975 resultados para depth perception
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Estimating the abundance of cetaceans from aerial survey data requires careful attention to survey design and analysis. Once an aerial observer perceives a marine mammal or group of marine mammals, he or she has only a few seconds to identify and enumerate the individuals sighted, as well as to determine the distance to the sighting and record this information. In line-transect survey analyses, it is assumed that the observer has correctly identified and enumerated the group or individual. We describe methods used to test this assumption and how survey data should be adjusted to account for observer errors. Harbor porpoises (Phocoena phocoena) were censused during aerial surveys in the summer of 1997 in Southeast Alaska (9844 km survey effort), in the summer of 1998 in the Gulf of Alaska (10,127 km), and in the summer of 1999 in the Bering Sea (7849 km). Sightings of harbor porpoise during a beluga whale (Phocoena phocoena) survey in 1998 (1355 km) provided data on harbor porpoise abundance in Cook Inlet for the Gulf of Alaska stock. Sightings by primary observers at side windows were compared to an independent observer at a belly window to estimate the probability of misidentification, underestimation of group size, and the probability that porpoise on the surface at the trackline were missed (perception bias, g(0)). There were 129, 96, and 201 sightings of harbor porpoises in the three stock areas, respectively. Both g(0) and effective strip width (the realized width of the survey track) depended on survey year, and g(0) also depended on the visibility reported by observers. Harbor porpoise abundance in 1997–99 was estimated at 11,146 animals for the Southeast Alaska stock, 31,046 animals for the Gulf of Alaska stock, and 48,515 animals for the Bering Sea stock.
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World Conference on Psychology and Sociology 2012
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Background Quality of cardiopulmonary resuscitation (CPR) is key to increase survival from cardiac arrest. Providing chest compressions with adequate rate and depth is difficult even for well-trained rescuers. The use of real-time feedback devices is intended to contribute to enhance chest compression quality. These devices are typically based on the double integration of the acceleration to obtain the chest displacement during compressions. The integration process is inherently unstable and leads to important errors unless boundary conditions are applied for each compression cycle. Commercial solutions use additional reference signals to establish these conditions, requiring additional sensors. Our aim was to study the accuracy of three methods based solely on the acceleration signal to provide feedback on the compression rate and depth. Materials and Methods We simulated a CPR scenario with several volunteers grouped in couples providing chest compressions on a resuscitation manikin. Different target rates (80, 100, 120, and 140 compressions per minute) and a target depth of at least 50 mm were indicated. The manikin was equipped with a displacement sensor. The accelerometer was placed between the rescuer's hands and the manikin's chest. We designed three alternatives to direct integration based on different principles (linear filtering, analysis of velocity, and spectral analysis of acceleration). We evaluated their accuracy by comparing the estimated depth and rate with the values obtained from the reference displacement sensor. Results The median (IQR) percent error was 5.9% (2.8-10.3), 6.3% (2.9-11.3), and 2.5% (1.2-4.4) for depth and 1.7% (0.0-2.3), 0.0% (0.0-2.0), and 0.9% (0.4-1.6) for rate, respectively. Depth accuracy depended on the target rate (p < 0.001) and on the rescuer couple (p < 0.001) within each method. Conclusions Accurate feedback on chest compression depth and rate during CPR is possible using exclusively the chest acceleration signal. The algorithm based on spectral analysis showed the best performance. Despite these encouraging results, further research should be conducted to asses the performance of these algorithms with clinical data.
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The length–weight relationships of 22 species of deep-sea fishes inhabiting the continental slopes beyond 250 m depth along the West Coast of India are presented. The parameters a and b of the equation W=a Lb were estimated. The fish samples were collected from trawl surveys during 1999 to 2001 on board the FORV Sagar Sampada at a depth range of 250 to 600 m in the area between 7°N and 20°N latitude. The value of b ranged from 1.94 to 3.36.
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The condition of soft-textured flesh in commercially harvested sablefish, Anoplopoma fimbria, from southeastern Alaska was investigated by National Marine Fisheries Service (NMFS) scientists from the Alaska Fisheries Science Center’s Auke Bay Laboratories (ABL) in Alaska and the Northwest Fisheries Science Center in Seattle, Wash. Sablefish were sampled by longline, pot, and trawl at five sites around Chichagof Island at depths of 259–988 m in the summer of 1985 and at depths of 259–913 m in the winter of 1986. At the time of capture and data collection, sablefish were categorized as being “firm” or “soft” by visual and tactile examination, individually weighed, measured for length, and sexed. Subsamples of the fish were analyzed and linear regressions and analyses of variance were performed on both the summer (n = 242) and winter (n = 439) data for combinations of chemical and physical analyses, depth of capture, weight vs. length, flesh condition, gonad condition, and sex. We successfully identified and selected sablefish with firm- and soft-textured flesh by tactile and visual methods. Abundance of firm fish in catches varied by season: 67% in winter and 40% in summer. Winter catches may give a higher yield than summer catches. Abundance of firm fish catches also varied with depth. Firm fish were routinely found shallower than soft fish. The highest percentage of firm fish were found at depths less than 365 m in summer and at 365–730 m in winter, whereas soft fish were usually more abundant at depths greater than 731 m. Catches of firm fish declined with increasing depth. More than 80% of the fish caught during winter at depths between 365 and 730 m had firm flesh, but this declined to 48% at these depths in summer. Longlines and pots caught similar proportions of firm and soft fish with both gears catching more firm than soft fish. Trawls caught a higher proportion of soft fish compared to longlines and pots in winter. Chemical composition of “firm” and “soft” fish differed. On average “soft” fish had 14% less protein, 12% more lipid, and 3% less ash than firm fish. Cooked yields from sablefish with soft-textured flesh were 31% less than cooked yields from firm fish. Sablefish flesh quality (firmness) related significantly to the biochemistry of white muscle with respect to 11 variables. Summer fish of all flesh conditions averaged 6% heavier than winter fish. Regulating depth of fishing could increase the yield from catches, but the feasibility and benefits from this action will require further evaluation and study. Results of this study provide a basis for reducing the harvest of sablefish with soft flesh and may stimulate further research into the cause and effect relationship of the sablefish soft-flesh phenomenon.