopathy (BSE), or ‘mad cow disease’, is one of several transmissable spongiform encephalopathies (TSEs) known to affect certain mammals and is spread through the ingestion of infected animal tissue. It is believed that the inadvertent contamination of meat and bone meal (MBM) with infected animal tissue and the subsequent use of this material as a feed supplement contributed to the spread of the disease in cattle. As a result, the use of processed animal proteins (PAPs) in animal feeds is regulated in many parts of the world. Although feed testing is the only definitive means to certify compliance, regulatory compliance often relies solely on paper certification. Recently, rapid methods have become available that can be used by regulators to determine compliance during routine inspections. We describe a rapid, immunochromatographic strip test that can detect 0.1% MBM in animal feed. The test takes 15 min to perform and large numbers of samples can be screened for PAPs simultaneously.     

Algorithms for multiprocessor scheduling with two job lengths and allocation restrictions

A variant of the High Multiplicity Multiprocessor Scheduling Problem with C job lengths is considered, in which jobs can be processed only by machines not greater than a given index. When C=2, polynomial algorithms are proposed, for the feasibility version of the problem and for maximizing the number of scheduled jobs.     

Refactoring and converting a TTCN-2 test suite

When deploying TTCN-3 at Nokia, we converted two TTCN-2 test systems to TTCN-3. We explain to which extend we have been able to do the conversion automatically. The conversion tool used provided a syntactically and semantically correct conversion of the TTCN-2 code. We define some improvements made on the tool to increase readability and maintainability of the resulting code. We cover aspects of converting also the non-TTCN-2 parts and describe the experiences we made as a set of lessons learnt.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.