ABSTRACT
Since the coronavirus disease 2019 occurred, the lateral flow immunoassay (LFIA) test strip has become a global testing tool for convenience and low cost. However, some studies have shown that LFIA strips perform poorly compared to other professional testing methods. This paper proposes a new method to improve the accuracy of LFIA strips using spectral signals. A spectrochip module is applied to disperse the reflected light from the LFIA strips. The obtained spectral signals will be used for supervised machine learning. After training, the trained model has 93.8% accuracy compared to the standard test. This result indicated that the evaluation method based on the spectrum of LFIA strips could enhance the detection performance. © 2022 IEEE.
ABSTRACT
Purpose The COVID-19 has become a global pandemic, which has caused large number of deaths and huge economic losses. These losses are not only caused by the virus but also by the related rumors. Nowadays, online social media are quite popular, where billions of people express their opinions and propagate information. Rumors about COVID-19 posted on online social media usually spread rapidly;it is hard to analyze and detect rumors only by artificial processing. The purpose of this paper is to propose a novel model called the Topic-Comment-based Rumor Detection model (TopCom) to detect rumors as soon as possible. Design/methodology/approach The authors conducted COVID-19 rumor detection from Sina Weibo, one of the most widely used Chinese online social media. The authors constructed a dataset about COVID-19 from January 1 to June 30, 2020 with a web crawler, including both rumor and non-rumors. The rumor detection task is regarded as a binary classification problem. The proposed TopCom model exploits the topical memory networks to fuse latent topic information with original microblogs, which solves the sparsity problems brought by short-text microblogs. In addition, TopCom fuses comments with corresponding microblogs to further improve the performance. Findings Experimental results on a publicly available dataset and the proposed COVID dataset have shown superiority and efficiency compared with baselines. The authors further randomly selected microblogs posted from July 1-31, 2020 for the case study, which also shows the effectiveness and application prospects for detecting rumors about COVID-19 automatically. Originality/value The originality of TopCom lies in the fusion of latent topic information of original microblogs and corresponding comments with DNNs-based models for the COVID-19 rumor detection task, whose value is to help detect rumors automatically in a short time.
ABSTRACT
Background: The thoracic surgery team of the Shanghai Chest Hospital has been publishing its annual report since 2018, summarizing the services and major progress over the last year. Methods: All patients receiving thoracic surgery services at the Department of Thoracic Surgery and the Department of Oncological Surgery at the Shanghai Chest Hospital in 2020 were enrolled. The number of surgical resections, types of surgical procedures, disease histological types, and perioperative outcomes were collected and compared with the results from previous years. Results: In the year 2020, the thoracic team of the Shanghai Chest Hospital faced the unprecedented challenge of the coronavirus disease 2019 (COVID-19) epidemic. A total of 15,664 patients received thoracic surgeries at the Shanghai Chest Hospital, only an 8.0% decrease compared with the previous year of 2019. These included 13,493 pulmonary procedures, 1,075 esophageal procedures, 969 mediastinal procedures, 66 tracheal procedures, 2 lung transplantations, and 59 other procedures. The rate of minimally invasive surgeries among all procedures was 91.1%, including 721 robotic-assisted thoracic surgeries, both of which increased from the year before. In addition, the average length of hospital stay continuously decreased, being only 3.82 days after pulmonary surgery and 10.96 days after esophageal surgery. Meanwhile, the quality of thoracic surgery has improved, with continuously lower rates of perioperative complications and an in-hospital mortality rate of only 0.14%. Conclusions: The services provided and progress made in 2020 by the thoracic surgery team of the Shanghai Chest Hospital were reviewed in this annual report, reflecting a consistent effort to help our patients with high-standard services and state-of-the-art surgical techniques. © 2022 Shanghai Chest. All rights reserved.
ABSTRACT
The low-carbon development of energy intensive industries is of vital importance to achieve China's energy and climate targets in 2030 while carbon market is an important mechanism to promote carbon reduction. This paper investigates three types of causality, including positive, negative and dark causality, between China carbon prices and four energy intensive stock indexes using Pattern Causality method from a nonlinear symbolic dynamic perspective. Our findings show that there exists weak bidirectional causality between these two markets, which is manifested in that the fluctuation of 1% in one market approximately cause the fluctuation of 0.15%–0.3% in the other market. Moreover, we further analysis the impact of policies on the causalities between these two markets by dividing the whole timescale into seven stages. The results indicated that the document announcing the formal launch of China's carbon trading system prompted the dominant market of their causality shifting from carbon market to stock markets. Carbon markets gradually show stronger causal influence on the stock markets before December 2017, and the opposite after April 2018. And the Covid-19 has further exacerbated the weakening role of the carbon finance. Finally, the delay effect of carbon market on power industry stock market can be identified when unveiling the dark causality type. © 2021 Elsevier Inc.
ABSTRACT
To prevent the spread of coronavirus disease 2019 (COVID-19), preliminary temperature measurement and mask detection in public areas are conducted. However, the existing temperature measurement methods face the problems of safety and deployment. In this paper, to realize safe and accurate temperature measurement even when a person's face is partially obscured, we propose a cloud-edge-terminal collaborative system with a lightweight infrared temperature measurement model. A binocular camera with an RGB lens and a thermal lens is utilized to simultaneously capture image pairs. Then, a mobile detection model based on a multi-task cascaded convolutional network (MTCNN) is proposed to realize face alignment and mask detection on the RGB images. For accurate temperature measurement, we transform the facial landmarks on the RGB images to the thermal images by an affine transformation and select a more accurate temperature measurement area on the forehead. The collected information is uploaded to the cloud in real time for COVID-19 prevention. Experiments show that the detection model is only 6.1M and the average detection speed is 257ms. At a distance of 1m, the error of indoor temperature measurement is about 3%. That is, the proposed system can realize real-time temperature measurement in public areas. © 2021 IEEE.
ABSTRACT
COVID-19's significant impact on economic and social life has rightfully garnered the attention of citizens and policymakers alike. In response to the pandemic, governments have issued strict guidelines and restrictions to shut down some cities and many rural villages in China. With no cure or vaccine on the horizon, governments are working to mitigate the damage of the lockdowns on rural cultural village. Over the past two decades, rural village has been negatively impacted by terrorism, lack of funding and loss of population. COVID-19 has had similar effect, but in an incredibly short period of time. During the control period of COVID-19, traditional data are widely used in village protection and renewal. Collect and sort out the original data of Huizhou culture to prepare for the subsequent calculation. After the data is ready, the data is processed as the basis of mining its potential application value. In this paper, the key words of Huizhou cultural resources are summarized. The data analysis platform is established. This paper analyzes people's preference for Huizhou cultural resources. To better realize the more effective and far-reaching development and exploitation of Huizhou cultural resources. © 2020 - IOS Press and the authors. All rights reserved.
ABSTRACT
Background: COVID-19(2019 novel coronavirus disease)has brought tremendous pressure to the prevention and control of the national epidemic due to its concealed onset, strong infectivity and fast transmission speed. Methods: In this retrospective study, 226 patients diagnosed with 2019 novel coronavirus pneumonia (NCP) in the Chongqing University Three Gorges Hospital were included. The patients' clinical data, including general information, initial symptoms at the onset, time of disease diagnosis, time to treatment in hospital, time of nucleic acid conversion to negative, disease classification, total time of hospitalization were collected. The clinical data of the mild and severe patients were compared. Results: Fever, cough, sore throat, poor appetite andfatigue were the main symptoms of the diagnosed patients. The time of diagnosis was significantly shorter in the mild patients (4.96 ± 4.10 days) than severe patients (7.63 ± 9.17 days) (P=0.004). Mild patients had shorter time to treatment in hospital (6.09 ± 4.47 vs. 8.71 ± 9.04 days) and less time of nucleic acid conversion to negative (7.58 ± 2.51 vs. 11.6 ± 4.67 days) compared to the severe patients. Conclusion: The above results can be used as a quantitative basis for the “five-early"(early detection, early screening, early diagnosis, early isolation treatment, and early recovery) model. The government, the masses, and the hospitals' joint prevention and optimization of the "five-early" model will provide important scientific reference for further prevention and control of the epidemics. © 2020, Iranian Journal of Public Health. All rights reserved.