Crisis management of authoritarian regimes in the digital era: the case of epidemic prevention and control in China

Purpose: The purpose of this paper is to figure out how authoritarian regimes conduct crisis management through application of technology, institutions and people. Design/methodology/approach: By means of a literature review, this paper briefly reviews the digital governance of authoritarianism and its approach in crisis management. Then, a case study with empirical analysis is conducted to explain how an authoritarian regime would perceive and manage crises in the digital era. Findings: China's response towards COVID-19 was mainly based on digitalised grid management. Government's perception of the crisis directly influences directions of institutions, while technology is developed, applied and iterated with the needs of institutions, rather than the public interests. And for the general public, the level of trust in the government directly affects the acceptance of technology. Originality/value: Previous studies on crisis management of authoritarian governments in the digital era have mostly been conducted from a techno-ethical perspective. However, this paper verifies that the use of technology in crisis management requires involvement of institutions and public. © 2023, Emerald Publishing Limited.

Digital transformation of the business models of Chinese sporting goods enterprises in the post-COVID-19 era: a knowledge-management perspective

Purpose: From a knowledge-management perspective, this paper aims to analyze the digital transformation of the business models of traditional Chinese sporting goods companies in the context of the pandemic crisis and to explore the role of their digital transformation in coping with the crisis. Design/methodology/approach: Using theoretical sampling, typical sporting goods companies are selected for case studies. We provide an in-depth analysis of how these companies achieve high performance levels through the digital transformation of their business models in the post-COVID-19 era and discuss the key role of knowledge management in this achievement. Findings: Focusing on the challenges faced by Chinese sporting goods enterprises during the pandemic crisis from the knowledge-management perspective, we find that through the digital transformation of their business models, enterprises can improve their knowledge-management capabilities, enhance their flexibility to respond to sudden crises and maintain a higher level of corporate performance. Research limitations/implications: This paper has significant implications for sporting goods companies wishing to achieve high corporate performance through the digital transformation of their business models in the post-COVID-19 era. Future research should address the dynamic mechanism of the digital transformation of business models to improve enterprise knowledge-management capabilities and the impact mechanism of knowledge-management capabilities on interenterprise organizational resilience. Originality/value: This paper proposes specific strategies in the process of the digital transformation of business models that are essential for improving enterprises' internal and external knowledge-management capabilities. © 2023, Emerald Publishing Limited.

Design of an Intelligent Educational Evaluation System Using Deep Learning

Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario. © 2013 IEEE.

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65% ± 1.86%, a specificity of 94.32% ± 2.07%, a precision of 94.30% ± 2.04%, an accuracy of 93.99% ± 1.78%, an F1-score of 93.97% ± 1.78%, Matthews Correlation Coefficient of 87.99% ± 3.56%, and Fowlkes-Mallows Index of 93.97% ± 1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective. © 2023 Centro Regional de Invest. Cientif. y Tecn.. All rights reserved.

Classifying COVID-19 Lung CT-Scans Using Complex-Valued CNN

In the past 1.5 years, the COVID-19 pandemic has taken countless lives and may continue to do so if we do not improve our ability to identify and contain emerging variants. One of the areas we may improve in is developing alternative forms of COVID testing, such as testing based on CT-scans of patient lungs. Traditional Real-valued CNNs have already been applied to this classification task and seen good results, but there we could also explore the potential benefits of applying a Complex-valued CNN model. In this paper, it shows how optimization of complex-valued CNN is a step further in the right direction to achieving lower loss rates. The specific ways to build a complex-valued CNN model upon a traditional real-valued 2D CNN is introduced. The paper compares traditional real-valued convolutional neural networks, in specific, a Lenet-5 model and Resnet-18, and a complex-valued CNN model in the application of image classification. A complex-valued CNN model will achieve a significantly lower training loss and a higher accuracy than a Lenet-5 model. For the same training amount, the complex-valued CNN will have much higher optimization efficiency than Resnet-18 and have as good performance in loss and accuracy as Resnet-18, a modern deep real-valued CNN. The complex-valued CNN is especially useful for cases that need to both high accuracy and efficiency, like Covid-19 CT scans detection. It also shows very good potential to as an efficient optimization method that do not need to keep increasing the depth of the neural network. However, the weight initialization tends to have a greater impact on the imaginary parts, which may cause an oscillating training loss for small datasets. This area needs further researches and optimization. Another potential area that worth further investigation is to combine the complex-valued neural network with the modern deep CNN networks like Vgg and ResNet. © 2022 IEEE.

Covid-19 Detection by Wavelet Entropy and Artificial Bee Colony

Computer analysis of patients' lung CT images has become a popular and effective way to diagnose COVID-19 patients amid repeated and evolving outbreaks. In this paper, wavelet entropy is used to extract features from CT images and integrate the information of various scales, including the characteristic signals of signals with transient components. Combined with the artificial bee colony optimization algorithm, we used the advantages of fewer parameters and simpler calculation to find the optimal solution and confirm COVID-19 positive. The use of K-fold cross validation allows the data set to avoid overfitting and unbalanced data set partition in small cases. The experimental results were compared with those of WE + BBO, GLCM-SVM, GLCM-ELM and WE-Jaya. Experimental data show that this method achieves our initial expectation. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Epidemic Data Visualization Analysis of the Covid-19 Development in China

Corona Virus Disease 2019 (Covid-19) is a war between all humans and viruses. The outbreak of the Covid-19 epidemic has produced a large amount of data related to case information. Current related visualization studies are difficult to analyze these data, so a visualization analysis method for the Covid-19 epidemic situation in China is proposed. In this study, we present an effort to compile and analyze epidemiological outbreak information of Covid-19 based on the epidemic news and data in China after January 10, 2020. Through the analysis of data, it is concluded that the Covid-19 has the characteristics of a high infection rate and rapid transmission rate, and it also reflects the great contribution made by the Chinese government in controlling the epidemic. This study can obtain the hidden value behind the data, facilitate the understanding of the results of data analysis, and provide a reference for the government. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

[Emergency training need and effect evaluation analysis of novel coronavirus pneumonia in centers of disease control and prevention].

A questionnaire was used to investigate the emergency training needs of novel coronavirus pneumonia of disease prevention and control institutions in provinces, deputy provincial level regions and cities specifically designated in the state plan, and the effect evaluation of emergency training activities conducted by Chinese Center for Disease Control and Prevention (China CDC). The results showed that 67.4% of 47 disease prevention and control institutions (31/46) believed that the emergency training at the initial stage of the epidemic should be conducted as soon as possible, and the form of network training should be given priority. The training should focus on the urgently needed technologies such as epidemiological investigation, formulation and response of prevention and control strategies, laboratory testing, etc. The teaching materials should highlight pertinence and practicability and be presented in the form of electronic video. The average satisfaction score of the video training conducted by China CDC was (8.81±1.125) and the score of audio-video courseware was (8.97±0.893). The needs analysis and evaluation of novel coronavirus pneumonia prevention and control in disease prevention and control institutions could provide reference for the follow-up training and improve the emergency training management.

Laboratory Effects of COVID-19 Infection in Pregnant Women and Their Newborns: A Systematic Review and Meta-Analysis.

Amidst the COVID-19 pandemic, there is a need for further research on its manifestation in pregnant women, since they are particularly prone to respiratory pathogens, like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), due to physiological changes during pregnancy. Its effects on infants born to mothers with COVID-19 are also not well-studied, and more evidence is needed on vertical transmission of the disease from mother to infant and on the transmission of IgG/IgM antibodies between mother and infant. We aim to systematically review and evaluate the effects of COVID-19 among SARS-CoV-2-positive pregnant women in late pregnancy and neonates with SARS-CoV-2-positive pregnant mothers using blood assays to find indicators of maternal and neonatal complications. We searched for original published articles in Google Scholar, Medline (PubMed), and Embase databases to identify articles in the English language from December 2019 to July 20, 2020. Duplicate entries were searched by their titles, authors, date of publication, and Digital Object Identifier. The selected studies were included based on patient pregnancy on admission, pregnant mothers with laboratory-confirmed COVID-19 virus, maternal/neonatal complications, and blood test results. We excluded duplicate studies, articles where full text was not available, other languages than English, opinions, and perspectives. The meta-analysis using the Generalized Linear Mixed model was conducted using the "meta" and "metaprop" packages in R code. Of the 1,642 studies assessed for eligibility, 29 studies (375 mothers and neonates) were included. Preterm birth rate was 34.2%, and cesarean section rate was 82.7%. Maternal laboratory findings found elevated neutrophils (71.4%; 95% CI: 38.5-90.9), elevated CRP (67.7%; 95%: 50.6-81.1), and low hemoglobin (57.3%; 95% CI: 26.0-87.8). We found platelet count, lactate dehydrogenase, and procalcitonin to be less strongly correlated with preterm birth than between high neutrophil counts (P = 0.0007), low hemoglobin (P = 0.0188), and risk of preterm birth. There is little evidence for vertical transmission. Elevated procalcitonin levels (23.2%; 95% CI: 8.4-49.8) are observed in infants born to mothers with COVID-19, which could indicate risk for neonatal sepsis. These infants may gain passive immunity to COVID-19 through antibody transfer via placenta. These results can guide current obstetrical care during the current SARS-CoV-2 pandemic.