Fear of COVID-19 and Perceived Stress: The Mediating Roles of Neuroticism and Perceived Social Support.

Background: Fear of COVID-19 leads to stress and may result in various kinds of mental health problems. Many factors are associated with an individual's perception of stress, including neuroticism and perceived social support. This study aimed to examine the role of neuroticism and perceived social support as mediators of fear of COVID-19 on perceived stress. Methods: Data from 3299 participants aged ≥18 years from the HOME-COVID-19 survey in 2020 were used for analysis. Measurements used included the Fear of COVID-19 and Impact on Quality of Life Scale, the Perceived Stress Scale-10, the Neuroticism inventory and the Multidimensional Scale of Perceived Social Support-12. A parallel mediation model within a structural equation modeling framework with 5000 bootstrapping sampling was used to test the mediating effect. Results: Fear of COVID-19 had a direct effect on perceived stress (B = 0.100, 95% CI = 0.080−0.121, p < 0.001), whereas neuroticism, but not perceived social support, partially mediated the relationship between fear of COVID-19 and perceived stress (B = 0.018, 95% CI = 0.000−0.036). Among all types of social support, only perceived support from friends was a significant mediator (B = 0.016, 95% CI = 0.006−0.025). Conclusions: Neuroticism and perceived support from friends are critical factors in the relationship between fear of COVID-19 and perceived stress.

Measurement of Water Velocity in Gas-Water Two-Phase Flow with the Combination of Electromagnetic Flowmeter and Conductance Sensor.

A method to measure the superficial velocity of the water phase in gas-water flow using an electromagnetic flowmeter (EMF) and rotating electric field conductance sensors (REFCSs) is introduced in this paper. An electromagnetic flowmeter instrument factor model is built and the correlation between electromagnetic flowmeter output and gas holdup in different flow patterns are explored through vertical upward gas-water flow dynamic experiments in a pipe with an inner diameter (ID) of 20 mm. Water superficial velocity is predicted based on pattern identification among bubble, churn, and slug flows. The experimental results show that water superficial velocity can be predicted fairly accurately for bubble, churn, and slug flows with a water cut higher than 60% (absolute average percentage deviation and absolute average deviation are 4.1057% and 0.0281 m/s, respectively). The output of the electromagnetic flowmeter is unstable and invalid in slug flows with a water cut below 60% due to the non-conducting gas slug is almost filling the pipe. Therefore, the electromagnetic flowmeter is not preferred to be used in such conditions.

A Differential Pressure Sensor Coupled with Conductance Sensors to Evaluate Pressure Drop Prediction Models of Gas-Water Two-Phase Flow in a Vertical Small Pipe.

In the process of production logging to evaluate fluid flow inside pipe, logging tools that force all flow to pass through a small measuring pipe are commonly utilized for measuring mixture density. For these logging tools, studying the fluid flow phenomenon inside the small diameter pipe and improving the prediction accuracy of pressure drop are beneficial to accurately measure mixture density. In this paper, a pressure drop prediction system is designed based on a combination of an eight-electrode rotating electric field conductance sensor (REFCS), plug-in cross-correlation conductance sensor, and differential pressure sensor. This combination overcomes the limitation of the existing pressure drop prediction model that the inlet flow velocity needs to be known. An experiment is conducted in a flow loop facility with 20 mm inner diameter small pipe. The responses of the combination sensors are collected. The REFCS is used to identify flow pattern and measure water holdup. During which five flow patterns are identified by recurrence plot method, i.e., slug flow, bubble flow, churn flow, bubble-slug transitional flow, and slug-churn transitional flow. The mixture velocity of two-phase flow is determined by the plug-in conductance sensor. The differential pressure sensor provides a differential pressure fluctuation signal. Five models of prediction of pressure drop are evaluated. The mixture friction factor of gas-water two-phase flow is obtained by a fitting method based on the measured parameters and flow pattern identification using the optimal model. Then, the pressure drop can be predicted according to the measurement results of a conductance sensor and fitting relationship. The results of pressure drop prediction show that the model proposed by Ansari et al. presents a higher accuracy compared with the other four differential pressure models with the absolute average percentage deviation (AAPD) of less than 2.632%. Moreover, the accuracy of pressure drop prediction of the Zhang et al. model is improved by using the mixture friction factor.