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Objectives To investigate the impact of COVID-19 on the well-being of surgeons and allied health professionals as well as the support provided by their institutions.Design This cross-sectional study involved distributing an online survey through medical organisations, social media platforms and collaborators.Setting It included all staff based in an operating theatre environment around the world.Participants 1590 complete responses were received from 54 countries between 15 July and 15 December 2020. The average age of participants was 30–40 years old, 64.9% were men and 32.5% of a white ethnic background. 79.5% were surgeons with the remainder being nurses, assistants, anaesthetists, operating department practitioners or classified other.Main outcome measures Participants that had experienced any physical illness, changes in mental health, salary or time with family since the start of the pandemic as well as support available based on published recommendations.Results 32.0% reported becoming physically ill. This was more likely in those with reduced access to personal protective equipment (OR 4.62; CI 2.82 to 7.56; p

The work presented in this paper demonstrates a method to obtain an inverse function of a trained neural network by connecting it with a PID controller in a close loop. This scheme is implemented to predict a process parameter controlling the efficiency of a compact axial flow gas liquid separator (I-SEP). The data is taken from an experimental study of I-SEP (compact separator) with air-water two phase flow. It was found during these experiments that by manipulating the pressure difference between the two outlets of separator and the inlet, the performance parameter i.e. Gas Carry-Under (GCU) and Liquid Carry-Over (LCO) could be controlled but nonlinearly. It requires a tedious job to set the differential pressure between tangential and axial outlet to control the GCU. A stacked neural network model consisting of several individual neural networks having different architecture is developed. The inverse function of the combined neural network was then determined by connecting this trained neural network with a PID controller in a closed loop, which is then used to predict the pressure at the two outlets of the I-SEP for a given GCU. The optimal weight determination techniques for stacked neural network is also studied and compared in this paper. [ABSTRACT FROM AUTHOR]