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.

Life on the Edge: Patterns of Formal and Informal Help to Older Adults in the United States and Sweden

Objectives Our objective in this study was to compare assistance received by individuals in the United States and Sweden with characteristics associated with low, moderate, or high 1-year placement risk in the United States. Methods We used longitudinal nationally representative data from 4,579 participants aged 75 years and older in the 1992 and 1993 waves of the Medicare Current Beneficiary Survey (MCBS) and cross-sectional data from 1,379 individuals aged 75 years and older in the Swedish Aging at Home (AH) national survey for comparative purposes. We developed a logistic regression equation using U.S. data to identify individuals with 3 levels (low, moderate, or high) of predicted 1-year institutional placement risk. Groups with the same characteristics were identified in the Swedish sample and compared on formal and informal assistance received. Results Formal service utilization was higher in Swedish sample, whereas informal service use is lower overall. Individuals with characteristics associated with high placement risk received more formal and less informal assistance in Sweden relative to the United States. Discussion Differences suggest formal services supplement informal support in the United States and that formal and informal services are complementary in Sweden.