The problem of water shortage needs to be solved urgently. The membrane-embedded microchannel structure based on the ion concentration polarization (ICP) desalination effect is a potential portable desalination device with low energy consumption and high efficiency. The electroosmotic flow in the microchannel of the cation exchange membrane and the desalination effect of the system are numerically analyzed. The results show that when the horizontal electric field intensity is 2 kV/m and the transmembrane voltage is 400 mV, the desalting efficiency reaches 97.3%. When the electric field strength increases to 20 kV/m, the desalination efficiency is reduced by 2%. In terms of fluid motion, under the action of the transmembrane voltage, two reverse eddy currents are formed on the surface of the membrane due to the opposite electric field and pressure difference on both sides of the membrane, forming a pumping effect. The electromotive force in the channel exhibits significant pressure-flow characteristics with a slip boundary at a speed approximately six times that of a non-membrane microchannel.

Distributed artificial intelligence (AI) is becoming an efficient approach to fulfill the high and diverse requirements for future vehicular networks. However, distributed intelligence tasks generated by vehicles often require diverse resources. A customized resource provision scheme is required to improve the utilization of multi-dimensional resources. In this work, a slice selection-based online offloading (SSOO) algorithm is proposed for distributed intelligence in future vehicular networks. First, the response time and energy consumption are reduced for processing tasks locally on the vehicles. Then, the offloading overheads, including latency and energy consumption, are calculated by considering the available resource amount, wireless channel states and vehicle conditions. The slice selection results is obtained by the deep reinforcement learning (DRL)-based method. Based on the selection solution, resource allocation results are achieved by KKT conditions and bisection method. Finally, the experimental results depict that the proposed SSOO algorithm outperforms other comparing algorithms in terms of energy consumption and task completion rate.

Thermophoresis of charged colloids in aqueous media has wide applications in biology. Most existing studies of thermophoresis focused on spherical particles, but biological compounds are usually non-spherical. The present paper reports a numerical analysis of the thermophoresis of a charged spheroidal colloid in aqueous media. The model accounts for the strongly coupled temperature field, the flow field, the electric potential field, and the ion concentration field. Numerical simulations revealed that prolate spheroids move faster than spherical particles, and oblate spheroids move slower than spherical particles. For the arbitrary electric double layer (EDL) thickness, the thermodiffusion coefficient of prolate (oblate) spheroids increases (decreases) with the increasing particle’s dimension ratio between the major and minor semiaxes. For the extremely thin EDL case, the hydrodynamic effect is significant, and the thermodiffusion coefficient for prolate (oblate) spheroids converges to a fixed value with the increasing particle’s dimension ratio. For the extremely thick EDL case, the particle curvature’s effect also becomes important, and the increasing (decreasing) rate of thermodiffusion coefficient for prolate (oblate) spheroids is reduced slightly.