Behavior of Science and Engineering Students to Digital Reading: Educational Disruption and Beyond

This paper investigates the impact of digital reading during educational disruption on science and engineering students' learning experience. Before the pandemic, some studies explored whether university students preferred using printed or digital resources for their academic readings. Amidst the pandemic, online learning became essential. Several studies showed students' preference for printed text. This paper extends a pilot study that was conducted during the first COVID-19 wave in China. A survey consisting of Likert questions and open questions was designed using MS-Forms. The survey was shared with the science and engineering students in Years 2–4 (Levels 1–3) of their study at SWJTU-Leeds Joint School, Southwest Jiaotong University in Chengdu, China. This covered students from four undergraduate programs: Civil Engineering with Transport, Electronic and Electrical Engineering, Mechanical Engineering, and Computer Science. In total, 223 students participated in this study. The survey was anonymous and was made available to students for a month. The participation rate is nearly 27%. Findings indicate that the behavior of science and engineering students toward digital reading was different than other majors, and it is generally favorable. The necessity for online learning during educational disruption has encouraged some students to develop their digital reading skills. © 2023 by the authors.

Personalized exercise recommendation method based on causal deep learning: Experiments and implications

The COVID-19 pandemic has accelerated innovations for supporting learning and teaching online. However, online learning also means a reduction of opportunities in direct communication between teachers and students. Given the inevitable diversity in learning progress and achievements for individual online learners, it is difficult for teachers to give personalized guidance to a large number of students. The personalized guidance may cover many aspects, including recommending tailored exercises to a specific student according to the student's knowledge gaps on a subject. In this paper, we propose a personalized exercise recommendation method named causal deep learning (CDL) based on the combination of causal inference and deep learning. Deep learning is used to train and generate initial feature representations for the students and the exercises, and intervention algorithms based on causal inference are then applied to further tune these feature representations. Afterwards, deep learning is again used to predict individual students' score ratings on exercises, from which the Top-N ranked exercises are recommended to similar students who likely need enhancing of skills and understanding of the subject areas indicated by the chosen exercises. Experiments of CDL and four baseline methods on two real-world datasets demonstrate that CDL is superior to the existing methods in terms of capturing students' knowledge gaps in learning and more accurately recommending appropriate exercises to individual students to help bridge their knowledge gaps. © 2022 The Author(s).

COVID-19 Literature Mining and Analysis Research

By 2019 COVID-19, since the epidemic, the number of relevant documents exponentially level rise. Faced with a large amount of literature, this research provides convenience for exploring the connection between research topics and fields and quickly understanding relevant literature information. We pass on the data set after data cleansing using the LDA(Latent Dirichlet allocation) methods, and Berts and K-means modeling method extracting topic keywords. Use knowledge graph tools to output relevant visual graphics and systematically extract adequate information. Through text mining of biomedical research papers related to COVID-19, the improved model is used to analyze and make recommendations to respond to and prevent the COVID-19 pandemic. This research can support the rapid and in-depth analysis of a large number of relevant documents and can be used in future research to support real-time scientific disease research.

A Gesture Controlled System to Train and Guide the Healthcare Workers While Donning and Doffing Personal Protective Equipment

During the COVID-19 outbreak, many healthcare workers (HCWs) have been infected because they failed to comply with the correct process of donning and doffing personal protective equipment (PPE). Based on this, we develop a gesture-controlled system that not only can train HCWs but also can give HCWs real-time guidance during the process of donning and doffing PPE. It can effectively prevent the infection of HCWs. We first use the hand detection algorithm to locate the position of the HCWs, helping them to enter the proper area. Then they can use our gesture recognition algorithm to control the playback of the videos which guides them in donning and doffing PPE. We verify the effectiveness of the system through a series of experiments. The results show the great value of our system in the protection of HCWs. © 2022 IEEE.

How to improve students' intrinsic motivation and 4C skills in the virtual e-learning community and evaluate its results

Many courses from Chinese Universities have to change the teaching model and content in suit for distance learning because of COVID-19 pandemic. This paper indicates a virtual learning community called Digital Philosophy Club (DPC) instead of learning philosophy in classroom. It uses three cooperative platforms to integrate an E-learning environment, in which students can participate online activities. They can motivate positively through explorative, and social learning experiences. To research the result of students' learning, consulting from semi-structured interviews with students from 8 faculties, analyses how to improve the intrinsic motivation and 4C skills (4Cs) in the virtual e-learning community. The research method is based on the Tripartite Model of Intrinsic Motivation and explains the results from consultation. Base on the questionnaire analysis, the following results are reached: 1. The practical methods of DPC can improve the students' positive motivation;2, Learning- support in DPC is conductive to enhance 4Cs abilities;3. The teaching methods in DPC are approved by the theory of Tripartite Model of Intrinsic Motivation from Psychology. © 2022 ACM.

Experts' consensus on severe acute respiratory syndrome coronavirus-2 vaccination of children.

At present, severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)infection is still rampant worldwide.As of September 10, 2021, there were about 222 million confirmed cases of corona virus disease 2019(COVID-19)and more than 4.6 million deaths worldwide.With the development of COVID-19 vaccines and the gradual vaccination worldwide, the increasing number of cases in children and unvaccinated young people has drawn attention.According to World Health Organization surveillance data, the proportion of COVID-19 infection cases in children gradually increased, and the proportion of cases in the age groups of under 5 years and 5-14 years increased from 1.0% and 2.5% in January 2020 to 2.0% and 8.7% in July 2021, respectively.At present, billions of adults have been vaccinated with various COVID-19 vaccines worldwide, and their protective effects including reducing infection and transmission, reducing severe disease and hospitalization, and reducing death, as well as high safety have been confirmed.Canada, the United States, Europe and other countries have approved the emergency COVID-19 vaccination in children and adolescents aged 12 to 17 years, and China has also approved the phased vaccination of COVID-19 vaccination in children and adolescents aged 3 to 17 years. For smooth advancement and implementation of COVID-19 vaccination in children, academic institutions, including National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, and The Society of Pediatrics, Chinese Medical Association organized relevant experts to reach this consensus on COVID-19 vaccination in children.Copyright © 2021 by the Chinese Medical Association.

Guidance on strengthening the management processes of children's fever in outpatient department during the novel coronavirus pneumonia epidemic period (First Edition).

Novel Coronavirus Pneumonia (NCP) is a class B infectious disease, which is prevented and controlled according to class A infectious diseases. Recently, children's NCP cases have gradually increased, and children's fever outpatient department has become the first strategic pass to stop the epidemic.Strengthening the management of the fever diagnosis process is very important for early detection of suspected children, early isolation, early treatment and prevention of cross-infection. This article proposes prevention and control strategies for fever diagnosis, optimizes processes, prevents cross-infection, health protection and disinfection of medical staff, based on the relevant diagnosis, treatment, prevention and control programs of the National Health and Health Commission and on the diagnosis and treatment experience of experts in various provinces and cities. The present guidance summarizes current strategies on pre-diagnosis;triage, diagnosis, treatment, and prevention of 2019-nCoV infection in common fever, suspected and confirmed children, which provide practical suggestions on strengthening the management processes of children's fever in outpatient department during the novel coronavirus pneumonia epidemic period.Copyright © 2020 by the Chinese Medical Association.

Twenty key issues on severe acute respiratory syndrome coronavirus-2 vaccination in children.

Severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)infection is still worldwide.As a vulnerable group, severe and dead pediatric cases are also reported.Under this severe epidemic situation, children should be well protected.With the widespread vaccination of SARS-CoV-2 vaccine in adults, the infection rate have decreased.Therefore, SARS-CoV-2 vaccine inoculation for children groups step by step is of great significance to the protection of children and the prevention and control of corona virus disease 2019(COVID-19) as a whole.But the safety of children vaccinated with SARS-CoV-2 vaccine is a main concern of parents.Therefore, in order to ensure the safety of vaccination and the implementation of vaccination work, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health and the Society of Pediatrics, Chinese Medical Association organized experts to interpret the main issue of parents about SARS-CoV-2 vaccine for children, in order to answer the doubts of parents.Copyright © 2021 by the Chinese Medical Association.

Diagnosis, treatment and prevention of 2019 novel coronavirus infection in children: experts' consensus statement (Third Edition).

2019 novel coronavirus(2019-nCoV) outbreak is one of the public health emergency of international concern.Since the 2019-nCoV outbreak, China has been adopting strict prevention and control measures, and has achieved remarkable results in the initial stage of prevention and control.However, some imported cases and sporadic regional cases have been found, and even short-term regional epidemics have occurred, indicating that the preventing and control against the epidemic remains grim.With the change of the incidence proportion and the number of cases in children under 18 years old, some new special symptoms and complications have appeared in children patients.In addition, with the occurrence of virus mutation, it has not only attracted attention from all parties, but also proposed a new topic for the prevention and treatment of 2019-nCoV infection in children of China.Based on the second edition, the present consensus further summarizes the clinical characteristics and experience of children's cases, and puts forward recommendations on the diagnostic criteria, laboratory examination, treatment, prevention and control of children's cases for providing reference for further guidance of treatment of 2019-nCoV infection in children.Copyright © 2021 Chinese Medical Association

Generation of APN-chimeric gene-edited pigs by CRISPR/Cas9-mediated knock-in strategy

The prevalence of porcine enteric coronaviruses (PECs), including transmissible gastroenteritis virus (TGEV), swine acute diarrhea syndrome coronavirus (SADS-CoV), porcine delta coronavirus (PDCoV), and porcine epidemic diarrhea virus (PEDV), poses a serious threat to animal and public health. Here, we aimed to further optimize the porcine aminopeptidase N (pAPN) gene editing strategy to explore the balance between individual antiviral properties and the biological functions of pAPN in pigs. Finally, APN-chimeric gene-edited pigs were produced through a CRISPR/Cas9-mediated knock-in strategy. Further reproductive tests indicated that these gene-edited pigs exhibited normal pregnancy rates and viability. Notably, in vitro viral challenge assays further demonstrated that porcine kidney epithelial cells isolated from F1-generation gene-edited pigs could effectively inhibit TGEV infection. This study is the first to report the generation of APN-chimeric pigs, which may provide a natural host animal for characterizing PEC infection with APN and help in the development of better antiviral solutions. © 2022 Elsevier B.V.

A comprehensive multi‐hazard risk assessment model for an urban agglomeration with multiple factors

To assist the Department of Emergency Management in understanding the overall risk characteristics and situation of an urban agglomeration for a reasonable risk prevention and control strategy, this study developed a comprehensive multi-hazard risk assessment model for an urban agglomeration with multiple factors. The proposed model includes disaster probability and disaster loss sub-models. The model evaluated four types of disaster risk in urban agglomerations: natural disasters, accidental disasters, public health incidents, and social security incidents. In addition, a variety of factors were integrated into the model, including the socioeconomic foundation of urban agglomerations, the oligopoly effect of core cities, historical disaster losses, the effect of disaster chains, the ability of disaster prevention and mitigation, and intercity coordinated rescue capabilities. Finally, the risk assessment model was applied to the Beijing-Tianjin-Hebei urban agglomeration. The assessment results were compared to the distribution of the new coronavirus pneumonia epidemic in the target urban agglomeration. The results showed that after analyzing the risk characteristics and evaluating the risk levels, the model not only showed the comprehensive risk levels and distribution of urban agglomerations but also revealed the high-risk areas and the key points of risk prevention and control. More importantly, the results obtained through the model can facilitate the strategic planning of disaster prevention and mitigation for urban agglomerations. © 2022

Tourist Trajectory Prediction Based on Improved LightGBM

Considering the influences of the COVID-19 disease, systemic risks with respect to the tourism industry and the erratic preferences of the tourists have fiercely affected the performance of machine learning models for tourist trajectory prediction. This paper introduces a noise-reduced and Bayesian optimized light gradient boosting machine(LightGBM) to forecast the likelihood of visitors entering the consequent scenic attraction, accommodating to the variability of tourism attributes. The empirical evidence of tourism data in Luoyang City Hall from March 2020 to November 2021 illustrates that our practice surpasses the baseline LightGBM mechanism as well as a random search-based technique regarding prediction loss by 5.39% and 4.42% correspondingly. The proposed research demonstrates a promising stride in the improvement of intelligent tourism in the experimental area by enhancing tourist experiences and allocating tourism resources efficiently, which can also be smoothly applied to other scenic spots. © 2023 SPIE.

A Hybrid Model for Spatio-Temporal Information Recognition in COVID-19 Trajectory Text

Since the outbreak of the COVID-19 epidemic at the end of 2019, the normalization of epidemic prevention and control has become one of the core tasks of the entire country. Health self-examination by checking the trajectory of diagnosed patients has gradually become everyone's basic necessity and essential to epidemic prevention. The COVID-19 patient's spatio-temporal information helps to facilitate the self-inspection of the masses of whether their trajectory overlaps with the confirmed cases, which promotes the epidemic prevention work. This paper, proposes a named entity recognition model to automatically identify the time and place information in the COVID-19 patient trajectory text. The model consists of an ALBERT layer, a Bi-GRU layer, and a GlobalPointer layer. The previous two layers jointly focus on extracting the context's characteristics and the semantic dependencies. And the GlobalPointer layer extracts the corresponding named entities from a global perspective, which improves the recognition ability for the long-nested place and time entities. Compared to the conventional name entity recognition models, our proposed model has high effectiveness because it has a smaller parameter scale and faster training speed. We evaluate the proposed model using a dataset crawled from the official COVID-19 trajectory text. The F1-score of the model has reached 92.86%, which outperforms four traditional named entity recognition models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

[Analysis of the whole genome traceability and transmission path simulation experiment of the local cluster COVID-19 epidemic].

Objective: To trace and characterize the whole genome of SARS-CoV-2 of confirmed cases in the outbreak of COVID-19 on July 31, 2021 in Henan Province. Method: Genome-wide sequencing and comparative analysis were performed on positive nucleic acid samples of SARS-CoV-2 from 167 local cases related to the epidemic on July 31, 2021, to analyze the consistency and evolution of the whole genome sequence of virus. Results: Through high-throughput sequencing, a total of 106 cases of SARS-CoV-2 whole genome sequences were obtained. The results of genome analysis showed that the whole genome sequences of 106 cases belonged to the VOC/Delta variant strain (B.1.617.2 clade), and the whole genome sequences of 106 cases were shared with the genomes of 3 imported cases from Myanmar admitted to a hospital in Zhengzhou. On the basis of 45 nucleotide sites, 1-5 nucleotide variation sites were added, and the genome sequence was highly homologous. Conclusion: Combined with the comprehensive analysis of viral genomics, transmission path simulation experiments and epidemiology, it is determined that the local new epidemic in Henan Province is caused by imported cases in the nosocomial area, and the spillover has caused localized infection in the community. At the same time, it spills over to some provincial cities and results in localized clustered epidemics.

Experimental Design of Self-Tracking Camera Gimbal Robot Based on OpenCV

The emergence of COVID-19 has reduced the opportunities for offline meetings, making people's work and study more transfer to the internet platform. However, the viewing angle and distance of the camera cannot be considered both. Therefore, machine vision is used to identify and track the presenter, and the camera pan-tilt control function of automatically tracking the presenter is realized. In many tests, the target tracking function works normally and works well. The experimental design involves relatively comprehensive disciplines, with good functional scalability and high practicability. It is an innovative experiment integrating robotics teaching, machine learning practice and embedded systems. © 2022 IEEE.

The Policy Choice and Economic Assessment of High Emissions Industries to Achieve the Carbon Peak Target under Energy Shortage—A Case Study of Guangdong Province

In recent years, due to the rise in energy prices and the impact of COVID-19, energy shortages have led to unsafe power supply environments. High emissions industries which account for more than 58% of the carbon emissions of Guangdong Province have played an important role in achieving the carbon peak goal, alleviating social energy shortage and promoting economic growth. Controlling high emissions industries will help to adjust the industrial structure and increase renewable energy investment. Therefore, it is necessary to comprehensively evaluate the policies of energy security and the investments of high emission industries. This paper builds the ICEEH-GD (comprehensive assessment model of climate, economy, environment and health of Guangdong Province) model, designs the Energy Security scenario (ES), the Restrict High Carbon Emission Sector scenario (RHS) and the Comprehensive Policy scenario (CP), and studies the impact of limiting high emissions industries and renewable energy policies on the transformation of investment structure, macro-economy and society. The results show that under the Energy Security scenario (ES), carbon emissions will peak in 2029, with a peak of 681 million tons. Under the condition of ensuring energy security, the installed capacity of coal-fired power generation will remain unchanged from 2025 to 2035. Under the Restrict High Carbon Emission Sector scenario (RHS), the GDP will increase by 8 billion yuan compared with the ES scenario by 2035. At the same time, it can promote the whole society to increase 10,500 employment opportunities, and more investment will flow to the low emissions industries. In the Comprehensive Policy scenario (CP), although the GDP loss will reach 33 billion yuan by 2035 compared with the Energy Security scenario (ES), the transportation and service industries will participate in carbon trading by optimizing the distribution of carbon restrictions in the whole society, which will reduce the carbon cost of the whole society by more than 48%, and promote the employment growth of 104,000 people through industrial structure optimization. Therefore, the power sector should increase investment in renewable energy to ensure energy security, limit the new production capacity of high emissions industries such as cement, steel and ceramics, and increase the green transition and efficiency improvement of existing high emissions industries. © 2022 by the authors.

Comparison of the SVR and ARIMA models for Prediction of daily imported new cases of COVID-19 in Shanghai, China

The outbreak and spread of COVID-19 poses a tremendous threat to the health of people all over the world. We collected the new imported COVID-19 cases daily in Shanghai, China from September 1, 2021 to January 17, 2022 from the National Commission on Health of the People's Republic of China website. The SVR and ARIMA models were constructed and compared. On this base, it is provided for the early warning of the outbreak of COVID-19 and the targeted preventive measures proposed for this infectious disease. © 2022 IEEE.