Unmanned aerial vehicles are rapidly advancing and becoming ubiquitous in an unlimited number of applications, from parcel delivery to people transportation. As unmanned aerial vehicle (UAV) markets expand, the increased acoustic nuisance on population becomes a more acute problem. Previous aircraft noise assessments have highlighted the necessity of a psychoacoustic metric for quantification of human audio perception. This study presents a framework for estimating propeller-based UAV auditory detection probability on the ground for a listener in a real-life scenario. The detection probability is derived by using its free-field measured acoustic background and estimating the UAV threshold according to a physiological model of the auditory pathway. The method is presented via results of an exemplar measurement in an anechoic environment with a single two- and five-bladed propeller. It was found that the auditory detection probability is primarily affected by the background noise level, whereas the number of blades is a less significant parameter. The significance of the proposed method lies in providing a quantitative evaluation of auditory detection probability of the UAV on the ground in the presence of a given soundscape. The results of this work are of practical significance since the method can aid anyone who plans a hovering flight mode.

In recent years, experimental studies have demonstrated that malfunction of the inner-hair cells and their synapse to the auditory nerve is a significant hearing loss (HL) contributor. This study presents a detailed biophysical model of the inner-hair cells embedded in an end-to-end computational model of the auditory pathway with an acoustic signal as an input and prediction of human audiometric thresholds as an output. The contribution of the outer hair cells is included in the mechanical model of the cochlea. Different types of HL were simulated by changing mechanical and biochemical parameters of the inner and outer hair cells. The predicted thresholds yielded common audiograms of hearing impairment. Outer hair cell damage could only introduce threshold shifts at mid-high frequencies up to 40 dB. Inner hair cell damage affects low and high frequencies differently. All types of inner hair cell deficits yielded a maximum of 40 dB HL at low frequencies. Only a significant reduction in the number of cilia of the inner-hair cells yielded HL of up to 120 dB HL at high frequencies. Sloping audiograms can be explained by a combination of gradual change in the number of cilia of inner and outer hair cells along the cochlear partition from apex to base.

Many biological systems rely on the ability to self-assemble target structures from different molecular building blocks using nonequilibrium drives, stemming, for example, from chemical potential gradients. The complex interactions between the different components give rise to a rugged energy landscape with a plethora of local minima on the dynamic pathway to the target assembly. Exploring a toy physical model of multicomponents nonequilibrium self-assembly, we demonstrate that a segmented description of the system dynamics can be used to provide predictions of the first assembly times. We show that for a wide range of values of the nonequilibrium drive, a log-normal distribution emerges for the first assembly time statistics. Based on data segmentation by a Bayesian estimator of abrupt changes (BEAST), we further present a general data-based algorithmic scheme, namely, the stochastic landscape method (SLM), for assembly time predictions. We demonstrate that this scheme can be implemented for the first assembly time forecast during a nonequilibrium self-assembly process, with improved prediction power compared to a naïve guess based on the mean remaining time to the first assembly. Our results can be used to establish a general quantitative framework for nonequilibrium systems and to improve control protocols of nonequilibrium self-assembly processes.