Orientation and navigation in bats: known unknowns or unknown unknowns?

Bats have been extensively studied with regard to their ability to orient, navigate and hunt prey by means of echolocation, but almost nothing is known about how they orient and navigate in situations such as migration and homing outside the range of their echolocation system. As volant animals, bats face many of the same problems and challenges as birds. Migrating bats must relocate summer and winter home ranges over distances as far as 2,000 km. Foraging bats must be able to relocate their home roost if they range beyond a familiar area, and indeed circumstantial evidence suggests that these animals can home from more than 600 km. However, an extensive research program on homing and navigation in bats halted in the early 1970s. The field of bird navigation has advanced greatly since that time and many of the mechanisms that birds are known to use for navigation were not known or widely accepted at this time. In this paper I discuss what is known about orientation and navigation in bats and use bird navigation as a model for future research in bat navigation. Technology is advancing such that previous difficulties in studying orientation in bats in the field can be overcome and so that the mechanisms of navigation in this highly mobile animal can finally be elucidated.     

Comparing computing formulas for estimating concentration ratios

The purpose of this paper is to provide guidance on the choice of computing formulas (estimators) for estimating average concentration ratios and other ratio-type measures of radionuclides and other environmental contaminant transfers between ecosystem components. Mathematical expressions for the expected value of three commonly used estimators (arithmetic mean of ratios, geometric mean of ratios, and the ratio of means) are obtained when the multivariate lognormal distribution is assumed. These expressions are used to explain why these estimators will not in general give the same estimate of the average concentration ratio. They illustrate that the magnitude of the discrepancies depends on the magnitude of measurement biases, and on the variance and correlations associated with spatial heterogeneity and measurement errors. This paper also reports on a computer simulation study that compares the accuracy of eight computing formulas for estimating a ratio relationship that is constant over time and/or space. Statistical models appropriate for both controlled spiking experiments and observational field studies for either normal or lognormal distributions are considered. Our results indicate that for either type of study the geometric mean is generally preferred if the lognormal distribution applies. However, the geometric mean has the disadvantage that its expected value depends on n, the number of measurements taken. Ricker's estimator, $\text{R}\text{?}{}_{\mathrm{rt}}$, appears to perform worse than the other estimators studied when the observations are lognormal. All eight estimators appear to be equally accurate for the controlled spiking study when data are normally distributed. For observational field studies when data are normally distributed the ratio of means or slight modifications thereof are preferred to other estimators investigated. Before one chooses a computing formula for estimating a concentration ratio, thought should be given to what target value needs to be estimated to satisfy study objectives, and to whether the normal or lognormal distribution is a more realistic model. The geometric mean performs well for lognormal distributions, but comparison of geometric means or of a geometric mean with environmental limits can be misleading if n is small. The arithmetic mean of ratios is a conservative choice in that it will always give a larger estimate than will the geometric mean. It may also be severely biased when data are lognormal and the variances of measurement errors are large. The ratio of the means is a reasonable choice if the distribution is normal. The median of the observed ratios, $\text{R}\text{?}{}_{\mathrm{<mtext>md</mtext>}}$, is useful estimate since it is easily obtained and has an easily understood interpretation as the point above which and below which 50% of the observed ratios lie. Also, it is appropriate no matter what the distribution of the observed ratios may be. Confidence limits on the median are also easily obtained. Finally, while this paper emphasizes applications in radionuclide research, our results should be applicable to a wide range of environmental contaminants since many contaminants have approximately lognormal distributions.     

一种改进的多学习教与学优化算法

针对传统教与学算法在解决复杂多峰函数优化问题时,具有局部最优且搜索开发能力较差的缺点,提出了一种改进的多学习教与学优化算法,新算法为学员的每一维加入不同的教学因子,设计了基于学员均值比较的教师选择策略和向教师及学员学习的多学习策略。基于多个单峰和多峰函数的仿真结果表明,新算法跟传统的、改进的教与学算法相比,在稳定性、寻优精度和收敛速度方面更具优势。     

The Contribution of Pre-impact Posture on Restrained Occupant Finite Element Model Response in Frontal Impact

Objective: The objective of this study was to discuss the influence of the pre-impact posture to the response of a finite element human body model (HBM) in frontal impacts.

Methods: This study uses previously published cadaveric tests (PMHS), which measured six realistic pre-impact postures. Seven postured models were created from the THUMS occupant model (v4.0): one matching the standard UMTRI driving posture as it was the target posture in the experiments, and six matching the measured pre-impact postures. The same measurements as those obtained during the cadaveric tests were calculated from the simulations, and biofidelity metrics based on signals correlation (CORA) were established to compare the response of the seven models to the experiments.

Results: The HBM responses showed good agreement with the PMHS responses for the reaction forces (CORA = 0.80 ± 0.05) and the kinematics of the lower part of the torso but only fair correlation was found with the head, the upper spine, rib strains (CORA= 0.50 ± 0.05) and chest deflections (CORA = 0.67 ± 0.08). All models sustained rib fractures, sternal fracture and clavicle fracture. The average number of rib fractures for all the models was 5.3 ± 1.0, lower than in the experiments (10.8 ± 9.0).

Variation in pre-impact posture greatly altered the time histories of the reaction forces, deflections and the rib strains, mainly in terms of time delay, but no definite improvement in HBM response or injury prediction was observed. By modifying only the posture of the HBM, the variability in the impact response was found to be equivalent to that observed in the experiments. The postured HBM sustained from 4 to 8 rib fractures, confirming that the pre-impact posture influenced the injury outcome predicted by the simulation.

Conclusions: This study tries to answer an important question: what is the effect of occupant posture on kinematics and kinetics. Significant differences in kinematics observed between HBM and PMHS suggesting more coupling between the pelvis and the spine for the models which makes the model response very sensitive to any variation in the spine posture. Consequently, the findings observed for the HBM cannot be extended to PMHS. Besides, pre-impact posture should be carefully quantified during experiments and the evaluation of HBM should take into account the variation in the predicted impact response due to the variation in the model posture.