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(1) Background: This study aims to assess the magnitude of, and factors associated with, depression and anxiety among Vietnamese frontline hospital healthcare workers in the fourth wave of COVID-19; (2) Methods: A hospital based cross-sectional study was carried out within two weeks, October 2020, at a central COVID-19 treatment hospital. Depression and anxiety were measured with PHQ-9 and GAD-7, respectively. Bivariate and multivariate logistic regression analysis were applied to recognize variables related to depression and anxiety, respectively; (3) Results: Among 208 frontline hospital healthcare workers, overall prevalence of depressive symptoms, anxiety symptoms, and both symptoms of depression and anxiety was 38.94%, 25.48% and 24.04%, respectively, in healthcare workers. In a reduced model after using multivariate stepwise logistic regression, age (OR = 0.9, p = 0.001), marital status (OR = 7.84, p = 0.027), profession (OR = 0.39, p = 0.028), having experienced traumatic stress following a work event (OR = 46.24, p < 0.001), feeling at very high risk for COVID-19 (OR = 0.02, p < 0.04), and affected by workplace conditions (OR = 5.36, p < 0.001) were associated with the symptoms of depression. With regard to symptoms of anxiety, single status (OR: 12.18, p = 0.002), being medical technician (OR: 68.89, p < 0.001), alcohol use (OR: 6.83, p = 0.014), using pain relief medications (OR: 25.50, p = 0.047), having experienced traumatic stress following a family event (OR: 130.32, p = 0.001), having experienced traumatic stress following a work event (OR: 181.55, p = 0.002), reporting at very high risk for COVID-19 (OR: 29.64, p = 0.011), treating moderate (OR: 6.46, p = 0.038) and severe (OR: 18.96, p = 0.004) COVID-19 patients, and being significantly affected by the community (OR: 6.33, p = 0.003) were increased risk factors for the symptoms of anxiety. Meanwhile, those living with 4–5 people (OR: 0.15, p = 0.011), specializing in infectious disease (OR: 0.13, p = 0.044)/resuscitation and emergency medicine (OR: 0.04, p = 0.046), and having knowledge preparation before participating in COVID-19 (OR: 0.008, p = 0.014) were less associated with the symptoms of anxiety; (4) Conclusions: There was a relatively high prevalence among Vietnamese hospital healthcare workers exhibiting symptoms of depression and anxiety during the ongoing pandemic. Greater attention to training in psychological skills should be suggested for those belonging to a younger age group, being single/widowed/divorced, treating moderate and severe COVID-19 patients, feeling at very high risk for COVID-19, being significantly affected a lot the community or workplace conditions, or experiencing traumatic stress following a family/work event in the past week.

It is vital to have high sensitivity in gas sensors to allow the exact detection of dangerous gases in the air and at room temperature. In this study, we used 2D MXenes and MoS

Deep convolutional neural networks revolutionized the area of automated objects detection from images. Can the same be achieved in the domain of time series forecasting? Can one build a universal deep network that once trained on the past would be able to deliver accurate predictions reaching deep into the future for any even most diverse time series? This work is a first step in an attempt to address such a challenge in the context of a FEDCSIS’2020 Competition dedicated to network device workload prediction based on their historical time series data. We have developed and pre-trained a universal 3-layer bi-directional Long-Short-Term-Memory (LSTM) regression network that reported the most accurate hourly predictions of the weekly workload time series from the thousands of different network devices with diverse shape and seasonality profiles. We will also show how intuitive human-led post-processing of the raw LSTM predictions could easily destroy the generalization abilities of such prediction model.

It is vital to have high sensitivity in gas sensors to allow the exact detection of dangerous gases in the air and at room temperature. In this study, we used 2D MXenes and MoS2 materials to create a Ti3C2–MoS2 composite with high metallic conductivity and a wholly functionalized surface for a significant signal. At room temperature, the Ti3C2–MoS2 composite demonstrated clear signals, cyclic response curves to NO2 gas, and gas concentration-dependent. The sensitivities of the standard Ti3C2–MoS2 (TM_2) composite (20 wt% MoS2) rose dramatically to 35.8%, 63.4%, and 72.5% when increasing NO2 concentrations to 10 ppm, 50 ppm, and 100 ppm, respectively. In addition, the composite showed reaction signals to additional hazardous gases, such as ammonia and methane. Our findings suggest that highly functionalized metallic sensing channels could be used to construct multigas-detecting sensors that are very sensitive in air and at room temperature.