Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 12 de 12
Filter
1.
Security and Communication Networks ; 2022, 2022.
Article in English | ProQuest Central | ID: covidwho-1832698

ABSTRACT

Robot Operating System (ROS) has received widespread utilization with the development of robotics, self-driving, etc., recently. Meanwhile, the other technology blockchain is frequently applied to various fields with its trustworthy characteristics and immutability in data storage. However, ROS has no ability to interact with the blockchain, which hinders research in related fields. Therefore, we wonder if we can develop a convenient tool to bridge ROS and blockchain. Inspired by this, we propose ROS-Ethereum. It bridges ROS and Ethereum, a widely used blockchain platform. ROS-Ethereum is based on the User Datagram Protocol (UDP) communication mechanism and the SM algorithm family along with Ethereum technology. Simply put, ROS-Ethereum allows users to invoke the contract when interacting with the blockchain, which makes this process easier and safer. We conduct experiments in real robots to verify the effectiveness of ROS-Ethereum and evaluate it from the following metrics: (1) the encryption efficiency and stability of the algorithm and (2) ROS-Ethereum transaction response time and packet loss rate.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315560

ABSTRACT

AI plays an important role in COVID-19 identification. Computer vision and deep learning techniques can assist in determining COVID-19 infection with Chest X-ray Images. However, for the protection and respect of the privacy of patients, the hospital's specific medical-related data did not allow leakage and sharing without permission. Collecting such training data was a major challenge. To a certain extent, this has caused a lack of sufficient data samples when performing deep learning approaches to detect COVID-19. Federated Learning is an available way to address this issue. It can effectively address the issue of data silos and get a shared model without obtaining local data. In the work, we propose the use of federated learning for COVID-19 data training and deploy experiments to verify the effectiveness. And we also compare performances of four popular models (MobileNet, ResNet18, MoblieNet, and COVID-Net) with the federated learning framework and without the framework. This work aims to inspire more researches on federated learning about COVID-19.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-315559

ABSTRACT

Federated Learning (FL) creates an ecosystem for multiple agents to collaborate on building models with data privacy consideration. The method for contribution measurement of each agent in the FL system is critical for fair credits allocation but few are proposed. In this paper, we develop a real-time contribution measurement method FedCM that is simple but powerful. The method defines the impact of each agent, comprehensively considers the current round and the previous round to obtain the contribution rate of each agent with attention aggregation. Moreover, FedCM updates contribution every round, which enable it to perform in real-time. Real-time is not considered by the existing approaches, but it is critical for FL systems to allocate computing power, communication resources, etc. Compared to the state-of-the-art method, the experimental results show that FedCM is more sensitive to data quantity and data quality under the premise of real-time. Furthermore, we developed federated learning open-source software based on FedCM. The software has been applied to identify COVID-19 based on medical images.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325172

ABSTRACT

With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment is proposed. Establish a "Scrapy-Redis-Bloomfilter" distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the depth of the seven emotions such as Hopeful, Happy, and Depressed. Finally, we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model. The results show that our model has better generalization ability and smaller discriminant error. We designed a large data visualization screen, which can clearly show the trend of public emotions, the proportion of various emotion categories, keywords, hot topics, etc., and fully and intuitively reflect the development of public opinion.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324417

ABSTRACT

New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. By understanding the development trend of a regional epidemic, the epidemic can be controlled using the development policy. The common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compared the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. And this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data;2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.

6.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-322526

ABSTRACT

The aim of our study was to describe the clinical characteristics and outcomes of patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia who underwent elective tracheostomies. We investigated all COVID-19 patients who underwent elective tracheostomies in intensive care units (ICUs) of 23 hospitals in Hubei Province, China, from January 8, 2020 to March 25, 2020. Demographic information, clinical characteristics, treatment, details of the tracheostomy procedure, successful weaning after tracheostomy, and living status were collected and analyzed. A total of 80 patients were included. The median duration from endotracheal intubation to tracheostomy was 17.5 [IQR 11.3-27.0] days. Most tracheotomies were performed by ICU physicians (62 (77.5%)) and using percutaneous techniques (63 (78.8%)) at the ICU bedside (76 (95.0%)). At 60 days after intubation, 31 (38.8%) patients experienced successful weaning from the ventilator, 17 (21.2%) patients were discharged from the ICU, and 43 (53.8%) patients had died. Higher 60-day mortality (22 (73.3%) vs 21 (42.0%)) was identified in patients who underwent early tracheostomy. In patients with SARS-CoV-2 pneumonia, tracheostomies were feasible to conduct by ICU physicians at bedside with few major complications. However, tracheostomies within 14 days of endotracheal intubation should be avoided.

7.
Front Med (Lausanne) ; 8: 753659, 2021.
Article in English | MEDLINE | ID: covidwho-1556286

ABSTRACT

Background: Invasive pulmonary aspergillosis (IPA) is a life-threatening complication in coronavirus disease 2019 (COVID-19) patients admitted to intensive care units (ICUs), but risk factors for COVID-19-associated IPA (CAPA) have not been fully characterized. The aim of the current study was to identify factors associated with CAPA, and assess long-term mortality. Methods: A retrospective cohort study of adult COVID-19 patients admitted to ICUs from six hospitals was conducted in Hubei, China. CAPA was diagnosed via composite clinical criteria. Demographic information, clinical variables, and 180-day outcomes after the diagnosis of CAPA were analyzed. Results: Of 335 critically ill patients with COVID-19, 78 (23.3%) developed CAPA within a median of 20.5 days (range 13.0-42.0 days) after symptom onset. Compared to those without CAPA, CAPA patients were more likely to have thrombocytopenia (50 vs. 19.5%, p < 0.001) and secondary bacterial infection prior to being diagnosed with CAPA (15.4 vs. 6.2%, p = 0.013), and to receive vasopressors (37.2 vs. 8.6%, p < 0.001), higher steroid dosages (53.9 vs. 34.2%, p = 0.002), renal replacement therapy (37.2 vs. 13.6%, p < 0.001), and invasive mechanical ventilation (57.7 vs. 35.8%, p < 0.001). In multivariate analysis incorporating hazard ratios (HRs) and confidence intervals (CIs), thrombocytopenia (HR 1.98, 95% CI 1.16-3.37, p = 0.012), vasopressor use (HR 3.57, 95% CI 1.80-7.06, p < 0.001), and methylprednisolone use at a daily dose ≥ 40 mg (HR 1.69, 95% CI 1.02-2.79, p = 1.02-2.79) before CAPA diagnosis were independently associated with CAPA. Patients with CAPA had longer median ICU stays (17 days vs. 12 days, p = 0.007), and higher 180-day mortality (65.4 vs. 33.5%, p < 0.001) than those without CAPA. Conclusions: Thrombocytopenia, vasopressor use, and corticosteroid treatment were significantly associated with increased risk of incident IPA in COVID-19 patients admitted to ICUs. The occurrence of CAPA may increase the likelihood of long-term COVID-19 mortality.

8.
Huanjing yu Zhiye Yixue = Journal of Environmental & Occupational Medicine ; 38(9):1029, 2021.
Article in English | ProQuest Central | ID: covidwho-1471185

ABSTRACT

The removal and defense mechanisms of the respiratory system of patients with pneumoconiosis are impaired. Once patients with pneumoconiosis and other underlying lung diseases are infected with novel coronavirus, they are likely to progress to severe cases with COVID-19, a tough condition with a high mortality and poor prognosis. Herein we presented a case of pneumoconiosis and tuberculosis complicated with severe COVID-19. Active administration of anti-viral, anti-infection, phlegm-removing, anti-asthmatic, and high-flow oxygen therapies did not alleviate the patient's acute respiratory distress syndrome symptoms. Then tracheal intubation, ventilator assisted breathing, and lung protective ventilation were given but did not effectively treat the patient's respiratory failure. Finally, the patient died clinically despite use of extracorporeal membrane oxygenation (ECMO).

9.
Front Med (Lausanne) ; 7: 611460, 2020.
Article in English | MEDLINE | ID: covidwho-1389196

ABSTRACT

Background: The data on long-term outcomes of patients infected by SARS-CoV-2 and treated with extracorporeal membrane oxygenation (ECMO) in China are merely available. Methods: A retrospective study included 73 patients infected by SARS-CoV-2 and treated with ECMO in 21 intensive care units in Hubei, China. Data on demographic information, clinical features, laboratory tests, ECMO durations, complications, and living status were collected. Results: The 73 ECMO-treated patients had a median age of 62 (range 33-78) years and 42 (63.6%) were males. Before ECMO initiation, patients had severe respiratory failure on mechanical ventilation with a median PO2/FiO2 of 71.9 [interquartile range (IQR), 58.6-87.0] mmHg and a median PCO2 of 62 [IQR, 43-84] mmHg on arterial blood analyses. The median duration from symptom onset to invasive mechanical ventilation, and to ECMO initiation was19 [IQR, 15-25] days, and 23 [IQR, 19-31] days. Before and after ECMO initiation, the proportions of patients receiving prone position ventilation were 58.9 and 69.9%, respectively. The median duration of ECMO support was 18.5 [IQR 12-30] days. During the treatments with ECMO, major hemorrhages occurred in 31 (42.5%) patients, and oxygenators were replaced in 21 (28.8%) patients. Since ECMO initiation, the 30-day mortality and 60-day mortality were 63.0 and 80.8%, respectively. Conclusions: In Hubei, China, the ECMO-treated patients infected by SARS-CoV-2 were of a broad age range and with severe hypoxemia. The durations of ECMO support, accompanied with increased complications, were relatively long. The long-term mortality in these patients was considerably high.

10.
Front Med (Lausanne) ; 7: 615845, 2020.
Article in English | MEDLINE | ID: covidwho-1016068

ABSTRACT

Background: The outbreak of coronavirus disease 2019 (COVID-19) has led to a large and increasing number of patients requiring prolonged mechanical ventilation and tracheostomy. The indication and optimal timing of tracheostomy in COVID-19 patients are still unclear, and the outcomes about tracheostomy have not been extensively reported. We aimed to describe the clinical characteristics and outcomes of patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia who underwent elective tracheostomies. Methods: The multi-center, retrospective, observational study investigated all the COVID-19 patients who underwent elective tracheostomies in intensive care units (ICUs) of 23 hospitals in Hubei province, China, from January 8, 2020 to March 25, 2020. Demographic information, clinical characteristics, treatment, details of the tracheostomy procedure, successful weaning after tracheostomy, and living status were collected and analyzed. Data were compared between early tracheostomy patients (tracheostomy performed within 14 days of intubation) and late tracheostomy patients (tracheostomy performed after 14 days). Results: A total of 80 patients were included. The median duration from endotracheal intubation to tracheostomy was 17.5 [IQR 11.3-27.0] days. Most tracheotomies were performed by ICU physician [62 (77.5%)], and using percutaneous techniques [63 (78.8%)] at the ICU bedside [76 (95.0%)]. The most common complication was tracheostoma bleeding [14 (17.5%)], and major bleeding occurred in 4 (5.0%) patients. At 60 days after intubation, 31 (38.8%) patients experienced successful weaning from ventilator, 17 (21.2%) patients discharged from ICU, and 43 (53.8%) patients had died. Higher 60 day mortality [22 (73.3%) vs. 21 (42.0%)] were identified in patients who underwent early tracheostomy. Conclusions: In patients with SARS-CoV-2 pneumonia, tracheostomies were feasible to conduct by ICU physician at bedside with few major complications. Compared with tracheostomies conducted after 14 days of intubation, tracheostomies within 14 days were associated with an increased mortality rate.

11.
Cmc-Computers Materials & Continua ; 64(3):1415-1434, 2020.
Article | WHO COVID | ID: covidwho-732586

ABSTRACT

With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed. Establish a "Scrapy-Redis-Bloomfilter" distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the depth of the seven emotions such as Hopeful, Happy, and Depressed. Finally, we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model. The results show that our model has better generalization ability and smaller discriminant error. We designed a large data visualization screen, which can clearly show the trend of public emotions, the proportion of various emotion categories, keywords, hot topics, etc., and fully and intuitively reflect the development of public opinion.

12.
Cmc-Computers Materials & Continua ; 64(3):1473-1490, 2020.
Article | WHO COVID | ID: covidwho-732585

ABSTRACT

New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long -Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. Furthermore, this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data. 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.

SELECTION OF CITATIONS
SEARCH DETAIL