Early-Career Researchers in China during the Pandemic: Qualitative Evidence from a Longitudinal Study

The outbreak of COVID-19 has changed the world in many aspects, and global scientific research has also been challenged. Early-career researchers (ECRs) who just start academic careers are prominently affected by the pandemic. To explore how Chinese ECRs have been affected, longitudinal qualitative interviews were conducted with twenty-four Chinese ECRs from different disciplines and universities in 2020 and 2021. As the results show, Chinese ECRs worry that their future prospects are limited by increased workloads and reduced productivity. However, the new wave of change did not occur for them, and their work resumed its routine and was relatively stable. Over two years, Chinese ECRs got used to working from home and giving online courses, but they were increasingly confused about when the pandemic will end. What bothers ECRs most is the inefficiency of communication and collaboration due to the travel bans, although they became familiar with the online way. © University of Toronto Press, 2023.

Electrospray-on-Electrospun Breathable, Biodegradable, and Robust Nanofibrous Membranes with Photocatalytic Bactericidal Activity

The increasing emergence of infectious diseases like COVID-19 has created an urgent need for filtration/purification materials coupled with multifunctional features such as mechanical integrity, excellent airflow/filtration, and antibacterial/antimicrobial properties. Polymer membranes and metal-organic frameworks (MOFs) have demonstrated high effectiveness in air filtration and purification. MOF nanoparticles have been introduced into electrospun polymer nanofibrous membranes through embedding or postsolution growth. However, the derived hybrids are still facing the issue of (1) limited MOF exposure, which leads to low efficacy;and (2) uncontrollable growth, which leads to pore blocking and low breathability. In this work, we customized an electrospray-on-electrospinning in situ process to dynamically integrate MOF nanoparticles into a robust and elastic continuous nanofibrous membrane for advanced properties including high mechanical strength and flexibility, excellent breathability, particle filtration, and good antimicrobial performance. Biodegradable polylactic acid was reinforced by the poly(hydroxybutyrate)-di-poly(DLA-CL)x copolymer (PHBR) and used as an electrospinning matrix, while MOF nanoparticles were simultaneously electrically sprayed onto the nanofibers with easily controllable MOF loading. The MOF nanoparticles were homogeneously deposited onto nanofibers without clogging the pores in the membrane. The collision of PLA and MOF under the wet status during electrospinning and the hydrogen bonding through C=O and N-H bonds strengthen the affinity between PLA nanofibers and MOF nanoparticles. Because of these factors, the MOF-incorporated PLA/PHBR nanofibrous membrane achieved over 95% particle filtration efficiency with enhanced mechanical properties while maintaining high breathability. Meanwhile, it exhibits excellent photocatalytic antibacterial performance, which is necessary to kill microbes. The electrospray-on-electrospinning in situ process provides an efficient and straightforward way to hybridize one-dimensional (1D) or two-dimensional (2D) nanomaterials into a continuous nanofibrous membrane with strong interaction and controllable loading. Upon integrating proper functionalities from the materials, the obtained hybrids are able to achieve multifunctionalities for various applications.

User Consumption Behavior Recognition Based on SMOTE and Improved AdaBoost

The sudden outbreak of COVID-19 has dealt a huge blow to traditional education and training companies. Institutions use the WeChat platform to attract users, but how to identify high-quality users has always been a difficult point for enterprises. In this paper, researchers proposed a classification algorithm based on SMOTE and the improved AdaBoost, which fuses feature information weights and sample weights to effectively solve the problems of overfitting and sample imbalance. To justify the study, it was compared with other traditional machine-learning algorithms. The accuracy and recall of the model increased by 19% and 36%, respectively, and the AUC value reached 0.98, indicating that the model could effectively identify the user's purchase intention. The proposed algorithm also ensures that it works well in spam identification and fraud detection. This research is of great significance for educational institutions to identify high-quality users of the WeChat platform and increase purchase conversion rate.

Explain systemic risk of commodity futures market by dynamic network

Since inflation of commodities is becoming more and more severe recently caused by many macro events, such as COVID-19 and Russian-Ukrainian conflict, systemic risk of commodity futures market is getting more attention from academic and industrial areas. Instead of using external factors to explain this risk as previous researches, we explain it by internal topology and structures of commodity futures market. This method helps us understand its key driving factors and their different impact to Chinese and international commodity futures markets. © 2023 Elsevier Inc.

ASSESSMENT OF BIRTH COHORT SCREENING OF CHRONIC HEPATITIS C IN COLORECTAL CANCER SCREENING PATIENTS IN BRITISH COLUMBIA

Background Birth cohort screening of chronic hepatitis C (CHC) is recommended in British Columbia since 2018 for baby boomers born between 1945 to 1964, with an estimated provincial prevalence of 2.31%. Though there remained a gap in care following anti-hepatitis C positivity, resulting in reflexive ribonucleic acid (RNA) testing provincially. Dual screening of CHC in patients referred to colorectal (CRC) screening programs can provide an opportunity to link patients with healthcare professionals to ensure appropriate follow-up. Purpose We aimed to assess the uptake of CHC screening amongst CRC screening patients after the release of British Columbia's birth cohort guidelines, both pre and post-COVID-19 pandemic. Method A retrospective review of patients referred to a CRC screening program in Vancouver from October 1st to December 31st, 2019, and December 1st – 31st, 2021, was performed. Collected data included demographics, liver disease history, and co-infection rates with hepatitis B (HBV) or human immunodeficiency virus (HIV). Dates of first-time hepatitis C antibody, RNA and viral load testing were gathered. Descriptive statistics were used to identify the proportion of screening and prevalence of CHC. Result(s) A total of 553 patients were referred for colonoscopy to the CRC screening program, of whom 458 (82.8%) patients were born between 1945 to 1964, and 273 (n=49%) were female. Among the 250 (45.2%) patients screened for CHC, 4 (0.72%) had positive anti-hepatitis C, all of whom were baby boomers. In 2019, 44% (n=183) of patients were screened for CHC;78.7% (n=144) were screened before colonoscopy referral. In 2020, 48.6% (n=67) of patients were screened for CHC;100% of cases were screened before colonoscopy referral. Conclusion(s) Birth cohort screening of CHC is underutilized in British Columbia. Dual screening of CHC at the time of referral to CRC screening provides a practical approach to linking patients to healthcare. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

Electrospray-on-Electrospun Breathable, Biodegradable, and Robust Nanofibrous Membranes with Photocatalytic Bactericidal Activity

The increasing emergence of infectious diseases like COVID-19 has created an urgent need for filtration/purification materials coupled with multifunctional features such as mechanical integrity, excellent airflow/filtration, and antibacterial/antimicrobial properties. Polymer membranes and metal-organic frameworks (MOFs) have demonstrated high effectiveness in air filtration and purification. MOF nanoparticles have been introduced into electrospun polymer nanofibrous membranes through embedding or postsolution growth. However, the derived hybrids are still facing the issue of (1) limited MOF exposure, which leads to low efficacy;and (2) uncontrollable growth, which leads to pore blocking and low breathability. In this work, we customized an electrospray-on-electrospinning in situ process to dynamically integrate MOF nanoparticles into a robust and elastic continuous nanofibrous membrane for advanced properties including high mechanical strength and flexibility, excellent breathability, particle filtration, and good antimicrobial performance. Biodegradable polylactic acid was reinforced by the poly(hydroxybutyrate)-di-poly(DLA-CL)x copolymer (PHBR) and used as an electrospinning matrix, while MOF nanoparticles were simultaneously electrically sprayed onto the nanofibers with easily controllable MOF loading. The MOF nanoparticles were homogeneously deposited onto nanofibers without clogging the pores in the membrane. The collision of PLA and MOF under the wet status during electrospinning and the hydrogen bonding through C═O and N-H bonds strengthen the affinity between PLA nanofibers and MOF nanoparticles. Because of these factors, the MOF-incorporated PLA/PHBR nanofibrous membrane achieved over 95% particle filtration efficiency with enhanced mechanical properties while maintaining high breathability. Meanwhile, it exhibits excellent photocatalytic antibacterial performance, which is necessary to kill microbes. The electrospray-on-electrospinning in situ process provides an efficient and straightforward way to hybridize one-dimensional (1D) or two-dimensional (2D) nanomaterials into a continuous nanofibrous membrane with strong interaction and controllable loading. Upon integrating proper functionalities from the materials, the obtained hybrids are able to achieve multifunctionalities for various applications. © 2023 American Chemical Society.

Intelligent classification of B-line and white lung from COVID-19 pneumonia ultrasound images using radiomics analysis

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.

A Novel Snore Detection and Suppression Method for a Flexible Patch With MEMS Microphone and Accelerometer

Sleep apnea impacts more and more people all over the world, and obstructive sleep apnea of which is the most frequent. Hence, research on snoring detection and related suppression methods is extremely urgent. In this article, a novel low-cost flexible patch with MEMS microphone and accelerometer is developed to detect snore event and sleeping posture, and a small vibration motor embedded in the patch is designed to suppress snoring. Theoretical analyses of short-time energy, piecewise average filtering (PAF), and Mel-frequency cepstral coefficients (MFCCs) processing are described in detail, and the improved MFCCs are put forward and used as the input of the convolutional neural network (CNN). Furthermore, the snore recognition method based on the combination of similarity analysis and CNN analysis is presented, followed by the snoring suppression method. Experimental results demonstrate that the main features of the sound signals can be extracted effectively by PAF and MFCCs processing, and the data compression ratio is about 99.41%. Besides, the locations of the eigenvectors can be found accurately based on short-time energy analysis. The numbers of high similarity of snoring signals within 30 s are larger than 3, while those of non-snoring signals are often less than 3. If the preliminary screening with similarity analysis is passed, CNN analysis will be conducted to judge whether there are snoring events. The accuracy of snore recognition with CNN analysis is calculated to be as high as 99.25%. Finally, the average snoring time measured by the smart patch with snoring suppression is reduced to 15 from 135 min, which indicates that the proposed snore recognition and suppression methods are effective.

Internet of Things (IoT)-blockchain-enabled pharmaceutical supply chain resilience in the post-pandemic era

During the COVID-19 pandemic, the current operating environment of pharmaceutical supply chain (PSC) has rapidly changed and faced increasing risks of disruption. The Internet of Things (IoT) and blockchain not only help enhance the efficiency of PSC operations in the information technology domain but also address complex related issues and improve the visibility, flexibility, and transparency of these operations. Although IoT and blockchain have been widely examined in the areas of supply chain and logistics management, further work on PSC is expected by the public to enhance its resilience. To respond to this call, this paper combines a literature review with semi-structured interviews to investigate the characteristics of PSC, the key aspects affecting PSC, and the challenges faced by PSC in the post-pandemic era. An IoT-blockchain-integrated hospital-side oriented PSC management model is also developed. This paper highlights how IoT and blockchain technology can enhance supply chain resilience and provides a reference on how PSC members can cope with the associated risks.

APPLICATION OF VIDEO MONITORING SYSTEM TO PREVENT SARS-COV-2 INFECTION

Objective: Discussion of the application effect of Video Monitoring System (VMS) to prevent SARS-CoV-2 infection during taking off personal protective equipment (PPE) in the designated hospital for coronavirus disease 2019 (COVID-19) patients. Method: Monitoring system, alarm system was installed in the buffer area of taking off PPE, with monitoring, guidance, and supervision by special personnel. Comparison with the effect of arranging dedicated staff for monitoring. Result: By application of video monitoring system, the infection prevention and control problems, as well as potential risks by process of taking off PPE, can be timelier discovered, compared with the traditional monitoring method of arranging dedicated staff, the number of non-conforming cases of taking off PPE within two weeks significantly reduced and the efficiency drastically increased. Conclusion: The video monitoring system plays a very effective role in preventing SARS-CoV-2 infection during the process of personal protective equipment taking off. Timely implementation of video monitoring system management can effectively avoid the risks caused by nonstandard PPE taking off and secure the safety of Healthcare personnel.

Dynamic efficiency of China's commodity futures market through the lens of high frequency data

In this paper, we use the permutation entropy algorithm to derive the static and dynamic permutation entropy of commodity futures, and to evaluate the effectiveness of main products in China's commodity futures market. The intraday data of six varieties belonging to six categories in China's commodity futures market are taken as samples. We find the following: (1) The return distribution of the main varieties shows high peaks, fat tails and asymmetry, and follows the biased random walk distribution characteristics;(2) The permutation entropy of all varieties decreases significantly in the same time window, during which the price volatility of major commodity markets rises. And the time window coincides with the impact time of COVID-19 epidemic;(3) By comparing the distribution of permutation entropy of main varieties in different stages of event shock, we found that the mean value of permutation entropy decreases significantly during the process of event shock, and the price fluctuates greatly. Therefore, the significant decrease of permutation entropy is a valuable warning signal for regulators and investors.

Exploit Social Distancing in Construction Scheduling: Visualize and Optimize Space-Time-Workforce Tradeoff

The COVID pandemic has given rise to the necessity of social distancing regulations, which has brought the importance of workspace management on the construction site to an unprecedented level. Understanding and visualizing the interaction and tradeoff among space, time, and workforce is critical for construction managers to schedule and deliver projects on time. Therefore, the objectives of this research are to investigate how the critical path method (CPM) and Takt-time planning methods utilize space, time, and workforce differently, develop a tool to visualize the space-time-workforce interactions, and investigate the space-time-workforce tradeoff based on different managers' preferences. This research selected a high-rise office building project and collected 889 sets of productivity data of five specialty trades. The research built a simulation model to investigate productivity and project performance under 267 scenarios of different combinations of the three resources. A dynamic tool was then developed to visualize workspace, time, and workforce interactions. Finally, a Choquet integral-based evaluation and decision tool was developed. The simulation results show that the Takt-time planning method can reduce up to 80% of workspace overlap compared with the actual production plan with less than 20% of duration extension. The contributions to the body of knowledge are (1) creating a visual framework for managers to understand the interaction and tradeoff among space, time, and workforce quickly and accurately, and (2) developing an innovative Choquet integral approach for managers to evaluate planning strategies according to project preferences. The framework and analysis method can be adapted to other construction projects to assist managers to visualize and optimize the space-time-workforce tradeoff under uncertain project drivers.

Beyond Entertainment: Unpacking Danmaku and Comments' Role of Information Sharing and Sentiment Expression in Online Crisis Videos

Online videos are playing an increasingly important role in timely information dissemination especially during public crises. Video commentary, synchronous or asynchronous, is indispensable in viewers' engagement and participation, and may in turn contribute to video with additional information and emotions. Yet, the roles of video commentary in crisis communications are largely unexplored, which we believe that an investigation not only provides timely feedback but also offers concrete guidelines for better information dissemination. In this work, we study two distinct commentary features of online videos: traditional asynchronous comments and emerging synchronous danmaku. We investigate how users utilize these two features to express their emotions and share information during a public health crisis. Through qualitative analysis and applying machine learning techniques on a large-scale danmaku and comment dataset of Chinese COVID-19-related videos, we uncover the distinctive roles of danmaku and comments in crisis communication, and propose comprehensive taxonomies for information themes and emotion categories of commentary. We also discover the unique patterns of crisis communications presented by danmaku, such as collective emotional resonance and style-based highlighting for emphasizing critical information. Our study captures the unique values and salient features of the emerging commentary interfaces, in particular danmaku, in the context of crisis videos, and further provides several design implications to enable more effective communications through online videos to engage and empower users during crises. © 2021 ACM.

Blockchain-based options for physical settlement of commodity futures

On April 20, the negative price in the last trading session of the Chicago Mercantile Exchange (CME) West Texas Intermediate (WTI) oil futures shocked many traders and investors globally. It caused much potential risk exposure for commodity futures trading and impacted pricing and valuation models in many derivatives. One of the essential hidden risks of commodity futures exposed by this event is high storage and delivery cost of their physical settlement, which reduces the hedging functionality of commodity futures during high volatile market periods, such as the coronavirus (COVID-19) pandemic. This chapter proposes options to hedge the risk by the fluctuation of physical settlement cost. Since both listed and over-the-counter (OTC) options may take a long time to be issued in the market, we propose to use blockchain technology to issue physical settlement rights by tokens. The transportation and storage provider can make profits by this covered-call like options strategies. At the same time, commodity futures traders can get proper insurance for this kind of recent unexpected risk for sky-rocketing high physical delivery cost. Blockchain technologies such as smart contract and decentralized settlement system can be more flexible to offer these options rather than traditional derivatives exchanges. And the premium can be paid by the commodity itself to the transportation and storage provider to link token value closely to physical goods and reduce inflation caused by currency over-issue. The simulation and scenario analysis are done to demonstrate the risk hedge functionality of these options for the April 20 event. And we show that based on economic theory, the combined price of commodity futures and its physical settlement options should be larger than zero if the economics is to keep moving forward. © 2021 World Scientific Publishing Company.

Mask Detection Algorithm for Public Places Entering Management During COVID-19 Epidemic Situation

COVID-19 now is spreading fast all over the world. Wearing masks has proven to be an effective way to prevent COVID-19 to some extent. This paper studies a mask detection algorithm for public places entering management during COVID-19 epidemic situation. Only people wearing masks are allowed to enter. Cameras are fixed at the entrances of public places and take photos of people who are coming in. Then, a series of pre-processing are performed, including face detection, normalization, etc. Residual network is used as the classifier. Simulation results show that the average recognition accuracy can reach 90%. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comparative efficacy of respiratory personal protective equipment against viral respiratory infectious diseases in healthcare workers: a network meta-analysis.

OBJECTIVE: With the epidemic of coronavirus disease 2019 (COVID-19), the healthcare workers (HCWs) require proper respiratory personal protective equipment (rPPE) against viral respiratory infectious diseases (VRIDs). It is necessary to evaluate which type of mask and manner of wearing is the best suitable rPPE for preventing the VRID. STUDY DESIGN: A Bayesian network meta-analysis was performed to comprehensively analyze the protective efficacy of various rPPE. METHODS: This network meta-analysis protocol was registered in an international prospective register of systematic reviews (CRD42020179489). Electronic databases were searched for cluster randomized control trials (RCTs) of comparing the effectiveness of rPPE and wearing manner in preventing HCWs from VRID. The primary outcome was the incidence of laboratory-confirmed viral respiratory infection reported as an odds ratio (OR) with the associated 95% credibility interval (CrI). The secondary outcome was the incidence of clinical respiratory illness (CRI) reported as an OR with the associated 95% CrI. Surface under the cumulative ranking curve analysis (SUCRA) provided a ranking of each rPPE according to the primary outcome and the secondary outcome as data supplement. RESULTS: Six studies encompassing 12,265 HCWs were included. In terms of the incidence of laboratory-confirmed viral respiratory infection, the continuous wearing of N95 respirators (network OR, 0.48; 95% CrI: 0.27 to 0.86; SUCRA score, 85.4) showed more effective than the control group. However, in terms of reducing the incidence of CRI, there was no rPPE showing superior protective effectiveness. CONCLUSIONS: There are significant differences in preventive efficacy among current rPPE. Our result suggests that continuous wearing of N95 respirators on the whole shift can serve as the best preventive rPPE for HCWs from the VRID.