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Purpose: The outbreak of COVID-19 in 2020 has had a profound impact on education institutions at all levels. Open universities, with their privileged delivery method, have been in an advantageous position. In the earlier stages of the pandemic they made remarkable contributions to assuring learning continuity. However, with more and more conventional universities migrating online, great changes have taken place in the field of higher education, and it is imperative for open universities to adjust their strategies in order to maintain their leading role in a technology-enabled education context. This paper aims to examine what challenges have been faced by open universities during the pandemic and how they will transform in the future. Design/methodology/approach: Six open universities in Asia, Africa and Europe were selected as cases in this research to make a comparative study based on the papers in the volume beyond distance education [1]. Similarities and differences among the cases were analyzed in order to identify the developing trend for open universities in the international context. Findings: The results showed that (1) open universities in these regions demonstrated their resilience in the pandemic, examples were that new technologies have been leveraged to implement totally online delivery with short notice;and huge amount of learning resources were offered to the society. (2) However, they encountered challenges of delivering fully online examination due to the lockdown and quarantine policies, and open universities in African and the sole private institution suffered financial pressure due to improving information and communication technology infrastructure and staff training. Another challenge was the fierce competition from conventional universities that open universities in Asia and Europe came across. (3) Four main areas were identified for future development in order to respond to the challenges: No.1 is that programs such as health care, psychology, epidemiology, virology, immunology, data analytics, biology, bio-informatics have stimulated interest for African open universities to develop in the future;No. 2 open universities were seeking to innovate their teaching formats, short courses, such as micro credentials might be developed as agile and flexible offerings which are expected to be suitable to learners in the pandemic context;No 3 is that programs and courses for upskilling in the context of digitalization will be implemented;and No. 4 is that lifelong learning is given a higher priority in order for open universities to stand securely in higher education sector. Originality/value: The study may give open university leaders a quick insight on their future development. © 2023, Songyan Hou.
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Objective To detect the active ingredients in the traditional Chinese medicine prescription and its molecular mechanisms against SARS-CoV-2 by prescription mining and molecular dynamics simulations. Methods Herein, prescription mining and virtual screening of drugs were performed to screen the potential inhibitors against SARS-CoV-2. Molecular docking and molecular dynamics (MDs) simulations were further performed to explore the molecular recognition and inhibition mechanism between the potential inhibitors and SARS-CoV-2. Results The natural compounds library was constructed by 143 prescriptions of traditional Chinese medicine, which contained 640 natural compounds. Ten compounds were screened out from the natural compounds library. Among the 10 compounds, 23-trans-p-coumaryhormentic acid, the main active constituent of the Loquat leaf, showed the best binding affinity targeting the recognizing interface of SARS-CoV-2 S protein/ACE2. Upon binding 23-trans-p-coumaryhormentic acid, the key interactions between SARS-CoV-2 S protein and ACE2 were almost interrupted. Conclusion Ten compounds targeting SARS-CoV-2 S protein/ACE2 interface were screened out from natural compound library. And we inferred that 23-trans-p-coumaryhormentic acid is a potential inhibitor against SARS-CoV-2, which would contribute to the development of the antiviral drug for SARS-CoV-2. © 2022 Institute of Biophysics,Chinese Academy of Sciences. All rights reserved.
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Xhaul, a mobile transport network, is a critical lifeline in imminent global crises: the combination of the COVID-19 pandemic and geopolitical conflict. Not only did the Russia-Ukraine war cause a global energy crisis, but it also put more energy stress on the 5G Xhaul. It also shows that the sustainability of a country depends on the unbroken Xhaul. Meanwhile, the COVID-19 outbreak has triggered the largest human-virus war of this century. It needs the ubiquitous 5G Xhaul to monitor the spread of COVID-19. Once crises occur, turning them into opportunities often requires new ways of seeing, considering, and responding to the 5G Xhaul provisioning. Facing more unpredictable situations, Chunghwa Telecom (CHT), the largest service provider in Taiwan, embraces the challenges and proposes practical solutions. This study aims to discuss the new 5G Xhaul provisioning strategies to achieve sustainable development goals in this turbulent era. © 2022 IEEE.
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Objective To detect the active ingredients in the traditional Chinese medicine prescription and its molecular mechanisms against SARS-CoV-2 by prescription mining and molecular dynamics simulations. Methods Herein, prescription mining and virtual screening of drugs were performed to screen the potential inhibitors against SARS-CoV-2. Molecular docking and molecular dynamics (MDs) simulations were further performed to explore the molecular recognition and inhibition mechanism between the potential inhibitors and SARS-CoV-2. Results The natural compounds library was constructed by 143 prescriptions of traditional Chinese medicine, which contained 640 natural compounds. Ten compounds were screened out from the natural compounds library. Among the 10 compounds, 23-trans-p-coumaryhormentic acid, the main active constituent of the Loquat leaf, showed the best binding affinity targeting the recognizing interface of SARS-CoV-2 S protein/ACE2. Upon binding 23-trans-p-coumaryhormentic acid, the key interactions between SARS-CoV-2 S protein and ACE2 were almost interrupted. Conclusion Ten compounds targeting SARS-CoV-2 S protein/ACE2 interface were screened out from natural compound library. And we inferred that 23-trans-p-coumaryhormentic acid is a potential inhibitor against SARS-CoV-2, which would contribute to the development of the antiviral drug for SARS-CoV-2.
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As two important features of COVID-19 pneumonia ultrasound, the B-line and white lung are easily confused in clinics. To classify the two features, a radiomics analysis technology was developed on a set of ultrasound images collected from patients with COVID-19 pneumonia in the study. A total of 540 filtered images were divided into a training set and a test set in the ratio of 7:3. A machine learning model was proposed to perform automated classification of the B-line and white lung, which included image segmentation, feature extraction, feature screening, and classification. The radiomic analysis was applied to extract 1688 high-throughput features. The principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) were used to perform feature screening for redundancy reduction. The support vector machine (SVM) was utilized to make the final classification. The confusion matrix was used to visualize the prediction performance of the model. In the result, the model with features selected using LASSO outperformed the model with PCA in terms of classification effectiveness. The number of high-throughput features closely related to the classification under the model with LASSO was 11, with the value of AUC, accuracy, specificity, precision and recall being 0.92, 0.92, 0.91, 0.92 and 0.92, respectively. Compared to the model with PCA, the values of the evaluation indicators of the model with LASSO increased by 13.94%, 13.26%, 15.79%, 22.23% and 5.66%, respectively. As a conclusion, the proposed models showed good performance in differentiation of the B-line and white lung, with potential application value in the clinics. © 2022 ACM.
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After the outbreak of COVID-19, it spread rapidly all over the world. A large number of infected patients have led to a sharp increase in medical waste. This puts great pressure on the medical waste treatment system. The disposal capacity of the system may not be able to meet such a large amount of medical waste, which may lead to delayed treatment of infectious medical waste (IMW) and accumulation of non-infectious medical waste (NMW). Therefore, this paper proposes that it is necessary to classify IMW and NMW, and reconstruct the domestic waste disposal plant to treat NMW to alleviate the pressure of the treatment system. This paper also establishes an eco-economics model to optimize the emergency disposal scheme. The effectiveness of the model is verified by a real case in Wuhan. It is found that the location and carbon emission coefficient of the domestic waste disposal plants are the key factors affecting its selection. At the same time, sufficient budget may lead to waste money. In addition, carbon emission and total cost always change in the opposite trend when the budget changes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Edge detection is an effective method for image segmentation and feature extraction. Therefore, extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019 (COVID-19) CT images is extremely important. Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy. In this paper, we propose a weak edge detection method based on Gaussian filtering and single-scale Retinex (GF_SSR), and improved multiscale morphology and adaptive threshold binarization (IMSM_ATB). As all the CT images have noise, we propose to remove image noise by Gaussian filtering. The edge of CT images is enhanced using the SSR algorithm. In addition, based on the extracted edge of CT images using improved Multiscale morphology, a particle swarm optimization (PSO) algorithm is introduced to binarize the image by automatically getting the optimal threshold. To evaluate our method, we use images from three datasets, namely COVID-19, Kaggle-COVID-19, and COVID-Chestxray, respectively. The average values of results are worthy of reference, with the Shannon information entropy of 1.8539, the Precision of 0.9992, the Recall of 0.8224, the F-Score of 1.9158, running time of 11.3000. Finally, three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed algorithm. Compared with the other four algorithms, the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction. © 2022 Tech Science Press. All rights reserved.
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Many people around the world have lost their lives due to COVID-19. The symptoms of most COVID-19 patients are fever, tiredness and dry cough, and the disease can easily spread to those around them. If the infected people can be detected early, this will help local authorities control the speed of the virus, and the infected can also be treated in time. We proposed a six-layer convolutional neural network combined with max pooling, batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients. In the 10-fold cross-validation methods, our method is superior to several state-of-the-art methods. In addition, we use Grad-CAM technology to realize heat map visualization to observe the process of model training and detection.
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In the fight against the COVID-19 pandemic, many social activities have moved online;society's overwhelming reliance on the complex cyberspace makes its security more important than ever. In this paper, we propose and develop an intelligent system named Dr.HIN to protect users against the evolving Android malware attacks in the COVID-19 era and beyond. In Dr.HIN, besides app content, we propose to consider higher-level semantics and social relations among apps, developers and mobile devices to comprehensively depict Android apps;and then we introduce a structured heterogeneous information network (HIN) to model the complex relations and exploit meta-path guided strategy to learn node (i.e., app) representations from HIN. As the representations of malware could be highly entangled with benign apps in the complex ecosystem of development, it poses a new challenge of learning the latent explanatory factors hidden in the HIN embeddings to detect the evolving malware. To address this challenge, we propose to integrate domain priors generated from different views (i.e., app content, app authorship, app installation) to devise an adversarial disentangler to separate the distinct, informative factors of variations hidden in the HIN embeddings for large-scale Android malware detection. This is the first attempt of disentangled representation learning in HIN data. Promising experimental results based on real sample collections from security industry demonstrate the performance of Dr.HIN in evolving Android malware detection, by comparison with baselines and popular mobile security products.
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During the pandemic caused by coronavirus disease (COVID-19), social media has played an important role by enabling people to discuss their experiences and feelings of this global crisis. To help combat the prolonged pandemic that has exposed vulnerabilities impacting community resilience, in this paper, based on our established large-scale COVID-19 related social media data, we propose and develop an integrated framework (named Dr.Emotion) to learn disentangled representations of social media posts (i.e., tweets) for emotion analysis and thus to gain deep insights into public perceptions towards COVID-19. In Dr.Emotion, for given social media posts, we first post-train a transformer-based model to obtain the initial post embeddings. Since users may implicitly express their emotions in social media posts which could be highly entangled with other descriptive information in the post content, to address this challenge for emotion analysis, we propose an adversarial disentangler by integrating emotion-independent (i.e., sentiment-neutral) priors of the posts generated by another post-trained transformer-based model to separate and disentangle the implicitly encoded emotions from the content in latent space for emotion classification at the first attempt. Extensive experimental studies are conducted to fully evaluate Dr.Emotion and promising results demonstrate its performance in emotion analysis by comparison with the state-of-the-art baseline methods. By exploiting our developed Dr.Emotion, we further perform emotion analysis over a large number of social media posts and provide in-depth investigation from both temporal and geographical perspectives, based on which additional work can be conducted to extract and transform the constructive ideas, experiences and support into actionable information to improve community resilience in responses to a variety of crises created by COVID-19 and well beyond. © 2021 ACM.
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The coronaviruses have inflicted health and societal crises in recent decades. Both SARS CoV-1 and 2 are suspected to spread through outdoor routes in high-density cities, infecting residents in apartments on separate floors or in different buildings in many superspreading events, often in the absence of close personal contact. The viability of such mode of transmission is disputed in the research literature, and there is little evidence on the dose–response relationship at the apartment level. This paper describes a study to examine the viability of outdoor airborne transmission between neighboring apartments in high density cities. A first-principles model, airborne transmission via outdoor route (ATOR), was developed to simulate airborne pathogen generation, natural decay, outdoor dispersion, apartment entry, and inhalation exposure of susceptible persons in neighboring apartments. The model was partially evaluated using a smoke tracer experiment in a mock-up high-density city site and cross-checking using the computational fluid dynamics (CFD) models. The ATOR model was used to retrospectively investigate the relationship between viral exposure and disease infection at an apartment level in two superspreading events in Hong Kong: the SARS outbreak in Amoy Gardens and the COVID-19 outbreak in Luk Chuen House. Logistic regression results suggested that the predicted viral exposure was positively correlated with the probability of disease infection at apartment level for both events. Infection risks associated with the outdoor route transmission of SARS can be reduced to <10%, if the quanta emission rate from the primary patient is below 30 q/h. Compared with the indoor route transmission, the outdoor route can better explain patterns of disease infection. A viral plume can spread upward and downward, driven by buoyancy forces, while also dispersing under natural wind. Fan-assistant natural ventilation in residential buildings may increase infection risks. Findings have implication for public health response to current and future pandemics and the ATOR model can serve as planning and design tool to identify the risk of airborne disease transmission in high-density cities. © Copyright © 2021 Huang, Jones, Zhang, Hou, Hang and Spengler.
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The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond with actionable strategies for community mitigation, leveraging the large-scale and real-time pandemic related data generated from heterogeneous sources (e.g., disease related data, demographic data, mobility data, and social media data), in this work, we propose and develop a data-driven system (named α-satellite), as an initial offering, to provide real-time COVID-19 risk assessment in a hierarchical manner in the United States. More specifically, given a location (either user input or automatic positioning), the system will automatically provide risk indices associated with the specific location, the county that location is in and the state as a whole to enable people to select appropriate actions for protection while minimizing disruptions to daily life to the extent possible. In α-satellite, we first construct an attributed heterogeneous information network (AHIN) to model the collected multi-source data in a comprehensive way;and then we utilize meta-path based schemes to model both vertical and horizontal information associated with a given location (i.e., point of interest, POI);finally we devise a novel heterogeneous graph neural network to aggregate its neighborhood information to estimate the risk of the given POI in a hierarchical manner. To comprehensively evaluate the performance of α-satellite in real-time COVID-19 risk assessment, a set of studies are first performed to validate its utility;based on a real-world dataset consisting of 6,538 annotated POIs, the experimental results show that α-satellite achieves the area of under curve (AUC) of 0.9378, which outperforms the state-of-the-art baselines. After we launched the system for public tests, it had attracted 51,190 users as of May 30. Based on the analysis of its large-scale users, we have a key finding that people from more severe regions (i.e., with larger numbers of COVID-19 cases) have stronger interests using the system for actionable information. Our system and generated benchmark datasets have been made publicly accessible through our website. © 2020 ACM.