Understanding Chinese students' online learning experiences with emergency remote teaching: a case study

Despite the growing body of research on college students' online learning experiences during the COVID-19 pandemic, little is known about how individual students perceive and experience emergency remote teaching in China. To fill this gap, this study seeks to explore college students' perceptions of emergency remote teaching as well as the factors deemed favourable and unfavourable to online learning. This study, adopting a photo elicitation method, investigated four college students' online learning experiences in an emergency remote instruction context. Our study revealed that students went through three stages of online learning and their perceptions of emergency remote teaching changed from one stage to another. Additionally, student-content interaction, strong teacher support and a high-level of digital inclusion were three factors that facilitated effective online learning, whereas lack of interaction with teachers and peers and dormitory confinement were two factors perceived as hindrance. The study explored possible explanations of the findings and made pedagogical recommendations to foster online learning success. The study bears significance for teachers and administrators practicing technology-supported teaching activities amid and beyond the pandemic.

Factors Influencing Covid-19 Risk: Insights from Molnupiravir Exposure-Response

Background: Exposure-response (E-R) models were developed for the primary endpoint of hospitalization or death in COVID-19 patients from the Phase 3 portion of the MOVe-OUT study (Clinicaltrials.gov NCT04577797). Beyond dose, these models can identify other determinants of response, highlight the relationship of virologic response with clinical outcomes, and provide a basis for differential efficacy across trials. Method(s): Logistic regression models were constructed using a multi-step process with influential covariates identified first using placebo arm data only. Subsequently the assessment of drug effect based on drug exposure was determined using placebo and molnupiravir (MOV) arm data. To validate the models, the rate of hospitalization/death was predicted for published studies of COVID-19 treatment. All work was performed using R Version 3.0 or later. Result(s): A total of 1313 participants were included in the E-R analysis, including subjects having received MOV (N=630) and placebo (N=683). Participants with missing baseline RNA or PK were excluded (79 from MOV and 16 from placebo arms). The covariates shown to be significant determinants of response were baseline viral load, baseline disease severity, age, weight, viral clade, and co-morbidities of active cancer and diabetes. Day 5 and Day 10 viral load were identified as strong on-treatment predictors of hospitalization/death, pointing to sustained high viral load as driving negative outcomes. Estimated AUC50 was 19900 nM*hr with bootstrapped 95% C.I. of (9270, 32700). In an external validation exercise based on baseline characteristics, the E-R model predicted the mean (95% CI) placebo hospitalization rates across trials of 9.3% (7.6%, 11.7%) for MOVe-OUT, 7.2% (5.3%, 9.8%) for the nirmatrelvir/ritonavir EPIC-HR trial, and 3.2% (1.9%, 5.5%) for generic MOV trials by Aurobindo and Hetero, consistent with the differing observed placebo rates in these trials. The relative reduction in hospitalization/death rate predicted with MOV treatment (relative to placebo) also varied with the above patient populations. Conclusion(s): Overall, the exposure-response results support the MOV dose of 800 mg Q12H for treatment of COVID-19. The results further support that many clinical characteristics impacted hospitalization rate beyond drug exposures which can vary widely across studies. These characteristics also influenced the magnitude of relative risk reduction achieved by MOV in the MOVe-OUT study.

Immune-Viral Dynamics Modeling of the Basis for Individual Variation in Covid-19

Background: Viral dynamics models provide mechanistic insights into viral disease and therapeutic interventions. A detailed, mechanistic model of COVID-19 was developed and fit to data from molnupiravir (MOV) trials to characterize the SARS-CoV-2 viral dynamics in MOV-treated and untreated participants and describe the basis for variation across individuals. Method(s): An Immune-Viral Dynamics Model (IVDM) incorporating mechanisms of viral infection, viral replication, and induced innate and adaptive immune response described the dynamics of viral load (VL) from pooled data from MOV Phase 2 and 3 trials (N=1958). Population approaches were incorporated to estimate variation across individuals and to conduct an extensive covariate analysis. Nineteen parameters in a system of five differential equations described SARS-CoV-2 viral dynamics in humans. Six population parameters were successfully informed through fitting to observed trial data while the remaining parameters were fixed based on literature values or calibrated via sensitivity analysis. Result(s): Final viral dynamics and immune response parameters were all estimated with high certainty and reasonable inter-individual variabilities. The model captured the viral load profiles across a wide range of subpopulations and predicted lymphocyte dynamics without using this data to inform the parameters, suggesting inferred immune response curves from this model were accurate. This mechanistic representation of COVID-19 disease indicated that the processes of cellular infection, viral production, and immune response are in a time-varying, non-equilibrium state throughout the course of infection. MOV mechanism of action was best described as an inhibitory process on the infectivity term with estimated AUC50 of 10.5 muM*hr. Covariates identified included baseline viral load on infectivity and age, baseline disease severity, viral clade, baseline viral load, and diabetes on immune response parameters. Greater variation was identified for immune parameters than viral kinetic parameters. Conclusion(s): These findings show that the variation in the human response (e.g., immune response) is more influential in COVID-19 disease than variations in the virus kinetics. The model indicates that immunocompromised patients (due to HIV, organ transplant, active cancer, immunosuppressive therapies) develop an immune response to SARS-CoV-2, albeit more slowly than in immunocompetent, and MOV is effective in further reducing viral loads in the immunocompromised.

Asymmetric volatility spillover among global oil, gold, and Chinese sectors in the presence of major emergencies

The volatility of international crude oil and gold markets has affected stock markets through several economic channels, and the impact tends to be more evident with the appearance of emergencies. However, the volatility linkages between commodities and Chinese sector stocks in the presence of emergencies are understudied. To examine the asymmetric relationship and time-varying connectedness between commodities and Chinese sector stocks, this paper first employs GJR-GARCH to capture the realized volatility of international oil, gold, and Chinese sector stocks. Secondly, we decompose the realized volatility of international oil and gold into bad and good volatility and then employ the TVP-VAR-DY approach to obtain the connectedness index. The final result shows asymmetric volatility spillover among oil, gold, and Chinese sector stocks. During the COVID-19 outbreak, the gold good volatility transmission is intenser than bad volatility. Thirdly, the analysis is also carried out under different subperiods. They include three international events: the global financial crisis and the European debt crisis, the oil crisis, and COVID-19. The result reveals heterogeneity exists in the impact of international oil and gold on the Chinese sector stocks under different emergencies. These findings are of great significance for policymakers to improve the sector management under the impact of different emergencies and for investors to design diversified portfolios according to the commodity-sector risk spillover effects. © 2023 Elsevier Ltd

‘Are we laughing at the same?': a contrastive analysis of Covid-related memes in Czech, Chinese and Spanish

Humour is often employed as a coping mechanism, with therapeutic effects on those producing and receiving it (Christopher 2015;Samson & Gross 2012). This buffering effect of humour might explain why, at the time of an international pandemic like Covid-19, human beings, independently of their cultural origin, have resorted to humour as a means of alleviating uncertainty and fear, and of enhancing feelings of connection and bonding with others. The proliferation of Covid-related humour has also led to a wide range of studies, with special attention to memes. However, contrastive studies are more limited, especially those comparing very different languages and cultural realities such as the Chinese, the Czech and the Spanish ones. This paper aims to redress this imbalance by analysing a corpus of 300 Covid-memes (100 memes per language). More specifically, we intend to answer the following questions: (i) what dimension(s) of humour are predominant in each language? (ii) what actors do the memes in the three countries target? and (iii) to what extent can these preferences relate to cultural differences/similarities? Applying a mixed-method approach, results show that there seems to be a global preference for affiliative humour while aggressive (and self-deprecating) humour appears to be more culturally bound, with a higher frequency in the Czech and Spanish datasets in contrast to the Chinese one. Likewise, the Czech and Spanish dataset share a significantly higher number of common frames, which might be pointing to a more European, Western type of humour in comparison to the Chinese approach (Jiang et al. 2019). © 2023,European Journal of Humour Research. All Rights Reserved.

Molnupiravir Exposure-Response Relationships for Clinical Outcomes, Virology, and Mechanism of Action Biomarkers Are Consistent in Treatment of Covid-19

BACKGROUND: Exposure-response (E-R) analysis supported molnupiravir phase 3 dose selection based on viral load (VL) and mechanism of action (MOA) markers from phase 2.1 This analysis evaluated how well these biomarkers predict the E-R for hospitalization or death in phase 3. METHOD(S): The following E-R models were developed and compared: (1) logistic regression of the primary outcome (hospitalization or death) from phase 3, (2) VL change from baseline (CFB) from phase 2 and 3, and (3) low frequency nucleotide substitutions (LNS), a measure of MOA, from phase 2. Individual estimates of exposure were derived from population PK modeling of sparse samples collected in all patients. All work was performed using R v3.0 or later. RESULT(S): All E-R relationships were best represented by an Emax model with AUC50 estimates of 19,900, 10,260, and 4,390 nM*hr for hospitalization, day 5 VL CFB, and LNS mutation rate, respectively. Normalized E-R relationships were overlaid, illustrating consistency in E-R shape (Figure). Plasma NHC AUC0-12 was identified as the PK driver. Patients at 800 mg achieved near maximal response. CONCLUSION(S): E-R results support the dose of 800 mg Q12H for treatment of COVID-19. E-R relationships for MOA and virology biomarkers were consistent with the clinical E-R. (Figure Presented).

Molnupiravir Exposure-Response of Clinical Outcomes in Treatment of Covid-19

BACKGROUND: The goal of this analysis was to investigate the relationship of molnupiravir pharmacokinetics (PK) and clinical outcomes (primary endpoint of hospitalization or death) in patients with COVID-19 in the phase 3 cohort of MOVe-OUT (clinicaltrials.gov NCT04577797).1 METHODS: Logistic regression models were constructed using a multi-step process with influential covariates identified first using placebo arm data only and subsequently assessment of drug effect as a function of exposures evaluated using placebo and MOV arm data. Individual estimates of exposure were derived from population PK modeling of sparse samples collected in all patients. All work was performed using R v3.0 or later. RESULT(S): A total of 1,313 participants were included in the exposure-response (E-R) analysis, including subjects on MOV (N = 630) and placebo (N = 683). Participants with missing PK or baseline RNA were excluded (79 from MOV and 16 from placebo arms). The covariates shown to be significant determinants of response were baseline viral load, baseline disease severity, age, weight, viral clade, active cancer, and diabetic risk factors. An additive AUC-based Emax model with a fixed hill coefficient of 1 best represented exposure-dependency in drug effect. Estimated AUC50 was 19,900 nM*hr with bootstrapped 95% confidence interval of (9,270, 32,700). Patients at 800 mg achieved near maximal response, which was larger than the response projected for 200 or 400 mg. CONCLUSION(S): Overall, the E-R results support the MOV dose of 800 mg Q12H for treatment of COVID-19. Many patient characteristics, beyond drug exposures, impacted the risk of hospitalization or death.

Family factors associated with handwashing behavior among children aged 3 to 12 years in Beijing

Objective To understand the proper handwashing behavior of preschool children and primary school students in Beijing and to analyze associated family factors to provide reference for further health intervention related to handwashing. Methods From November to December 2020 parents of 36 kindergartens and 18 primary schools in 9 districts of Beijing were investigated online by using a self-designed questionnaire with questionnaire star software. The contents of the survey included the basic situation of children and their families parents' correct knowledge of the prevention of novel coronavirus pneumonia their perception of the epidemic risk the provision of handwashing guidance for children and children's handwashing behavior. Results The proportion of proper handwashing of preschool children was 70.2% which was higher than that of primary school students 61.9% χ2 = 57.63 P<0.01. The proportion of parents of preschool children who correctly knew handwashing related knowledge 36.2% 33.4% had low perception of epidemic risk 28.9% 25.4% and provided handwashing guidance 99.1% 97.9% was higher than that of parents of primary school students and the differences were statistically significant χ2 = 6.72 22.84 18.68 P<0.05. But the proportion of parents of preschool children who had high self-efficacy 75.7% 78.2% was lower compared to parents of primary school students χ2 = 6.43 P = 0.04. Multivariate regression results showed that whether preschool children or primary school students urban areas and parents had high self-efficacy low risk perception and provided hand washing guidance for children children were more likely to wash their hands correctly. For preschool children non-only children were 0.79 95%CI= 0.69-0.92 times more likely to wash their hands correctly than only children. For primary school students girls were 1.21 95%CI = 1.06-1.39 times more likely to wash their hands correctly than boys and parents who know knowledge correctly were 1.20 95%CI = 1.04-1.40 times more likely to know it incorrectly P<0.05. Conclusion Proper hand washing behavior of preschool children is higher than that of primary school students. Parental awareness of COVID-19 epidemic handwashing behavior self-efficacy and guidance behavior have effects on the development of children's health behavior. Measures should be taken to enhance parents' awareness of infectious diseases and their ability and self-efficacy of guiding children in disease prevention. © 2022 The authors.

COVID-19 Detection Based on 6-Layered Explainable Customized Convolutional Neural Network

This paper presents a 6-layer customized convolutional neural network model (6L-CNN) to rapidly screen out patients with COVID-19 infection in chest CT images. This model can effectively detect whether the target CT image contains images of pneumonia lesions. In this method, 6L-CNN was trained as a binary classifier using the dataset containing CT images of the lung with and without pneumonia as a sample. The results show that the model improves the accuracy of screening out COVID-19 patients. Compared to other methods, the performance is better. In addition, the method can be extended to other similar clinical conditions. © 2023 Tech Science Press. All rights reserved.

The "problem” of teacher quality: exploring challenges and opportunities in developing teacher quality during the Covid-19 global pandemic in England

Teachers and teacher education are often presented as "problems” to be solved, with policy solutions that focus on ways to make teachers "better” and improve teacher "quality” by introducing prescriptive strategies. We investigate the ways Covid-19-related changes to university and school-based facets of Initial Teacher Education (ITE) in England influence teacher quality in relation to both student teachers and early career teachers, working in secondary schools. Drawing on 34 interviews with school leaders, school mentors and ITE tutors, we critically explore the ways in which teacher quality was developed through key aspects of teachers' pedagogy and practice during the pandemic crisis when schools were closed and teaching moved online. Our findings show that the pandemic crisis has highlighted the different facets of teacher quality which arguably disrupt narrow and prescriptive understandings of what constitutes "quality” in policy terms. Although there were many instances of challenge in the development of new and student teachers, our data also shows how ITE tutors, school mentors and leaders responded creatively to the crisis. Participants highlighted the opportunities afforded by the pandemic to develop diverse and innovative pedagogies and practice, enhance students' subject knowledge, as well as overcome some of the challenges in other areas of pedagogy and practice. Furthermore, the study shows that teacher quality was not substantially reduced despite the challenges arising from the pandemic and concerns that pre-service teachers would not be ready and prepared for a career in the classroom. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Asymmetric Differential Privacy

Differential privacy (DP) is attracting considerable research attention as a privacy definition when publishing statistics of a dataset. This study focused on addressing the limitation that DP inevitably causes two-sided errors. For example, consider a threshold query that asks whether a counting is above a given threshold or not. An answer through the DP mechanism can cause error. This phenomenon is not desirable for sensitive analysis such as the counting of COVID-19-infected individuals (in a dataset) visiting a specific location;misinformation can result in incorrect decision-making which can increase the epidemic. To the best of our knowledge, the problem is yet to be solved. We proposed a variation of DP, namely asymmetric DP (ADP) to solve the problem. ADP can provide reasonable privacy protection and achieve one-sided errors. Finally, experiments were conducted to evaluate the utility of the proposed mechanism for the epidemic analysis using a real-world dataset. The results of study revealed the feasibility of proposed mechanisms. © 2022 IEEE.

[Progress on specific and non-specific immune effects of Bacille Calmette-Guerin].

Currently, Bacille Calmette-Guerin(BCG) is still the only admitted vaccine to prevent tuberculosis around the world. The target population is infants and children, but its protective efficacy is limited. As more and more studies have shown that re-vaccination with BCG protects against tuberculosis in adults, BCG can also induce non-specific immunity against other respiratory diseases and some chronic diseases by training immunity, especially the immune effects against COVID-19. At present, the epidemic of COVID-19 has not been effectively contained, and it is worth considering whether BCG vaccine can be used as an intervention to prevent COVID-19. The WHO and China do not have a policy to support BCG revaccination, and as more and more BCG vaccines are discovered, whether selective revaccination can be carried out in some high-risk populations and whether the vaccine can be used more widely have led to intense discussions. This article reviewed the effects of specific immunity and non-specific immunity of BCG on tuberculosis and non-tuberculous diseases.

Individual Surveillance Around Parked Aircraft at Nighttime: Thermal Infrared Vision-Based Human Action Recognition

The COVID-19 crisis has led to an unusually large number of commercial aircraft being currently parked or stored. For airlines, airports, and civil aviation authorities around the world, monitoring, and protecting these parked aircraft to prevent them from causing human-made damage are becoming urgent problems that are receiving increasing attention. In this study, we use thermal infrared monitoring videos to establish a framework for individual surveillance around parked aircraft by proposing a human action recognition (HAR) algorithm. As the focus of this article, the proposed HAR algorithm seamlessly integrates a preprocessing module in which a novel data structure is constructed to introduce spatiotemporal information of the action;a convolutional neural network-based module for spatial feature extraction;a triple-layer convolutional long short-term memory network for temporal feature extraction;and two fully connected layers for classification. Moreover, because no infrared dataset is available for the HAR task on airport grounds at nighttime, we present a dataset called IIAR-30, which consists of eight action categories that frequently occur on airport grounds and 2000 video clips. The experimental results on the IIAR-30 dataset demonstrated that the recognition accuracy of the proposed method was higher than 96%. We also further evaluated the effectiveness of the proposed method by comparing it with five baselines and four other methods. © 2022 IEEE.

[Epidemiological investigation on the local epidemic situation in Zhengzhou High-Tech Zone caused by SARS-CoV-2 Delta variant].

This study collected epidemic data of COVID-19 in Zhengzhou from January 1 to January 20 in 2022. The epidemiological characteristics of the local epidemic in Zhengzhou High-tech Zone caused by the SARS-CoV-2 Delta variant were analyzed through epidemiological survey and big data analysis, which could provide a scientific basis for the prevention and control of the Delta variant. In detail, a total of 276 close contacts and 599 secondary close contacts were found in this study. The attack rate of close contacts and secondary close contacts was 5.43% (15/276) and 0.17% (1/599), respectively. There were 10 confirmed cases associated with the chain of transmission. Among them, the attack rates in close contacts of the first, second, third, fourth and fifth generation cases were 20.00% (5/25), 17.86% (5/28), 0.72% (1/139) and 14.81% (4/27), 0 (0/57), respectively. The attack rates in close contacts after sharing rooms/beds, having meals, having neighbor contacts, sharing vehicles with the patients, having same space contacts, and having work contacts were 26.67%, 9.10%, 8.33%, 4.55%, 1.43%, and 0 respectively. Collectively, the local epidemic situation in Zhengzhou High-tech Zone has an obvious family cluster. Prevention and control work should focus on decreasing family clusters of cases and community transmission.

Novel Ionizable Lipid Nanoparticles for SARS-CoV-2 Omicron mRNA Delivery

mRNA-based therapy has emerged as the most promising nucleic acid therapy in the fight against COVID-19. However, a safe and efficacious systemic delivery remains a challenge for mRNA therapy. Lipid nanoparticles (LNPs) are currently widely used in mRNA delivery vehicles. Here, series of ionizable LNPs are rationally designed. YK009-LNP is an optimal delivery platform to carry mRNA. YK009-LNP exhibited higher mRNA delivery efficiency, a more favorable biodistribution pattern, and better safety than the approved MC3-LNP. In addition, mRNA encoding SARS-CoV-2 Omicron receptor binding domain protein was synthesized, and intramuscular administration of mice with YK009-LNP-Omicron mRNA induced a robust immune response and immune protective effect. Our study provides a novel mRNA delivery vehicle with more powerful delivery efficiency and better safety than the approved LNPs. This article is protected by copyright. All rights reserved.

Temporal and spatial variation of NO2 in oasis cities in Xinjiang based on hyperspectral remote sensing technology

In order to promote the implementation of the "14th Five-Year Plan for the Prevention and Control of Air Pollution in Key Regions”, and to control NO2 pollution in Xinjiang more effectively. Based on hyperspectral remote sensing technology, combined with ground monitoring technology, this paper studies the temporal and spatial changes of NO2 pollutants in valley-type oasis cities in Xinjiang. The research results show that: (1) The overall characteristics of NO2 concentration are as follows: "Twelfth Five-Year” period > "Eleventh Five-Year” period > "Thirteenth Five-Year” period;the largest decrease (−11.45%) during the "Eleventh Five-Year" period in autumn;During the "Twelfth Five-Year Plan” period, the winter has the largest decrease (−9.74%). In the past 15 years, the center of NO2 pollution concentration in northern Xinjiang was near the Ganquanbao Industrial Park at the junction of the southeastern suburbs of Urumqi and the southwestern part of Changji Prefecture, and the concentration in the study area of southern Xinjiang was near Aksu City. The NO2 concentration in Xinjiang is influenced by both local sources and air mass transport. The potential source areas of northern Xinjiang are mainly distributed in the central part of Xinjiang, mainly through Wuchangshi and other areas. The potential source areas of the southern slope of the Tianshan Mountains are mainly distributed in the southern part of Xinjiang, mainly through the Korla and Aksu areas. During the COVID-19 epidemic, the control of man-made pollution sources has played a significant role in air pollution control. This study can provide data reference for the prevention and control of air pollution in Xinjiang. © 2022 Science Press. All rights reserved.

METHODS FOR FORECASTING DEMAND IN REPAIRING AN AIRLINE'S REPAIRABLE LINE REPLACEABLE UNIT PARTS

Verspreide mislukkingsfrekwensie, veranderlike en komplekse bei'nvloedende faktore, en 'n lae akkuraatheid in die voorspelling van voorraadaanvraag is kenmerke van lynvervangbare eenheid (LRU) onderdele. Sommige duur herstelbare LRU (HR-LRU) onderdele het 'n aansienlike impak op die koste van vliegtuigonderdele. Baie lugrederye stel baie belang om die vraag na HR-LRU-onderdele te voorspel. Hierdie studie bied prosedures aan om die optimale model vir die voorspelling van die vraag na HR-LRU-onderdele te identifiseer. Eerstens is 'n tradisionele voorspellingsmodel, sewe enkelmetingsmodelle en vier gekombineerde modelle gekies en gebruik om mislukkingsdata te voorspel. Vervolgens is evalueringsindekse vir assessering gekies om die optimale model te verkry. Laastens het ons die werklike en voorspelde waardes vergelyk om die gevolgtrekkings wat tydens die vorige evalueringstap gemaak is, te verifieer. Die resultate het aangedui dat, onder die enkelmodelle, die negatiewe binomiale regressiemodel en die Holt-Winters model die mees geskikte was vir HR-LRU dele. Die SSE en MAE van die negatiewe binomiale regressie was die laagste op 118.4114 en 1.97352 onderskeidelik, en die Holt-Winters model se MAE was die laagste op 1. 13270. Die IOWA operateur voorspellingsmodel en die fout wederkerige veranderlike gewig kombinasie metode het voorspellings opgelewer wat die naaste aan die werklike waardes was onder die gekombineerde modelle. Die voorspellingsfoute van die negatiewe binomiale regressiemodel en die IOWA-operateurmodel was slegs 0,169 3 en 1,411 3 in 2018. Benewens die samestelling van 'n stel prosesse om die vraag na HR-LRU-onderdele te voorspel, bespreek ons ook die graad van passing van verskillende metodes, die redes vir die verandering in die gewaarborgde koers van HR-LRU-onderdele, en die redes vir die voorkoms van spesiale jare. Ons vergelyk ook die ooreenkomste en verskille tussen hierdie artikel en ander navorsingsartikels.Alternate :Scattered failure frequency, variable and complex influencing factors, and a low accuracy in predicting inventory demand are characteristics of line replaceable unit (LRU) parts. Some high-priced repairable LRU (HR-LRU) parts have a considerable impact on the cost of aircraft spare parts.This study presents procedures to identify the optimal model for forecasting the demand for HR-LRU parts. First, a traditional prediction model, seven single measurement models, and four combined models were selected and used to predict failure data. Subsequently, evaluating indexes were selected for assessment to obtain the optimal model. Finally, we compared the actual and predicted values to verify the conclusions drawn during the previous evaluation step. The results indicated that, among the single models, the negative binomial regression model and the Holt-Winters model were most suitable for HRLRU parts. The SSE (sum of squares error) and MAE (mean absolute error) of the negative binomial regression were the lowest at 118.4114 and 1.97352 respectively, and the Holt-Winters model's MAE was the lowest at 1. 13270. The IOWA operator prediction model and the error reciprocal variable weight combination method produced predictions closest to the actual values among the combined models. In addition to constructing a set of processes to prediction, we also discuss the fit of different methods, the reasons for the change in the guaranteed rate, and the reasons for the occurrence of special years. We also compare the similarities and differences between this article and other papers.

Intelligent classification of B-line and white lung from COVID-19 pneumonia ultrasound images using radiomics analysis

As two important features of COVID-19 pneumonia ultrasound, the B-line and white lung are easily confused in clinics. To classify the two features, a radiomics analysis technology was developed on a set of ultrasound images collected from patients with COVID-19 pneumonia in the study. A total of 540 filtered images were divided into a training set and a test set in the ratio of 7:3. A machine learning model was proposed to perform automated classification of the B-line and white lung, which included image segmentation, feature extraction, feature screening, and classification. The radiomic analysis was applied to extract 1688 high-throughput features. The principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) were used to perform feature screening for redundancy reduction. The support vector machine (SVM) was utilized to make the final classification. The confusion matrix was used to visualize the prediction performance of the model. In the result, the model with features selected using LASSO outperformed the model with PCA in terms of classification effectiveness. The number of high-throughput features closely related to the classification under the model with LASSO was 11, with the value of AUC, accuracy, specificity, precision and recall being 0.92, 0.92, 0.91, 0.92 and 0.92, respectively. Compared to the model with PCA, the values of the evaluation indicators of the model with LASSO increased by 13.94%, 13.26%, 15.79%, 22.23% and 5.66%, respectively. As a conclusion, the proposed models showed good performance in differentiation of the B-line and white lung, with potential application value in the clinics. © 2022 ACM.

An Accurate, Flexible and Private Trajectory-Based Contact Tracing System on Untrusted Servers

Infections by the Covid-19 coronavirus have proliferated since the end of 2019, and many privacy-protective contact tracing systems have been proposed to limit infections from spreading. However, the existing Bluetooth-based contact tracking systems lack accuracy and flexibility. In addition, it is desirable to have a contact tracing system that, in the future, can contribute to limiting the proliferation of new coronaviruses and as yet unknown viruses. In this study, we propose a method to extend a contact tracing system to be more flexible, accurate, and capable of dealing with unknown viruses by using trajectory data and infection factor information while protecting privacy. We also implemented the proposed extension method and measured its execution time and confirmed its practicality. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Predicting ICU Admissions for Hospitalized COVID-19 Patients with a Factor Graph-based Model

This paper presents a factor graph-based model that takes comorbidities and clinical measurements as inputs and predicts intensive care unit (ICU) admissions 3 days and 7 days in advance for hospitalized COVID-19 patients. We applied the proposed model on a COVID-19 cohort from a large medical center in Chicago (with records from March 2020 to August 2021). We used the first occurrence of the Delta variant in the U.S., February 2021, as the threshold to divide the dataset into pre-Delta data (533 patients) and post-Delta data (56 patients). Our model demonstrated 0.82 AUC on the pre-Delta data and 0.87 AUC on the post-Delta data in 7-day predictions. Our contribution is a model that (i) explains relationships between different clinical features and provides interpretations for ICU admissions, (ii) outperforms existing methods for 7-day predictions, and (iii) maintains more robustness than existing models in predictions under the influence of the Delta variant. The proposed model could be used as a predictive tool in clinical practice to help clinicians in decision-making by predicting which patients will need ICU support in the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.