The prevalence of self-reported anxiety, depression, and associated factors among Hanoi Medical University's students during the first wave of COVID-19 pandemic.

BACKGROUND: Medical students are known to have higher levels of these issues than the general population but in Vietnam the effects of the pandemic on medical student mental health was not documented. OBJECTIVES: To estimate the prevalence and identify factors associated with self-reported anxiety disorder, depression, and perception of worsening mental health among Vietnamese medical students during the COVID-19 pandemic. METHOD: A cross-sectional study was conducted from April 7th to 29th, 2020. All students in Doctor of General Medicine, Doctor of Preventive Medicine, and Bachelor of Nursing tracks at Hanoi Medical University (3672 students) were invited to participate. Data were collected using an online questionnaire including demographic characteristics, Generalized Anxiety Disorder 7 items, Patient Health Questionnaire 9 items, Fear of COVID-19 scale, and question about worsening mental health status. Robust Poisson regression was used to assess the association between mental health status and associated factors. RESULTS: Among 1583 students (43.1% response rate), the prevalence of students screened positive for anxiety disorder was 7.3%(95%C.I.:6.0-8.7), depression was 14.5%(95%C.I.:12.8-16.3), and perceiving worsening mental health was 6.9%(95%C.I.:5.7-8.3). In multivariable regression models, significant factors associated with self-reported anxiety disorder included being male (PR = 1.99,95%C.I.:1.35-2.92), difficulty in paying for healthcare services (PR = 2.05,95%C.I.:1.39-3.01), and high level of fear of COVID-19 (Q3:PR = 2.36,95%C.I.:1.38-4.02 and Q4:PR = 4.75,95%C.I.:2.65-8.49). Significant factors associated with self-reported depression were difficulty in paying for healthcare services (PR = 1.78,95%C.I.:1.37-2.30), and high level of fear of COVID-19 (Q3:PR = 1.41,95%C.I.:1.02-1.95 and Q4:PR = 2.23,95%C.I.:1.51-3.29). Significant factors associated with perceived worsening mental health status included having clinical experience (PR = 1.83,95%C.I.:1.17-2.88) and having atypical symptoms of COVID-19 (PR = 1.96,95%C.I.:1.31-2.94). CONCLUSION: The prevalence of self-reported depression, anxiety disorder, and worsening mental health among Vietnamese students during the first wave of COVID-19 was lower than in medical students in other countries. Further investigation is needed to confirm this finding.

Adapting a Dehazing System to Haze Conditions by Piece-Wisely Linearizing a Depth Estimator.

Haze is the most frequently encountered weather condition on the road, and it accounts for a considerable number of car crashes occurring every year. Accordingly, image dehazing has garnered strong interest in recent decades. However, although various algorithms have been developed, a robust dehazing method that can operate reliably in different haze conditions is still in great demand. Therefore, this paper presents a method to adapt a dehazing system to various haze conditions. Under this approach, the proposed method discriminates haze conditions based on the haze density estimate. The discrimination result is then leveraged to form a piece-wise linear weight to modify the depth estimator. Consequently, the proposed method can effectively handle arbitrary input images regardless of their haze condition. This paper also presents a corresponding real-time hardware implementation to facilitate the integration into existing embedded systems. Finally, a comparative assessment against benchmark designs demonstrates the efficacy of the proposed dehazing method and its hardware counterpart.

Visibility Restoration: A Systematic Review and Meta-Analysis.

Image acquisition is a complex process that is affected by a wide variety of internal and environmental factors. Hence, visibility restoration is crucial for many high-level applications in photography and computer vision. This paper provides a systematic review and meta-analysis of visibility restoration algorithms with a focus on those that are pertinent to poor weather conditions. This paper starts with an introduction to optical image formation and then provides a comprehensive description of existing algorithms as well as a comparative evaluation. Subsequently, there is a thorough discussion on current difficulties that are worthy of a scientific effort. Moreover, this paper proposes a general framework for visibility restoration in hazy weather conditions while using haze-relevant features and maximum likelihood estimates. Finally, a discussion on the findings and future developments concludes this paper.

Single Image Haze Removal from Image Enhancement Perspective for Real-Time Vision-Based Systems.

Vision-based systems operating outdoors are significantly affected by weather conditions, notably those related to atmospheric turbidity. Accordingly, haze removal algorithms, actively being researched over the last decade, have come into use as a pre-processing step. Although numerous approaches have existed previously, an efficient method coupled with fast implementation is still in great demand. This paper proposes a single image haze removal algorithm with a corresponding hardware implementation for facilitating real-time processing. Contrary to methods that invert the physical model describing the formation of hazy images, the proposed approach mainly exploits computationally efficient image processing techniques such as detail enhancement, multiple-exposure image fusion, and adaptive tone remapping. Therefore, it possesses low computational complexity while achieving good performance compared to other state-of-the-art methods. Moreover, the low computational cost also brings about a compact hardware implementation capable of handling high-quality videos at an acceptable rate, that is, greater than 25 frames per second, as verified with a Field Programmable Gate Array chip. The software source code and datasets are available online for public use.