Near optimal combination of sensory and motor uncertainty in time during a naturalistic perception-action task.

Most behavioral tasks have time constraints for successful completion, such as catching a ball in flight. Many of these tasks require trading off the time allocated to perception and action, especially when only one of the two is possible at any time. In general, the longer we perceive, the smaller the uncertainty in perceptual estimates. However, a longer perception phase leaves less time for action, which results in less precise movements. Here we examine subjects catching a virtual ball. Critically, as soon as subjects began to move, the ball became invisible. We study how subjects trade-off sensory and movement uncertainty by deciding when to initiate their actions. We formulate this task in a probabilistic framework and show that subjects' decisions when to start moving are statistically near optimal given their individual sensory and motor uncertainties. Moreover, we accurately predict individual subject's task performance. Thus we show that subjects in a natural task are quantitatively aware of how sensory and motor variability depend on time and act so as to minimize overall task variability.

Vibration and stress analysis in the presence of structural uncertainty

At medium to high frequencies the dynamic response of a built-up engineering system, such as an automobile, can be sensitive to small random manufacturing imperfections. Ideally the statistics of the system response in the presence of these uncertainties should be computed at the design stage, but in practice this is an extremely difficult task. In this paper a brief review of the methods available for the analysis of systems with uncertainty is presented, and attention is then focused on two particular "non- parametric" methods: statistical energy analysis (SEA), and the hybrid method. The main governing equations are presented, and a number of example applications are considered, ranging from academic benchmark studies to industrial design studies. © 2009 IOP Publishing Ltd.

Altitudinal Ranging of Black-Crested Gibbons at Mt. Wuliang, Yunnan: Effects of Food Distribution, Temperature and Human Disturbance

We studied the altitudinal ranging of one habituated group of black-crested gibbons (Nomascus concolor) at Dazhaizi, Mt. Wuliang, Yunnan, China, between March 2005 and April 2006. The group ranged from 1,900 to 2,680 m above sea level. Food distribution was the driving force behind the altitudinal ranging patterns of the study group. They spent 83.2% of their time ranging between 2,100 and 2,400 m, where 75.8% of important food patches occurred. They avoided using the area above 2,500 m despite a lack of human disturbance there, apparently because there were few food resources. Temperature had a limited effect on seasonal altitudinal ranging but probably explained the diel altitudinal ranging of the group, which tended to use the lower zone in the cold morning and the higher zone in the warm afternoon. Grazing goats, the main disturbance, were limited to below 2,100 m, which was defined as the high-disturbance area (HDA). Gibbons spent less time in the HDA and, when ranging there, spent more time feeding and travelling and less time resting and singing. Human activities directly influenced gibbon behaviour, might cause forest degradation and create dispersal barriers between populations. Copyright (C) 2010 S. Karger AG, Basel

Model structure and uncertainty for stochastic non-point source modelling applications

The uncertainty associated with a rainfall-runoff and non-point source loading (NPS) model can be attributed to both the parameterization and model structure. An interesting implication of the areal nature of NPS models is the direct relationship between model structure (i.e. sub-watershed size) and sample size for the parameterization of spatial data. The approach of this research is to find structural limitations in scale for the use of the conceptual NPS model, then examine the scales at which suitable stochastic depictions of key parameter sets can be generated. The overlapping regions are optimal (and possibly the only suitable regions) for conducting meaningful stochastic analysis with a given NPS model. Previous work has sought to find optimal scales for deterministic analysis (where, in fact, calibration can be adjusted to compensate for sub-optimal scale selection); however, analysis of stochastic suitability and uncertainty associated with both the conceptual model and the parameter set, as presented here, is novel; as is the strategy of delineating a watershed based on the uncertainty distribution. The results of this paper demonstrate a narrow range of acceptable model structure for stochastic analysis in the chosen NPS model. In the case examined, the uncertainties associated with parameterization and parameter sensitivity are shown to be outweighed in significance by those resulting from structural and conceptual decisions. © 2011 Copyright IAHS Press.

Inhomogeneous soils and their effect on ground vibration due to underground railways

Ground vibration due to underground railways is a significant source of disturbance for people living or working near subways. Numerical models are commonly used to predict vibration levels; however, uncertainty inherent to these simulations must be understood to give confidence in the predictions. A semi-analytical approach is developed herein to investigate the effect of uncertainty in soil material properties on the surface vibration of layered halfspaces excited by an underground railway. The half-space is simulated using the thin-layer method coupled with the pipe-in-pipe (PiP) method for determining the load on the buried tunnel. The K-L expansion method is employed to smoothly vary the material properties throughout the soil by up to 10%. The simulation predicts a surface rms velocity variation of 5-10dB compared to a homogeneous, layered halfspace. These results suggest it may be prudent to include a 5dB error band on predicted vibration levels when simulating areas of varied material properties.