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Adults aged 65 years and older are the fastest growing age group worldwide. Future autonomous vehicles may help to support the mobility of older individuals; however, these cars will not be widely available for several decades and current semi-autonomous vehicles often require manual takeover in unusual driving conditions. In these situations, the vehicle issues a takeover request in any uni-, bi- or trimodal combination of visual, auditory, or tactile alerts to signify the need for manual intervention. However, to date, it is not clear whether age-related differences exist in the perceived ease of detecting these alerts. Also, the extent to which engagement in non-driving-related tasks affects this perception in younger and older drivers is not known. Therefore, the goal of this study was to examine the effects of age on the ease of perceiving takeover requests in different sensory channels and on attention allocation during conditional driving automation. Twenty-four younger and 24 older adults drove a simulated SAE Level 3 vehicle under three conditions: baseline, while performing a non-driving-related task, and while engaged in a driving-related task, and were asked to rate the ease of detecting uni-, bi- or trimodal combinations of visual, auditory, or tactile signals. Both age groups found the trimodal alert to be the easiest to detect. Also, older adults focused more on the road than the secondary task compared to younger drivers. Findings may inform the development of next-generation of autonomous vehicle systems to be safe for a wide range of age groups. 

The availability of high quality surface observations of precipitation and volume observations by polarimetric operational radars make it possible to constrain, evaluate, and validate numerical models with a wide variety of microphysical schemes. In this article, a novel particle-based Monte-Carlo microphysical model (called McSnow) is used to simulate the outer rain bands of Hurricane Dorian which traversed the densely instrumented precipitation research facility operated by NASA at Wallops Island, Virginia. The rain bands showed steady stratiform vertical profiles with radar signature of dendritic growth layers near −15 °C and peak reflectivity in the bright band of 55 dBZ along with polarimetric signatures of wet snow with sizes inferred to exceed 15 mm. A 2D-video disdrometer measured frequent occurrences of large drops >5 mm and combined with an optical array probe the drop size distribution was well-documented in spite of uncertainty for drops <0.5 mm due to high wind gusts and turbulence. The 1D McSnow control run and four numerical experiments were conducted and compared with observations. One of the main findings is that even at the moderate rain rate of 10 mm/h collisional breakup is essential for the shape of the drop size distribution 

In order to peak emissions before 2030 and to achieve the net‐zero ambition around 2060, China urgently needs to accelerate low‐carbon transition, especially in the power system. Previous studies were mainly focused on deterministic optimization, with some of them being followed by sensitivity analyses. To tackle the gaps and to support the net‐zero ambition, this study develops a multi‐region power system risk management (MPRM) model to analyze composite effects of renewable energy development and inter‐regional electricity transmission under uncertainties, and their combinations to achieve carbon neutrality by 2060. In detail, MPRM can (a) reveal the downward trend in costs of renewable energy and the increasing in inter‐regional electricity transmission; (b) tackle the uncertainties expressed as intervals; (c) support the low‐carbon transition of the power system. Under the renewable‐dominated power structure, 90% of China's electricity demands can be derived from non‐fossil sources by 2060. Inter‐regional electricity transmission will continue to expand due to the dramatic decreases in the costs of renewables and fast‐growing demands for electricity. Northwest and east regions will be the main exporter and importer of renewable electricity. Carbon emissions from power system will peak in 2030 (about 6.21% above the 2020 level) and be eliminated by 96% (of 2030 levels) by 2060. These results can provide support for expansion of renewable capacities, acceleration of low‐carbon transition in power structure, elimination of barriers in electricity trading across regions, and exploration of the trade‐off between system costs and risk.