Review of Complete Crime and Punishment Series

Some examples of topics covered include undocumented immigrants, guns, and terrorism within Crime and Criminal Behavior, vigilantes, Miranda warnings, and zero-tolerance policing within Police and Law Enforcement; insanity laws, DNA evidence, and victims' rights within Courts, Law, and Justice; gangs and prison violence, capital punishment, and prison privatization within Corrections; and school violence, violent juvenile offenders, and age of responsibility within Juvenile Crime and Justice. Note that Sage offers numerous reference works that provide focused analysis of key topics in the field of criminal justice, such as the Encyclopedia of Crime and Punishment (2002), the Encyclopedia of Race and Crime (2009), the Encyclopedia of Victimology and Crime Prevention (2010), the Encyclopedia of White Collar & Corporate Crime (2004), and the Encyclopedia of Interpersonal Violence (2008), available in print or as e-books via Sage Reference online.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.