Effect of Environmental Noise, Distance and Warning Sound on Pedestrians' Auditory Detectability of Electric Vehicles.

With developments in science and technology, the number of electric vehicles will increase, and they will even replace ICE vehicles. Thus, perceiving the presence of approaching electric vehicles on the road has become an important issue. In this study, the auditory detectability of the electric vehicle warning sound at different volumes, distances, and environmental noise levels was investigated. To this end, the detection rate was recorded in experiments with three environmental noise levels (50, 60, and 70 dBA), two sound pressure levels (SPLs) of the warning sound (46 and 51 dBA), three frequency combinations of the warning sound (5000, 2500, 1250, and 630 Hz for high frequencies; 2500, 1250, 630, and 315 Hz for medium frequencies; and 1250, 630, 315, and 160 Hz for low frequencies), and five distances (2, 4, 6, 8, and 10 m). The main results showed that the detection rate at 51 dBA was significantly higher than that at 46 dBA under a high-frequency warning sound; however, the detection rates were similar under medium- and low-frequency warning sounds. The participants' rates of detection for warning sounds were less than 20% under all experimental conditions, and a high-frequency warning sound was not affected by environmental noise. With regard to distances, no significant effects were observed between the distances and the detection rate at any of the three frequencies. In addition, auditory thresholds based on high-, medium-, and low-frequency warning sounds were found through logistic regression analysis results. The results of this study can be used as a reference for the future design of warning sounds.

The personal genome browser: visualizing functions of genetic variants.

Advances in high-throughput sequencing technologies have brought us into the individual genome era. Projects such as the 1000 Genomes Project have led the individual genome sequencing to become more and more popular. How to visualize, analyse and annotate individual genomes with knowledge bases to support genome studies and personalized healthcare is still a big challenge. The Personal Genome Browser (PGB) is developed to provide comprehensive functional annotation and visualization for individual genomes based on the genetic-molecular-phenotypic model. Investigators can easily view individual genetic variants, such as single nucleotide variants (SNVs), INDELs and structural variations (SVs), as well as genomic features and phenotypes associated to the individual genetic variants. The PGB especially highlights potential functional variants using the PGB built-in method or SIFT/PolyPhen2 scores. Moreover, the functional risks of genes could be evaluated by scanning individual genetic variants on the whole genome, a chromosome, or a cytoband based on functional implications of the variants. Investigators can then navigate to high risk genes on the scanned individual genome. The PGB accepts Variant Call Format (VCF) and Genetic Variation Format (GVF) files as the input. The functional annotation of input individual genome variants can be visualized in real time by well-defined symbols and shapes. The PGB is available at http://www.pgbrowser.org/.