BackgroundPrimary care medical staffs’ knowledge, attitude and behavior about health emergency and the response capacity are directly related to the control and prevention of public health emergencies. Therefore, it is of great significance for improving primary care to gain in-depth knowledge about knowledge, attitude and behavior and the response capacity of primary care medical staffs. The main objective of this study is to explore knowledge, attitude and behavior, and the response capacity of primary care medical staffs of Guangdong Province, China.MethodsStratified clustered sample method was used in the anonymous questionnaire investigation about knowledge, attitude and behavior, and the response capacity of 3410 primary care medical staffs in 15 cities of Guangdong Province, China from July, 2010 to October 2010. The emergency response capacity was evaluated by 33 questions. The highest score of the response capacity was 100 points (full score), score of 70 was a standard.Results62.4% primary care medical staffs believed that public health emergencies would happen. Influenza (3.86 ± 0.88), food poisoning (3.35 ± 0.75), and environmental pollution events (3.23 ± 0.80) (the total score was 5) were considered most likely to occur. Among the 7 public health emergency skills, the highest self-assessment score is “public health emergency prevention skills” (2.90 ± 0.68), the lowest is “public health emergency risk management (the total score was 5)” (1.81 ± 0.40). Attitude evaluation showed 66.1% of the medical staffs believed that the community awareness of risk management were ordinary. Evaluation of response capacity of health emergency showed that the score of primary care medical staffs was 67.23 ± 10.61, and the response capacity of senior physicians, public health physicians and physicians with relatively long-term practice were significantly better (P <0.05). Multiple linear stepwise regression analysis showed gender, title, position, type of work, work experience and whether to participate relative training were the main factors affecting the health emergency response capacity.ConclusionsThe knowledge, attitude and behavior about public health emergencies and the response capacity of primary care medical staffs of Guangdong Province (China) were poor. Health administrative departments should strengthen the training of health emergency knowledge and skills of the primary care medical staffs to enhance their health emergency response capabilities.
Pedestrians in videos are usually in a moving state, resulting in serious spatial misalignment like scale variations and pose changes, which makes the video-based person re-identification problem more challenging. To address the above issue, in this paper, we propose a Frame-Guided Region-Aligned model (FGRA) for discriminative representation learning in two steps in an end-to-end manner. Firstly, based on a frame-guided feature learning strategy and a non-parametric alignment module, a novel alignment mechanism is proposed to extract well-aligned region features. Secondly, in order to form a sequence representation, an effective feature aggregation strategy that utilizes temporal alignment score and spatial attention is adopted to fuse region features in the temporal and spatial dimensions, respectively. Experiments are conducted on benchmark datasets to demonstrate the effectiveness of the proposed method to solve the misalignment problem and the superiority of the proposed method to the existing video-based person re-identification methods.
In order to provide an accurate estimation of energy consumption, this work proposes a novel energy consumption modeling and prediction approach for a milling process from a multistage perspective. Based on its work stages, each stage’s energy consumption model is established by sliding filter, multiple linear regression, and improved gene expression programming (variable neighborhood search–based gene expression programming) methods and then the total energy consumption is predicted through their combination. A case study is given to illustrate the proposed model and its effectiveness. Compared with the full quadratic model, which can fully consider the interaction between cutting factors, the proposed method can achieve the higher accuracy to predict the energy consumption of the milling process.
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