Research on the classification of CT images of COVID-19 by pre-training model based on self-attention mechanism

Nowadays, with the outbreak of COVID-19, the prevention and treatment of COVID-19 has gradually become the focus of social disease prevention, and most patients are also more concerned about the symptoms. COVID-19 has symptoms similar to the common cold, and it cannot be diagnosed based on the symptoms shown by the patient, so it is necessary to observe medical images of the lungs to finally determine whether they are COVID-19 positive. As the number of patients with symptoms similar to pneumonia increases, more and more medical images of the lungs need to be generated. At the same time, the number of physicians at this stage is far from meeting the needs of patients, resulting in patients unable to detect and understand their own conditions in time. In this regard, we have performed image augmentation, data cleaning, and designed a deep learning classification network based on the data set of COVID-19 lung medical images. accurate classification judgment. The network can achieve 95.76% classification accuracy for this task through a new fine-tuning method and hyperparameter tuning we designed, which has higher accuracy and less training time than the classic convolutional neural network model. © 2023 SPIE.

Under the background of 100 million tons of crude oil purchase and sale contract: countermeasures for smooth operation of the Alashankou-Dushanzi-Urumqi crude oil pipeline

A 100 million ton crude oil purchases and sale contract signed between China and Russia, and the crude oil will pass through Kazakhstan and flow along the Alashankou-Dushanzi-Urumchi crude oil pipeline to western Region. As an important crude oil import channel, this paper analyzes the current situation of Alashankou-Dushanzi-Urumchi crude oil pipeline and puts forward some countermeasures. Both Russia and Kazakhstan are major crude oil exporters, with internal demand for exporting crude oil to China. There is a huge gap between domestic crude oil demand, and social and economic development depends on crude oil import. The geographical environment of the crude oil pipeline is conducive to pipeline protection. The security environment of Xinjiang has improved. The situation in Russia and Ukraine and the civil strife in Kazakhstan have limited impact on the pipeline which will have good development opportunities. Thus, this paper proposes the following management strategies. First of all, strengthen communication and coordination between upstream and downstream enterprises. Second, reduce cost and increase efficiency. Third, attach importance of the application of new technologies. Fourth, establish emergency plans and hold emergency drills for emergencies such as COVID-19 outbreak and pipeline leakage. Fifth, strengthen anti-terrorism and riot control. Sixth, strengthen corporate culture and talent team construction. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).

Cellular and Molecular Atlas of Peripheral Blood Mononuclear Cells from a Pregnant Woman after Recovery from COVID-19

Objective This study aimed to investigate the immune response of a pregnant woman who recovered from the coronavirus disease 2019 (COVID_RS) by using single-cell transcriptomic profiling of peripheral blood mononuclear cells (PBMCs) and to analyze the properties of different immune cell subsets. Methods PBMCs were collected from the COVID_RS patient at 28 weeks of gestation, before a cesarean section. The PBMCs were then analyzed using single-cell RNA sequencing. The transcriptional profiles of myeloid, T, and natural killer (NK) cell subsets were systematically analyzed and compared with those of healthy pregnant controls from a published single-cell RNA sequencing data set. Results We identified major cell types such as T cells, B cells, NK cells, and myeloid cells in the PBMCs of our COVID_RS patient. The increase of myeloid and B cells and decrease of T cells and NK cells in the PBMCs in this patient were quite distinct compared with that in the control subjects. After reclustering and Augur analysis, we found that CD16 monocytes and mucosal-Associated invariant T (MAIT) cells were mostly affected within different myeloid, T, and NK cell subtypes in our COVID_RS patient. The proportion of CD16 monocytes in the total myeloid population was increased, and the frequency of MAIT cells in the total T and NK cells was significantly decreased in the COVID-RS patient. We also observed significant enrichment of gene sets related to antigen processing and presentation, T-cell activation, T-cell differentiation, and tumor necrosis factor superfamily cytokine production in CD16 monocytes, and enrichment of gene sets related to antigen processing and presentation, response to type II interferon, and response to virus in MAIT cells. Conclusion Our study provides a single-cell resolution atlas of the immune gene expression patterns in PBMCs from a COVID_RS patient. Our findings suggest that CD16-positive monocytes and MAIT cells likely play crucial roles in the maternal immune response against severe acute respiratory syndrome coronavirus 2 infection. These results contribute to a better understanding of the maternal immune response to severe acute respiratory syndrome coronavirus 2 infection and may have implications for the development of effective treatments and preventive strategies for the coronavirus disease 2019 in pregnant women.Copyright © Wolters Kluwer Health, Inc. All rights reserved.

Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread worldwide and threatened human's health. With the passing of time, the epidemiology of coronavirus disease 2019 evolves and the knowledge of SARS-CoV-2 infection accumulates. To further improve the scientific and standardized diagnosis and treatment of maternal SARS-CoV-2 infection in China, the Chinese Society of Perinatal Medicine of Chinese Medical Association commissioned leading experts to develop the Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection under the guidance of the Maternal and Child Health Department of the National Health Commission. This recommendations includes the epidemiology, diagnosis, management, maternal care, medication treatment, care of birth and newborns, and psychological support associated with maternal SARS-CoV-2 infection. It is hoped that the recommendations will effectively help the clinical management of maternal SARS-CoV-2 infection.Copyright © Wolters Kluwer Health, Inc. All rights reserved.

FRD: Few-shot rumor detection for the novel coronavirus pneumonia epidemic

With the full arrival of the digital era, fueled by both information technology and business marketing, rumors are produced and spread endlessly on social networks. During the recent novel coronavirus pneumonia epidemic, online rumors have continued to flourish. Most existing studies on traditional rumor detection rely on a large number of features in practical applications. However, the current severe epidemic scenarios have limited rumor information features, and it remains a challenging problem to detect epidemic rumors with high accuracy using only limited information. As a result, we propose a novel Few-shot Rumor Detection model (FRD) for the novel coronavirus pneumonia, which is combined with meta-learning to be able to accurately identify rumors as soon as possible in crises. Specifically, we started by using the BERT+BiLSTM combination for rumor text feature extraction and representation to generate the historical rumor sample-wise vector and epidemic rumor sample-wise vector;secondly, the prototypical network was introduced to summarize the historical rumor data, and the feature vectors of samples belonging to the same category were averaged to obtain the prototype representation of historical rumor category;finally, we utilize the modified cosine similarity measure function to calculate the distance between the class-wise vector of historical rumor text and the sample-wise vector of epidemic rumor, and complete the rumor detection according to the nearest neighbor method. Our experimental results on English datasets show that the FRD rumor detection model proposed in this paper is superior to other baseline algorithms in terms of accuracy, precision, recall and macro F1 value. From the comparison of experimental results, the FRD model can effectively improve conventional rumor detection methods, and better realize the early detection of sudden epidemic rumors. © 2022 IEEE.

SARS-CoV-2 Neutralization Assay System using Pseudo-lentivirus

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects humans' lower respiratory tracts and causes coronavirus disease-2019 (COVID-19). Neutralizing antibodies is one of the adaptive immune system responses that can reduce SARS-CoV-2 infection. This study aimed to develop a SARS-CoV-2 neutralization assay system using pseudo-lentivirus. METHODS: The plasmid used for pseudo-lentivirus production was characterized using restriction analysis. The gene encoding for SARS-CoV-2 spike protein was confirmed using sequencing. The transfection pseudolentivirus optimal condition was determined by choosing the transfection reagents and adding centrifugation step. Optimal pseudo-lentivirus infection was analysed using fluorescent assay and luciferase assay. The optimal condition of pseudo-lentivirus infection was determined by the target cell type and the number of pseudo-lentiviruses used for neutralization test. SARS-CoV-2 pseudo-lentivirus was used to detect neutralizing antibodies from serum samples. RESULTS: The plasmid used for pseudo-lentivirus production was characterized and confirmed to have no mutations. Lipofectamine 2000 reagent generated pseudolentivirus with a higher ability to infect target cells, as indicated by a percentage green fluorescent protein (GFP) of 12.68%. Pseudo-lentivirus centrifuged obtained more stable results in luciferase expression. Optimal pseudo-lentivirus infection conditions were obtained using puromycinselected HEK 293T-ACE2 cells as target cells. The number of pseudo-lentiviruses used in the neutralization assay system was multiplicity of infection (MOI) 0.075. Serum A samples with a 1:10 dilution had the highest neutralizing antibody activity. CONCLUSION: This study shows that SARS-CoV-2 neutralization assay system using pseudo-lentivirus successfully detected neutralizing antibodies in human serum, which were indicated by a decrease in the percentage of pseudo-lentivirus infections. © 2023 The Prodia Education and Research Institute

Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study

Background The transmission dynamics of influenza were affected by public health and social measures (PHSMs) implemented globally since early 2020 to mitigate the COVID-19 pandemic. We aimed to assess the effect of COVID-19 PHSMs on the transmissibility of influenza viruses and to predict upcoming influenza epidemics. Methods For this modelling study, we used surveillance data on influenza virus activity for 11 different locations and countries in 2017-22. We implemented a data-driven mechanistic predictive modelling framework to predict future influenza seasons on the basis of pre-COVID-19 dynamics and the effect of PHSMs during the COVID-19 pandemic. We simulated the potential excess burden of upcoming influenza epidemics in terms of fold rise in peak magnitude and epidemic size compared with pre-COVID-19 levels. We also examined how a proactive influenza vaccination programme could mitigate this effect. Findings We estimated that COVID-19 PHSMs reduced influenza transmissibility by a maximum of 17.3% (95% CI 13.3-21.4) to 40.6% (35.2-45.9) and attack rate by 5.1% (1.5-7.2) to 24.8% (20.8-27.5) in the 2019-20 influenza season. We estimated a 10-60% increase in the population susceptibility for influenza, which might lead to a maximum of 1-5-fold rise in peak magnitude and 1-4-fold rise in epidemic size for the upcoming 2022-23 influenza season across locations, with a significantly higher fold rise in Singapore and Taiwan. The infection burden could be mitigated by additional proactive one-off influenza vaccination programmes. Interpretation Our results suggest the potential for substantial increases in infection burden in upcoming influenza seasons across the globe. Strengthening influenza vaccination programmes is the best preventive measure to reduce the effect of influenza virus infections in the community. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.

CdcSegNet: Automatic COVID-19 Infection Segmentation from CT Images

It has been more than two years since the outbreak of COVID-19, which has spread to almost every corner of the world and killed a great number of people. Rapid detection and screening have become an important means of controlling the spread of COVID-19. Segmentation of COVID-19 infected tissue from computed tomography (CT) images of a patient’s lungs can provide clinicians with important information to quantify and diagnose COVID-19. However, the accuracy of medical image segmentation is seriously affected by such factors as the low contrast between the infected tissue and the edge of the surrounding environment, the large variation of the infected tissue and the lack of labeling data. Therefore, a deep learning model called CdcSegNet to accurately segment lung lesions from CT images infected by COVID-19 is proposed. In our method, transfer learning is introduced to solve the problem of lack of annotation data, and three modules, i.e., continuous dilated convolution module (CDC), parallel dual attention module (PDA) and additional multi-core pooling layer (AMP) are innovatively proposed to solve the problem of fuzzy segmentation boundary and to segment effectively infected tissues. Extensive experiments and comparison studies are made, and demonstrate that our model CdcSegNet has high accuracy in COVID-19 segmentation, and is superior to the state-of-the-art models in terms of DICE, SEN, SPE, PPV, and VOE. IEEE

Emergency surgical strategies for patients with acute abdomen during the COVID-19 outbreak.

Objective: To investigate the emergency surgical strategies for patients with acute abdomen during the Corona Virus Disease 2019 (COVID-19) outbreak. Method(s): The retrospective and descriptive study was conducted. The clinical data of 20 patients with acute abdomen who were admitted to the Union Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology between January 18, 2020 and February 10, 2020 were collected. There were 13 males and 7 females, aged from 25 to 82 years, with an average age of 57 years. All the patients with emergency surgeries received pulmonary computed tomography (CT) examination before surgery, and completed nucleic acid detection in throat swab if necessary. Patients excluded from COVID-19 underwent regular anesthesia, suspected and confirmed cases were selected a proper anesthesia based on their medical condition and surgical procedure. Patients excluded from COVID-19 underwent emergency surgeries following the regular procedure, suspected and confirmed cases underwent emergency surgeries following the three-grade protection. Observation indicators: (1) surgical situations;(2) postoperative situations. Measurement data with normal distribution were represented as average (range). Count data were described as absolute numbers. Result(s): (1) Surgical situations: of the 20 patients with acute abdomen, 16 patients were excluded from COVID-19, and 4 were not excluded. All the 20 patients underwent emergency abdominal surgeries successfully, of whom 2 received surgeries under epidural anesthesia (including 1 with open appendectomy, 1 with open repair of duodenal bulbar perforation), 18 received surgeries under general anesthesia (including 9 with laparoscopic repair of duodenal bulbar perforation, 3 with open partial enterectomy, 3 with laparoscopic appendectomy, 1 with laparoscopic left hemicolectomy, 1 with laparoscopic right hemicolectomy, 1 with cholecystostomy). The operation time of patients was 32-194 minutes, with an average time of 85 minutes. The volume of intraoperative blood loss was 50-400 mL, with an average volume of 68 mL. (2) Postoperative situations: 16 patients excluded from COVID-19 preopratively were treated in the private general ward postoperatively. One of the 16 patients had fever at the postoperative 5th day and was highly suspected of COVID-19 after an emergency follow-up of pulmonary CT showing multiple ground-glass changes in the lungs. The patient was promptly transferred to the isolation ward for treatment, and results of nucleic acid detection in throat swab showed double positive. Medical history described by the patient showed that the patient and family members were residents of Wuhan who were not isolated at home during the epidemic. There was no way to confirm whether they had a history of exposure to patients with COVID-19. Medical staffs involved in this case did not show COVID-19 related symptoms during 14 days of medical observation. The other 15 patients recovered well postoperatively. The 4 patients who were not excluded from COVID-19 preoperatively based on medical history and results of pulmonary CT examination were directly transferred to the isolation ward for treatment postoperatively. They were excluded from COVID-19 for two consecutive negative results of nucleic acid detection in the throat swab and recovered well. Two of the 20 patients with acute abdomen had postoperative complications. One had surgical incision infection and recovered after secondary closure following opening incision, sterilizing and dressing, the other one had intestinal leakage and was improved after conservative treatment by abdominal drainage. There was no death in the 20 patients with acute abdomen. Conclusion(s): Patients with acute abdomen need to be screened through emergency forward. Patients excluded from COVID-19 undergo emergency surgeries following the regular procedure, and patients not excluded from COVID-19 undergo emergency surgeries following the three-grade protection. The temperature, blood routine test and other l boratory examinations are performed to monitor patients after operation, and the pulmonary CT and throat nucleic acid tests should be conducted if necessary. Patients excluded from COVID-19 preopratively are treated in the private general ward postoperatively, and they should be promptly transferred to the isolation ward for treatment after being confirmed. Patients who are not excluded from COVID-19 preoperatively based on medical history should be directly transferred to the isolation ward for treatment postoperatively.Copyright © 2020 by the Chinese Medical Association.

Emergency surgical strategies for patients with acute abdomen during the COVID-19 outbreak.

Objective: To investigate the emergency surgical strategies for patients with acute abdomen during the Corona Virus Disease 2019 (COVID-19) outbreak. Method(s): The retrospective and descriptive study was conducted. The clinical data of 20 patients with acute abdomen who were admitted to the Union Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology between January 18, 2020 and February 10, 2020 were collected. There were 13 males and 7 females, aged from 25 to 82 years, with an average age of 57 years. All the patients with emergency surgeries received pulmonary computed tomography (CT) examination before surgery, and completed nucleic acid detection in throat swab if necessary. Patients excluded from COVID-19 underwent regular anesthesia, suspected and confirmed cases were selected a proper anesthesia based on their medical condition and surgical procedure. Patients excluded from COVID-19 underwent emergency surgeries following the regular procedure, suspected and confirmed cases underwent emergency surgeries following the three-grade protection. Observation indicators: (1) surgical situations;(2) postoperative situations. Measurement data with normal distribution were represented as average (range). Count data were described as absolute numbers. Result(s): (1) Surgical situations: of the 20 patients with acute abdomen, 16 patients were excluded from COVID-19, and 4 were not excluded. All the 20 patients underwent emergency abdominal surgeries successfully, of whom 2 received surgeries under epidural anesthesia (including 1 with open appendectomy, 1 with open repair of duodenal bulbar perforation), 18 received surgeries under general anesthesia (including 9 with laparoscopic repair of duodenal bulbar perforation, 3 with open partial enterectomy, 3 with laparoscopic appendectomy, 1 with laparoscopic left hemicolectomy, 1 with laparoscopic right hemicolectomy, 1 with cholecystostomy). The operation time of patients was 32-194 minutes, with an average time of 85 minutes. The volume of intraoperative blood loss was 50-400 mL, with an average volume of 68 mL. (2) Postoperative situations: 16 patients excluded from COVID-19 preopratively were treated in the private general ward postoperatively. One of the 16 patients had fever at the postoperative 5th day and was highly suspected of COVID-19 after an emergency follow-up of pulmonary CT showing multiple ground-glass changes in the lungs. The patient was promptly transferred to the isolation ward for treatment, and results of nucleic acid detection in throat swab showed double positive. Medical history described by the patient showed that the patient and family members were residents of Wuhan who were not isolated at home during the epidemic. There was no way to confirm whether they had a history of exposure to patients with COVID-19. Medical staffs involved in this case did not show COVID-19 related symptoms during 14 days of medical observation. The other 15 patients recovered well postoperatively. The 4 patients who were not excluded from COVID-19 preoperatively based on medical history and results of pulmonary CT examination were directly transferred to the isolation ward for treatment postoperatively. They were excluded from COVID-19 for two consecutive negative results of nucleic acid detection in the throat swab and recovered well. Two of the 20 patients with acute abdomen had postoperative complications. One had surgical incision infection and recovered after secondary closure following opening incision, sterilizing and dressing, the other one had intestinal leakage and was improved after conservative treatment by abdominal drainage. There was no death in the 20 patients with acute abdomen. Conclusion(s): Patients with acute abdomen need to be screened through emergency forward. Patients excluded from COVID-19 undergo emergency surgeries following the regular procedure, and patients not excluded from COVID-19 undergo emergency surgeries following the three-grade protection. The temperature, blood routine test and other l boratory examinations are performed to monitor patients after operation, and the pulmonary CT and throat nucleic acid tests should be conducted if necessary. Patients excluded from COVID-19 preopratively are treated in the private general ward postoperatively, and they should be promptly transferred to the isolation ward for treatment after being confirmed. Patients who are not excluded from COVID-19 preoperatively based on medical history should be directly transferred to the isolation ward for treatment postoperatively.Copyright © 2020 by the Chinese Medical Association.

Digital twins to fight against COVID-19 pandemic

This study is aimed to explore the anti-epidemic effect of artificial intelligence (AI) algorithms such as digital twins on the COVID-2019 (novel coronavirus disease 2019), so that the information security and prediction accuracy of epidemic prevention and control (P & C) in smart cities can be further improved. It addresses the problems in the current public affairs governance strategy for the outbreak of the COVID-2019 epidemic, and uses digital twins technology to map the epidemic P & C situation in the real space to the virtual space. Then, the blockchain technology and deep learning algorithms are introduced to construct a digital twins model of the COVID-2019 epidemic (the COVID-DT model) based on blockchain combined with BiLSTM (Bi-directional Long Short-Term Memory). In addition, performance of the constructed COVID-DT model is analyzed through simulation. Analysis of network data security transmission performance reveals that the constructed COVID-DT model shows a lower average delay, its data message delivery rate (DMDR) is basically stable at 80%, and the data message disclosure rate (DMDCR) is basically stable at about 10%. The analysis on network communication cost suggests that the cost of this study does not exceed 700 bytes, and the prediction error does not exceed 10%. Therefore, the COVID-DT model constructed shows high network security performance while ensuring low latency performance, enabling more efficient and accurate interaction of information, which can provide experimental basis for information security and development trends of epidemic P & C in smart cities. © 2022

An Efficient Medicine Identification and Delivery System Based on Mobile Manipulation Robot

In recent years, with the emergence of COVID-19, the shortage of medical resources has become increasingly obvious. However, current environments such as hospital wards still require a large number of medical staff to deliver medicines. In this paper, we propose a mobile robot that can complete medicine grabbing and delivery in a hospital ward scenario. First, a lightweight neural network is built to improve the detection efficiency of Faster R-CNN algorithm for boxed medicine. Then, the pose of the robotic arm grasping the pill box is determined by point cloud matching to control the mechanical grasping of the pill box. Finally, a discomfort function representing the collision risk between the robot and the pedestrian is incorporated into the Risk-RRT algorithm to improve the navigation performance of the algorithm. By building a real experimental platform, the experiments verify the performance of our proposed medicine delivery robot system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Biomedicine Big Data-Trends and Prospect

This forward-looking reviewfocuses on the development and applications for Biomedicine Big Data (BMBD), and its role in the engineering system for data management, scientific and technological research and development, as well as in social and economic transformation. The review starts with an elaboration on the complex connotations of BMDB from the inter-disciplinary point of view. It then explores the implications of BMDB in sectors such as life science research, medical and health institutions, and biotechnology and bio-medicine industries in connection with the challenges and opportunities faced by social and economic development. The recent COVID-19 outbreak is used as an illustrative case study. The review ends with an analysis of a decade of BMBD practice, both domestically and abroad, with suggestions for policy-making and solutions to tacklemajor challenges from China's perspective. It is hoped that anyBMBD-related institutions, including administrative, academic, industrial, financial and social organizations, practitioners and users will benefit from this insightful summary drawn from the past decades ofBMBDpractice. Any critical comments and constructive suggestions are sincerely welcomed by the authors. © Publishing House of Electronics Industry 2021.

Epidemiology reveals mask wearing by the public is crucial for COVID-19 control

Objective: The pandemic 2019 Coronavirus disease (COVID-19) is the greatest concern globally. Here we analyzed the epidemiological features of China, South Korea, Italy and Spain to find out the relationship of major public health events and epidemiological curves. Study design: In this study we described and analyzed the epidemiological characteristics of COVID-19 in and outside China. We used GAM to generate the epidemiological curves and simulated infection curves with reported incubation period. Result(s): The epidemiological curves derived from the GAM suggested that the infection curve can reflect the public health measurements sensitively. Under the massive actions token in China, the infection curve flattened at 23rd of January. While surprisingly, even before Wuhan lockdown and first level response of public emergency in Guangdong and Shanghai, those infection curve came to the reflection point both at 21st of January, which indicated the mask wearing by the public before 21st Jan were the key measure to cut off the transmission. In the countries outside China, infection curves also changed in response to measures, but its rate of decline was much smaller than the curve of China's. Conclusion(s): The present analysis comparing the epidemiological curves in China, South Korea, Italy and Spain supports the importance of mask wearing by the public. Analysis of the infection curve helped to clarify the impact of important public health events, evaluate the efficiencies of prevention measures, and showed wearing masks in public resulted in significantly reduced daily infected cases.Copyright © 2020 The Author(s)

The Graph Convolutional Networks Framework for Predicting Pandemic Impact on Stock Prices

Covid-19 has dealt an unprecedented hit to the global economy and all industries, with varying degrees of decline from retail to real estate. This volatility is most evident in stock prices. Previous stock price forecasting methods typically used historical data for each stock as a separate input into the system. This paper proposes an attention-based parallel graph convolutional network framework, which consists of two parallel GCNs. The first GCN takes stock features as input, and the second GCN takes other industry features as input, and sets an attention model to reflect the pairwise interactions between networks. Experimental results on selected stock data show that the model outperforms both the LSTM model and the GCN model in accuracy and F1 score. © 2022 IEEE.

Influence of COVID-19 on asymmetric cost behavior and intellectual capital efficiency: a comparison of Australian and Chinese listed firms

This study examines whether intellectual capital efficiency affects the asymmetric cost behavior of managers and whether such influences were impacted by the COVID-19 pandemic in Australia and China. The sample consists of Australian and Chinese-listed firms from 2018 to 2021. The results found that intellectual capital efficiency increases the cost stickiness in general for both countries. However, the degree of cost stickiness caused by intellectual capital efficiency is significantly more pronounced in Australia than in China. When Chinese firms have government connections, the degree of cost stickiness caused by the intellectual capital efficiency increases and the significant difference in cost stickiness between China and Australia ceases. In addition, this study found that COVID-19 affected the degree of cost stickiness in China more profoundly than in Australia. This study presents important implications for external stakeholders to assess a firm's cost behavior by considering a firm's intellectual capital efficiency as the determinant of asymmetric cost behavior. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Developing a VR Socially Assistive Robot Simulator Employing Game Development Tools

Long-term care facilities (LTCFs) face challenges due to the personalized care required by people with developmental disabilities. The COVID-19 pandemic exacerbated the issues associated with limited staff who are overworked. Such a scenario also disrupted the research and development of socially assistive robots (SARs) as access to care facilities and the elderly was restricted. The restriction sparked creative thinking and innovation aimed at addressing the challenges introduced by the pandemic. Such is the case for developing Aether™, a SAR designed to monitor falls and engage in playful activities with users. This paper presents the use of game technologies to develop a Virtual reality digital twin simulator for overcoming the lack of access to LTCFs and the elderly by creating synthetic data that simulates the robot's behavior, interactions with the environment, and virtual avatars, before its deployment. Our approach additionally allows overcoming the limitation with traditional datasets for training machine learning where depicted people and actions are not representative of the elderly population. Our preliminary results indicate that combining DTs and VR expedites robot development. We tested and compared the robot navigation, person detection, and inspection behavior while observing COVID-19 restrictions. © 2022 IEEE.

Effect of environmental factors on the concentration distribution of bioaerosols with different particle sizes in an enclosed space

COVID-19 has alerted us about the need to quantify the effect of different environmental factors on the concentration distribution of bioaerosols. An experimental investigation was carried out to evaluate the effect of environmental factors, including air temperature, relative humidity, airflow speed and ultraviolet (UV) radiation, on the potential dispersion risk of bioaerosols in an enclosed space by tracking the Serratia marcescens as the tiny organisms. Research results indicated that the concentration of bioaerosols is the highest at the indoor air temperature of 25°C among the tested conditions (20°C, 25°C, 30°C and 35°C). The particle size of bioaerosols can be influenced by temperature, resulting in changes in the amount of settling. Increasing relative humidity from 50% to 80% and airflow speed from 1.5 m/s to 2.2 m/s have a negative impact on the dispersion of bioaerosols as the amount of particle settlement increases accordingly. As for the UV radiation parameters, a better disinfection efficiency was achieved at a radiation distance of 40 cm in the tested range of 20–50 cm and a radiation exposure time of 30 min in the tested range of 10–50 min. This study delivered novel data for the concentration distribution of bioaerosol under different environmental factors for creating a safe indoor environment. © The Author(s) 2022.