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ObjectiveThe clinical trial unit (CTU) workforce in the UK have been delivering COVID-19 research since the inception of the pandemic. Challenges associated with COVID-19 research have impacted the global healthcare communities differently. Thus, the overall objective of the study was to determine the mental health impact among CTU staff working during the COVID-19 pandemic.DesignA mixed-methods based observational study was designed using a new workforce impact questionnaire using validated mental health assessments of Vancouver Index of Acculturation (VIA), Hospital Anxiety and Depression Scale (HADS), Insomnia Severity Index (ISI), Pandemic Stress Index (PSI), Burnout Assessment Too-12 (BAT-12), General Self Efficacy Scale (GSE) and The Everyday Discrimination Scale (EDS).SettingThe Qualtrics platform was used to deploy the questionnaire where a quantitative analysis was conducted. The qualitative part of the study used the Microsoft Teams digital application to complete the interviews.ParticipantsAll participants were CTU staff within the United Kingdom.

SHAD oilfield, located in one of the fields with the highest onshore production in the Middle East; more than 70% of its production wells are flowing wells, with an average single well production capacity of more than 3000 bbl/d. Its main oil production formation is loose sandstone reservoir. The completion style of its production wells are generally completed casing perforation, and sand production is obvious after a period of exploitation. In the process of production, sand cleaning and sand discharging are often needed in the perforated section of oil well; however, after 3-5 years of production, serious sand burial still forces developers to give up the original production formation. In addition to the problem of sand production, there are many complex layers, some of them are oil-water layer, and the increase of water in the liquid production gradually affects the well flowing. Fishable and sectional expandable sand screen (ESS) completion technology is developed to deal with sand production and the increase of water production. The upper of the original production formation of the old wells is as the new production formation. The new formation is divided into several sections due to different physical properties. Perforation is carried out at the same time for all sections. After this, each the ESS completion string is run into each section. The top sealing hanger and the bottom packer will seal each ends of the annular between ESS and perforating casing, thus to form independent production section. In later process of production, plugging tools is run for sections if needed, so as to realize selective open/ close of different sections. Special fishing tools are developed, combined with the mechanical plastic characteristics of ESS, which can realize the fishing of the completion string. This solves the problem of fishing long string, and provides good conditions for subsequent operations. The research results show that two good results are obtained. One is that arranging several sections of ESS completion strings in the old well bore gives full play to the characteristics of ESS with strong flow capacity, good sand control effect, and is suitable for sand control in high-yield wells. The other is that selective open/close is realized for different sections which meets the sand control and production needs of in SHAD oilfield. The research results have applied to 9 wells in SHAD oilfield, and the success rate is 100%. Among them, one single well is divided into three sections, and the production is stable, the sand production is near to 0, and the sand control effect is significant. The successful application of this technology fills up the gap of multi-stage ESS completion in the Middle East.

IntroductionClinical Trials Units (CTUs) are a key component of delivering non-commercial and commercial clinical research globally. Within the UK, CTUs are seen as a specialist and independent entity available to all researchers requiring support to setup, conduct and deliver clinical trials. Therefore, an involvement of a CTU is highly recommended by national regulators and positively accepted by funders, especially for drug and/or medical device and/or complex intervention trials.AimThis study aims to determine the challenges associated with the management of Covid-19 research managed via the CTU workforce, including the challenges associated with quality assurance, trial setup and data management. Additionally, this study will explore the by-stander effect on trial staff by way of evaluating the mental and physical health impact.Methods/ DesignThis is a mixed methods study. An online novel questionnaire survey study will be conducted among the UK CTU workforce. Quantitative data will be collected using the Qualtrics XM platform. We aim to recruit up to 1,500 CTU staff across the UK workforce. A subgroup sample will be randomly invited to take part in semi-structured interviews. Therefore, this survey will generate both quantitative and qualitative data inclusive of demographic data.ResultsThe findings will inform current initiatives and identify key themes for prioritising in further research to develop robust approaches to support CTU staff, including the development of a start-re-start framework for CTUs for any future pandemics relevant to developing and delivering communicable diseases and non-communicable diseases-based research.Strengths/LimitationsThe validation of the EPIC impact questionnaire used qualitative and quantitative methods which is a strength of the study. However, the study has a single timepoint to obtain data with the secondary outcome measures to be completed at two timepoints as this is an exploratory study attempting to obtain a wider data pool.

Images taken under water usually suffer from the problems of quality degradation, such as low contrast, blurring details, color deviations, non-uniform illumination, etc. As an important problem in image processing and computer vision, the restoration and enhancement of underwater image are necessary for numerous practical applications. Over the last few decades, underwater image restoration and enhancement have been attracting an increasing amount of research effort. However, a comprehensive and in-depth survey of related achievements and improvements is still missing, especially the survey of underwater image dataset which is a key issue in underwater image processing and intelligent application. In this exposition, we first summarize more than 120 studies about the latest progress in underwater image restoration and enhancement, including the techniques, datasets, available codes, and evaluation metrics. We analyze the contributions and limitations of existing methods to facilitate the comprehensive understanding of underwater image restoration and enhancement. Furthermore, we provide detailed objective evaluations and analysis of the representative methods on five types of underwater scenarios, which verifies the applicability of these methods in different underwater conditions. Finally, we discuss the potential challenges and open issues of underwater image restoration and enhancement and suggest possible research directions in the future.

This work addresses a realization of a compact ROV (Remotely Operated Vehicle) for monitoring in a dark and turbid aquaculture environment. A six degree-of-freedom ROV with four thrusters is implemented, in which a novel structure is proposed to solve the heeling problem better. In addition, a lighting system with wide and uniform light field and a biaxial imaging platform are designed based on the principle of convex lens scattering analysis. The simulation and tank experiment results illustrate the effectiveness of the designed ROV on steadily and flexibly. It achieves moving forward at a velocity of 0.17m/s at 50HZ artificial flow, and cruising at a fixed depth with up to 5 hours battery life. The designed ROV exhibits better comprehensive performance than other small ROVs for the turbid aquaculture environments.

Underwater images play an essential role in acquiring and understanding underwater information. High-quality underwater images can guarantee the reliability of underwater intelligent systems. Unfortunately, underwater images are characterized by low contrast, color casts, blurring, low light, and uneven illumination, which severely affects the perception and processing of underwater information. To improve the quality of acquired underwater images, numerous methods have been proposed, particularly with the emergence of deep learning technologies. However, the performance of underwater image enhancement methods is still unsatisfactory due to lacking sufficient training data and effective network structures. In this paper, we solve this problem based on a conditional generative adversarial network (cGAN), where the clear underwater image is achieved by a multi-scale generator. Besides, we employ a dual discriminator to grab local and global semantic information, which enforces the generated results by the multi-scale generator realistic and natural. Experiments on real-world and synthetic underwater images demonstrate that the proposed method performs favorable against the state-of-the-art underwater image enhancement methods.

Background estimation is a crucial step in underwater image dehazing. Most of the current estimation methods assume a uniform background light in the underwater environment and select the brightest pixel in the dark channel as the candidate, which fails to explain the real interactions of light rays and particles in the water medium and causes over-saturation in dehazed images. In this paper, the relationship between the maxima of dark channel and the background light in offshore underwater images is initially illustrated, and a contradiction of the assumption related to the dark channel prior used in underwater image restoration is addressed. To the best of our knowledge, this is the first work studying the statistical facts of underwater background light. Furthermore, a machine learning based background light estimation and reconstruction method is proposed based on the learning of the maximum areas of a dark channel. The subjective and objective restoration results of the state-of-the-art algorithms with and without applying the proposed background light estimation method to the offshore images are compared. The results show that the proposed method better simulated the directional distribution of the background light in a turbid water environment, and the foggy ambiguity caused by the backscattering was removed more efficiently in comparison with existing methods.

The design of the poloidal field (PF) system includes the ohmic heating field system and the equilibrium (EQ) field system, and is the basis for the design of a magnetic confinement fusion device. A coupling between the poloidal and plasma currents, especially the eddy current in the stabilizing shell, yields design difficulties. The effects of the eddy current in the stabilizing shell on the poloidal magnetic field also cannot be ignored. A new PF system design is thus proposed. By using a low-μ material (μ = 0.001, e = 1) instead of a conductive shell, an electromagnetic model is established that can provide a continuous eddy current distribution on the conductive shell. In this model, a 3D time-domain problem with shells translates into a 2D magnetostatic problem, and the accuracy of the calculation is improved. Based on these current distributions, we design the PF system and analyze how the EQ coils and conductive shell affect the plasma EQ when the plasma ramps up. To meet the mainframe design requirements and achieve an efficient power-supply design, the position and connection of the poloidal coils are optimized further.