BACKGROUND Unmanned aerial vehicles (UAVs) are a recently developed aerial spraying technology. However, the effect of spray volume variation on deposition and pesticide control efficacy is unknown. The effect of three UAV spray volumes (9.0, 16.8 and 28.1 L ha−1) using three different nozzle sizes on droplet deposition and wheat aphid and powdery mildew control efficacy was assessed. An electric air‐pressure knapsack (EAP) sprayer was used as a comparison. RESULTS Different spray volumes significantly influenced the deposition and control efficacy of the UAV and EAP. For the UAV, a low spray volume of 9.0 L ha−1 with a fine nozzle (nozzle LU120‐01) resulted in lower deposition and control efficacy. Optimal control efficacy was achieved with coarser nozzles (nozzles LU120‐02, ‐03) at > 16.8 L ha−1 volume with systemic insecticide, and at 28.1 L ha−1 with contact insecticide and fungicide. For EAP, a high spray volume led to run‐off, and a spray volume of 225 L ha−1 achieved better deposition and control efficacy. CONCLUSION The UAV had comparable deposition and efficacy control to the EAP at a higher spray volume (> 16.8 L ha−1) with coarse nozzles, but exhibited inferior deposition and efficacy control at a lower spray volume (<9.0 L ha−1) with fine nozzles. © 2019 Society of Chemical Industry

This paper examines the workload of Facebook's photoserving stack and the effectiveness of the many layers of caching it employs. Facebook's image-management infrastructure is complex and geographically distributed. It includes browser caches on end-user systems, Edge Caches at~20 PoPs, an Origin Cache, and for some kinds of images, additional caching via Akamai. The underlying image storage layer is widely distributed, and includes multiple data centers.We instrumented every Facebook-controlled layer of the stack and sampled the resulting event stream to obtain traces covering over 77 million requests for more than 1 million unique photos. This permits us to study traffic patterns, cache access patterns, geolocation of clients and servers, and to explore correlation between properties of the content and accesses. Our results (1) quantify the overall traffic percentages served by different layers: 65.5% browser cache, 20.0% Edge Cache, 4.6% Origin Cache, and 9.9% Backend storage, (2) reveal that a significant portion of photo requests are routed to remote PoPs and data centers as a consequence both of load-balancing and peering policy, (3) demonstrate the potential performance benefits of coordinating Edge Caches and adopting S4LRU eviction algorithms at both Edge and Origin layers, and (4) show that the popularity of photos is highly dependent on content age and conditionally dependent on the social-networking metrics we considered.

Modern Web services rely extensively upon a tier of in-memory caches to reduce request latencies and alleviate load on backend servers. Within a given cache, items are typically partitioned across cache servers via consistent hashing, with the goal of balancing the number of items maintained by each cache server. Effects of consistent hashing vary by associated hashing function and partitioning ratio. Most real-world workloads are also skewed, with some items significantly more popular than others. Inefficiency in addressing both issues can create an imbalance in cache-server loads.We analyze the degree of observed load imbalance, focusing on read-only traffic against Facebook's graph cache tier in Tao. We investigate the principal causes of load imbalance, including data co-location, non-ideal hashing scenarios, and hot-spot temporal effects. We also employ trace-drive analytics to study the benefits and limitations of current loadbalancing methods, suggesting areas for future research.

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Existing work in energy demand side management focuses on the interaction between the utility grid and consumers. However, the previous technique is not focused on energy trading in local community of a renewable energy generation, distributed demand side management and not suitable for real-time environment. This paper presents a distributed demand side management system among multiple homes in community microgrid, with the integration of the internet of things smart meter and in the presence of renewable energy sources. The proposed energy consumption game is formulated for minimizing the cost of electricity in the individual home and the total cost of energy consumption in the whole community. The smart home users are playing game by optimizing their own daily energy consumption of appliances. The multiple participants include the self renewable generation of users, shared community microgrid and optional utility company. Each participant applies its best strategy to minimize energy consumption cost and users can maintain their own privacy of energy consumption. Moreover, the proposed scheme is distributed on blockchain, which provides a trusted communication medium between the participants. It enforces the autonomous monitoring of smart appliances and the billing of electricity consumption via smart contracts. Solidity smart contract is deployed to facilitate the execution of transactions without the involvement of third party in the smart community. Comparison of the results show that the proposed approach minimizes the total cost of energy consumption as well as each user's energy consumption cost. INDEX TERMS Distributed demand side management, community microgrid, appliances scheduling, smart home, Internet of Things, blockchain, smart contracts.

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As a new low volume application technology, unmanned aerial vehicle (UAV) application is developing quickly in China. The aim of this study was to compare the droplet deposition, control efficacy and working efficiency of a six-rotor UAV with a self-propelled boom sprayer and two conventional knapsack sprayers on the wheat crop. The total deposition of UAV and other sprayers were not statistically significant, but significantly lower for run-off. The deposition uniformity and droplets penetrability of the UAV were poor. The deposition variation coefficient of the UAV was 87.2%, which was higher than the boom sprayer of 31.2%. The deposition on the third top leaf was only 50.0% compared to the boom sprayer. The area of coverage of the UAV was 2.2% under the spray volume of 10 L/ha. The control efficacy on wheat aphids of UAV was 70.9%, which was comparable to other sprayers. The working efficiency of UAV was 4.11 ha/h, which was roughly 1.7–20.0 times higher than the three other sprayers. Comparable control efficacy results suggest that UAV application could be a viable strategy to control pests with higher efficiency. Further improvement on deposition uniformity and penetrability are needed.

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