The moving k nearest neighbor query, which computes one's k nearest neighbor set and maintains it while at move, is gaining importance due to the prevalent use of smart mobile devices such as smart phones. Safe region is a popular technique in processing the moving k nearest neighbor query. It is a region where the movement of the query object does not cause the current k nearest neighbor set to change. Processing a moving k nearest neighbor query is a continuing process of checking the validity of the safe region and recomputing it if invalidated. The size of the safe region largely decides the frequency of safe region recomputation and hence query processing efficiency. Existing moving k nearest neighbor algorithms lack efficiency due to either computing small safe regions and have to recompute frequently or computing large safe regions (i.e., an order-k Voronoi cell) with a high cost.In this paper, we take a third approach. Instead of safe regions, we use a small set of safe guarding objects. We prove that, as long as the the current k nearest neighbors are closer to the query object than the safe guarding objects, the current k nearest neighbors stay valid and no recomputation is required. This way, we avoid the high cost of safe region recomputation. We also prove that, the region defined by the safe guarding objects is the largest possible safe region. This means that the recomputation frequency of our method is also minimized. We conduct extensive experiments comparing our method with the state-of-the-art method on both real and synthetic data sets. The results confirm the superiority of our method.

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BackgroundAccumulating evidence indicates that stroke risk may be increased following herpes zoster. The aim of this study is to perform a meta-analysis of current literature to systematically analyze and quantitatively estimate the short and long-term effects of herpes zoster on the risk of stroke.MethodsEmbase, PubMed and Cochrane library databases were searched for relevant studies up to March 2016. Studies were selected for analysis based on certain inclusion and exclusion criteria. Relative risks with 95% confidence interval (CI) were extracted to assess the association between herpes zoster and stroke.ResultsA total of 8 articles were included in our analysis. The present meta-analysis showed that the risks of stroke after herpes zoster were 2.36 (95% CI: 2.17–2.56) for first 2 weeks, 1.56 (95% CI: 1.46–1.66) for first month, 1.17 (95% CI: 1.13–1.22) for first year, and 1.09 (95% CI: 1.02–1.16) for more than 1 year, respectively.ConclusionThe results of our study demonstrated that herpes zoster was associated with a higher risk of stroke, but the risks decreased along with the time after herpes zoster.

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There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means and C-means. Meanwhile, Artificial Intelligence (AI) is widely used in data analysis and prediction. However, K-means and C-means cannot directly process heterogeneous data, and AI algorithms require equipment with high computing and storage capabilities. IoT equipment of underground mining cannot perform complex calculation due to the limitation of energy consumption. Therefore, many existing methods cannot be directly used for IoT applications in underground mining. In this paper, a multi-sensors data anomaly detection method based on edge computing is proposed. Firstly, an edge computing model is designed, and according to the computing capabilities of different types of devices, anomaly detection tasks are migrated to different edge devices, which solve the problem of insufficient computing capabilities of the devices. Secondly, according to the requirements of different anomaly detection tasks, edge anomaly detection algorithms for sensor nodes and sink nodes are designed respectively. Lastly, an experimental platform is built for performance comparison analysis, and the experimental results show that the proposed algorithm has better performance in anomaly detection accuracy, delay, and energy consumption.

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