Emergency tracheal intubation in 202 patients with COVID-19 in Wuhan, China:lessons learnt and international expert recommendations

Tracheal intubation in coronavirus disease 2019 (COVID-19) patients creates a risk to physiologically compromised patients and to attending healthcare providers. Clinical information on airway management and expert recommendations in these patients are urgently needed. By analysing a two-centre retrospective observational case series from Wuhan, China, a panel of international airway management experts discussed the results and formulated consensus recommendations for the management of tracheal intubation in COVID-19 patients. Of 202 COVID-19 patients undergoing emergency tracheal intubation, most were males (n=136; 67.3%) and aged 65 yr or more (n=128; 63.4%). Most patients (n=152; 75.2%) were hypoxaemic (Sao2 <90%) before intubation. Personal protective equipment was worn by all intubating healthcare workers. Rapid sequence induction (RSI) or modified RSI was used with an intubation success rate of 89.1% on the first attempt and 100% overall. Hypoxaemia (Sao2 <90%) was common during intubation (n=148; 73.3%). Hypotension (arterial pressure <90/60 mm Hg) occurred in 36 (17.8%) patients during and 45 (22.3%) after intubation with cardiac arrest in four (2.0%). Pneumothorax occurred in 12 (5.9%) patients and death within 24 h in 21 (10.4%). Up to 14 days post-procedure, there was no evidence of cross infection in the anaesthesiologists who intubated the COVID-19 patients. Based on clinical information and expert recommendation, we propose detailed planning, strategy, and methods for tracheal intubation in COVID-19 patients

Energy efficiency optimization of ships based on particle swarm optimization

In response to the severe energy-saving and emission-reduction situation, it is of great practical significance to reduce the fuel consumption of ships’ main engines and carbon dioxide emissions, and to realize the energy-saving and emission reduction of ships. In this study, We use the particle swarm optimization algorithm to optimize and analyze the energy consumption of ships based on the energy consumption data of ships during navigation, and test the energy efficiency of ships through the ship energy efficiency operation index. The results of the present study show that in order to reduce the energy consumption of ships and ultimately achieve the purpose of energy saving and emission reduction, it is theoretically feasible to use particle swarm optimization algorithm to optimize the speed of ships

Analog and Photon Signal Splicing for CO₂-DIAL Based on Piecewise Nonlinear Algorithm

In the CO2 differential absorption lidar (DIAL) system, signals are simultaneously collected through analog detection (AD) and photon counting (PC). These two kinds of signals have their own characteristics. Therefore, a combination of AD and PC signals is of great importance to improve the detection capability (detection range and accuracy) of CO2-DIAL. The traditional signal splicing algorithm cannot meet the accuracy requirements of CO2 inversion due to unreasonable data fitting. In this paper, a piecewise least square splicing algorithm is developed to make signal splicing more flexible and efficient. First, the lidar signal is segmented, and according to the characteristics of each signal, the best fitting parameters are obtained by using the least square fitting with different steps. Then, all the segmented and fitted signals are integrated to realize the effective splicing of the near-field AD signal and the far-field PC signal. A weight gradient strategy is also adopted in signal splicing, and the weights of the AD and PC signals in the spliced signal change with the height. The splicing effect of the improved algorithm is evaluated by the measured signal, which are obtained in Wuhan, China, and the splice of the AD and PC signals in the range of 800–1500 m are completed. Compared with the traditional method, the evaluation parameter R2 and the residual sum of squares of the spliced signal are greatly improved. The linear relationship between the AD and PC signals is improved, and the fitting R2 of differential absorption optical depth reaches 0.909, indicating that the improved signal splicing algorithm can well splice the near-field AD signal and the far-field PC signal

Comparison of Satellite-Observed XCO2 from GOSAT, OCO-2, and Ground-Based TCCON

CO2 is one of the most important greenhouse gases. Its concentration and distribution in the atmosphere have always been important in studying the carbon cycle and the greenhouse effect. This study is the first to validate the XCO2 of satellite observations with total carbon column observing network (TCCON) data and to compare the global XCO2 distribution for the passive satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT), which are on-orbit greenhouse gas satellites. Results show that since GOSAT was launched in 2009, its mean measurement accuracy was −0.4107 ppm with an error standard deviation of 2.216 ppm since 2009, and has since decreased to −0.62 ppm with an error standard deviation of 2.3 ppm during the past two more years (2014–2016), while the mean measurement accuracy of the OCO-2 was 0.2671 ppm with an error standard deviation of 1.56 ppm from September 2014 to December 2016. GOSAT observations have recently decreased and lagged behind OCO-2 on the ability to monitor the global distribution and monthly detection of XCO2. Furthermore, the XCO2 values gathered by OCO-2 are higher by an average of 1.765 ppm than those by GOSAT. Comparison of the latitude gradient characteristics, seasonal fluctuation amplitude, and annual growth trend of the monthly mean XCO2 distribution also showed differences in values but similar line shapes between OCO-2 and GOSAT. When compared with the NOAA statistics, both satellites’ measurements reflect the growth trend of the global XCO2 at a low and smooth level, and reflect the seasonal fluctuation with an absolutely different line shape

Parallel Learning of Dynamics in Complex Systems

Dynamics always exist in complex systems. Graphs (complex networks) are a mathematical form for describing a complex system abstractly. Dynamics can be learned efficiently from the structure and dynamics state of a graph. Learning the dynamics in graphs plays an important role in predicting and controlling complex systems. Most of the methods for learning dynamics in graphs run slowly in large graphs. The complexity of the large graph’s structure and its nonlinear dynamics aggravate this problem. To overcome these difficulties, we propose a general framework with two novel methods in this paper, the Dynamics-METIS (D-METIS) and the Partitioned Graph Neural Dynamics Learner (PGNDL). The general framework combines D-METIS and PGNDL to perform tasks for large graphs. D-METIS is a new algorithm that can partition a large graph into multiple subgraphs. D-METIS innovatively considers the dynamic changes in the graph. PGNDL is a new parallel model that consists of ordinary differential equation systems and graph neural networks (GNNs). It can quickly learn the dynamics of subgraphs in parallel. In this framework, D-METIS provides PGNDL with partitioned subgraphs, and PGNDL can solve the tasks of interpolation and extrapolation prediction. We exhibit the universality and superiority of our framework on four kinds of graphs with three kinds of dynamics through an experiment

Feasibility Study of Multi-Wavelength Differential Absorption LIDAR for CO2 Monitoring

To obtain a better understanding of carbon cycle and accurate climate prediction models, highly accurate and temporal resolution observation of atmospheric CO2 is necessary. Differential absorption LIDAR (DIAL) remote sensing is a promising technology to detect atmospheric CO2. However, the traditional DIAL system is the dual-wavelength DIAL (DW-DIAL), which has strict requirements for wavelength accuracy and stability. Moreover, for on-line and off-line wavelengths, the system’s optical efficiency and the change of atmospheric parameters are assumed to be the same in the DW-DIAL system. This assumption inevitably produces measurement errors, especially under rapid aerosol changes. In this study, a multi-wavelength DIAL (MW-DIAL) is proposed to map atmospheric CO2 concentration. The MW-DIAL conducts inversion with one on-line and multiple off-line wavelengths. Multiple concentrations of CO2 are then obtained through difference processing between the single on-line and each of the off-line wavelengths. In addition, the least square method is adopted to optimize inversion results. Consequently, the inversion concentration of CO2 in the MW-DIAL system is found to be the weighted average of the multiple concentrations. Simulation analysis and laboratory experiments were conducted to evaluate the inversion precision of MW-DIAL. For comparison, traditional DW-DIAL simulations were also conducted. Simulation analysis demonstrated that, given the drifting wavelengths of the laser, the detection accuracy of CO2 when using MW-DIAL is higher than that when using DW-DIAL, especially when the drift is large. A laboratory experiment was also performed to verify the simulation analysis