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In statistical modeling, cross-validation refers to the practice of fitting a model with part of the available data, and then using predictions of the unused data to test and improve the fitted model. In accident reconstruction, cross-validation is possible when two different measurements can be used to estimate the same accident feature, such as when measured skidmark length and pedestrian throw distance each provide an estimate of impact speed. In this case a Bayesian cross-validation can be carried out by (1) using one measurement and Bayes theorem to compute a posterior distribution for the impact speed, (2) using this posterior distribution to compute a predictive distribution for the second measurement, and then (3) comparing the actual second measurement to this predictive distribution. An actual measurement falling in an extreme tail of the predictive distribution suggests a weakness in the assumptions governing the reconstruction.

Particle concentrations and size distributions were measured in the exhaust of a turbocharged, aftercooled, direct-injection, Diesel engine equipped with a ceramic filter (trap). Measurements were performed both upstream and downstream of the filter using a two-stage, variable residence time, micro-dilution system, a condensation particle counter and a scanning mobility particle sizer set up to count and size particles in the 7-320 nm diameter range. Engine operating conditions of the ISO 11 Mode test were used. The engine out (upstream of filter) size distribution has a bimodal, log normal structure, consisting of a nuclei mode with a geometric number mean diameter, DGN, in the 10-30 nm range and an accumulation mode with DGN in the 50-80 nm range. The modal structure of the size distribution is less distinct downstream of the filter. Nearly all the particle number emissions come from the nuclei mode, are nanoparticles (Dp < 50nm), and are volatile.

The history of off-road equipment manufacturing has been based on proven designs and long times between model updates. In sharp contrast with this strategy is the electronic manufacturing services (EMS) industry. The EMS industry is driven by the larger consumer product industry's continuing pressure for lower costs. Because of this, EMS tools, processes, and practices have evolved to support rapid technology and component changes. However the increasing consumer demand for features like better user-interfaces, more efficient fuel consumption, and the desire for increased operational controls in equipment have forced the off-road industry to increase the frequency of product updates to meet customers' needs. Equipment manufacturers make running changes leading to a “Learning-by-doing” development and manufacturing process. But rapid changes sometimes have an unpredictable impact on the reliability of the final product.

The critical speed method uses measurements of the radii of yawmarks left by vehicles, together with values for centripetal acceleration, to estimate the speeds of the vehicles when the yawmarks were made. Several field studies have indicated that equating the centripetal force with braking friction produced biased estimates, but that the biases tended to be small (e.g. within 10%-15% on average) and led to underestimates, suggesting that the method can be useful for forensic purposes. Other studies, however, have challenged this conclusion. The critical speed method has also seen use in safety-related research, where it is important to have a reliable assessment of the uncertainty associated with a speed estimate. This paper describes a variant of the critical speed method, where data from field tests lead to an informative prior probability distribution for the centripetal acceleration.