Epidemiological study on hand, foot and mouth disease in Tongzhou District, Beijing, 2013-2017.

OBJECTIVE: To study the epidemiological characteristics of hand, foot and mouth disease (HFMD) in Tongzhou District, Beijing between 2013 and 2017. METHODS: Data on HFMD infections from 1 January 2013 to 31 December 2017 were collected from the Notifiable Infectious Diseases Reporting Information System and analysed. Serotyping of enteroviruses from samples from patients with HFMD was undertaken using reverse transcription-polymerase chain reaction. RESULTS: A total of 15 341 patients with HFMD were reported and 32 patients (0.2%) were classified as having severe HFMD. The annual mean incidence rate of HFMD was 219.3/100 000 of the general population. The incidence and case-severity rates of HFMD generally decreased between 2013 and 2017. In the floating migrant population, the incidence and cases-severity rates of HFMD were significantly higher than in the local population. The peak incidence and severity-case rates were at 2 years of age and > 90% of patients were ≤5 years. Enterovirus A71 and Coxsackievirus A16 were the predominant pathogens in 2013-2017. CONCLUSIONS: During the 5-year period 2013-2017, the incidence rate and case-severity rate of HFMD generally decreased in Tongzhou District, Beijing. The floating migrant population and children ≤5 years of age were at the highest risk of HFMD.

Multiple chaos synchronization system for power quality classification in a power system.

This document proposes multiple chaos synchronization (CS) systems for power quality (PQ) disturbances classification in a power system. Chen-Lee based CS systems use multiple detectors to track the dynamic errors between the normal signal and the disturbance signal, including power harmonics, voltage fluctuation phenomena, and voltage interruptions. Multiple detectors are used to monitor the dynamic errors between the master system and the slave system and are used to construct the feature patterns from time-domain signals. The maximum likelihood method (MLM), as a classifier, performs a comparison of the patterns of the features in the database. The proposed method can adapt itself without the need for adjustment of parameters or iterative computation. For a sample power system, the test results showed accurate discrimination, good robustness, and faster processing time for the detection of PQ disturbances.