ABSTRACT
Flexible humidity sensors with high sensitivity, fast response time, and outstanding reliability have the potential to revolutionize electronic skin, healthcare, and non-contact sensing. In this study, we employed a straightforward nanocluster deposition technique to fabricate a resistive humidity sensor on a flexible substrate, using molybdenum oxide nanoparticles (MoOx NPs). We systematically evaluated the humidity-sensing behaviors of the MoOx NP film-based sensor and found that it exhibited exceptional sensing capabilities. Specifically, the sensor demonstrated high sensitivity (18.2 near zero humidity), a fast response/recovery time (1.7/2.2 s), and a wide relative humidity (RH) detection range (0-95%). The MoOx NP film, with its closely spaced granular nanostructure and high NP packing density, exhibited insensitivity to mechanical deformation, small hysteresis, good repeatability, and excellent stability. We also observed that the device exhibited distinct sensing kinetics in the range of high and low RH. Specifically, for RH > 43%, the response time showed a linear prolongation with increased RH. This behavior was attributed to two factors: the higher physical adsorption energy of H2O molecules and a multilayer physical adsorption process. In terms of applications, our sensor can be easily attached to a mask and has the potential to monitor human respiration owing to its high sensing performance. Additionally, the sensor was capable of dynamically tracking RH changes surrounding human skin, enabling a non-contact sensing capability. More significantly, we tested an integrated sensor array for its ability to detect moisture distribution in the external environment, demonstrating the potential of our sensor for contactless human-machine interaction. We believe that this innovation is particularly valuable during the COVID-19 epidemic, where cross-infection may be averted by the extensive use of contactless sensing. Overall, our findings demonstrate the tremendous potential of MoOx NP-based humidity sensors for a variety of applications, including healthcare, electronic skin, and non-contact sensing.
ABSTRACT
Economic and fiscal policies devised by international organizations, governments, and central banks rely significantly on economic projections, particularly during times of economic instability such as the one we have recently seen with the COVID-19 virus spreading globally. However, the accuracy of economic forecasting and now casting models remains a challenge since modern economies are prone to multiple shocks that make forecasting and now casting activities extremely difficult, both in the short and medium range. The purpose of the paper is to identify the key aspects, which must be taken into account in big data sentiment analysis to solve the problem of forecasting and now casting tasks. The work has developed an mpBC-ELMo based BNM-cBLSTM for financial sentiment analysis in European Bond markets. The proposed framework is processed based on collection of big data that are formed into a corpus. Initially, the corpus data is subjected to preprocessing, which performs tokenization, Lemmatization, URL removal, punctuations removal, Dependency Parsing, Noun Phrases, Named Entity recognition, and Coreference Resolution in order to make the data healthier and to get a potential impact for analyzing the sentiments. Thereafter, the data is converted into a vector using mpBC-ELMo, which addresses the complex characteristics of the word as well as polarity and handles stock bond correlation invariant, behavioral biases. Finally, BNM-cBLSTM analyses the sentiments and provides an accurate as well as optimized improvisation. In comparison to existing state-of-the-art methods, experimental results show that the work tends to deliver a precise sentiment analysis and avoids erroneous prediction rate. © 2022 IEEE.
ABSTRACT
Objective To investigate parents' acceptance to the COVID-19 vaccine booster shots for their children aged 3-11 years in Nanjing based on the theory of planned behavior and to provide a scientific theoretical basis for carrying out COVID-19 booster vaccination among children of this age group. Methods A total of 1 286 parents of children aged 3-11 years in Nanjing were selected by multistage stratified cluster sampling. A questionnaire survey based on the theory of planned behavior was used to investigate their willingness to vaccinate their children with the COVID-19 booster dose and structural equation model was used to conduct data analysis. Results About 90.2% of parents were willing to give their children a booster dose of the COVID-19 vac￣cine. The model constructed based on the theory of planned behavior could explain 42.2% of the variance of vaccination acceptance. Attitudes and perceived behavioral control had a direct positive effect on parents' acceptance to booster dose of the COVID-19 vac￣cine path coefficients were 0.47 and 0.18 P<0.01 The direct effect of subjective norms on vaccination acceptance was not sta￣tistically significant Perceived behavioral control and subjective norms could have indirect positive effects on vaccination accept￣ance through attitudes path coefficients were 0.27 and 0.13 P<0.01 . Conclusion Parents in Nanjing have a higher acceptance to vaccinate their children aged 3-11 years with the COVID-19 vaccine booster dose. The theory of planned behavior shows a good explanatory ability on parents' acceptance to vaccinate their children and attitude plays an important role in the formation of vacci￣nation acceptance. © 2022 Chinese Journal of School Health. All right reserved.
ABSTRACT
With the global epidemic of the COVID-19, various rumors spread wantonly on social networks, which has seriously affected the stability and harmony of the entire society. To purify the network environment, some researchers have proposed to fight rumors from the perspectives of tracing the source of rumors, detecting the authenticity of information, and predicting explosive fake news. But their works are fragmented, and their performance are not significant. So we need strong antirumor methods to fight rumors. To this end, this article proposes a more comprehensive antirumor mechanism, which can realize rumors source location, rumor detection, and popularity prediction (RLDP). In particular, in the task of localization, we propose graph neural network-based method, which does not need to specify the underlying propagation mode and the number of rumor sources;in the task of detection, utilizing lightGBM, we construct a rumor detection model;in the task of popularity prediction, we construct a model based on contrastive learning while considering user engagements and information propagation, and the text of rumor. Finally, we verify the performance of the proposed RLDP by conducting extensive experiments. IEEE
ABSTRACT
The current coronavirus disease 2019 (COVID-19) pandemic has caused significant challenges throughout the world and a rapid, reliable diagnostic test is in high demand. Real-time reverse transcription polymerase chain reaction (RT-PCR) was one of the most quickly established methods of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection and is considered to be the gold standard. In this report, we share our experience of using two different testing platforms: the cobas 6800 SARS-CoV-2 test, an automated system that was recently granted Emergency Use Authorization by the FDA, and a laboratory-developed test based on the protocol from the Taiwan Centers for Disease Control (CDC). There was an overall 96.2% agreement between the two platforms. However, the positive agreement between the two platforms was only 80.0%. We found 3 instances of discordance between the two systems and this emphasized the need for timely diagnosis with a reliable testing platform.
ABSTRACT
Introduction: Patients on maintenance hemodialysis (MHD) are vulnerable to COVID-19. MHD individuals are usually old with chronic comorbidities while these features are all risk factors of poor prognosis. Case Description: Our patient was a 79-year-old female with ESRD and had been on MHD for 12 years in Wuhan. She had a history of severe coronary heart disease. The patient firstly reported fever, chest tightness and cough. One week later, she developed diarrhea and the chest CT indicated “viral pneumonia”. Her COVID-19 testing was reported positive, and she was admitted to the hospital with SpO2 of 92% and high level of hs-CRP, cardiac troponin I (TNI) and creatine kinase MB isoenzyme. She was prescribed oxygen therapy, methylprednisolone, intravenous immunoglobulin, prophylactic anticoagulation and CRRT therapy. Her respiratory condition improved gradually and COVID-19 testing showed negative. However, on admission day 19, the patient presented psychiatric symptom, and was considered as secondary higher-level cognitive impairment since she received regular CRRT therapy without using drugs which could cause mental disorders. Two days later, her chest X-ray showed lung infection advanced with increased hs-CRP and PCT indicating bacterial infection. We prescribed meropenem and vancomycin. Meanwhile, there was an increasing trend of TNI, and electrocardiogram indicated acute coronary syndrome (ACS). Clopidogrel, aspirin and atorvastatin were prescribed immediately. Unfortunately, on admission day 22, the patient suffered cardiac arrest and was declared dead after active rescue. The cause of death was considered as cardiogenic shock and ACS. Discussion: To our experience, personalized immunomodulation therapies against “cytokine storm” including steroid, intravenous immunoglobulin and CRRT might playimportant role in treating severe COVID-19 patients with MHD. Cardiovascular disease is the leading cause of death in ESRD patients. The cardiovascular events of this patient might be combined with COVID-19 associated heart injury and worsen of pre-existing CHD. We suggested lack of family support during quarantine might be a reason of psychiatric symptom. In conclusion, personalized immunomodulation therapies might be helpful in treating severe COVID-19 patients with MHD. Cardiovascular events were associated with poor outcome.
ABSTRACT
Objective: To investigate the positive rates of 2019-nCoV nucleic acid in different specimens from confirmed COVID-19 cases during hospitalization and after discharge. Methods: Patients with confirmed COVID-19 were enrolled from designated hospitals. Nasal swabs, throat swabs, and specimens of stool, urine and blood were collected during hospitalization. After the patients were discharged, nasal swabs, throat swabs and stool specimens were collected during follow-up. Real-time RT-PCR was used to detect 2019-nCoV nucleic acid. Results: This study involved 25 confirmed COVID-19 cases. During hospitalization, all patients tested positive in both nasal and throat swab 2019-nCoV nucleic acid tests, and nine of them (36.00%) were positive in stool specimen test. Urine and blood specimen test results were all negative. Nasal swabs, throat swabs and stool specimens were collected from each patient 7 d and 14 d after discharge. Two patients (8.00%) tested positive for 2019-nCoV nucleic acid again in nasal and throat swab tests on 7 d, while all stool specimen tests were negative. No 2019-nCoV nucleic acid was detected in nasal swabs, throat swabs or stool samples on 14 d. Conclusions: 2019-nCoV nucleic acid was detected in stool samples of confirmed COVID-19 cases during hospitalization. Nasal and throat swab nucleic acid tests turned positive again in some patients after discharge.