Detection for Dangerous Goods Vehicles in Expressway Service Station Based on Surveillance Videos

To improve the safety capabilities of expressway service stations, this study proposes a method for detecting dangerous goods vehicles based on surveillance videos. The information collection devices used in this method are the surveillance cameras that already exist in service stations, which allows for the automatic detection and position recognition of dangerous goods vehicles without changing the installation of the monitoring equipment. The process of this method is as follows. First, we draw an aerial view image of the service station to use as the background model. Then, we use inverse perspective mapping to process each surveillance video and stitch these videos with the background model to build an aerial view surveillance model of the service station. Next, we use a convolutional neural network to detect dangerous goods vehicles from the original images. Finally, we mark the detection result in the aerial view surveillance model and then use that model to monitor the service station in real time. Experiments show that our aerial view surveillance model can achieve the real-time detection of dangerous goods vehicles in the main areas of the service station, thereby effectively reducing the workload of the monitoring personnel.

A COVID-19 Rehabilitation Prospective Surveillance Model for Use by Physiotherapists

The long-term sequelae of coronavirus disease 2019 (COVID-19) are only now beginning to be defined, but it is already known that the disease can have direct and indirect impacts mainly on the cardiorespiratory and neuromuscular systems and may affect mental health. A role for rehabilitation professionals from all disciplines in addressing COVID-19 sequelae is recognised, but it is essential that patient assessment be systematic if health complications are to be identified and treated and, if possible, prevented. The aim is to present a COVID-19 prospective surveillance model based on sensitive and easily used assessment tools, which is urgently required. Following the Oxford Centre for Evidence-Based Medicine Level of Evidence Tool, an expert team in cardiorespiratory, neuromuscular and mental health worked via telemeetings to establish a model that provides guidelines to rehabilitation professionals working with patients who require rehabilitation after suffering from COVID-19. A COVID-19 prospective surveillance model is proposed for use by rehabilitation professionals and includes both face-to-face and telematic monitoring components. This model should facilitate the early identification and management of long-term COVID-19 sequelae, thus responding to an arising need.

Well-Flux Surveillance and Ramp-Up Method for Openhole Standalone Screen Completion

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 201662, “A Well-Flux Surveillance and Ramp-Up Method for Openhole Standalone Screen Completion,” by Mehmet Karaaslan and George K. Wong, University of Houston, and Kevin L. Soter, SPE, Shell, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5-7 October. The paper has not been peer reviewed. Production and surveillance engineers need practical models to help maximize production while avoiding ramping up the well to an extent that the completion is damaged, causing well impairment or failure. The complete paper presents a well-flux surveillance method to monitor and ramp up production for openhole standalone screen (OH-SAS) completions that optimizes production by considering risks of production impairment and screen-erosion failure. Challenges of Increased Production vs. Well Failure The problem of increased production vs. the risk of well impairment or failure is a pressing problem for sand-control wells in deepwater, where projects tend to have a small number of high-rate wells. In such environments, any well impairments or failures greatly affect the project economics. Following unloading, well surveillance faces the critical step of ramping up to-ward the well’s designed peak rate for the first time when the actual well performance is uncertain. To reduce risk of well impairment or failure, surveillance information and models are needed to help make adjustments during the ramp-up process. Different models are available, from simple to complex and from small to large amounts of input data and computational efforts. Simple nonsurveillance models use field-derived operating limits of completion pressure drop and flow velocity or flux. They are non-surveillance models in the sense that no direct linkage of surveillance results to update flux calculations exists. Simple surveillance models use pressure transient analysis (PTA) results and completion information to evaluate changing well performance and adjust the ramp-up and long-term surveillance operations. The complex surveillance model evaluates well performance and adjusts well operations using probabilistic completion failure risks and coupled reservoir and completion simulations. These models mainly focus on cased-hole gravel pack and frac-pack applications. For openhole completions with sand control, the literature offers limited ramp-up surveillance references. The objective of the well-flux model described in the complete paper is to ramp up the well safely and optimize production using PTA results as surveillance inputs to calculate completion fluxes for well impairment or failure assessment. The method follows an approach presented in the literature.

COVID-19 in pregnancy: creating an outpatient surveillance model in a public hospital system

ObjectivesWe describe a standardized, scalable outpatient surveillance model for pregnant women with COVID-19 with several objectives: (1) to identify and track known, presumed, and suspected COVID-positive pregnant patients both during their acute illness and after recovery, (2) to regularly assess patient symptoms and escalate care for those with worsening disease while reducing unnecessary hospital exposure for others, (3) to educate affected patients on warning symptoms, hygiene, and quarantine recommendations, and (4) to cohort patient care, isolating stable infected patients at home and later within the same physical clinic area upon their return to prenatal care.MethodsPregnant women in an urban public hospital system with presumed or confirmed COVID-19 were added to a list in our electronic medical record as they came to the attention of providers. They received a series of phone calls based on their illness severity and were periodically assessed until deemed stable.ResultsA total of 83 patients were followed between March 19 and May 31, 2020. Seven (8%) were asymptomatic, 62 (75%) had mild disease, 11 (13%) had severe disease, and three (4%) had critical illness.ConclusionsWe encourage others to develop and utilize outpatient surveillance systems to facilitate appropriate care and to optimize maternal and fetal well-being.

New Means of Workplace Surveillance Model: From the Gaze of the Supervisor to the Digitalization of Employee Performance

Based on a data collection using the puzzle method. This method allowed us, first, to collect new types of data on Tunisian workers, in order to better quantify and analyse their activities. As a result, this study shows the consequences of the excessive use of employee behaviour monitoring tools and control devices established in Tunisian companies after the events of 14 January 2011. Indeed, it has been found that the system of control and monitoring of employee behaviors can feed the sources of stress and burn-out. Finally, some recommendations were proposed to address these issues.