•Fever and cough are the most common symptoms in patients with COVID-19.•The most prevalent comorbidities are hypertension and diabetes which are associated with the severity of COVID-19.•ARDS and ...ACI may be the main obstacles to treatment recovery for patients.•The case severe rate and mortality is lower than that of SARS and MERS.
Since being first reported in Wuhan, China, in December 8, 2019, the outbreak of the novel coronavirus, now known as COVID-19, has spread globally. Some case studies regarding the characteristics and outcome of patients with COVID-19 have been published recently. We conducted a meta-analysis to evaluate the risk factors of COVID-19.
Medline, SinoMed, EMBASE, and Cochrane Library were searched for clinical and epidemiological studies on confirmed cases of COVID-19.
The incidence of fever, cough, fatigue, and dyspnea symptoms were 85.6 % (95CI 81.3–89.9 %), 65.7 % (95CI 60.1–71.4 %), 42.4 % (95CI 32.2–52.6 %) and 21.4 % (95CI 15.3–27.5 %). The prevalence of diabetes was 7.7 % (95CI 6.1–9.3 %), hypertension was 15.6 % (95CI 12.6–18.6 %), cardiovascular disease was 4.7 % (95CI 3.1–6.2 %), and malignancy was 1.2 % (95CI 0.5–1.8 %). The complications, including ARDS risk, ranged from 5.6–13.2 %, with the pooled estimate of ARDS risk at 9.4 %, ACI at 5.8 % (95CI 0.7–10.8 %), AKI at 2.1 % (95CI 0.6–3.7 %), and shock at 4.7 % (95CI 0.9–8.6 %). The risks of severity and mortality ranged from 12.6 to 23.5% and from 2.0 to 4.4 %, with pooled estimates at 18.0 and 3.2 %, respectively. The percentage of critical cases in diabetes and hypertension was 44.5 % (95CI 27.0–61.9 %) and 41.7 % (95CI 26.4–56.9 %), respectively.
Fever is the most common symptom in patients with COVID-19. The most prevalent comorbidities are hypertension and diabetes which are associated with the severity of COVID-19. ARDS and ACI may be the main obstacles for patients to treatment recovery. The case severe rate and mortality is lower than that of SARS and MERS.
Objective
This study aims to explore the indicators for severity of coronavirus disease 2019 (COVID‐19) in young patients between the ages of 18 and 40 years.
Methods
This retrospective cohort study ...included 65 consecutively admitted patients with COVID‐19 who were between 18 and 40 years old in Zhongnan Hospital of Wuhan University in Wuhan, China. Among them, 53 were moderate cases, and 12 were severe or critical cases. Epidemiological, clinical, and laboratory characteristics and treatment data were collected. A multivariate logistic regression analysis was implemented to explore risk factors.
Results
The patients with severe/critical cases had obviously higher BMI (average 29.23 vs. 22.79 kg/m2) and lower liver computed tomography value (average 50.00 vs. 65.00 mU) than the group of moderate cases. The patients with severe/critical cases had higher fasting glucose, alanine aminotransferase, aspartate aminotransferase, and creatinine compared with patients with moderate cases (all P < 0.01). More severe/critical cases (58.33% vs. 1.92%) had positive urine protein levels. The severe/critical cases also experienced a significant process of serum albumin decline. Logistic regression analysis showed that male sex, high BMI (especially obesity), elevated fasting blood glucose, and urinary protein positivity were all risk factors for young patients with severe COVID‐19.
Conclusions
Obesity is an important predictor of COVID‐19 severity in young patients. The main mechanism is related to damage of the liver and kidney.
This article investigates the attitude stabilization control problem with low-frequency communication to actuators in the framework of sampled-data control. A novel event-triggered sampling control ...policy is proposed by employing an integral-type triggering function where the determination of all sampling times is by judging the properties of this function including the integration of measurement errors. Using the Lyapunov-based approach, we show that the stability of the closed-loop system can be guaranteed in the presence of external disturbance, inertia uncertainty, and actuator fault. Compared with conventional attitude control policies, the proposed algorithm significantly reduces the data-rate requirement in updating the actuator while providing high reliability and accurate performance for attitude stabilization. Compared with the traditional event-triggered sampling, the proposed policy is no longer by judging the instantaneous state of measurement errors, which reduces the sampling frequency and does not increase the computational burden. Numerical simulations are conducted to show a decent performance of the algorithm.
This brief investigates event-triggered control for spacecraft attitude stabilization under the influence of actuator output nonlinearities. Under a designed event-triggered policy, the influences of ...actuators' inherent nonlinearity and network quantized transmission are evaluated under a unified framework. Compared with the previous results, the influences of data transmission and actuators' inherent nonlinearities are uniformly described as the actuator output nonlinearities, and the event-triggered policy reduces the effects of constants commonly introduced into triggering conditions on the final state convergence regions and achieves Zeno-free triggering. Simulations verify theoretical results.
This paper addresses the consensus problem of double-integrator networks via event- and self-triggered control approaches. In the proposed control protocol, only relative state information is ...utilised to synchronise all agents. First, an event-triggering algorithm with periodic event detection is designed to determine the event times of each agent. Then, a self-triggered control algorithm is provided to further reduce resource consumption. Two numerical simulations are conducted to illustrate the effectiveness of the results.
Abstract
The dynamical history of stars influences the formation and evolution of planets significantly. To explore the influence of dynamical history on the planet formation and evolution using ...observations, we assume stars that experienced significantly different dynamical histories tend to have different relative velocities. Utilizing the accurate Gaia–Kepler Stellar Properties Catalog, we select single main-sequence stars and divide these stars into three groups according to their relative velocities, i.e., high-
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, medium-
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, and low-
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stars. After considering the known biases from Kepler data and adopting prior and posterior correction to minimize the influence of stellar properties on planet occurrence rate, we find that high-
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stars have a lower occurrence rate of super-Earths and sub-Neptunes (1–4
R
⊕
,
P
< 100 days) and a higher occurrence rate of sub-Earth (0.5–1
R
⊕
,
P
< 30 days) than low-
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stars. Additionally, high-
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stars have a lower occurrence rate of hot Jupiter-sized planets (4–20
R
⊕
,
P
< 10 days) and a slightly higher occurrence rate of warm or cold Jupiter-sized planets (4–20
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⊕
, 10 <
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< 400 days). After investigating multiplicity and eccentricity, we find that high-
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planet hosts prefer a higher fraction of multiplanet systems and lower average eccentricity, which are consistent with the eccentricity–multiplicity dichotomy of Kepler planetary systems. All of these statistical results favor the scenario that high-
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stars with large relative velocity may experience fewer gravitational events, while low-
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stars may be influenced by stellar clustering significantly.
In order to solve the problems of high investment and low box office losses in the film industry, this study analyzes the topic of film box office and film and television reviews based on social ...network big data. Firstly, the factors that affect the box office of the movie are analyzed. Secondly, continuous and discrete feature parts, text parts, and fusion parts are merged. The box office prediction model of mixed features using deep learning is established, and the movie box office is predicted. Finally, compared with other algorithms and models, the box office prediction model of mixed features using deep learning is verified. The results show that compared with other models, the prediction accuracy of the mixed feature movie box office prediction model using depthwise separable convolution (DSC)-Transformer is higher than that of other algorithm models. Its optimal mean square error (MSE) value is 0.6549, and the optimal mean absolute error (MAE) value is 0.1706. The constructed model predicts the box office of nine movies, and the error between the predicted value and the true value is about 10%. Therefore, the established movie box office prediction model has a good effect. This study can predict movies' box office to reduce investment risk, so it is of great significance to movie investors and the social economy.
This brief studies the synchronization problem of nonlinear multi-agent systems (MASs) with the quadratic condition (QUAD-condition) via prescribed performance control (PPC), where a novel ...performance function is employed that can configure the convergence rate arbitrarily. With the designed protocol, all relative states' evolution is within the preset performance envelopes, and the system achieves state synchronization asymptotically. In comparison with the existing studies, we first investigate the coordinated control of QUAD nonlinear MASs with prescribed trajectory behaviors. Moreover, the convergence rate of the proposed performance constraint function can be selected arbitrarily, which has better versatility. Simulations on coupled chaotic circuit networks verify the theoretical results.
This article designs performance adjustable event- and self-triggered policies for nonlinear multiagent systems with controller output fluctuations. In the provided event-triggered mechanism, a ...performance adjustable function is introduced, which can freely adjust the system sampling frequencies and state convergence rates. In the proposed self-triggered policy, continuous measurement error monitoring and neighbors' event listening are avoided, thereby further saving system sensor resources. Furthermore, under a unified framework, the controllers' inherent nonlinearities and the influence of data quantization in digital communication networks are studied. In comparison with the previous event-triggered sampling schemes, the developed policies do not only study the multiagent coordinated control in terms of system sampling performance and state convergence performance but are also suitable for the scenarios with controller output fluctuations. Numerical simulation results verify the effectiveness and superiority of the proposed policies.
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