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Background Stroke severely influences the quality of human life, and imposes a great burden on the society, patients and their families. Although China has vigorously promoted the prevention and treatment of stroke, the prevalence of pre-hospital delay in stroke patients is still relatively high. The stroke pre-hospital delay behavioral intention may predict the possibility of stroke pre-hospital delay to some extent, but theassociated factors have rarely been studied. Objective To investigate the stroke pre-hospital delay behavioral intention and associated factors in residents. Methods Convenience sampling was used to select 645 Guangdong residents as the participants from July to September 2021. The Demographic Information Questionnaire developed by us, Chinese version of Ten-Item Personality Inventory, Chinese version of Perceived Social Support Scale, Simplified Coping Style Questionnaire and Stroke Pre-Hospital Delay Behavior Intention scale (SPDBI) were used in a survey for understanding the participants' personality characteristics, social support, coping styles and stroke pre-hospital delay behavioral intention. Multiple linear regression was used to explore the influential factors of stroke pre-hospital delayed behavioral intention. Results A total of 645 valid questionnaires were collected (92.1%) . Of the 645 respondents, 312 (48.4%) knew nothing about stroke, and 262 (40.6%) had heard of the formula "stroke 120". The stroke pre-hospital delay behavior intention of the participants was in the intermediate level〔mean SPDBI score (71.3±18.7) 〕. Compared with the Chinese norm, these respondents had higher mean dimension score of warning of stroke symptoms, but lower mean total score of SPDBI, and lower mean scores of four dimensions (rationalization of non-healthcare seeking behavior, symptom attribution, habitual response style, emergency system use) (P

The data acquisition of Internet of Things (IoT) is mostly brought out by wireless sensor networks (WSNs), and the efficiency of IoT is directly affected by the time delay in the process of data acquisition, which is researched mainly by the data aggregation of WSNs. Minimizing the delay in the data aggregation process is one of the most important operations. In order to reduce the data aggregation delay, a hybrid delay-minimization data aggregation scheduling (HDDAS) algorithm is proposed, which optimizes the delay from the two main aspects that affect the delay, namely, the transmission path and the collision rate. Accordingly, the algorithm is divided into two phases: the aggregation tree construction and the data transmission scheduling. In the aggregation tree construction phase, the fat tree (FT) is introduced to generate an optimal aggregation tree with the shortest path; in the data transmission scheduling phase, the maximum parallel transmission set (MPTS) is adopted to increase the amount of the collision-free data transmission. The simulation experiment results demonstrate that the HDDAS algorithm proposed in the paper has performed favorably in terms of the data aggregation delay. [ABSTRACT FROM AUTHOR]

Nowadays, location based service (LBS) is one of the most popular mobile apps and following with humongous of location data been produced. The publishing of location data can provide benefit for promoting the quality of service, optimizing the commercial environment as well as harmonizing the infrastructure construction. However, as location data may contain some sensitive or confidential information, the publishing may reveal privacy and bring hazards. So the published data had to be disposed to protect the privacy. In order to cope with this problem, a number of algorithms based on the strategy of k-anonymity were proposed, but this is not enough for the privacy protection, as the correlation between the sensitive region and the background knowledge can be used to infer the real location. Thus, consider about this condition, in this paper a ε-sensitive correlation privacy protection scheme is proposed, and provides correlation indistinguishable to the location data. In this scheme, entropy is first used to determine the location centroid of each cell to build up the voronoi diagram. Then the coordinate of the untreated location data that is located in the cell is transferred into the centroid vicinity. Accordingly, the sensitive correlation is destroyed by the coordinate of each published data. The process of transferring the location data is determined by metrics of ε-sensitive correlation privacy, and is rigorous in mathematical justification. At last, security analysis is proposed in this paper to verify the privacy ability of our proposed algorithm based on voronoi diagram and entropy, and then we utilize the comparative experiment to further affirm the advantage of this algorithm in the location data privacy protection as well as the availability of published data.

Deep neural networks have made great achievements in remote sensing image analyses; however, previous studies have shown that deep neural networks exhibit incredible vulnerability to adversarial examples, which raises concerns about regional safety and production safety. In this paper, we propose an adversarial denoising method based on latent representation guidance for remote sensing image scene classification. In the training phase, we train a variational autoencoder to reconstruct the data using only the clean dataset. At test time, we first calculate the normalized mutual information between the reconstructed image using the variational autoencoder and the reference image as denoised by a discrete cosine transform. The reconstructed image is selectively utilized according to the result of the image quality assessment. Then, the latent representation of the current image is iteratively updated according to the reconstruction loss so as to gradually eliminate the influence of adversarial noise. Because the training of the denoiser only involves clean data, the proposed method is more robust against unknown adversarial noise. Experimental results on the scene classification dataset show the effectiveness of the proposed method. Furthermore, the method achieves better robust accuracy compared with state-of-the-art adversarial defense methods in image classification tasks. [ABSTRACT FROM AUTHOR]