Prevalence of mental health problems and associated risk factors among military healthcare workers in specialized COVID-19 hospitals in Wuhan, China: A cross-sectional survey.

INTRODUCTION: China has been severely affected by coronavirus disease 2019 (COVID-19) since December 2019. Military healthcare workers in China have experienced many pressures when combating COVID-19. This study aimed to investigate the current psychological status and associated risk factors among military healthcare workers. METHODS: We collected data from 194 military healthcare workers from three inpatient wards in two specialized COVID-19 hospitals using a web-based cross-sectional survey. The survey covered demographic information, the patient health questionnaire-9, the Generalized Anxiety Disorder-7, and the patient health questionnaire-15. Hierarchical regression analysis was used to explore potential risk factors for mental health problems. RESULTS: The overall prevalence rates of depressive, generalized anxiety, and somatic symptoms were 37.6%, 32.5%, and 50%, respectively. Rates of severe depression, generalized anxiety, and somatic symptoms were 5.2%, 3.6%, and 15.5%, respectively. In 22.7% of cases, comorbidities existed between depression, generalized anxiety, and somatization. A junior-grade professional title was associated with depression, older age was associated with generalized anxiety and somatization, and short sleep duration and poor sleep quality were associated with all three symptoms. DISCUSSION: The prevalence of depression, generalized anxiety, and somatic symptoms among military healthcare workers in specialized COVID-19 hospitals is high during the current COVID-19 outbreak. A junior-grade professional title, older age, short sleep duration, and poor sleep quality significantly affect military healthcare workers' mental health. Continuous surveillance and monitoring of the psychological consequences of the COVID-19 outbreak should be routine to promote mental health among military healthcare workers.

A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System.

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.