ABSTRACT
In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available. © 2023 Association for Computational Linguistics.
ABSTRACT
An organizational crisis is a low-probability, high-impact event that threatens the survival of organizations and individuals, often with little warning. In response, people seek clarity, reassurance, and hope from organiza-tional leaders. Yet, crises also vary in nature and impact (e.g., a product failure versus the COVID-19 pan-demic), which presents diverse challenges to leaders and differing stakeholder perceptions. Based on a critical analysis of 69 empirical articles, we provide a comprehensive, systematic, interdisciplinary review of the crisis leadership literature. Our review utilizes the Coombs and Holladay (1996) crisis typology, where crises are categorized according to mutually exclusive attributional dimensions (i.e., internal-external and intentional-unintentional). We conduct a thematic analysis of crisis leadership within and across these four cri-sis categories and find that each is associated with a different leadership theme. We also examine the method-ological quality and rigor of the qualitative and quantitative articles in our review. Based on our findings, we also offer suggestions to guide future crisis leadership research, and provide guidance for organizational lead-ers in how to respond to various crises.
ABSTRACT
Balint group helps health professionals to get emotional support and different perspectives of feedback, inspire reflection, and alleviate job burnout. During the outbreak of COVID-19, it was difficult for medical staff to carry out the traditional form of in person Balint group. Referring to the work of international pilot online Balint group, leaders of Balint group all over China have accumulated some experience and encountered new problems by using the internet to carry out discussion. In order to assist and standardize the work of online Balint group and enrich the ways of expanding Balint work, the Working Committee on Doctor-patient Relationship, Chinese Psychiatrist Association, Chinese Medical Doctor Association organized experts to have two rounds of discussion, and developed the consensus on: Principles and forms of online Balint group, the way of using web platforms for demonstration and learning, matters needing attention, the future development, and so on.Copyright © 2021, Peking Union Medical College Hospital. All rights reserved.
ABSTRACT
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.
ABSTRACT
Purpose: The high-pressure nature of the construction industry, along with the COVID-19 pandemic, triggered abusive supervision (i.e. workplace bullying and incivility behaviour) that has diminished workers' well-being. However, despite the growing prevalence in practice and increasing concern in academia, abusive supervision remains largely unexplored by construction management scholars. This study aims to fill the gap in the current literature by analysing the effects of abusive supervision on construction workers' well-being, the mediating role of guanxi closeness and the moderating role of trust in the manager. Design/methodology/approach: A questionnaire survey was completed by 258 Chinese construction workers. The data underwent mediation and moderation analyses using PROCESS macro 3.5 for SPSS. Findings: The results revealed that managers' abusive supervision reduced construction workers' well-being at work and in life. Guanxi closeness between manager and workers mediated the relationship between managers' abusive supervision and construction workers' well-being. Additionally, trust in managers moderated the mediating effect of guanxi closeness. This study further revealed that the emotional connection between construction managers and workers, such as expressive guanxi closeness and affective-based trust, is important in handling the impact of abusive supervision on the workers. Practical implications: The findings of this study provide support for recent calls to address negative manager behaviours such as abusive supervision in construction management. They aid the development of a more comprehensive internal mechanism that considers the influence of guanxi closeness on the outcomes of abusive supervision by managers at construction sites. Additionally, interventions that develop trust in managers may be particularly effective in alleviating the tension of abusive supervision. More attention should be paid to managers' emotional connections in daily construction project management. Originality/value: Rather than concentrate on positive leadership, this study shifts the focus to negative leadership in construction project management by identifying abusive supervision as a negative primary antecedent of workers' well-being. While prior research has highlighted how negative manager behaviours affect workers' well-being from the conservation of resources theory (COR) perspective, this study is the first, to the authors' knowledge, to adopt a social exchange theory perspective by introducing guanxi closeness as a mediator. It contributes to a greater understanding of how trust in the manager alleviates the negative effect of the person's abusive supervision on construction workers. © 2023, Emerald Publishing Limited.
ABSTRACT
The medical system has been targeted by the cyber attackers, who aim to bring down the health security critical infrastructure. This research is motivated by the recent cyber-attacks happened during COVID 19 pandemics which resulted in the compromise of the diagnosis results. This study was carried to demonstrate how the medical systems can be penetrated using AI-based Directory Discovery Attack and present security solutions to counteract such attacks. We then followed the NIST (National Institute of Standards and Technology) ethical hacking methodology to launch the AI-based Directory Discovery Attack. We were able to successfully penetrate the system and gain access to the core of the medical directories. We then proposed a series of security solutions to prevent such cyber-attacks. © 2022 Creative Commons.
ABSTRACT
Densely occupied spaces (e.g., classrooms) are generally over-crowded and pose a high risk of cross-infection during the pandemic of COVID-19. Among various ventilation systems, impinging jet ventilation (IJV) system might be promising for such spaces. However, the exhaust location of the IJV system used for densely occupied classrooms is unclear. This study aims to investigate the effects of exhaust location on the removal of exhaled contaminants in a classroom (15 × 7 × 5 m3) occupied by 50 students. Exhaled contaminants are modeled by a tracer gas released at the top of each manikin. The reference case has three exhausts evenly distributed in the ceiling. The results indicate that: a) a recirculation airflow entraining exhaled contaminants exists above the occupied zone;b) this recirculation air flow entrains contaminants and accumulates them at the upper part of the room near the diffuser;c) locating merely one exhaust on the same side of the supply diffuser leads to the best indoor air quality, i.e., it reduces the mean age of air from 278 s to 243 s, the mass fraction of CO2 from 753 ppm to 726 ppm, and the concentration of tracer gas from 305 ppm to 266 ppm;d) this layout still performs the best when the supply velocity drops to 0.5 m/s. It is worth noting that the proposed layout has fewer exhausts than the reference case but performs better. These results conclude that the exhaust for large spaces is not evenly distributed but depends on the indoor airflow pattern: the key is locating the exhaust near the region with high contaminant concentration. Factors determining the recirculation airflow are suggested to be further studied. © 2023 Elsevier Ltd
ABSTRACT
We have designed, fabricated, and tested a MEMS-based impedance biosensor for accurate and rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) using of clinical samples. The device consists of focusing region that concentrate low quantities of the virus present in the samples to a detectable threshold, trap region hat maximize the captured virus, and detection region to detect the virus with high selectivity and sensitivity, using an array of interdigitated electrodes (IDE) coated with a specific antibody. Changes in the impedance value due to the binding of the SARS-COV-2 antigen to the antibody will indicate positive or negative result. The device was able to detect inactivated SARS-COV-2 antigen present in phosphate buffer saline (PBS) with a concentration as low as 50 TCID50/ml in 30 minutes. In addition, the biosensor was able to detect SARS-COV-2 in clinical samples (swabs) with a sensitivity of 84 TCID50/ml, also in 30 minutes. © 2023 IEEE.
ABSTRACT
The raging of COVID-19 has caused a huge impact on all countries. This paper selects China, which has adopted a "strict strategy” in response to the epidemic, to observe the correlation between changes in COVID-19 data and ICT statistics, so as to analyze the impact of COVID-19 on the ICT industry. Due to availability of the data, this paper mainly analyzes the impact on telecommunication industry, mobile Internet, Internet business and software industry, which are more consumption-oriented in the ICT industry. In this paper, data from different fields at different time periods are collected and organized into four sets of graphs, and each graph is analyzed using pearson correlation data model and simple linear regression model. It can be concluded that the revenue of ICT industry in different fields was affected differently during the epidemic period. The specific impact needs to be discussed according to the different types of business in relation to the development of the epidemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Conventional X-ray imaging architectures feature data redundancy and hardware consumption due to the separated sensory terminal and computing units. In-sensor computing architectures is promising to overcome such drawbacks. However, its realization in X-ray range remains elusive. We propose ion distribution induced reconfigurable mechanism, and demonstrate the first X-ray band in-sensor computing array based on Pb-free perovskite. Redistribution of Br- ion in perovskite induces the switching of PN and NP modes under electrical pooling. X-ray detection sensitivity can be switched between two stable self-power sensing modes with 4373±298 and -7804±429 mu mathrm{CGy}-{ mathrm{a} mathrm{i} mathrm{r}}{}{-1} mathrm{cm}{-2} respectively, which are superior than that of commercial a-Se detectors (20 mu mathrm{C} mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}}{}{-1} mathrm{c} mathrm{m}{-2}). Both modes exhibit low detection limit of 48.4 mathrm{n} mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}} mathrm{s}{-1}, which is two orders lower than typical medical dose rate of 5.5 mu mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}} mathrm{s}{-1}. The perovskite array sensors can integrate with thin film transistors (TFTs) with low-temperature (80oC) process with good uniformity. An in-sensor computing algorithm of attention mechanism is performed on array sensors for chest X-ray images COVID-19 recognition, which enables an accuracy improvement up to 98.2%. Our results can pave the way for future intelligent X-ray imaging. © 2022 IEEE.
ABSTRACT
Knowledge tracing (KT) is based on modeling students' behavior sequences to obtain students' knowledge state and predict students' future performance. The KT task aims to model students' knowledge state in real-time according to their historical learning behavior, so as to predict their future learning performance. Online education has become more critical in recent years due to the impact of COVID-19, and KT has also attracted much attention due to its importance in the education field. However, previous KT models generally have the following three problems. Firstly, students' learning and forgetting behaviors affect their knowledge state, and past KT models have yet to exploit this fully. Secondly, the input of traditional KT models is mainly limited to students' exercise sequence and answers. In the learning process, students' answering performance can reflect their knowledge level. Finally, the context of students' learning sequence also affects their judgment of the knowledge state. In this paper, we combined educational psychology theories to propose enhanced learning and forgetting behavior for contextual knowledge tracing (LFEKT). LFEKT enriches the features of exercises by introducing difficulty information and considers the influence of students' answering behavior on the knowledge state. In order to model students' learning and forgetting behavior, LFEKT integrates multiple influencing factors to build a knowledge acquisition module and a knowledge retention module. Furthermore, LFEKT introduces a long short-term memory (LSTM) network to capture the contextual relations of learned sequences. From the experimental results, it can be seen that LFEKT had better prediction performance than existing models on four public datasets, which indicates that LFEKT can better trace students' knowledge state and has better prediction performance. © 2023 by the authors.
ABSTRACT
With the long-lasting impact of the COVID-19 pandemic, online learning has gradually become one of the mainstream learning methods in Chinese universities. The effectiveness of online learning is significantly influenced by learning engagement, and studies into this topic can help learners by providing them with process-based learning support and focused teaching interventions. Based on the online learning environment, this research constructs an online learning engagement analysis model. Additionally, this study explores the relationship between students' online learning engagement and their online learning performance by taking the Secondary School Geography Curriculum Standards and Textbooks Research, a small-scale private online course (SPOC) of the geography education undergraduate course at Nanjing Normal University, as an example. The findings are as follows: In the cognitive engagement dimension, only "analyze” is significantly positively correlated with learning performance;in the behavioral engagement dimension, the "number of question and answer (Q&A) topic posts,” the "replies to others,” and the "teachers' replies” are all significantly positively correlated with learning performance. In terms of the emotional engagement dimension, "curiosity” and "pleasure” are positively correlated with learning performance;as for the social engagement dimension, "point centrality” and "intermediary centrality” are positively correlated with learning performance. The findings of this case study reveal that the student's engagement in higher-order cognitive learning is obviously insufficient. Students' online learning performance can be enhanced both by behavioral engagement in knowledge reprocessing and positive emotional engagement. Further research should be focused on finding ways to increase students' enthusiasm for social engagement. © 2023 by the authors.
ABSTRACT
Messenger RNA (mRNA) is the template for protein biosynthesis and is emerging as an essential active molecule to combat various diseases, including viral infection and cancer. Especially, mRNA-based vaccines, as a new type of vaccine, have played a leading role in fighting against the current global pandemic of COVID-19. However, the inherent drawbacks, including large size, negative charge, and instability, hinder its use as a therapeutic agent. Lipid carriers are distinguishable and promising vehicles for mRNA delivery, owning the capacity to encapsulate and deliver negatively charged drugs to the targeted tissues and release cargoes at the desired time. Here, we first summarized the structure and properties of different lipid carriers, such as liposomes, liposome-like nanoparticles, solid lipid nanoparticles, lipid-polymer hybrid nanoparticles, nanoemulsions, exosomes and lipoprotein particles, and their applications in delivering mRNA. Then, the development of lipid-based formulations as vaccine delivery systems was discussed and highlighted. Recent advancements in the mRNA vaccine of COVID-19 were emphasized. Finally, we described our future vision and perspectives in this field.Copyright © 2023 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences
ABSTRACT
The Corona Virus Disease 2019 (COVID-19) that occurred December of 2019 has a wide range of impacts, and its epidemic situation is grim. China has a large population of liver cancer, accounting for 50% of new cases of liver cancer worldwide. How to ensure the diagnosis, treatment and rehabilitation of liver cancer patients while preventing and controlling the epidemic situation is an issue that urgently need specialists pay attention to. The authors propose an overall management model for patients with liver cancer, combined with their own experience, in order to guide specialists to safely and effectively carry out clinical diagnosis and treatment of liver cancer during the prevention and control of epidemics, and to help liver cancer patients receive treatment.Copyright © 2020 by the Chinese Medical Association.
ABSTRACT
Under the influence of the COVID-19, people's awareness of physical health and immunity has increased significantly. Chitooligosaccharide is an oligomer of β-(1, 4)-linked D-glucosamine, furthermore, is one of the most widely studied immunomodulators. Chitooligosaccharide can be prepared from the chitin or chitosan polymers through enzymatically, chemically or physically processes. Chitooligosaccharide and its derivatives have been proven to have a wide range of biological activities including intestinal flora regulation, immunostimulant, anti-tumor, anti-obesity and anti-oxidation effects. This review summarizes the latest research of the preparation methods, biological activities in immunity and safety profiles of Chitooligosaccharide and its derivatives. We recapped the effect mechanisms of Chitooligosaccharide basing on overall immunity. Comparing the effects of Chitooligosaccharide with different molecular weights and degree of aggregation, a reference range for usage has been provided. This may provide a support for the application of Chitooligosaccharide in immune supplements and food. In addition, future research directions are also discussed. © 2023, Sociedade Brasileira de Ciencia e Tecnologia de Alimentos, SBCTA. All rights reserved.
ABSTRACT
Research on automated social media rumour verification, the task of identifying the veracity of questionable information circulating on social media, has yielded neural models achieving high performance, with accuracy scores that often exceed 90%. However, none of these studies focus on the real-world generalisability of the proposed approaches, that is whether the models perform well on datasets other than those on which they were initially trained and tested. In this work we aim to fill this gap by assessing the generalisability of top performing neural rumour verification models covering a range of different architectures from the perspectives of both topic and temporal robustness. For a more complete evaluation of generalisability, we collect and release COVID-RV, a novel dataset of Twitter conversations revolving around COVID-19 rumours. Unlike other existing COVID-19 datasets, our COVID-RV contains conversations around rumours that follow the format of prominent rumour verification benchmarks, while being different from them in terms of topic and time scale, thus allowing better assessment of the temporal robustness of the models. We evaluate model performance on COVID-RV and three popular rumour verification datasets to understand limitations and advantages of different model architectures, training datasets and evaluation scenarios. We find a dramatic drop in performance when testing models on a different dataset from that used for training. Further, we evaluate the ability of models to generalise in a few-shot learning setup, as well as when word embeddings are updated with the vocabulary of a new, unseen rumour. Drawing upon our experiments we discuss challenges and make recommendations for future research directions in addressing this important problem. © 2022 The Author(s)
ABSTRACT
This paper proposes a novel and efficient method, called S-PDB, for the analysis and classification of Spike (S) protein structures of SARS-CoV-2 and other viruses/organisms in the Protein Data Bank (PDB). The method first finds and identifies protein structures in PDB that are similar to a protein structure of interest (SARS-CoV-2 S) via a protein structure comparison tool. The amino acid (AA) sequences of identified protein structures, downloaded from PDB, and their aligned amino acids (AAA) and secondary structure elements (ASSE), that are stored in three separate datasets, are then used for the reliable detection/classification of SARS-CoV-2 S protein structures. Three classifiers are used and their performance is compared by using six evaluation metrics. Obtained results show that two classifiers for text data (Multinomial Naive Bayes and Stochastic Gradient Descent) performed better and achieved high accuracy on the dataset that contains AAA of protein structures compared to the datasets for AA and ASSE, respectively. © 2022 IEEE.
ABSTRACT
COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. This paper proposes a novel decomposition-ensemble forecasting method to forecast container throughput under the impact of major events. Combining this with change-point analysis and empirical mode decomposition (EMD), this paper uses the decomposition-ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, we tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. To validate the proposed method, we apply it to a forecast of the container throughput of Shanghai port. The results show that the proposed forecasting model significantly outperforms its rivals, including EMD-SVR, SVR, and ARIMA.
ABSTRACT
This paper proposes a novel and efficient method, called S-PDB, for the analysis and classification of Spike (S) protein structures of SARS-CoV-2 and other viruses/organisms in the Protein Data Bank (PDB). The method first finds and identifies protein structures in PDB that are similar to a protein structure of interest (SARS-CoV-2 S) via a protein structure comparison tool. The amino acid (AA) sequences of identified protein structures, downloaded from PDB, and their aligned amino acids (AAA) and secondary structure elements (ASSE), that are stored in three separate datasets, are then used for the reliable detection/classification of SARS-CoV-2 S protein structures. Three classifiers are used and their performance is compared by using six evaluation metrics. Obtained results show that two classifiers for text data (Multinomial Naive Bayes and Stochastic Gradient Descent) performed better and achieved high accuracy on the dataset that contains AAA of protein structures compared to the datasets for AA and ASSE, respectively. © 2022 IEEE.