With the emerging vehicular applications such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in Vehicular Fog Computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a non-deterministic polynomial-time hard (NP-hard) problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a trade-off is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation (DTA) framework using Linear Programming based Optimization (LBO) and Binary Particle Swarm Optimization (BPSO). To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%.

The rapid development of 3D printing (or additive manufacturing) technologies demands new materials with novel properties and functionalities. Superhydrophobic materials, owing to their ultralow water adhesion, self‐cleaning, anti‐biofouling, or superoleophilic properties are useful for myriad applications involving liquids. However, the majority of the methods for making superhydrophobic surfaces have been based on surface functionalization and coatings, which are challenging to apply to 3D objects. Additionally, these coatings are vulnerable to abrasion due to low mechanical stability and limited thickness. Here, a new materials concept and methodology for 3D printing of superhydrophobic macroscopic objects with bulk nanostructure and almost unlimited geometrical freedom is presented. The method is based on a specific ink composed of hydrophobic (meth)acrylate monomers and porogen solvents, which undergoes phase separation upon photopolymerization to generate inherently nanoporous and superhydrophobic structures. Using a desktop Digital Light Processing printer, superhydrophobic 3D objects with complex shapes are demonstrated, with ultralow and uniform water adhesion measured with scanning droplet adhesion microscopy. It is shown that the 3D‐printed objects, owing to their nanoporous structure throughout the entire volume, preserve their superhydrophobicity upon wear damage. Finally, a superhydrophobic 3D‐printed gas‐permeable and water‐repellent microfluidic device and a hierarchically structured 3D‐printed super‐oil‐absorbent are demonstrated.

Droplets slip and bounce on superhydrophobic surfaces, enabling remarkable functions in biology and technology. These surfaces often contain microscopic irregularities in surface texture and chemical composition, which may affect or even govern macroscopic wetting phenomena. However, effective ways to quantify and map microscopic variations of wettability are still missing, because existing contact angle and force-based methods lack sensitivity and spatial resolution. Here, we introduce wetting maps that visualize local variations in wetting through droplet adhesion forces, which correlate with wettability. We develop scanning droplet adhesion microscopy, a technique to obtain wetting maps with spatial resolution down to 10 µm and three orders of magnitude better force sensitivity than current tensiometers. The microscope allows characterization of challenging non-flat surfaces, like the butterfly wing, previously difficult to characterize by contact angle method due to obscured view. Furthermore, the technique reveals wetting heterogeneity of micropillared model surfaces previously assumed to be uniform.

The origin of the idea of moving objects by acoustic vibration can be traced back to 1787, when Ernst Chladni reported the first detailed studies on the aggregation of sand onto nodal lines of a vibrating plate. Since then and to this date, the prevailing view has been that the particle motion out of nodal lines is random, implying uncontrollability. But how random really is the out-of-nodal-lines motion on a Chladni plate? Here we show that the motion is sufficiently regular to be statistically modelled, predicted and controlled. By playing carefully selected musical notes, we can control the position of multiple objects simultaneously and independently using a single acoustic actuator. Our method allows independent trajectory following, pattern transformation and sorting of multiple miniature objects in a wide range of materials, including electronic components, water droplets loaded on solid carriers, plant seeds, candy balls and metal parts.