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We observed 2863 trout in the wild to determine habitat utilization in small streams of the Kings River basin in California's Sierra Nevada mountains. The habitat utilization data were used to develop habitat suitability functions that provide input variables to the instream flow incremental methodology (IFIM) habitat assessment model of the U.S. Fish and Wildlife Service.Observations of habitat utilization of rainbow trout (Salmo gairdneri), brown trout (Salmo trutta), and brook trout (Salwelinwfontinalis) were obtained during the summer months of 1983 and 1984. The observations were made in small streams with discharges ranging from 0.7 m3 s-' to 0.03 m3 s-'. The streams are at elevations of 1250 to 2530 m. Equal effort was applied to observing undisturbed trout in all habitat types. Snorkeling proved to be the most effective method of observation.Individual trout of all species and life stages were most often observed in the lower half of the water column, utilizing low-velocity currents of less than 3.0cm s-'.From the depth and velocity utilization data, several forms of habitat suitability functions were developed and evaluated:1. Univariate depth and velocity functions derived from frequency histogram analysis.2. Univariate depth and univariate velocity exponential polynomial models. Bivariate depth and velocity exponential polynomial models.Univariate exponential polynomial models provided the best fit to the data for each species, based on visual comparisons of response surfaces and contour plots, and comparisons of computed sums of squared errors. Bivariate models fitted to the data resulted in greater sums of squared errors than multiplicative aggragation of univariate models, and frequently predicted utilization at zero depth.The habitat suitability functions derived from the univariate exponential polynomial models provided the best input to the IFIM habitat assessment models. KEY woKI>s lnstrcam flow Trout Suitability critcrin

Improved respirator test headforms are needed to measure the fit of N95 filtering facepiece respirators (FFRs) for protection studies against viable airborne particles. A Static (i.e., non-moving, non-speaking) Advanced Headform (StAH) was developed for evaluating the fit of N95 FFRs. The StAH was developed based on the anthropometric dimensions of a digital headform reported by the National Institute for Occupational Safety and Health (NIOSH) and has a silicone polymer skin with defined local tissue thicknesses. Quantitative fit factor evaluations were performed on seven N95 FFR models of various sizes and designs. Donnings were performed with and without a pre-test leak checking method. For each method, four replicate FFR samples of each of the seven models were tested with two donnings per replicate, resulting in a total of 56 tests per donning method. Each fit factor evaluation was comprised of three 86-sec exercises: “Normal Breathing” (NB, 11.2 liters per min (lpm)), “Deep Breathing” (DB, 20.4 lpm), then NB again. A fit factor for each exercise and an overall test fit factor were obtained. Analysis of variance methods were used to identify statistical differences among fit factors (analyzed as logarithms) for different FFR models, exercises, and testing methods. For each FFR model and for each testing method, the NB and DB fit factor data were not significantly different (P > 0.05). Significant differences were seen in the overall exercise fit factor data for the two donning methods among all FFR models (pooled data) and in the overall exercise fit factor data for the two testing methods within certain models. Utilization of the leak checking method improved the rate of obtaining overall exercise fit factors ≥100. The FFR models, which are expected to achieve overall fit factors ≥ 100 on human subjects, achieved overall exercise fit factors ≥ 100 on the StAH. Further research is needed to evaluate the correlation of FFRs fitted on the StAH to FFRs fitted on people.

Nowadays advances in robotics and computer science have made possible the development of sociable and attractive robots. A challenging objective of the field of humanoid robotics is to make robots able to interact with people in a believable way. Recent studies have demonstrated that human-like robots with high similarity to human beings do not generate the sense of unease that is typically associated to human-like robots. For this reason designing of aesthetically appealing and socially attractive robots becomes necessary for realistic human-robot interactions. In this paper HEFES (Hybrid Engine for Facial Expressions Synthesis), an engine for generating and controlling facial expressions both on physical androids and 3D avatars is described. HEFES is part of a software library that controls a human robot called FACE (Facial Automaton for Conveying Emotions). HEFES was designed to allow users to create facial expressions without requiring artistic or animatronics skills and it is able to animate both FACE and its 3D replica. The system was tested in human-robot interaction studies aimed to help children with autism to interpret their interlocutors' mood through facial expressions understanding. © 2012 IEEE

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