Vanadium-based
materials have been extensively studied as promising
cathode materials for zinc-ion batteries because of their multiple
valences and adjustable ion-diffusion channels. However, the sluggish
kinetics of Zn-ion intercalation and less stable layered structure
remain bottlenecks that limit their further development. The present
work introduces potassium ions to partially substitute ammonium ions
in ammonium vanadate, leading to a subtle shrinkage of lattice distance
and the increased oxygen vacancies. The resulting potassium ammonium
vanadate exhibits a high discharge capacity (464 mAh g–1 at 0.1 A g–1) and excellent cycling stability
(90% retention over 3000 cycles at 5 A g–1). The
excellent electrochemical properties and battery performances are
attributed to the rich oxygen vacancies. The introduction of K+ to partially replace NH4
+ appears to alleviate the irreversible deammoniation
to prevent structural collapse during ion insertion/extraction. Density
functional theory calculations show that potassium ammonium vanadate
has a modulated electron structure and a better zinc-ion diffusion
path with a lower migration barrier.
Rumors on social media have always been an important issue that seriously endangers social security. Researches on timely and effective detection of rumors have aroused lots of interest in both academia and industry. At present, most existing methods identify rumors based solely on the linguistic information without considering the temporal dynamics and propagation patterns. In this work, we aim to solve rumor detection task under the framework of representation learning. We first propose a novel way to construct the propagation graph by following the propagation structure (who replies to whom) of posts on Twitter. Then we propose a gated graph neural network based algorithm called PGNN, which can generate powerful representations for each node in the propagation graph. The proposed PGNN algorithm repeatedly updates node representations by exchanging information between the neighbor nodes via relation paths within a limited time steps. On this basis, we propose two models, namely GLO-PGNN (rumor detection model based on the global embedding with propagation graph neural network) and ENS-PGNN (rumor detection model based on the ensemble learning with propagation graph neural network). They respectively adopt different classification strategies for rumor detection task, and further improve the performance by including attention mechanism to dynamically adjust the weight of each node in the propagation graph. Experiments on a real-world Twitter dataset demonstrate that our proposed models achieve much better performance than state-of-the-art methods both on the rumor detection task and early detection task.
It is feasible and safe to use unmanned aerial vehicle (UAV) as the data collection platform of the Internet of things (IoT). In order to save the energy loss of the platform and make the UAV perform the collection work effectively, it is necessary to optimize the deployment of UAV. The objective problem is to minimize the sum of the lost energy of UAV and the loss of data transmission of Internet of things devices. The key to solving the problem is to calculate the location of the docking points and the number of docking points when the UAV is working to collect data. This paper proposes a coding scheme based on swarm intelligence optimization, which encapsulates the docking position of UAV into a dimension, so the number of docking points to be calculated is the dimension number of optimization objective. This problem is considered as a dynamic dimension optimization problem. Each individual in swarm intelligence algorithm is a solution. When adjusting the dimension, the best individual is added or deleted to achieve dynamic search in the evolutionary process. Collaborative search among multiple individuals can improve the local optimal limit of search to a certain extent. Finally, the validity of the swarm intelligence-based coding approach is verified by simulation experiments under seven different IoT device distribution scenarios. INDEX TERMS UAV deployment optimization, Internet of things data collection, Swarm intelligence, Dynamic dimension optimization.
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