Microfluidic Investigation of the Ion-Specific Effect on Bubble Coalescence in Salt Solutions.

A microfluidic method was developed to study the ion-specific effect on bubble coalescence in salt solutions. Compared with other reported methods, microfluidics provides a more direct and accurate means of measuring bubble coalescence in salt solutions. We analyzed the coalescence time and approach velocity between bubbles and used simulation to investigate the pressure evolution during the coalescence process. The coalescence time of the three salt solutions decreased initially and then increased as the concentration of the salt solution was increased. The concentration with the shortest coalescence time is considered as the transition concentration (TC) and exhibits ion-specific. At the TC, the change in coalescence time indicates a shift in the effect of salt on bubble coalescence from facilitation to initial inhibition. Meanwhile, it can be seen that the sodium halide solutions significantly inhibit the bubble coalescence and the inhibition capability follows the order NaCl > NaBr > NaI. The results of the approach velocity show that the coalescence time decreases with increasing approach velocity, as well as the approach velocity was strongly influenced by concentration. The approach velocity undergoes a significant change at the TC. Furthermore, simulations of bubble coalescence in the microchannel indicate that the vertical pressure gradient at the center point of the bubble pairs increases as bubbles approach, driving liquid film drainage until bubble coalescence. The pressure at the center of the bubble pair reaches the maximum when the bubbles have first coalesced. It was further revealed that the concentration of the salt solution has a significant impact on the maximum pressure, as evidenced by the observed trend of decreasing pressure values with increasing concentrations.

Self-Adaptive Learning of Task Offloading in Mobile Edge Computing Systems.

Mobile edge computing (MEC) focuses on transferring computing resources close to the user's device, and it provides high-performance and low-delay services for mobile devices. It is an effective method to deal with computationally intensive and delay-sensitive tasks. Given the large number of underutilized computing resources for mobile devices in urban areas, leveraging these underutilized resources offers tremendous opportunities and value. Considering the spatiotemporal dynamics of user devices, the uncertainty of rich computing resources and the state of network channels in the MEC system, computing resource allocation in mobile devices with idle computing resources will affect the response time of task requesting. To solve these problems, this paper considers the case in which a mobile device can learn from a neighboring IoT device when offloading a computing request. On this basis, a novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system. SAda adopts a distributed working mode and has a perception function to adapt to the dynamic environment in reality; it does not require frequent access to equipment information. Extensive simulations demonstrate that SAda achieves preferable latency performance and low learning error compared to the existing upper bound algorithms.