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Using Unmanned Aerial Vehicle(UAV) swarm to deal with emergencies is an efficient handling mode. In order to establish a comprehensive security prevention and control system from the ground to the air, from the plane to the three-dimensional, the UAV swarm emergency response system model for emergencies is built by using the Department of Defense Architecture Framework(DoDAF), and the system structure, task activities, organizational relationships, ability indicators and other models of the system are established based on the task requirements. With AHP(Analytic Hierarchy Process)and fuzzy synthetic evaluation method, the capability requirements are analyzes combining qualitative and quantitative methods. The analysis provides support and research perspectives for the application requirements and demonstration for UAV emergency warfare.

With the continuous expansion of UAV applications, especially the large-scale use of “low, slow and small” UAV, it has brought a huge threat to national security and regional stability. Therefore, it is urgent to study the countermeasure equipment of “low, slow and small” UAV. By analyzing the characteristics and threats of UAV, combined with the typical mission scenarios of “low, slow and small” UAV, the requirements of countermeasure equipment is drawn. On the premise of in-depth study of the current situation of “low, slow and small”UAV countermeasure equipment at home and abroad, this paper analyzes and summarizes the key technologies, principles and existing problems of countermeasures, looks forward to the future development trend and research direction of countermeasure equipment and key technologies, and provides an important reference for the relevant research of “low, slow and small” UAV countermeasures.

Tensor data is common in real-world applications, such as recommendation system and air quality monitoring. But such data is often sparse, noisy, and fast produced. CANDECOMP/PARAFAC (CP) is a popular tensor decomposition model, which is both theoretically advantageous and numerically stable. However, learning the CP model in a Bayesian framework, though promising to handle data sparsity and noise, is computationally challenging, especially with fast produced data streams. The fundamental problem addressed by the paper is mainly tackles the efficient processing of streaming tensor data. In this work, we propose BS-CP, a quick and accurate structure to dynamically update the posterior of latent factors when a new observation tensor is received. We first present the BS-CP1 algorithm, which is an efficient implementation using assumed density filtering (ADF). In addition, we propose BS-CP2 algorithm, using Gauss–Laguerre quadrature method to integrate the noise effect which shows better empirical result. We tested BS-CP1 and BS-CP2 on generic real recommendation system datasets, including Beijing-15k, Beijing-20k, MovieLens-1m and Fit Record. Compared with state-of-the-art methods, BS-CP1 achieve 31.8% and 33.3% RMSE improvement in the last two datasets, with a similar trend observed for BS-CP2. This evidence proves that our algorithm has better results on large datasets and is more suitable for real-world scenarios. Compared with most other comparison methods, our approach has demonstrated an improvement of over 10% and exhibits superior stability. [ABSTRACT FROM AUTHOR]

Our aim was to investigate the prevalence of psychological disorders, sleep disturbance, and stressful life events in Chinese patients with ankylosing spondylitis (AS) and healthy controls, to assess the correlation between psychological and disease-related variables, and finally to detect powerful factors in predicting anxiety and depression. AS patients diagnosed with the modified New York criteria and healthy controls were enrolled from China. Participants completed a set of questionnaires, including demographic and disease parameters, Zung self-rating anxiety scale (SAS), Zung self-rating depression scale (SDS), the Pittsburgh Sleep Quality Index questionnaire (PSQI), and the Social Readjustment Rating Scale (SRRS). The relationship between psychological and other variables was explored. Stepwise multiple regression was used to determine the contributors to each disorder. Of all the 2772 AS patients, 79.1% were male. Mean age was 28.99 ± 8.87 years. Prevalence of anxiety, depression, and sleep disturbance was 31.6% (95% CI, 29.9, to 33.4), 59.3% (95% CI, 57.5, to 61.2), and 31.0% (95% CI, 29.3, to 36.7), respectively. 35.3% had stimulus of psychological and social elements (SPSE). Compared with healthy controls, AS patients had more severe psychological disorders, sleep disturbance, and stressful life events ( P < 0.01). SDS, overall pain, BASFI, and sleep disturbance were significant contributors of the SAS scores ( P < 0.03). SAS, less years of education, and sleep duration were significant contributors of SDS ( P < 0.01). AS patients had more anxiety, depression, stressful life events, and sleep disturbance than healthy controls. Pain, functional limitation, sleep disturbance, and education were major contributors to psychological disorders. [ABSTRACT FROM AUTHOR]