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[1] The focus of this paper is to study the relationship between sporadic Fe (Fe s ) and Na (Na s ) layers through simultaneous and common volume Fe and Na lidar observations. A total of 37 sporadic layering events were identified from one year (195 hours) of observations at Wuhan (30.5°N, 114.4°E), China. Out of the 37 events, 23 (62%) are characterized by the simultaneous formation of Fe s and Na s layers. The most prominent feature for each of the 23 events is that the Fe s and Na s layers occurred in overlapping altitude ranges and moved following almost the same track. On occasion the Fe s and Na s layers exactly simultaneously reached their maximum peak densities at nearly the same altitude. These observational results strongly suggest that Fe s and Na s layers are formed via the same or very similar mechanisms. This conclusion contradicts the previous suggestion based on those independent observations of Fe s and Na s layers, that the Fe s and Na s layers may be formed via different mechanisms. Out of the 37 events, 14 (38%) belong to single-species sporadic layering events. It is noticed that the formation of each single-species sporadic atom layer was usually accompanied by a weak density enhancement in the other metal atom. This supports the suggestion that Fe s and Na s layers are formed via the same or very similar mechanisms. Both the Fe s and Na s layers over Wuhan showed a tendency to strengthen with decreasing occurrence altitude. This tendency is consistent with the earlier Na s layer observations at high and low latitudes. Moreover, it is noticed by comparing the currently available Fe s and Na s layer characteristics, which came from the observations at five different locations (including Wuhan) during different periods, that a lower average altitude could link to a higher average peak density and vice versa. The statistics-based link might perhaps represent a universal feature of sporadic layers. From our simultaneous Fe and Na density data we have found that the undersides of the normal Fe and Na layers follow nearly the exact movements and occur at nearly the same altitude. The normal Fe layer tended to be narrower than the corresponding Na layer nearly at all times, and this difference was generally reflected in the extent of the upper edge of the layer. The nearly persistent underside overlap strongly suggests that on the undersides of these meteoric metal layers there exist some sink mechanisms leading to the concurrent removal of different sorts of free neutral metal atoms.

As a novel computing technology closer to business ends, edge computing has become an effective solution for delay sensitive business of power Internet of Things (IoT) and promotes the application and development of the IoT technology in smart grids. However, the inherent characteristics of a single edge node with limited resources may fail to meet the delay requirements for access ubiquitous IoT businesses of massive access. Multiple edge nodes are needed to cooperate with each other to optimize workload allocation to provide lower delay services. To this end, this paper proposes a workload allocation mechanism, orienting edge computing-based power IoT, which minimizes service delay. The workload optimization allocation model is established, and the optimal workload allocation oriented on delay among multiple edge nodes is further realized on the basis of computing resource optimization within the single edge node. The balanced initialization, resource allocation, and task allocation (BRT) algorithm are proposed. Based on the balanced initialization of workload within edge nodes, the particle swarm algorithm modified by the pheromone strategy is used to solve the problem of the computing resources' allocation inside edge nodes. Finally, the task allocation among multiple edge servers is converted into a semi-definite programming problem. The simulation results show that the proposed BRT algorithm reduces the service delay by 9.1%, 16.9%, and 26.4%, and the service delay growth rate by 24.6%, 34.5%, and 38.7%, respectively, compared with the simulated annealing algorithm (SAA), LoAd Balancing (LAB), and Latency-awarE workloAd offloaDing (LEAD) algorithms. INDEX TERMS Edge computing, multiple business, power Internet of Things, service delay, workload allocation.

Schwertmannite-mediated removal of arsenic from contaminated water has attracted increasing attention. However, schwertmannite chemical synthesis behavior under different H2O2 supply rates for ferrous ions oxidation is unclear. This study investigated pH, ferrous ions oxidation efficiency, and total iron precipitation efficiency during schwertmannite synthesis by adding H2O2 into FeSO4·7H2O solution at different supply rates. Specific surface area and arsenic (III) removal capacity of schwertmannite have also been studied. Results showed that pH decreased from ~3.48 to ~1.96, ~2.06, ~2.12, ~2.14, or ~2.17 after 60 h reaction when the ferrous ions solution received the following corresponding amounts of H2O2: 1.80 mL at 2 h (treatment 1); 0.90 mL at 2 h and 14 h (treatment 2); 0.60 mL at 2, 14, and 26 h (treatment 3); 0.45 mL at 2, 14, 26, and 38 h (treatment 4), or 0.36 mL at 2, 14, 26, 38, and 50 h (treatment 5). Slow H2O2 supply significantly inhibited the total iron precipitation efficiency but improved the specific surface area or arsenic (III) removal capacity of schwertmannite. For the initial 50.0 μg/L arsenic (III)-contaminated water under pH ~7.0 and using 0.25 g/L schwertmannite as an adsorbent, the total iron precipitation efficiency, specific surface area of the harvested schwertmannite, and schwertmannite arsenic(III) removal efficiency were 29.3%, 2.06 m2/g, and 81.1%, respectively, in treatment 1. However, the above parameters correspondingly changed to 17.3%, 16.30 m2/g, and 96.5%, respectively, in treatment 5.

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