Two-year chronic bioassay study of rats exposed to a 1.6 GHz radiofrequency signal.

The purpose of this study was to determine whether long-term exposure to a 1.6 GHz radiofrequency (RF) field would affect the incidence of cancer in Fischer 344 rats. Thirty-six timed-pregnant rats were randomly assigned to each of three treatment groups: two groups exposed to a far-field RF Iridium signal and a third group that was sham exposed. Exposures were chosen such that the brain SAR in the fetuses was 0.16 W/kg. Whole-body far-field exposures were initiated at 19 days of gestation and continued at 2 h/day, 7 days/week for dams and pups after parturition until weaning (approximately 23 days old). The offspring (700) of these dams were selected, 90 males and 90 females for each near-field treatment group, with SAR levels in the brain calculated to be as follows: (1) 1.6 W/kg, (2) 0.16 W/kg and (3) near-field sham controls, with an additional 80 males and 80 females as shelf controls. Confining, head-first, near-field exposures of 2 h/day, 5 days/week were initiated when the offspring were 36 +/- 1 days old and continued until the rats were 2 years old. No statistically significant differences were observed among treatment groups for number of live pups/litter, survival index, and weaning weights, nor were there differences in clinical signs or neoplastic lesions among the treatment groups. The percentages of animals surviving at the end of the near-field exposure were not different among the male groups. In females a significant decrease in survival time was observed for the cage control group.

Error analysis for estimation of trace vapor concentration pathlength in stack plumes.

Near-infrared hyperspectral imaging is finding utility in remote sensing applications such as detection and quantification of chemical vapor effluents in stack plumes. Optimizing the sensing system or quantification algorithms is difficult because reference images are rarely well characterized. The present work uses a radiance model for a down-looking scene and a detailed noise model for dispersive and Fourier transform spectrometers to generate well-characterized synthetic data. These data were used with a classical least-squares-based estimator in an error analysis to obtain estimates of different sources of concentration-pathlength quantification error in the remote sensing problem. Contributions to the overall quantification error were the sum of individual error terms related to estimating the background, atmospheric corrections, plume temperature, and instrument signal-to-noise ratio. It was found that the quantification error depended strongly on errors in the background estimate and second-most on instrument signal-to-noise ratio. Decreases in net analyte signal (e.g., due to low analyte absorbance or increasing the number of analytes in the plume) led to increases in the quantification error as expected. These observations have implications on instrument design and strategies for quantification. The outlined approach could be used to estimate detection limits or perform variable selection for given sensing problems.