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Summary This study looks at how digital technologies disrupted signalling and signal cost calculations in public diplomacy within the context of Covid-19. The pandemic presented a noteworthy opportunity to observe how countries attempt to navigate a relatively unknown communication landscape as a result of external shock and a crisis for states' images and reputations. We position the communicative outcomes of the pandemic as an exploratory case to discuss how countries use social media to engage with target audiences. We study American and Chinese messaging on Twitter about Covid-19 employing an analytical model of signal cost developed from signalling theory. Using a data set of 1,512 tweets coming from nine different American and Chinese accounts, we investigated their signal cost through content and network analyses. Our findings describe and operationalise signal cost in digital public diplomacy through signaller, signal content and outreach. Keywords © 2023 Authors. All rights reserved.
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Physiological signals indicate a person's physical and mental state at any given time. Accordingly, many studies extract physiological signals from the human body with non-contact methods, and most of them require facial feature points. However, under COVID-19, wearing a mask has become a must in many places, so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research. In this study, RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate, blood pressure, respiratory rate, and forehead temperature for people wearing masks due to the pandemic. Using the green (G) minus red (R) signal in the RGB image, the region of interest (ROI) is established in the forehead and nose bridge regions. The photoplethysmography (PPG) waveforms of the two regions are obtained after the acquired PPG signal is subjected to the optical flow method, baseline drift calibration, normalization, and bandpass filtering. The relevant parameters in Deep Neural Networks (DNN) for the regression model can correctly predict the heartbeat and blood pressure. In addition, the temperature change in the ROI of the mask after thermal image processing and filtering can be used to correctly determine the number of breaths. Meanwhile, the thermal image can be used to read the temperature average of the ROI of the forehead, and the forehead temperature can be obtained smoothly. The experimental results show that the above-mentioned physiological signals of a subject can be obtained in 6-s images with the error for both heart rate and blood pressure within 2%∼3% and the error of forehead temperature within ±0.5°C. © 2023 Tech Science Press. All rights reserved.
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Wearing masks can effectively inhibit the spread and damage of COVID-19. A device-edge-cloud collaborative recognition architecture is designed in this paper, and our proposed device-edge-cloud collaborative recognition acceleration method can make full use of the geographically widespread computing resources of devices, edge servers, and cloud clusters. First, we establish a hierarchical collaborative occluded face recognition model, including a lightweight occluded face detection module and a feature-enhanced elastic margin face recognition module, to achieve the accurate localization and precise recognition of occluded faces. Second, considering the responsiveness of occluded face detection services, a context-aware acceleration method is devised for collaborative occluded face recognition to minimize the service delay. Experimental results show that compared with state-of-the-art recognition models, the proposed acceleration method leveraging device-edge-cloud collaborations can effectively reduce the recognition delay by 16%while retaining the equivalent recognition accuracy. IEEE
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Background: The occurrence of deep vein thrombosis (DVT) in COVID-19 pneumonia has raised wide concern recently, but few studies have reported the incidence of DVT in other types of pneumonia. We evaluate the prevalence, risk factors and treatment of DVT in the elderly inpatients with pneumonia. Method(s): A cohort of 550 elderly inpatients (>= 75 years old) with pneumonia between 2017 and 2021 were reviewed. They were divided into DVT group and non-DVT groups on the basis of whether pneumonia was combined with new-found DVT. Clinical data were collected retrospectively. Patients with DVT were divided into anticoagulant group and non-anticoagulant groups on the basis of whether they received anticoagulant therapy. Result(s): Ninety-seven patients were included in the DVT group;453 in the non-DVT group. The incidence of DVT was 17.64%. Hospital stays were significantly longer for DVT patients than for non-DVT counterparts (p = 0.005). Coronary heart disease, heart failure, hyperlipidemia, bed rest, and elevated D-dimer were independent risk factors for DVT (p < 0.05). The rate of anticoagulant therapy in DVT group was 63.92% (62/97 cases). Follow-up showed that the continuous anticoagulant treatment rate was 48.39% (30/62 cases) at 3 months and 30.65% (19/62 cases) at 6 months. Conclusion(s): Elderly inpatients with pneumonia are at high risk of DVT. The combination of DVT and pneumonia may lead to prolonged hospitalization. Coronary heart disease, heart failure, hyperlipidemia, bed rest and elevated D-dimer are independent risk factors for DVT in these patients. The rate of regular anticoagulant treatment is low because of the high risk of bleeding.Copyright © 2022, Taiwan Society of Geriatric Emergency & Critical Care Medicine.
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The health risks of socially vulnerable groups, such as the elderly, the sick, and the disabled, are significantly elevated under the COVID-19 epidemic. Therefore, the different factors affecting the use of information technology by socially vulnerable groups under COVID-19 are explored at the level of the use of emerging information technology. The impact on the information behavior of socially vulnerable groups under COVID-19 is also explored at the level of information behavior, including health information needs, the digital divide phenomenon, and the utilization of public information services. Based on the above findings, the current status of information behavior research for socially vulnerable groups is combined. Future research directions of information technology and information behavior for socially vulnerable groups are proposed. First, to improve the research theory of information behavior of socially vulnerable groups regarding information technology. Second, to apply big data technology and data analysis technology to explore the information technology adoption behavior of socially vulnerable groups in-depth. Third, to construct the information behavior model of socially vulnerable groups based on empirical research cases. Fourth, to use information technology for socially vulnerable groups according to information technology and the barriers faced by socially vulnerable groups in using information technology, and to provide strategies for using information technology that meet the needs of socially vulnerable groups. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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COVID-19 has become a global pandemic, and COVID-19 patients are in a medical dilemma with no effective treatment and no effective drugs. The questions and answers in the social Q&A community can reveal the characteristics and evolution rules of the health information needs of COVID-19 patients. Using the Q&A data in Baidu Zhidao (https://zhidao.baidu.com/ ) as the research object, using the web crawlers to capture the data, automatic topic recognition on the acquired data by constructing an LDA topic model, exploring the content of COVID-19 patients' health information needs, and revealing the change rule of Q&A publication volume and health information need topics from the time dimension. Combining statistical information such as the number of answers, the number of likes, and the level of respondents, cluster analysis is used to reveal the changing rules of social characteristics and health information need topics. By analyzing the Q&A data on COVID-19 patients in Baidu Zhidao, it is found that the topic distribution of health information needs topic is relatively concentrated. Moreover, the number of Q&A and the types of health information needs to be changed in different development periods. There are differences in social characteristics that correspond to different topics of health information needs. Through in-depth analysis of the characteristics of health information needs of COVID-19 patients in the social Q&A community, on the one hand, it is beneficial for COVID-19 patients to obtain the required health information content timely. On the other hand, it is beneficial to optimize the community information display mechanism and improve the organization of information resources. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The pandemic of 2019 novel coronavirus (2019-nCoV) infection since 2020 caused Coronavirus Disease 2019 (COVID-19) leads the serious threaten to global public health. It is urgent to diagnose COVID-19, guide epidemiological measures, control the infection rates, research/develop the antiviral treatment and promote the vaccine research. The application of nano-material based biosensors (the nano-biosensors) has achieved the high-performance detection of a variety of biomarkers due to their small device size, label free detection, high sensitivity, good specificity, short detection time, and has been considered as great potential to become a point-of-care testing tool for detecting 2019-nCoV. Therefore, by summarizing the working principle and classification of nano-biosensors, and focusing on the research progress of nano-biosensors in the detection of 2019-nCoV reported in the recent years, our review provides the challenges and future development prospects of the nano-biosensor in clinical laboratory.Copyright © 2022 Chin J Lab Med. All rights reserved.
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The pandemic of 2019 novel coronavirus (2019-nCoV) infection since 2020 caused Coronavirus Disease 2019 (COVID-19) leads the serious threaten to global public health. It is urgent to diagnose COVID-19, guide epidemiological measures, control the infection rates, research/develop the antiviral treatment and promote the vaccine research. The application of nano-material based biosensors (the nano-biosensors) has achieved the high-performance detection of a variety of biomarkers due to their small device size, label free detection, high sensitivity, good specificity, short detection time, and has been considered as great potential to become a point-of-care testing tool for detecting 2019-nCoV. Therefore, by summarizing the working principle and classification of nano-biosensors, and focusing on the research progress of nano-biosensors in the detection of 2019-nCoV reported in the recent years, our review provides the challenges and future development prospects of the nano-biosensor in clinical laboratory.
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This paper developed a factor-based robust approach to improve the tracking fund's stability. Similar to the financial crisis, the recent coronavirus pandemic amplify the global market volatility significantly, which suggests that healthcare-based factor can be used to hedge against the jump risk. The index tracking fund is constructed by a developed cardinality constrained conic programming. To overcome the large-scale computational challenge, we decompose the problem into two simplified cases and quickly calculate the tighter lower bound and its feasible upper bound. In addition, a subgradient-based inequalities are derived to exclude the suboptimal points that have been traveled in previous iterations. It turns out that the proposed model, along with the designed solving technique, can be used as an alternative to build reliable tracking portfolios. We demonstrate the effectiveness and robustness of the proposed method by testing different large real data sets. © 2022 Elsevier Ltd
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Overall survival (OS) is considered the standard clinical endpoint to support effectiveness claims in new drug applications globally, particularly for lethal conditions such as cancer. However, the source and reliability of OS in the setting of clinical trials have seldom been doubted and discussed. This study first raised the common issue that data integrity and reliability are doubtful when we collect OS information or other time-to-event endpoints based solely on simple follow-up records by investigators without supporting material, especially since the 2019 COVID-19 pandemic. Then, two rounds of discussions with 30 Chinese experts were held and 12 potential source scenarios of three methods for obtaining the time of death of participants, including death certificate, death record and follow-up record, were sorted out and analysed. With a comprehensive assessment of the 12 scenarios by legitimacy, data reliability, data acquisition efficiency, difficulty of data acquisition, and coverage of participants, both short-term and long-term recommended sources, overall strategies and detailed measures for improving the integrity and reliability of death date are presented. In the short term, we suggest integrated sources such as public security systems made available to drug inspection centres appropriately as soon as possible to strengthen supervision. Death certificates provided by participants' family members and detailed standard follow-up records are recommended to investigators as the two channels of mutual compensation, and the acquisition of supporting materials is encouraged as long as it is not prohibited legally. Moreover, we expect that the sharing of electronic medical records and the legal disclosure of death records in established health registries can be realized with the joint efforts of the whole industry in the long-term. The above proposed solutions are mainly based on the context of China and can also provide reference for other countries in the world. © 2022 The Authors
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Background:Coronavirus disease 2019 (COVID-19) is a serious and even lethal respiratory illness. The mortality of critically ill patients with COVID-19, especially short term mortality, is considerable. It is crucial and urgent to develop risk models that can predict the mortality risks of patients with COVID-19 at an early stage, which is helpful to guide clinicians in making appropriate decisions and optimizing the allocation of hospital resoureces.Methods:In this retrospective observational study, we enrolled 949 adult patients with laboratory-confirmed COVID-19 admitted to Tongji Hospital in Wuhan between January 28 and February 12, 2020. Demographic, clinical and laboratory data were collected and analyzed. A multivariable Cox proportional hazard regression analysis was performed to calculate hazard ratios and 95% confidence interval for assessing the risk factors for 30-day mortality.Results:The 30-day mortality was 11.8% (112 of 949 patients). Forty-nine point nine percent (474) patients had one or more comorbidities, with hypertension being the most common (359 [37.8%] patients), followed by diabetes (169 [17.8%] patients) and coronary heart disease (89 [9.4%] patients). Age above 50 years, respiratory rate above 30 beats per minute, white blood cell count of more than10 × 109/L, neutrophil count of more than 7 × 109/L, lymphocyte count of less than 0.8 × 109/L, platelet count of less than 100 × 109/L, lactate dehydrogenase of more than 400 U/L and high-sensitivity C-reactive protein of more than 50 mg/L were independent risk factors associated with 30-day mortality in patients with COVID-19. A predictive CAPRL score was proposed integrating independent risk factors. The 30-day mortality were 0% (0 of 156), 1.8% (8 of 434), 12.9% (26 of 201), 43.0% (55 of 128), and 76.7% (23 of 30) for patients with 0, 1, 2, 3, ≥4 points, respectively.Conclusions:We designed an easy-to-use clinically predictive tool for assessing 30-day mortality risk of COVID-19. It can accurately stratify hospitalized patients with COVID-19 into relevant risk categories and could provide guidance to make further clinical decisions. © 2021 The Chinese Medical Association, Published by Wolters Kluwer Health, Inc.
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Through a case study of Kunshan, China, this paper shows how a local state utilised place-based leadership to enhance regional economic resilience under the COVID-19 pandemic crisis. It unpacks how Kunshan effectively mitigated early economic disturbances induced by the COVID-19 pandemic, by two ways of leadership actions, namely, enacting jurisdictional power (that is formal leadership), and mobilising wide official and interpersonal networks (that is network leadership). Four specific local-state-led adaptive resilience processes or strategies are identified: stabilising labour supply, mitigating supply-chain disruptions, alleviating financial strains and reconfiguring market orientations. Through these proactive endeavours, the local state played an enabling role in aligning diverse stakeholders and resources across places, scales and sectors, thereby allaying economic shocks and enhancing regional economic resilience. This study contributes to the resilience literature by developing an agency-centric perspective to understanding regional economic resilience during the COVID-19 pandemic.
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Objective To analyse monitoring absenteeism due to respiratory symptoms/diseases among preschoolers in Guang-zhou, and to provide reference for risk prevention and control of respiratory infectious diseases in kindergartens. Methods Data of absenteeism due to symptoms and diseases in kindergartens were collected from "Guangzhou Student Health Monitoring System", and was analyzed by using R 4.1.3 software. Results During 2018-2020 academic year, there were 1 965, 2 019, 2 236 kinder-gartens being monitored respectively. The absenteeism rate of kindergarten's children due to respiratory symptoms and diseases in Guangzhou were 3.08 ‰ and 2.02 ‰. The absenteeism rates due to respiratory symptoms were 3.75 ‰, 4.17 ‰, 2.97 ‰ and 2.09 ‰ in baby class, junior class, middle class and senior class, respectively. The absenteeism rates due to respiratory diseases were 2.34 ‰, 2.60 ‰, 1.94 ‰ and 1.50 ‰, respectively. The absenteeism rate in higher grade was lower than that in lower grade (X2 = 65 197.95, 27 929.44, P<0.01). The absenteeism rates of boys (3.11 ‰, 2.05 ‰ ) due to respiratory symptoms/diseases were signif-icantly higher than those of girls (3.06 ‰, 1.97 ‰ ) (X2 =57.71, 229.45, P<0.01). The absenteeism of preschoolers due to respiratory symptoms/diseases showed two peaks in December of the this year and May of the following year. At the beginning of the second se-mester of 2019 academic year (after the outbreak of Coronavirus disease 2019), the absenteeism rate due to respiratory symptoms/diseases were lower than those of the same period in previous years. Conclusions The absences due to respiratory symptoms or dis-eases not only accounted for half of the total absences due to illness, but also had seasonal characteristics. Children in the younger-age group and most boys are treated as a focus group for absence due to respiratory symptoms/illnesses. It's necessary to give full use of timely warning function of health monitoring system. © 2022 by the Author(s).
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The pandemic of 2019 novel coronavirus (2019-nCoV) infection since 2020 caused Coronavirus Disease 2019 (COVID-19) leads the serious threaten to global public health. It is urgent to diagnose COVID-19, guide epidemiological measures, control the infection rates, research/develop the antiviral treatment and promote the vaccine research. The application of nano-material based biosensors (the nano-biosensors) has achieved the high-performance detection of a variety of biomarkers due to their small device size, label free detection, high sensitivity, good specificity, short detection time, and has been considered as great potential to become a point-of-care testing tool for detecting 2019-nCoV. Therefore, by summarizing the working principle and classification of nano-biosensors, and focusing on the research progress of nano-biosensors in the detection of 2019-nCoV reported in the recent years, our review provides the challenges and future development prospects of the nano-biosensor in clinical laboratory. © 2022 Chin J Lab Med. All rights reserved.
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Over the past several years the electric power sector has been challenged by a number of extreme events around the globe. Significant societal and economic shocks were due to the rapid spread of COVID-19 around the world. In addition to the pandemic, there have been several extreme weather and societal disruptions to the electricity sector, such as the February 2021 Texas power outage and the 9 p.m. nine-minute blackout event in India. © 2003-2012 IEEE.
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COVID-19 is an epidemic threat to human health caused by a novel coronavirus. Different countries are still struggling with whether to extend lockdown policies considering economic recession outcomes or lift the lockdown and recover the fragile economy. This research narrows the knowledge gap by applying a decision-making approach in a fuzzy environment to consider the uncertainties in the supply chain risk assessment of the construction industry during the COVID-19 pandemic. Besides, risk factors (RFs) are identified and interrelationships between RFs are depicted. RFs are categorized using expert panel surveys and literature reviews. The interdependencies between different RFs are evaluated using a combination of interval-valued intuitionistic fuzzy numbers, decision making trial and evaluation laboratory, and the Choquet integral to allow a strong interrelationship between the sum of rows and the sum of columns of RFs in structural correlation analysis. Next, priorities of RFs are obtained by considering interdependencies between different RFs using a combination of the technique for order preference by similarity to the ideal solution (TOPSIS) method with the intuitionistic fuzzy set. An intuitionistic fuzzy weighted average operator is utilized to aggregate individual opinions of decision makers for rating the importance of RFs. Results reported that the scarcity of material and financial resources due to movement restrictions with overall weights of 0.0614 and 0.413 are the most prominent RFs in the supply chain due to the COVID-19 pandemic. The results of this research can assist decision-makers in developing strategic policies to prevail over the risk challenges and subsequently aid experts in redesigning supply chains. IEEE
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Photobiomodulation (PBM) therapy is a therapeutic method that can produce a range of physiological effects in cells and tissues using certain wavelengths. The reparative benefits of PBM therapy include wound healing, bone regeneration, pain reduction, and the mitigation of inflammation. Advances in the development of laser instruments, including the use of high-intensity lasers in physiotherapy, have recently led to controllable photothermal and photomechanical treatments that enable therapeutic effects to be obtained without damaging tissue. The combination of PBM therapy with acupuncture may provide new perspectives for investigating the underlying therapeutic mechanisms of acupuncture and promote its widespread application.
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Previous studies have documented an association of D-dimer levels with COVID-19 severity. Elevated D-dimer is reported to be associated with patient demographics, comorbidities, lab results, and overall higher incidence of critical illness. However, due to small sample sizes, limited availability of data on essential covariates, and lack of standardization of the admission laboratory protocol, the role of D-dimer in the progression of COVID-19 remains uncertain and needs further investigation using data from larger cohorts. The objectives of this study were to study the factors predicting elevated D-dimer level and to characterize the risk factors that predict D-dimer elevation over the course of inpatient admission. We used statistical modeling, applying machine learning methods to maximally leverage all the available clinical and care variables without being limited by the assumptions of traditional regression analysis methods. Our sample consisted of 1005 COVID-19 inpatients admitted to a large US hospital from March 2020 to July 2020, using detailed data on various clinical and biochemical laboratory test results at admission and throughout the course of hospital stay. Analytic methods used in this study included a) descriptive statistics at baseline using chi-square tests to compare patients with normal and elevated D-dimer at baseline, b) adjusted multivariable regression modeling, and c) evaluation of importance of each feature using two decision-tree-based supervised machine learning algorithms, random forest and XGBoost methods. Results show that machine learning methods could identify 20 important features that predict D-dimer some of which could be used to prevent the processes that lead to D-dimer elevation. Our study suggests that continual laboratory monitoring of D-dimer levels from the time of detection of COVID-19 infection, and monitoring of selected risk factors out of the panel of identified risk factors may enable clinicians to triage patients into risk levels, initiate appropriate therapeutic strategies, and tailor care management to each patient in order to minimize the morbidity and mortality of COVID-19. © 2022 IEEE.
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Although previous studies using limited data have documented an association of D-dimer levels with COVID-19 severity, the role of D-dimer in the progression of COVID-19 remains unclear and requires further investigation using data from larger cohorts. We used traditional statistical modeling and machine learning methods to examine critical factors influencing the D-dimer elevation and to characterize associated risk factors of D-dimer elevation over the course of inpatient admission. We identified 20 important features to predict D-dimer levels, some of which could be used to predict and prevent the D-dimer elevation. Laboratory monitoring of D-dimer level and its risk factors at early stage can mitigate severe or death cases in COVID-19. © 2022 IEEE.
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From the start of the coronavirus disease 2019 (COVID-19) pandemic in 2020, COVID-19 infection in the pediatric population has aroused great attention. This article presents dynamic epidemiological characteristics of COVID-19 infection in pediatric patients from January 2020 to March 2022 in China. These data contributed essential insights and shared experience on the management of COVID-19 in children. To date, the unvaccinated population and events with children need more attention. Copyright © 2022 E-Century Publishing Corporation. All rights reserved.