Experimental study on the heating/cooling and temperature uniformity performance of the microchannel temperature control device for nucleic acid PCR amplification reaction of COVID-19

The nucleic acid detection is an effective way for the prevention and control of COVID-19. PCR amplification is an important process in the nucleic acid detection. At present, PCR amplification has the problem of low heating/cooling rates, and poor temperature uniformity. This paper proposes a microchannel temperature control device for the nucleic acid detection. Five groups of parallel serpentine channels are used to increase the cooling rate of the PCR amplification. A gradual thermal conductivity design is applied to the reaction module to increase the temperature uniformity. The experimental results show that the best temperature uniformity is obtained when the materials of the inner and outer layers of the reaction module are copper and aluminum alloys, respectively. The limit and average heating/cooling rate are 7.2, 6.12, 5.52 and 5.28 °C/s, respectively, when the input power of the thermoelectric cooler is 11.07 W/cm2, the temperature and flow rate of the cooling water are 15℃ and 700 ml/min, and the thermal conductivity of the thermal grease is 6 W/(m·K). Compared with the commercial fan-fin cooling method, the limit and average heating/cooling rates are increased by 38.02%, 80.82%, 86.49% and 208.77%, respectively, with the help of microchannel cooling method. © 2023 Elsevier Ltd

Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification

The COVID-19 pandemic has threatened global health. Many studies have applied deep convolutional neural networks (CNN) to recognize COVID-19 based on chest 3D computed tomography (CT). Recent works show that no model generalizes well across CT datasets from different countries, and manually designing models for specific datasets requires expertise;thus, neural architecture search (NAS) that aims to search models automatically has become an attractive solution. To reduce the search cost on large 3D CT datasets, most NAS-based works use the weight-sharing (WS) strategy to make all models share weights within a supernet;however, WS inevitably incurs search instability, leading to inaccurate model estimation. In this work, we propose an efficient Evolutionary Multi-objective ARchitecture Search (EMARS) framework. We propose a new objective, namely potential, which can help exploit promising models to indirectly reduce the number of models involved in weights training, thus alleviating search instability. We demonstrate that under objectives of accuracy and potential, EMARS can balance exploitation and exploration, i.e., reducing search time and finding better models. Our searched models are small and perform better than prior works on three public COVID-19 3D CT datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Enhancing Online Epidemic Supervising System by Compartmental and GRU Fusion Model

The global pandemic, COVID-19, is an acute respiratory infectious disease caused by the 2019 novel coronavirus. Building the online epidemic supervising system to provide COVID-19 dynamic prediction and analysis has attracted the attention of the industry and applications community. In previous studies, the compartmental models and deep neural networks (DNNs) played important roles in predicting and analyzing the dynamics of the pandemic. Nevertheless, the compartmental model has limited ability to fit historical data and thus leads to unsatisfactory prediction accuracy due to the difficulty in parameter estimation. For DNNs, the lack of interpretability makes it difficult to explain the prediction results;thus, it cannot provide an in-depth understanding of the transmission mechanism of the pandemic. We propose a fusion model to leverage the merits of both models and resolve their shortcomings. The fusion model extracts epidemic-related knowledge from the state-of-the-art SEIDR compartmental model to guide the training of the GRU model, which can preserve the interpretability and achieve a good performance in predicting epidemic dynamics. This model can help to enhance the online epidemic supervising system by providing more accurate prediction results and deeper analysis. Our extensive experiments across multiple epidemic datasets from six European countries demonstrate that our model outperforms existing state-of-the-art baselines in predicting the active confirmed cases. More importantly, by analyzing the effective reproductive number, our method can reveal the risk of the second wave of the epidemic in Europe and justify the importance of social distancing to control the outbreak of the epidemic. © 2022 Junyi Ma et al.

Industry 4.0 Powered Process Technology Innovation, Firm's Leanness, and Eco-Environmental Performance During the COVID-19 Phase

Amid rising market competitiveness, Industry Revolution (IR) 4.0 oriented technological integration is considered an imperative driver of sustainable organizational performances and green supply chain management. This study explores the role of IR 4.0 powered process technology innovation in enhancing Leanness, Green Supply Chain Management, and Organizational Performance (including operational, economic, and environmental) during COVID-19. For this purpose, a novel conceptual framework was developed, and Partial Least Square-Structural Equation Modelling (PLSM) was employed on primary data of 314 respondents collected from Chinese manufacturing industries. Moreover, Multi-Group Analysis was also implemented to compare firms' willingness to implement IR 4.0 technologies powered process innovation. The results exhibit that Green IR 4.0 powered process technology innovation improves firm's leanness and stimulates environmental, optional, and economic performances. Similar findings are endorsed through the green supply chain management channel. Manifestly, COVID-19 instigated firms to adopt IR 4.0-based technological processes for efficient supply chain management. Based on these results, organizations are recommended to integrate IR 4.0 induced technology innovation to spur manufacturing firms' eco-economic and operational performance.

Survey of Studies of COVID-19 Diagnosis Based on Deep Learning

The Corona Virus Disease 2019 COVID-19 is highly infectious and pathogenic, posing a serious threat to public safety.  Rapid and accurate detection and diagnosis of COVID-19 is key to the epidemic control. The existing detection and diagnosis methods are mainly based on nucleic acid tests or manual diagnosis using medical images.  However, nucleic acid tests are time-consuming and require special test boxes, while the manual diagnosis relies heavily on professional knowledge, takes longer time for analysis and often fail to detect concealed lesions. Since then, with the development of X-ray and Computer Tomography CT image datasets, researchers have built many deep learning-based COVID-19 detection and diagnosis models which effectively assist medical experts in the efficient diagnosis and treatment of COVID-19. This paper lists the mainstream image datasets for the detection and diagnosis of COVID-19 and related evaluation metrics. Then, it introduces the existing deep learning-based models for COVID-19 diagnosis from the perspectives of the model task and the image data type, and on this basis compares and analyzes the detection performance of the models in six different dimensions: Backbone network, data sets, image types, model performance, classification task types and park opening situation. In addition, this paper introduces the excellent application systems used to fight against COVID-19, and discusses the development trend of the studies in this field. © 2021, Editorial Office of Computer Engineering. All rights reserved.

The Effect of Multi-mode Stimuli of Feedforward and Eye Tracking on Metacognition— An Exploratory Study Using Digital Dictionaries

Metacognition is regarded as a retrospective skill promoting learners’ learning performance, deep thinking, and academic well-being. Stimulated Recall (SR) is regarded as a reliable approach to inspiring learners’ metacognition in the classroom. However, the outbreak of COVID-19, causing widespread class suspension, may impair the effect of SR on cultivating distance learners’ metacognition. The current study, employing multi-mode stimuli of learners’ eye movements and feedforward, aimed to develop the effect of SR on activating learners’ metacognition in remote settings. Forty-eight university graduates were recruited to participate in an eye-tracking experiment using digital dictionaries. Their feedforward and eye movements were collected as multi-mode stimuli. By reviewing the consistency and discrepancies between their feedforward and eye movements, participants were invited to conduct an SR interview, which stimulated them to retrospect on their prior cognitive behaviors. The results of the metacognition scale pre-post test showed that learners’ metacognitive skills were significantly improved by the stimulated recall with multi-mode stimuli. The findings theoretically enrich the metacognition strategy in the Cognitive Theories of Multimedia Learning, and practically extend the implementation of stimulated recall in distance learning contexts. © 2022,Educational Technology and Society. All Rights Reserved.

MMCoVaR: Multimodal COVID-19 vaccine focused data repository for fake news detection and a baseline architecture for classification

The outbreak of COVID-19 has resulted in an "infodemic"that has encouraged the propagation of misinformation about COVID-19 and cure methods which, in turn, could negatively affect the adoption of recommended public health measures in the larger population. In this paper, we provide a new multimodal (consisting of images, text and temporal information) labeled dataset containing news articles and tweets on the COVID-19 vaccine. We collected 2,593 news articles from 80 publishers for one year between Feb 16th 2020 to May 8th 2021 and 24184 Twitter posts (collected between April 17th 2021 to May 8th 2021). We combine ratings from two news media ranking sites: Medias Bias Chart and Media Bias/Fact Check (MBFC) to classify the news dataset into two levels of credibility: reliable and unreliable. The combination of two filters allows for higher precision of labeling. We also propose a stance detection mechanism to annotate tweets into three levels of credibility: reliable, unreliable and inconclusive. We provide several statistics as well as other analytics like, publisher distribution, publication date distribution, topic analysis, etc. We also provide a novel architecture that classifies the news data into misinformation or truth to provide a baseline performance for this dataset. We find that the proposed architecture has an F-Score of 0.919 and accuracy of 0.882 for fake news detection. Furthermore, we provide benchmark performance for misinformation detection on tweet dataset. This new multimodal dataset can be used in research on COVID-19 vaccine, including misinformation detection, influence of fake COVID-19 vaccine information, etc. © 2021 ACM.

A Wi-Fi harvesting enabled energy optimization scheme for smart clothing

The outbreak of the COVID-19 and the rapid aging population have made isolated medical treatment and home based care become the new norm. As a distributed wireless body sensor network (WBSN), smart clothing is of great importance for the monitoring of infectious diseases and chronic diseases. Yet, limited energy supply and real-time data transmission are challenging for regular use. In this paper, we propose an energy harvesting powered smart clothing architecture, which is optimized by reinforcement learning, PSO and static transferring algorithm to extend battery life while balancing the time delay of data transmission. To evaluate the optimization performance, the reinforcement learning, the static transferring algorithm and the particle swarm optimization(PSO) with two kinds of weight are conducted. The simulation results show that reinforcement learning has the optimal performance in terms of total cost, energy consumption and battery loss without considering the time delay. ©2021 IEEE

Automated Model Design and Benchmarking of Deep Learning Models for COVID-19 Detection with Chest CT Scans

The COVID-19 pandemic has spread globally for several months. Because its transmissibility and high pathogenicity seriously threaten people's lives, it is crucial to accurately and quickly detect COVID-19 infection. Many recent studies have shown that deep learning (DL) based solutions can help detect COVID-19 based on chest CT scans. However, most existing work focuses on 2D datasets, which may result in low quality models as the real CT scans are 3D images. Besides, the reported results span a broad spectrum on different datasets with a relatively unfair comparison. In this paper, we first use three state-of-the-art 3D models (ResNet3D101, DenseNet3D121, and MC3 18) to establish the baseline performance on three publicly available chest CT scan datasets. Then we propose a differentiable neural architecture search (DNAS) framework to automatically search the 3D DL models for 3D chest CT scans classification and use the Gumbel Softmax technique to improve the search efficiency. We further exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results. The experimental results show that our searched models (CovidNet3D) outperform the baseline human-designed models on three datasets with tens of times smaller model size and higher accuracy. Furthermore, the results also verify that CAM can be well applied in CovidNet3D for COVID-19 datasets to provide interpretability for medical diagnosis. Code: https://github.com/HKBU-HPML/CovidNet3D.

SIMULATION OF AN ANNULAR LIQUID JET WITH A COAXIAL SUPERSONIC GAS JET IN A MEDICAL INHALER

Medical inhalers have been used for the treatment of a wide range of respiratory diseases including COVID-19. In this study, we investigate the annular liquid jet breakup with a coaxial supersonic gas jet using large eddy simulations. This contributes to a further understanding and improvement of medical inhaler designs. The liquid is sucked in by the low pressure as a result of the high velocity gas jet and breaks up due to the interaction with the gas jet. This type of spray nozzle configuration is commonly used in medical inhalers. Two different gas nozzle diameters are studied. The simulated liquid structure is compared with preliminary, qualitative experimental results. The gas jet pressure and radial velocity of the liquid are found to be coupled and the interaction between them plays an important role in formation of the liquid structure. The effect of gas nozzle diameter on flow rate, mean radius of the liquid, and mean radial velocity, as well as its oscillation behavior has been investigated. The power development of dominate frequencies of averaged radial liquid velocity along the flow direction is shown. The growth of the instabilities can be observed from these results.

PB-Trace: Privacy-Preserving Blockchain-based Contact Tracing to Combat Pandemics

The eruption of a pandemic, such as COVID-19, can cause an unprecedented global crisis. Contact tracing, as a pillar of communicable disease control in public health for decades, has shown its effectiveness on pandemic control. Despite intensive research on contact tracing, existing schemes are vulnerable to attacks and can hardly simultaneously meet the requirements of data integrity and user privacy. The design of a privacy-preserving contact tracing framework to ensure the integrity of the tracing procedure has not been sufficiently studied and remains a challenge. In this paper, we propose P2B-Trace, a privacy-preserving contact tracing initiative based on blockchain. First, we design a decentralized architecture with blockchain to record an authenticated data structure of the user's contact records, which prevents the user from intentionally modifying his local records afterward. Second, we develop a zero-knowledge proximity verification scheme to further verify the user's proximity claim while protecting user privacy. We implement P2B-Trace and conduct experiments to evaluate the cost of privacy-preserving tracing integrity verification. The evaluation results demonstrate the effectiveness of our proposed system. © 2021 ACM.

CRI: Measuring City Infection Risk amid COVID-19

The outbreak of COVID-19 has brought incalculable economy and life losses. Accurately assessing the risk of a certain city can help formulate effective measures to prevent and control COVID-19 in time. It will be of great significance for us to measure city risk in infection amid epidemics. City risk in infection is related to many factors. To address this problem, this paper proposes city risk index (CRI) to measure city risk in infection, considering the following four perspectives: Economy (i.e., GDP and FCI), technology (i.e., education and innovation), population, and geographical position (i.e., latitude and longitude). The experimental results show that CRI can be effectively employed to measure city risk in infection amid COVID-19 as well as other similar epidemics. The proposed CRI can be used to guide policymakers for better emergency management policies making when coping with COVID-19. © 2020 IEEE.

BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing

The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented health crisis for the global. Digital contact tracing, as a transmission intervention measure, has shown its effectiveness on pandemic control. Despite intensive research on digital contact tracing, existing solutions can hardly meet users’ requirements on privacy and convenience. In this paper, we propose BU - Trace, a novel permissionless mobile system for privacy-preserving intelligent contact tracing based on QR code and NFC technologies. First, a user study is conducted to investigate and quantify the user acceptance of a mobile contact tracing system. Second, a decentralized system is proposed to enable contact tracing while protecting user privacy. Third, an intelligent behavior detection algorithm is designed to ease the use of our system. We implement BU - Trace and conduct extensive experiments in several real-world scenarios. The experimental results show that BU - Trace achieves a privacy-preserving and intelligent mobile system for contact tracing without requesting location or other privacy-related permissions. © 2021, Springer Nature Switzerland AG.

Data mining of weibo for public sentiment evolution regarding COVID-19

This article proposes a Weibo public opinion evolution model based on data mining for COVID-19. Python crawlers are used to collect Weibo public content, and the evolution process is classified into four phases according to the popularity of public opinion. Naive Bayes classifier is employed for sentiment analysis, and data visualization method is adopted to explore the frequent word. Regional heat characteristics at each phase, the temporal and spatial laws of public opinion are discussed accordingly. The analysis results show that through the data mining of Weibo public opinion, the evolution pattern and hot content of each phase can be identified. This study suggests the government should focus on the evolution of public opinion and take effective guidance measures timely. © 2020 IEEE.