Travel behavior adjustment based epidemic spreading model and prediction for COVID-19

Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people' s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took "ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word "ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word "ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers. © 2023 Chinese Physical Society.

Simulation Study on Airflow Organization and Environment in Reconstructed Fangcang Shelter Hospital Based on CFD

With frequent outbreaks of COVID-19, the rapid and effective construction of large-space buildings into Fangcang shelter hospitals has gradually become one of the effective means to control the epidemic. Reasonable design of the ventilation system of the Fangcang shelter hospital can optimize the indoor airflow organization, so that the internal environment can meet the comfort of patients and at the same time can effectively discharge pollutants, which is particularly important for the establishment of the Fangcang shelter hospital. In this paper, through the reconstruction of a large-space gymnasium, CFD software is used to simulate the living environment and pollutant emission efficiency of the reconstructed Fangcang shelter hospital in summer under different air supply temperatures, air supply heights and exhaust air volume parameters. The results show that when the air supply parameters are set to an air supply height of 4.5 m, an air supply temperature of 18 °C, and an exhaust air volume of a single bed of 150 m3/h, the thermal comfort can reach level I, and the ventilation efficiency for pollutants can reach 69.6%. In addition, the ventilation efficiency is 70.1% and 70.3% when the exhaust air volume of a single bed is continuously increased to 200 and 250 m3/h, which can no longer effectively improve the pollutant emission and will cause an uncomfortable blowing feeling to patients. © 2023 by the authors.

Subsidy strategy for reserving flexible capacity of emergency supply production

This paper investigates a government's subsidy strategy for motivating a manufacturer to set up a flexible production line for emergency supplies. Four subsidy strategies are proposed to ensure a desired service level in case of an emergency: zero subsidy, a fixed subsidy, a marginal subsidy, and a hybrid subsidy. We develop a game theoretical model to examine how the government can induce a manufacturer to set up a flexible production line that can respond promptly to an emergency, based on the manufacturer's cost structure (fixed and marginal costs). We find that when the marginal profit of an emergency product is higher than that of the manufacturer's regular product, a fixed (marginal) subsidy is the dominant strategy if the manufacturer's fixed (marginal) cost is high, while a hybrid subsidy strategy is dominant if both costs are high. When the marginal profit of an emergency product is lower than that of the manufacturer's regular product, neither a fixed subsidy nor a zero subsidy will be the dominant strategy. We also find that a marginal subsidy can ensure the effectiveness of the strategy, while a fixed subsidy helps improve strategy efficiency. We use government subsidy strategies implemented for Chinese COVID-19 emergency supplies as examples to demonstrate the effectiveness and efficiency of the subsidy strategies under the proposed framework. We also extend the discussion by considering the manufacturer's social consciousness.

Resilience and recovery of public transport use during COVID-19

To better understand how public transport use varied during the first year of COVID-19, we define and measure travel behavior resilience. With trip records between November 2019 and September 2020 in Kunming, China, we identify people who relied on traveling by subway both before and after the first pandemic wave. We investigate whether and how travelers recover to their pre-pandemic mobility level. We find that public transport use recovered slowly, as urban mobility is a result of urban functionality, transport supply, social context, and inter-personal differences. In general, urban mobility represents a strengthened revisiting tendency during COVID-19, as individual's trips occur within a more limited space. We confirm that travel behavior resilience differs by groups. Commuters recover travel frequency and length, while older people decrease frequency but retain activity space. The study suggests that policymakers take group heterogeneity and travel behavior resilience into account for transport management and city restoration.

PQSCT: Pseudo-Siamese BERT for Concept Tagging With Both Questions and Solutions

The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which extracts knowledge index structures and knowledge representations for exercises. Unfortunately, to the best of our knowledge, existing tagging approaches based on exercise content either ignore multiple components of exercises, or ignore that exercises may contain multiple concepts. To this end, in this paper, we present a study of concept tagging. First, we propose an improved pre-trained BERT for concept tagging with both questions and solutions (QSCT). Specifically, we design a question-solution prediction task and apply the BERT encoder to combine questions and solutions, ultimately obtaining the final exercise representation through feature augmentation. Then, to further explore the relationship between questions and solutions, we extend the QSCT to a pseudo-siamese BERT for concept tagging with both questions and solutions (PQSCT). We optimize the feature fusion strategy, which integrates five different vector features from local and global into the final exercise representation. Finally, we conduct extensive experiments on real-world datasets, which clearly demonstrate the effectiveness of our proposed models for concept tagging. IEEE

A Study on the Influence of Task-driven Instruction on Learning Outcomes in Online Learning under COVID-19

Interest in online learning is increasing due to its advantages and pedagogical potential. However, few studies have investigated the effects of task-driven instruction on learning outcomes. This study examines the effectiveness of the application of task-driven instruction as a means of verifying that the use of task-driven instruction in online learning is effective by comparing changes in students' grades, intrinsic motivation, perceived social presence, and perceived cognitive load before and after the application of the method. Eighty high school students (33 males) were recruited for this experiment. Prior to the experiment, the purpose and steps of the study were explained frankly and candidly, problems and risks that might arise from participation in the study were pointed out, the benefits that would result from participation in the study were explained, and the possibility of voluntarily withdrawing from the study at any time was clearly communicated and approved by the study subjects or guardians. They were divided into experimental group I and control group II, with 40 students in each group. The results of the study showed that after the implementation of the instruction, the experimental group I performed significantly better than the control group II. In addition, the experimental group II outperformed the control group II in terms of perceived intrinsic motivation, social presence, and cognitive load. © 2023 ACM.

Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread worldwide and threatened human's health. With the passing of time, the epidemiology of coronavirus disease 2019 evolves and the knowledge of SARS-CoV-2 infection accumulates. To further improve the scientific and standardized diagnosis and treatment of maternal SARS-CoV-2 infection in China, the Chinese Society of Perinatal Medicine of Chinese Medical Association commissioned leading experts to develop the Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection under the guidance of the Maternal and Child Health Department of the National Health Commission. This recommendations includes the epidemiology, diagnosis, management, maternal care, medication treatment, care of birth and newborns, and psychological support associated with maternal SARS-CoV-2 infection. It is hoped that the recommendations will effectively help the clinical management of maternal SARS-CoV-2 infection.Copyright © Wolters Kluwer Health, Inc. All rights reserved.

An Examination of the Spatial Spillover Effects of Tourism Transportation on Sustainable Development from a Multiple-Indicator Cross-Perspective

Tourism is linked to multiple dimensions, such as the economy, society, and environment, and the relationships among its influencing factors are complex, diverse, and overlapping. This study constructed an evaluation index system to measure the degree of coordinated development of tourism, transportation, and the regional economy, then built a tourism-transportation-based Spatial Durbin Model (SDM) regarding the process of the coordinated development of tourism in the Beijing-Tianjin-Hebei region (BTHR) from 2010 to 2020. This paper explains the current status of sustainable tourism development in the BTHR and the impact and spillover effects of transportation on tourism development. The results show that the normalized tourism coordinated development index (NTCDI) of the BTHR increased from 13.61 in 2010 to 18.75 in 2019, then decreased to 14.45 in 2020. The results of SDM show that different transportation modes have different spillover effects on tourism. Specifically, civil aviation transportation has a positive impact and significant spillover on a city's tourism revenue (TR), while high-speed railway transportation has a negative spillover effect. The model results also show that the degree of openness of the city and city economic development level have significant positive effects and spillover effects on tourism development. Finally, the implications of related variables are discussed, and some suggestions are put forward on tourism development in the BTHR. However, there are some limitations in this study. In the future, international cooperation and data sharing will be strengthened, and multivariate methods such as social network analysis, artificial intelligence, and machine learning will be further integrated to achieve accurate simulation and prediction of the spatial spillover effects of tourism transportation.

Politicizing Global Governance Institutions in Times of Crisis The Case of World Health Organization during the Coronavirus Pandemic

This article examines the politicization of the World Health Organization (WHO) over the course of the coronavirus pandemic ( January-December 2020), a paradigmatic case of politicization of global governance institutions. During the pandemic, the WHO was subjected to considerable scrutiny and contestation. This research focuses on politicization at the level of behavior and discourse. Conceptually, it leverages the analytic purchase of politicization and framing. Empirically, it is based on a corpus comprising 505 texts gathered from key actors involved. The analysis not only lays bare the varying demands and arguments vis-a-vis the WHO, but foregrounds the broad consensus among the actors examined (barring the Donald Trump administration) on the imperative to support the organization. Additionally, seven distinct frames on the WHO are identified: Puppet, Handcuffed, Scapegoat, Irreplaceable, Botched, Comme il faut, and Battleground. Together, they offer a holistic overview of the diverse perspectives on the WHO and its pandemic response.

Interactive Strategies of Chinese Cloud Classroom Based on the Perspective of Meta Universe

With the American social software Facebook officially announced its name change to "Meta”, the concept of "Meta Universe” has really entered the public field. The meta universe is an Internet social form based on the integrated application of various emerging information technologies. It has transformative value for the future development of all walks of life, including the education industry. Since the outbreak of covid-19, online Chinese learning of most foreign students has changed from a short-term accidental state to a normal state. In this way, we must pay attention to how to seize the opportunity of the development of meta universe technology, combine the meta universe cloud classroom with AI digital people, and create a new scene of educational application integrating multiple technologies under the new AI ecology. Only in this way can we build a new educational paradigm change, and solve some bottlenecks and difficulties in the current development of Chinese cloud classroom interaction for foreign students, so that classroom teaching can truly promote the development of students' knowledge, ability and emotion, and finally cultivate students' innovative spirit and practical ability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Developing a Place–Time-Specific Transmissibility Index to Measure and Examine the Spatiotemporally Varying Transmissibility of COVID-19

The transmission rate of COVID-19 varies by location and time. A proper measure of the transmissibility of an infectious disease should be place- and time-specific, which is currently unavailable. This research aims to better understand the spatiotemporally changing transmissibility of COVID-19. It contributes to COVID-19 research in three ways. First, it presents a generally applicable modeling framework to estimate the transmissibility of COVID-19 in a specific place and time based on daily reported case data, called space-time effective reproduction number, denoted as (Formula presented.) Then, the developed model is used to create a spatiotemporal data set of (Formula presented.) values at the county level in the United States. Second, it investigates relationships between (Formula presented.) and dynamically changing context factors with multiple machine learning and spatial modeling techniques. The research examines the relationships from a cross-sectional perspective and a longitudinal perspective separately. The longitudinal view allows us to understand how local human dynamics and policy factors influence changes in (Formula presented.) over time in the place, whereas the cross-sectional view sheds light on the demographic, socioeconomic, and environmental factors behind spatial variations of (Formula presented.) at a specific time slice. Some general trends of the relationships are found, but the level of impact by each context factor varies geographically. Third, the best performing local longitudinal models have promising potential to simulate or forecast future transmissibility. The random forest and the exponential regression models based on time-series data gave the best performances. These models were further evaluated against ground truth data of county-level reported cases. Their good prediction accuracies in the case study prove that these machine learning models are promising in their ability to predict transmissibility in hypothetical or foreseeable scenarios. © 2023 by American Association of Geographers.

Online 8-Form Tai Chi Chuan Training and Evaluation System Based on Pose Estimation

Exercise at home has already been a common behavior in the current world, especially in the post-COVID-19 era, even some athletes need to do physical fitness at home to keep their state due to the quarantine. So, the importance of online physical training and evaluation is highly increasing. In this work, we build an online 8-form Tai Chi Chuan (TCC) training and evaluation system, which provides a platform for coaches and users to conduct TCC training and evaluation online. Coaches formulate an evaluation rule and upload coaching videos to the platform, then users watch videos online and submit their own recordings, finally, users will get a score of their recordings. To complete this task, we propose a video capture method to record users' sports exercise videos from different perspectives, construct a 3D pose estimation model to identify human pose from captured video, and propose an evaluation model which can judge users' performance and assign a score to each video. To test our proposed models, we make a dataset consisting of key pose frames of TCC, and the key pose frames are extracted from users' TCC exercise videos. We use the dataset to train our models and assign scores to key poses, then compare the results with scores given by professional TCC players. In addition, we add all key pose scores from every single user together and obtain the whole score of an exercise video. The experiment results show that the error between scores assigned by our models and scores given by professional players does not exceed 1.6 in most scoring of a whole exercise video, and Root Mean Square Error (RMSE) is about 0.75 in the scoring of each key pose. © 2022 IEEE.

Development of an Indirect ELISA Antibodies Detection Method on Tandem-epitope Peptide of African Swine Fever Virus p30 and p54 Proteins.

Here, we report the development of an indirect ELISA antibodies detection method for African swine fever virus (ASFV). Two purified monoclonal antibodies (mAbs) against ASFV p30 and p54 protein were used as targets and a phage-displayed 12-mer peptide library was used to conduct four rounds of biopanning to screen peptide epitopes, then amino acids GGG was used as a linker to synthesize tandem-epitope peptide of ASFV p30 and p54 protein which was used as coating antigen. The optimum reaction conditions of indirect ELISA were determined by chessboard titration, and clinical serum samples were used to evaluate the specificity, sensitivity, stability and conformity of this method. The biopanning experiment indicated that 146PAEPYTT152 was a core domain of the B cell linear epitope of p54 protein. The optimization results of ELISA reaction conditions showed that the tandem-epitope peptide coupled with ovalbumin (OVA) at N-terminal had low background of non-specific serum reaction. And the optimum reaction effect was obtained when the polypeptide antigen was coated with carbonate buffer in 2 mug.mL-1, the serum was diluted 100-fold with blocking solution (1% gelatin solution), and the HRP-antibody was diluted 5 000 times with 0.05% PBST solution. The cut-off value was determined to be 0.339. Furthermore, the results of specificity, sensitivity and stability tests showed that there is no cross-reaction in positive serum samples of classical swine fever virus (CSFV), porcine reproductive and respiratory syndrome virus (PRRSV), porcine circovirus type 2 (PCV2), porcine parvovirus (PPV) and porcine pseudorabies virus (PRV), the detection limit of ASFV positive sera is 1:1 600, and the method had high repeatability. Finally, Total 320 swine serum samples were detected simultaneously by the present established method and commercial ASFV antibody detection kit. The results showed that the relative specificity and sensitivity of the two methods were 97.6% and 97.3%, respectively. And the coincidence rate was 97.5%. In conclusion, this method showed good specificity, sensitivity, repeatability and coincidence rate, that had the potential value of developing clinical diagnostic kit.Copyright © 2022 Editorial Board, Institute of Animal Science of the Chinese Academy of Agricultural Sciences. All rights reserved.

Initial five and ten-minute regional cerebral oxygen saturation as a predictor of the futility of resuscitation for out-of-hospital cardiac arrest

This study aimed to investigate the usefulness of cerebral regional oxygen saturation (rSO2) during the initial 5 and 10 minutes of cardiopulmonary resuscitation (CPR) compared with an initial rSO2 and mean rSO2 during entire CPR to predict the futility of resuscitation for patients without of-hospital-cardiac arrest (OHCA). This was a prospective study involving 52 adult patients presenting in OHCA and whose cerebral rSO2 values were measured until either CPR was terminated or sustained return of spontaneous circulation (ROSC) was achieved. Receiver operating characteristics analyses were used to evaluate which time and type of measurement is better to predict non-ROSC. The area under the curve (AUC) of each rSO2 value according to measurement time (overall, initial 5 minutes and 10 minutes) were the highest value of 0.743, 0.724, and 0.739, mean values of 0.724, 0.677 and 0.701 and rSO2 (Changes in values of regional cerebral oxygen) value of 0.722, 0.734 and 0.724, respectively, while all of the initial values had a poor AUC (<0.7) and also were not statistically significant. The optimal cut-off value of each rSO2 values during overall, initial 5 minutes and 10 minutes were the highest value of 26% (sensitivity, 53.9% specificity, 92.3%), 24% (sensitivity, 56.4% specificity, 92.3%), and 30% (sensitivity, 61.5% specificity, 84.6%), mean value of 15.2%, 15.3% and 16%, respectively. None of the patients with a persistent rSO2 <=18% during the overall period achieved ROSC. Initial 5 minutes and 10 minutes cerebral rSO2 values an out-of-hospital-cardiac arrest (OHCA) are a better predictor in deciding the futility of CPR, compared to initial and overall measurements.Copyright © 2023 The Author(s). Published by MRE Press.

A Predictive Model of COVID-19 mRNA Vaccine Reactivity based on Dual Attention for Federated Learning Scenarios

The base reactivity of the mRNA sequence has a significant impact on the effectiveness of the mRNA vaccine in fighting against the pandemic of COVID-19. The annotation of mRNA sequence reactivity value is a time-consuming and labor-intensive work, which belongs to the private digital assets of each medical institution. It is not practical to train a predictive model by pooling private data from various parties. Fortunately, federated learning techniques can serve to collaboratively train a predictive model among medical institutions while preserving respective digital assets. However, due to the scarcity of data from each participant, conventional sequential prediction mod-els often fail to perform well. To overcome such a challenge, we propose a reactivity value prediction model based on both the self-attention and the convolutional attention mechanisms only requiring a small dataset of labeled samples. Inspired by BERT, we first train a self-attention feature extraction model through self-supervision using both labeled and unlabeled mRNA samples. In this way, the information of mRNA in the semantic space is deeply mined. Then, a convolutional attention block follows the self-attention block, to extract the attention matrix from the base-pair probability matrix and adjacency matrix. By doing so, the attention matrix can compensate for the insensitivity of the self-attention mechanism to the spatial information of mRNA. By using the Open Vaccine RNA database, experiments show that our prediction model for unseen mRNA has a better performance than other state-of-the-art deep learning models that are used to process gene sequences. Further ablation experiments demonstrate that the existence of the dual attention mechanism reduces the risk of overfitting, resulting in an excellent generalization capability of our model. © 2022 IEEE.

Randomized controlled study protocol: Shensong Yangxin in persistent atrial fibrillation after radiofrequency catheter ablation (SS-ADJUST)

Introduction: Atrial fibrillation (AF) is one of the most common forms of arrhythmia in the clinic. There are about 10 million AF patients in China, of which 1/3 are paroxysmal AF, and the remaining 2/3 are persistent or permanent AF. Long-term AF impairs cardiac function and leads to heart failure and thromboembolism. Moreover, AF increases the risk of mortality and ischemic stroke. Drug therapy and radiofrequency catheter ablation (RFCA) are still the mainstream treatment for AF patients. However, drug therapy has its drawbacks because of the high recurrence rate and side effects. Therefore, the current antiarrhythmic drugs could not meet all the clinical needs of patients with AF. RFCA is superior to antiarrhythmic drugs in maintaining sinus rhythm, improving symptoms and exercise tolerance, and improving quality of life. The role of RFCA in the treatment of persistent AF has gradually been recognized and affirmed. Although RFCA has been progressively used in the treatment of AF, there is still a high recurrence rate of AF after RFCA, especially in patients with persistent AF. Hence, it is meant to solve the high recurrence rate of AF after RFCA. Shensong Yangxin (SSYX) capsule has been proven to treat arrhythmia both in animal studies and clinical research. SSYX capsule could regulate multi-ion channels, improve cardiomyocyte metabolism and regulate autonomic nervous function. In addition, randomized, double-blind, multicenter clinical research indicated that the SSYX capsule exhibited good clinical efficacy in treating ventricular premature beats and paroxysmal AF. However, the effect of SSYX on recurrence after RFCA for patients with persistent AF remains unclear. High-level randomized controlled trials (RCTs) could offer clinicians high-quality evidence regarding the usage of SSYX capsule, especially in persistent AF patients who received RFCA. Hence, the RCTs aim to evaluate the effect of SSYX capsules on the prognosis in patients with persistent AF after RFCA through multicenter, double-blind RCTs. Method(s): This trial will be conducted with a total of 920 participants diagnosed with persistent AF who received RFCA. The participants will be randomized (1:1) into groups receiving either SSYX or Placebo for 1 year. The primary endpoint includes the recurrence of AF within 1 year after RFCA. The secondary outcome measures include changes of AF load at 3 months, 6 months, 9 months, and 1 year after treatment, the time of first atrial flutter/AF, the incidence of cardioversion 1 year after treatment, changes of transthoracic echocardiographic parameters 1 year after treatment, the incidence of stroke and thromboembolism at 6 months and 1 year after treatment, the score of SF-36 within 1 year after treatment. Application: The trial is ongoing. The trial started in September 2019 and recruiting patients. Data collection will be completed after all participants have completed the treatment course and follow-up assessments (expected in 2022, pending COVID-19). Next Steps/Future: The SS-ADJUST study is a randomized control study of TCM in persistent AF after RFCA. It will determine the place of SSYX capsule as a new treatment approach and provide additional and innovative information regarding TCM and the specific use of SSYX in persistent AF after RFCA.