A bibliometric analysis of the trends, topics, and findings of research publications on asynchronous and synchronous online language learning over three decades

Since the first study on computer-mediated communication tools in support of language learning was published in 1992, asynchronous and synchronous tools have been widely adopted;however, few reviews have been conducted to explore the research status in this field. As COVID-19 has increased the use of online tools in education, the need to understand how asynchronous and synchronous tools are being used in language education has grown. In this bibliometric analysis, we reviewed asynchronous and synchronous online language learning (ASOLL) by analyzing the trends, topics, and findings of 319 articles on ASOLL. The results indicate that interest in ASOLL has increased over the past three decades with ASOLL for oral proficiency development and collaborative ASOLL being the two main research issues. Interest in three topics collaborative ASOLL, emotions, and corrective feedback - was especially apparent. The review contributes to the understanding of ASOLL while providing practical implications for using information communication technologies to enhance language learning.

Extracellular Disintegration of Viral Proteins as an Innovative Strategy for Developing Broad-Spectrum Antivirals Against Coronavirus

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has claimed millions of lives and caused innumerable economic losses worldwide. Unfortunately, state-of-the-art treatments still lag behind the continual emergence of new variants. Key to resolving this issue is developing antivirals to deactivate coronaviruses regardless of their structural evolution. Here, we report an innovative antiviral strategy involving extracellular disintegration of viral proteins with hyperanion-grafted enediyne (EDY) molecules. The core EDY generates reactive radical species and causes significant damage to the spike protein of coronavirus, while the hyperanion groups ensure negligible cytotoxicity of the molecules. The EDYs exhibit antiviral activity down to nanomolar concentrations, and the selectivity index of up to 20,000 against four kinds of human coronavirus, including the SARS-CoV-2 Omicron variant, suggesting the high potential of this new strategy in combating the COVID-19 pandemic and a future "disease X."

The Mechanisms of Luteolin in Treating COVID-19 Based on Bioinformatics Analysis and Molecular Docking

Luteolin is a natural flavonoid that has a variety of pharmacological activities, such as anti-inflammatory, anti-allergic, anti-bacterial, anti-viral, apoptosis inhibition, cell autophagy regulation, and anti-tumor activity. It is one of the main ingredients of an expert-recommended herbal formula for the prevention and treatment of coronavirus disease 2019 (COVID-19). This suggests that luteolin has strong pharmacological effects on the prevention and treatment of COVID-19. The aims of this study were to identify the molecular targets of luteolin and to infer the possible mechanisms by which it exerts its pharmacological effects. The GSE159787 data set was obtained from the Gene Expression Omnibus online database, and differentially expressed genes were analyzed. There were 22 upregulated differentially expressed genes enriched in the COVID-19 signaling pathway, suggesting that the upregulation of these genes may be closely related to the occurrence of COVID-19. Molecular docking results showed that luteolin had strong binding efficiency to 20 of these 22 key genes. Six of these genes (CFB, EIF2AK2, OAS1, MAPK11, OAS3, and STAT1) showed strong binding activity. Luteolin can regulate the COVID-19 signaling pathway by combining with these targets, which may have a therapeutic effect on COVID-19. © 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.

A Novel Graph Indexing Approach for Uncovering Potential COVID-19 Transmission Clusters

The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission chains so as to prevent an outbreak of COVID-19 as early as possible. In this article, we study the problem of potential transmission cluster discovery based on the spatio-temporal logs. Given a query of patient user q and a timestamp of confirmed infection tq, the problem is to find all potential infected users who have close social contacts to user q before time tq. We motivate and formulate the potential transmission cluster model, equipped with a detailed analysis of transmission cluster property and particular model usability. To identify potential clusters, one straightforward method is to compute all close contacts on-the-fly, which is simple but inefficient caused by scanning spatio-temporal logs many times. To accelerate the efficiency, we propose two indexing algorithms by constructing a multigraph index and an advanced BCG-index. Leveraging two well-designed techniques of spatio-temporal compression and graph partition on bipartite contact graphs, our BCG-index approach achieves a good balance of index construction and online query processing to fast discover potential transmission cluster. We theoretically analyze and compare the algorithm complexity of three proposed approaches. Extensive experiments on real-world check-in datasets and COVID-19 confirmed cases in the United States validate the effectiveness and efficiency of our potential transmission cluster model and algorithms. © 2023 Association for Computing Machinery.

Research on two-dimensional code scanning and automatic recording device for human body temperature measurement based on stm32

With the outbreak and normal development of COVID-19, the effective detection and recording of body temperature has become a new focus of our attention. At present, there is no complete system to measure temperature, automatic record and specific information at home and abroad. To this end, combined with professional knowledge, our team designed a two-dimensional code scanning and human body temperature automatic recording device with STM32F1 as the core. The device STM32F1 development board is the main control chip. By connecting the WIFI module through the serial port, STM32F1 uses the function of wireless communication. Through the communication protocol, the link between the router and the ESC cloud server of Ali Cloud is utilized. The router or mobile data is transmitted to the user side (APP, applets) according to the specified communication protocol. Inside the development board, the code of each part is written to complete the device integrating code scanning and temperature measurement, which can be displayed and alarm through the node (OLED display screen). This will play a good role in preventing the spread of COVID-19. The system can be used in hospitals, communities, railway stations, shopping malls and many other public places. © 2022 IEEE.

Advances and challenges in intranasal vaccines development for respiratory infectious diseases

The great challenge to prevent transmission makes widespread of respiratory infectious diseases easily occur. Intranasal immunization is considered to be a promising route of vaccination to prevent it. Different from parenteral vaccines, intranasal vaccines can induce mucosal immune in respiratory tracts in addition to systemic immune, which provide the first line of defense against respiratory pathogen infection and further prevent transmission. Safe and effective intranasal spray flu vaccines have been licensed. Since the outbreak of COVID-19, intranasal administration has been applied in different vaccine platforms. This article has reviewed the progress of intranasal vaccines for respiratory infectious diseases that have been licensed or are under evaluation in the clinical trials, meanwhile discusses its unique advantages and challenges faced. © 2023, Publication Centre of Anhui Medical University. All rights reserved.

International Workshop on Learning with Knowledge Graphs: Construction, Embedding, and Reasoning

A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering. During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. Moreover, graph neural networks have also brought new opportunities to KG learning. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning. © 2023 Owner/Author.

A Tabu-Based Multi-objective Particle Swarm Optimization for Irregular Flight Recovery Problem

Air transportation is eminent for its fast speed and low cargo damage rate among other ways. However, it is greatly limited by emergent factors like bad weather and current COVID-19 epidemic, where irregular flights may occur. Confronted with the negative impact caused by irregular flight, it is vital to rearrange the preceding schedule to reduce the cost. To solve this problem, first, we established a multi-objective model considering cost and crew satisfaction simultaneously. Secondly, due to the complexity of irregular flight recovery problem, we proposed a tabu-based multi-objective particle swarm optimization introducing the idea of tabu search. Thirdly, we devised an encoding scheme focusing on the characteristic of the problem. Finally, we verified the superiority of the tabu-based multi-objective particle swarm optimization through the comparison against MOPSO by the experiment based on real-world data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mental status in patients with cerebral infarction in central China at the early stage of coronavirus disease-19 pandemic

Aims: The study aimed to analyze the changes in mental health and social support in patients with cerebral infarction during the recovery period at the early stage of coronavirus disease pandemic. Subjects and Methods: During January-March 2020, 98 patients with cerebral infarction during the recovery period were selected from Wuhan city. Among them, 42 patients were living alone (called the solitary group) and 56 patients lived with their spouses (called the spouse group). The Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) were used to evaluate anxiety and depression, respectively, and Multi-Dimensional Scale of Perceived Social Support (MSPSS), social support for patients. Statistical Analysis Used: The statistical calculations were carried out using GraphPad Prism 5.01 software (GraphPad, San Diego, California, USA). Results: At the early stage of the pandemic, patients with cerebral infarction in the solitary group and the spouse group experienced varying degrees of anxiety and depression. The SAS and SDS scores in the solitary group were significantly higher than those in the spouse group (P < 0.01). The subscale scores of MSPSS in the solitary group were lower than those in the spouse group (P < 0.01). Conclusions: It is necessary for medical staff to help the patients to overcome anxiety and depression and provide more social support to patients, especially for those patients living alone. © 2022 Heart and Mind ;Published by Wolters Kluwer - Medknow.

Factors Influencing Students' Continuance Usage Intention with Virtual Classroom during the COVID-19 Pandemic: An Empirical Study

The constant variation of COVID-19 has intensified the spread and recurrence of the epidemic, and education continues to be hard hit in most countries. The virtual classroom has become the main platform to replace the traditional classroom in the COVID-19 pandemic context. Due to the lack of a comprehensive understanding of college students' perceptions of the platform system, it is essential to explore the factors and mechanisms that influence students' willingness to use virtual classrooms consistently to improve the learning efficiency and optimize the effect of educational communication during the epidemic. This study integrates the Delone and McLean (D&M) information systems (IS) success model, expectation–confirmation model (ECM), and instructor quality factor to construct an operational model, and it used a structural equation model to analyze the 411 valid samples received from online questionnaires. The results reveal that the determinants of college students' perceived usefulness of virtual classrooms are service quality, instructor quality, and confirmation, while system quality has no effect on perceived usefulness in the context of the COVID-19 pandemic. Secondly, system quality, service quality, and instructor quality are three critical antecedents of confirmation, and perceived usefulness and confirmation positively affect satisfaction. Finally, perceived usefulness and satisfaction directly affect college students' continuance intention. © 2023 by the authors.

Advances and challenges in intranasal vaccines development for respiratory infectious diseases.

The great challenge to prevent transmission makes widespread of respiratory infectious diseases easily occur. Intranasal immunization is considered to be a promising route of vaccination to prevent it. Different from parenteral vaccines, intranasal vaccines can induce mucosal immune in respiratory tracts in addition to systemic immune, which provide the first line of defense against respiratory pathogen infection and further prevent transmission. Safe and effective intranasal spray flu vaccines have been licensed. Since the outbreak of COVID-19, intranasal administration has been applied in different vaccine platforms. This article has reviewed the progress of intranasal vaccines for respiratory infectious diseases that have been licensed or are under evaluation in the clinical trials, meanwhile discusses its unique advantages and challenges faced.Copyright © 2023, Publication Centre of Anhui Medical University. All rights reserved.

POCT detection device based on thermal convection fluorescence PCR amplification

This article is based on the principle of thermal convection PCR and nucleic acid fluorescence intensity detection technology. The principle of thermal convection PCR is to form a temperature difference by separately controlling the upper temperature and the bottom temperature of the reaction tube. The lower temperature liquid at the upper part has relatively high density or specific gravity, and the upper and lower liquids will produce convection, which drives the flow of molecules in the tubular chamber. The reaction solution is formed into thermal convection in the reaction test tube and subjected to different temperatures, so as to meet the required conditions for the reaction of different enzymes, and realize the pre-denaturation, annealing and extension processes in the nucleic acid PCR amplification in a short time. Nucleic acid fluorescence intensity detection technology involves embedded system design for device control and signal analysis, optical system design for optical signal filtering and collection, and differential amplifier circuit design. The embedded system design is based on the development of precise temperature control system, motion system and signal analysis system based on Stm32 single-chip microcomputer. The temperature control system includes independent temperature control to control the heaters at the bottom of the reaction tube and the top of the reaction tube respectively;the motion system includes sample switching and switching of the light source in the imaging system. The optical system design includes 540nm FAM excitation light source, 570nm CY3 excitation light source and spherical lens focusing excitation system. This device uses a photodiode to convert the optical signal into an electrical signal, and then amplifies the collected electrical signal with a two-stage operational amplifier through a two-color light differential amplifier circuit, and then uses the signal analysis system to record and display the electrical signal changes in real time, and Make a qualitative analysis. This device not only has the advantages of low cost and high sensitivity, but also solves the key problem of the long time (more than 2 hours) of the whole process of real-time fluorescent quantitative PCR in the detection of new crown nucleic acid and cannot be screened quickly on site. The PCR time of this device is from 2 The hour is reduced to 30 minutes, which is suitable for POCT inspections, and achieves rapid screening goals for crowds of people, which is conducive to responding to acute nucleic acid detection and large-scale nucleic acid detection. This device is currently used with COVID-19 detection reagents to detect new coronaviruses, and realize the detection of 20 copies of nucleic acid sensitivity within 30 minutes. Four samples can be processed in batches at a time, and the sample size for single processing can be increased appropriately according to needs. This device provides rapid and sensitive screening methods for global epidemic prevention and control, and is of great significance to improve human health. This device can also be applied to other rapid nucleic acid detection fields. With different nucleic acid detection reagents, this device can detect different gene loci, and has a broad development space and application fields. © 2023 SPIE.

Black Businesses Matter: A Longitudinal Study of Black-Owned Restaurants in the COVID-19 Pandemic Using Geospatial Big Data

Black communities in the United States have been disproportionately affected by the COVID-19 pandemic;however, few empirical studies have been conducted to examine the conditions of Black-owned businesses in the United States during this challenging time. In this article, we assess the circumstances of Black-owned restaurants during the entire year of 2020 through a longitudinal quantitative analysis of restaurant patronage. Using multiple sources of geospatial big data, the analysis reveals that most Black-owned restaurants in this study are disproportionately affected by the COVID-19 pandemic among different cities in the United States over time. The finding reveals the need for a more in-depth understanding of Black-owned restaurants' situations during the pandemic and further indicates the significance of carrying out place-based relief strategies. Our findings also urge big tech companies to improve existing Black-owned business campaigns to enable sustainable support. As the first to systematically examine the racialization of locational information, this article implies that geographic information systems (GIS) development should not be detached from human experience, especially that of minorities. A humanistic rewiring of GIS is envisioned to achieve a more racially equitable world. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

A GIS-based analytical framework for evaluating the effect of COVID-19 on the restaurant industry with big data

COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy. However, what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales, as well as its relationship with the neighborhood characteristics of customers' origins. Based on the Point of Interest (POI) measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US, our study takes Lower Manhattan, New York City, as the pilot study, and aims to examine 1) the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak, 2) the areas where restaurant customers live, and 3) the association between the neighborhood characteristics of these areas and lost customers. By doing so, we provide a geographic information system-based analytical framework integrating the big data mining, web crawling techniques, and spatial-economic modelling. Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.

Value of internet of things-based diagnosis-treatment model in improving the quality of medical services during COVID-19 outbreak

Objective: To demonstrate the value of Internet of things (IoT)-based diagnosis-treatment model in improving medical service quality during the novel coronavirus pneumonia (COVID-19) outbreak. Methods: In this retrospective analysis, 483 patients with chronic diseases treated between January 2020 and March 2021 were selected and grouped as follows based on different intervention methods: a research group (the Res group) with 229 patients that were given IoT-based diagnosis and treatment, and a control group (the Con group) with 254 patients that were treated with routine diagnosis and treatment. The qualified rate of medical records, the missing rate of medical records, and the incidence of doctor-patient disputes were compared between the two groups. In addition, investigations were made regarding patients' daily living ability, psychological state, health behavior, self-care ability, quality of life, as well as treatment satisfaction. Results: There was no difference in the qualified rate of medical records between the Res group and the Con group (P>0.05), but the missing rate of medical records and the incidence of doctor-patient disputes were lower in the Res group (both P<0.05). An obviously improved living ability was observed in both groups after the treatment (both P<0.05), with no statistical significance between groups (P>0.05). Besides, the Res group presented lower scores of SAS and SDS but higher scores of SRAHP, ES-CA and SF-36 than the Con group after treatment (all P<0.05). Finally, according to the satisfaction survey, more patients in the Res group were very satisfied but fewer cases were dissatisfied with the medical service they received as compared with the Con group (both P<0.05). Conclusions: The IoT-based diagnosis-treatment model can effectively improve the quality of medical services and patients' self-care ability, which is extremely important and promising for addressing the current medical limitations during the COVID-19 epidemic.

Modeling and numerical analysis of a fractional order model for dual variants of SARS-CoV-2

This paper considers the novel fractional-order operator developed by Atangana-Baleanu for transmission dynamics of the SARS-CoV-2 epidemic. Assuming the importance of the non-local Atangana-Baleanu fractional-order approach, the transmission mechanism of SARS-CoV-2 has been investigated while taking into account different phases of infection and var-ious transmission routes of the disease. To conduct the proposed study, first of all, we shall formu-late the model by using the classical operator of ordinary derivatives. We utilize the fractional order derivative and the model will be extended to a model containing fractional order derivatives. The operator being used is the fractional differential operator and has fractional order U1. The model is analyzed further and some basic aspects of the model are investigated besides calculating the basic reproduction number and the possible equilibria of the proposed model. The equilibria of the model are examined for stability purposes and necessary conditions for stability are obtained. Stability is also necessary in terms of numerical setup. The theory of non-linear functional analysis is employed and Ulam-Hyers's stability of the model is presented. The approach of newton's polynomial is con-sidered and a new numerical scheme is developed which helped in presenting an iterative process for the proposed ABC system. Based on this scheme, sample curves are obtained for various values of U1 and a pattern is derived between the dynamics of the infection and the order of the derivative. Further simulations are presented which show the cruciality and importance of various parameters and the impact of such parameters on the dynamics and control of the disease is presented. The findings of this study will also provide strong conceptual insights into the mechanisms of contagious diseases, assisting global professionals in developing control policies.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Tourism Demand Interval Forecasting Amid COVID-19: A Hybrid Model With a Modified Multi-Objective Optimization Algorithm

A hybrid tourism demand interval forecasting system is proposed consisting of two parts: the construction of forecasting interval based on lower and upper bound estimates, and the forecasting interval adjustment based on an optimized reduction coefficient. Coronavirus factors are added as input variables to improve forecasting performance. A new multi-objective optimization algorithm is proposed to construct a feature selection method, optimize the forecasting model, and estimate the optimal reduction coefficient. The results of the experiments show that the proposed system has a powerful interval forecasting ability, which provides crucial guidance for balancing the recovery of the tourism industry and the control of the epidemic spread during the COVID-19 pandemic, and contributes to contingency planning for tourism practitioners and managers.

A Pose Detection based Continuous Authentication System Design via Gait Feature Analysis

In recent years, with the increasing demand for public safety and the impact of pneumonia (Corona Virus Disease 2019, COVID-19), long-distance, contactless authentication has become a hot topic. Gait recognition technology has broad application prospect in computer vision field because of its ability of long-distance gait recognition and identification verification. On the other hand, with the development of big data, cloud computing, 5G, IoT and other technologies, which makes the Continuous authentication based on cameras is already possible. Therefore, we propose a continuous authentication system based on human pose estimation framework by analyzing and extracting gait characteristics. This system not only has the advantages of easy acquisition, long distance, contactless, and hard to disguise gait recognition, but also has the functions of dynamic authorization and continuous authentication, This method will bring a new development direction for the research of human pose estimation and gait recognition and other related fields. © 2022 IEEE.