A Droplet Generator Using Piezoelectric Ceramics to Impact Metallic Pellets.

Metal micro-droplet ejection technology has attracted attention for its potential applications in the rapid prototyping of micro-metal parts and microelectronic packaging. The current micro-droplet ejection device developed based on this technology faces challenges such as the requirement of a micro-oxygen ejection environment, a complex feeding structure, and high costs. Therefore, a drop-on-demand droplet generator for metallic pellets with impact feed ejection is designed in this paper.

Effects of a color gradient and emoji in AR-HUD warning interfaces in autonomous vehicles on takeover performance and driver emotions.

Objective: This study examined the effects of color gradients and emojis in an augmented reality-head-up display (AR-HUD) warning interface on driver emotions and takeover performance.

Methods: A total of 48 participants were grouped into four different warning interfaces for a simulated self-driving takeover experiment. Two-way analysis of variance and the Kruskal-Wallis test was used to analyze takeover time, mood, task load, and system availability.

Microchannels Formed Using Metal Microdroplets.

The metal microdroplet deposition manufacturing technique has gained extensive attention due to its potential applications in microstructure fabrication. In order to fabricate components such as microchannel heat sinks and microchannel reactors, this paper investigates the interactions and influences between microdroplets and substrates, as well as between microdroplets themselves. The transient phenomena during the fusion of metal microdroplets in contact with the substrate and the formation of inclined columns, as well as the solid-liquid coupling and morphology formation processes during the collision between microdroplets, are analyzed.

Influence of Multi-Modal Warning Interface on Takeover Efficiency of Autonomous High-Speed Train.

As a large-scale public transport mode, the driving safety of high-speed rail has a profound impact on public health. In this study, we determined the most efficient multi-modal warning interface for automatic driving of a high-speed train and put forward suggestions for optimization and improvement. Forty-eight participants were selected, and a simulated 350 km/h high-speed train driving experiment equipped with a multi-modal warning interface was carried out.

The impact of different AR-HUD virtual warning interfaces on the takeover performance and visual characteristics of autonomous vehicles.

Objective: The objective of this study was to determine the different effects of the arrow-pointing augmented reality head-up display (AR-HUD) interface, virtual shadow AR-HUD interface, and non-AR-HUD interface on autonomous vehicle takeover efficiency and driver eye movement characteristics in different driving scenarios.

Methods: Thirty-six participants were selected to carry out a simulated driving experiment, and the eye movement index and takeover time were analyzed.

Results: The arrow pointing AR-HUD interface and the virtual shadow AR-HUD interface could effectively reduce the driver's visual distraction, improve the efficiency of obtaining visual information, reduce the number of times the driver's eyes leave the road, and improve the efficiency of the takeover compared with the non-AR-HUD interface, but there was no significant difference in eye movement indexes between the arrow pointing AR-HUD interface and the more eye-catching virtual shadow AR-HUD interface.