Deploying the Minimum Number of Rechargeable UAVs for a Quarantine Barrier

To control the rapid spread of COVID-19, we consider deploying a set of Unmanned Aerial Vehicles (UAVs) to form a quarantine barrier such that anyone crossing the barrier can be detected. We use a charging pile to recharge UAVs. The problem is scheduling UAVs to cover the barrier, and, for any scheduling strategy, estimating theminimum number of UAVs needed to cover the barrier forever. We propose breaking the barrier into subsegments so that each subsegment can be monitored by a single UAV. We then analyze two scheduling strategies, where the first one is simple to implement and the second one requires fewer UAVs. The first strategy divides UAVs into groups with each group covering a subsegment. For this strategy, we derive a closed-form formula for the minimum number of UAVs. In the case of insufficient UAVs, we give a recursive function to compute the exact coverage time and give a dynamic-programming algorithm to allocate UAVs to subsegments to maximize the overall coverage time. The second strategy schedules all UAVs dynamically. We prove a lower and an upper bound on the minimum number of UAVs. We implement a prototype system to verify the proposed coverage model and perform simulations to investigate the performance.

Impact of La3+doping on temperature coefficient of resistivity and peak temperature of La x Ca0.89-xSr0.11MnO3 films prepared by sol-gel spin coating method

Herein, refined LaxCa0.89-xSr0.11MnO3 (LCSMO, x = 0.65, 0.68, 0.71 and 0.74) films were prepared through the sol-gel spin-coating. The influence of La3+ content on the structural properties of LCSMO films was investigated by X-ray diffraction and Atomic force microscope, demonstrating that LCSMO films can grow well on SrTiO3 (00l) substrate. Besides, X-ray photoemission spectroscopy verified the double exchange (DE) effect was weakened with La3+ dopant. The La3+ doping and interconnected grains boundaries (GBs) led to the weakening DE effect and GBs scattering, respectively. Due to superior GBs connectivity, the resistivity of LCSMO films was less than 7.1 x 10(-4) Omega.cm at low temperature of 100 K. Importantly, it is an effective control method to keep the temperature (T-k) corresponding to temperature coefficient of resistivity (TCR) at room temperature with Sr2+ content as constant in LCSMO films. At x = 0.71, the peak TCR value was found to be 8.84%/K and corresponding T-k was 283.15 K. These results are beneficial for advanced application of uncooling infrared bolometer.

Anti-Fraud Analysis during the COVID-19 Pandemic: A Global Perspective

The ongoing coronavirus disease 2019 (COVID-19) pandemic has brought unexpected economic downturns and accelerated digital transformation, leading to stronger financial fraud motives and more complicated fraud schemes. Although scholars, practitioners, and regulators have begun to focus on the new characteristics of financial fraud, a systematic and effective anti-fraud strategy during the pandemic still needs to be explored. This paper comprehensively analyzes the lessons of anti-fraud that we should learn from the COVID-19 pandemic. By exploring the complex motives and schemes of fraud, we summarize the characteristics of financial fraud activities and further analyze the regulatory challenges posed by financial fraud during the outbreak. To better cope with the fraudulent activities during the pandemic, policy proposals on how to improve the supervision of financial fraud activities are put forward. In particular, the panoramic data and graph-based techniques are powerful tools for future fraud detection.

A Novel Graph Indexing Approach for Uncovering Potential COVID-19 Transmission Clusters

The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission chains so as to prevent an outbreak of COVID-19 as early as possible. In this article, we study the problem of potential transmission cluster discovery based on the spatio-temporal logs. Given a query of patient user q and a timestamp of confirmed infection tq, the problem is to find all potential infected users who have close social contacts to user q before time tq. We motivate and formulate the potential transmission cluster model, equipped with a detailed analysis of transmission cluster property and particular model usability. To identify potential clusters, one straightforward method is to compute all close contacts on-the-fly, which is simple but inefficient caused by scanning spatio-temporal logs many times. To accelerate the efficiency, we propose two indexing algorithms by constructing a multigraph index and an advanced BCG-index. Leveraging two well-designed techniques of spatio-temporal compression and graph partition on bipartite contact graphs, our BCG-index approach achieves a good balance of index construction and online query processing to fast discover potential transmission cluster. We theoretically analyze and compare the algorithm complexity of three proposed approaches. Extensive experiments on real-world check-in datasets and COVID-19 confirmed cases in the United States validate the effectiveness and efficiency of our potential transmission cluster model and algorithms. © 2023 Association for Computing Machinery.

Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic

Social media data can be a very salient source of information during crises. User-generated messages provide a window into people's minds during such times, allowing us insights about their moods and opinions. Due to the vast amounts of such messages, a large-scale analysis of population-wide developments becomes possible. In this paper, we analyze Twitter messages (tweets) collected during the first months of the COVID-19 pandemic in Europe with regard to their sentiment. This is implemented with a neural network for sentiment analysis using multilingual sentence embeddings. We separate the results by country of origin, and correlate their temporal development with events in those countries. This allows us to study the effect of the situation on people's moods. We see, for example, that lockdown announcements correlate with a deterioration of mood in almost all surveyed countries, which recovers within a short time span. © ACL 2020.All right reserved.

Toward a Sociotechnical ramework for Misinformation Policy Analysis

The challenge of widespread misinformation has expanded during the COVID-19 pandemic. Governments worldwide have adopted or are designing a variety of information policies and tools to cope with the exacerbated challenge of misinformation. To understand the complexity and nuanced realities of government misinformation regulatory practices, we developed an analytical framework, drawing from policy design studies and the social informatics perspectives, which emphasize two aspects of misinformation policies. First, we identified agents, actions, and target groups as the essential components of policy design. We then incorporated three sociotechnical dimensions related to misinformation regulation—the context from which misinformation policies originate;the specific issues, topics, and forms of misinformation;and the channels for its creation and spread. We applied this framework to 139 policy documents systematically collected from the federal government of the United States and the central government of China, for the purpose of understanding and comparing misinformation regulations in two distinct contexts. Beyond those well-known political narratives in each country, this chapter identified the nuanced differences in their misinformation policies and the different stages or maturity of misinformation policymaking. The empirical findings showcase the analytical power of this framework and shed light on policy practices and the direction of future research. © 2023 selection and editorial matter, Shengnan Yang, Xiaohua Zhu and Pnina Fichman;individual chapters, the contributors.

Analysis of Students' Online Learning Engagement during the COVID-19 Pandemic: A Case Study of a SPOC-Based Geography Education Undergraduate Course

With the long-lasting impact of the COVID-19 pandemic, online learning has gradually become one of the mainstream learning methods in Chinese universities. The effectiveness of online learning is significantly influenced by learning engagement, and studies into this topic can help learners by providing them with process-based learning support and focused teaching interventions. Based on the online learning environment, this research constructs an online learning engagement analysis model. Additionally, this study explores the relationship between students' online learning engagement and their online learning performance by taking the Secondary School Geography Curriculum Standards and Textbooks Research, a small-scale private online course (SPOC) of the geography education undergraduate course at Nanjing Normal University, as an example. The findings are as follows: In the cognitive engagement dimension, only "analyze” is significantly positively correlated with learning performance;in the behavioral engagement dimension, the "number of question and answer (Q&A) topic posts,” the "replies to others,” and the "teachers' replies” are all significantly positively correlated with learning performance. In terms of the emotional engagement dimension, "curiosity” and "pleasure” are positively correlated with learning performance;as for the social engagement dimension, "point centrality” and "intermediary centrality” are positively correlated with learning performance. The findings of this case study reveal that the student's engagement in higher-order cognitive learning is obviously insufficient. Students' online learning performance can be enhanced both by behavioral engagement in knowledge reprocessing and positive emotional engagement. Further research should be focused on finding ways to increase students' enthusiasm for social engagement. © 2023 by the authors.

Typical es of COVID-19 Pneumonia in Adult

Medical History and Clinical Manifestation © Henan Science and Technology Press 2020.

Introduction: Social Informatics in the Context of the COVID-19 Pandemic

The COVID-19 pandemic has been accelerating digital transformation around the globe since the beginning of the COVID-19 outbreak in early 2020. Individuals, organizations, and governments have experienced unprecedented information and communication technologies (ICT) adoption and use, willingly or not. The backdrop of the pandemic provides a unique opportunity for us to reflect on and reexamine our relationships with ICTs, as we envision the postpandemic future. This chapter introduces the entire book, especially the core idea of "social informatics” that serves as the underlying theoretical foundation of the book and its chapters to echo the concerns about ICT use and consequences during the pandemic. This edited volume presents empirical cases from different countries and our theoretical exploration of ICT usage and consequences by covering four major themes—governance, information behavior, community, and everyday life. These chapters shed light on the importance of ICTs and unpack the complexity of technology-related practices during the global crisis. We hope to intrigue other researchers to examine the emerging social changes and evolving use of digital tools in a still-unfolding pandemic era. © 2023 selection and editorial matter, Shengnan Yang, Xiaohua Zhu and Pnina Fichman;individual chapters, the contributors.

The Usage and Impact of ICTs during the COVID-19 Pandemic

This book takes a holistic view of the roles of ICTs during the pandemic through the lens of social informatics, as it is critical to our understanding of the relations between society and technology. Specific attention is given to various stakeholders and social contexts, with analysis at the individual, group, community, and society levels. Pushing the boundaries of information science research with timely and critical research questions, this edited volume showcases information science research in the context of COVID-19, by specifically accentuating sociotechnical practices, activities, and ICT interventions during the pandemic. Its social informatics focus appeals to a broad audience, and its global and international orientation provides a timely, innovative, and much-needed perspective to information science.This book is unique in its interdisciplinary nature as it consists of research studies on the intersections between ICTs and health, culture, social interaction, civic engagement, information dissemination, work, and education.Chapters apply a range of research methods, including questionnaire surveys, content analyses, and case studies from countries in Asia, Europe, and America, as well as global and international comparisons.The book's primary target audience includes scholars and students in information and library science, particularly those interested in the social aspect of the information society. It may be of interest to information professionals, library practitioners, educators, and information policymakers, as well as scholars and students in science and technology studies, cultural studies, political science, public administration, sociology, and communication studies © 2023 selection and editorial matter, Shengnan Yang, Xiaohua Zhu and Pnina Fichman;individual chapters, the contributors.

[Establishment and preliminary application of quantitative real-time PCR assay for the detection of SARS-CoV-2 subgenomic nucleocapsid RNA].

Objective: To establish a rapid and specific quantitative real-time PCR (qPCR) method for the detection of SARS-CoV-2 subgenomic nucleocapsid RNA (SgN) in patients with COVID-19 or environmental samples. Methods: The qPCR assay was established by designing specific primers and TaqMan probe based on the SARS-CoV-2 genomic sequence in Global Initiative of Sharing All Influenza Data (GISAID) database. The reaction conditions were optimized by using different annealing temperature, different primers and probe concentrations and the standard curve was established. Further, the specificity, sensitivity and repeatability were also assessed. The established SgN and genomic RNA (gRNA) qPCR assays were both applied to detect 21 environmental samples and 351 clinical samples containing 48 recovered patients. In the specimens with both positive gRNA and positive SgN, 25 specimens were inoculated on cells. Results: The primers and probes of SgN had good specificity for SARS-CoV-2. The minimum detection limit of the preliminarily established qPCR detection method for SgN was 1.5×102 copies/ml, with a coefficient of variation less than 1%. The positive rate of gRNA in 372 samples was 97.04% (361/372). The positive rates of SgN in positive environmental samples and positive clinical samples were 36.84% (7/19) and 49.42% (169/342), respectively. The positive rate and copy number of SgN in Wild strain were lower than those of SARS-CoV-2 Delta strain. Among the 25 SgN positive samples, 12 samples within 5 days of sampling time were all isolated with virus; 13 samples sampled for more than 12 days had no cytopathic effect. Conclusion: A qPCR method for the detection of SARS-CoV-2 SgN has been successfully established. The sensitivity, specificity and repeatability of this method are good.

Effective multipurpose sewage sludge and food waste reduction strategies: A focus on recent advances and future perspectives

Energy crisis and increasing rigorous management standards pose significant challenges for solid waste management worldwide. Several emerging diseases such as COVID-19 aggravated the already complex solid waste management crisis, especially sewage sludge and food waste streams, because of the increasingly large production year by year. As mature waste disposal technologies, landfills, incineration, composting, and some other methods are widespread for solid wastes management. This paper reviews recent advances in key sewage sludge disposal technologies. These include incineration, anaerobic digestion, and valuable products oriented-conversion. Food waste disposal technologies comprised of thermal treatment, fermentation, value-added product conversion, and composting have also been described. The hot topic and dominant research foci of each area are summarized, simultaneously compared with conventional technologies in terms of organic matter degradation or conversion performance, energy generation, and renewable resources production. Future perspectives of each technology that include issues not well understood and predicted challenges are discussed with a positive effect on the full-scale implementation of the discussed disposal methods. © 2022 Elsevier Ltd

Vehicle Detection of Autonomous Sanitation Robot in Campus

Under the COVID-19 and other terrible environments workers are constrained to sweep campus and public area. Intelligent and driverless sanitation robot can solve the problem. Obstacle avoidance and garbage cleanup are its important functions. Based on the driverless sanitation robot project introduced by Sanda University, this paper carries out recognition of campus vehicles and improves its obstacle avoidance function. Through image processing, the object features of different environment and climate conditions are extracted, analyzed and recognized, so as to achieve more accurate recognition of campus vehicles. And opencv and python language are used to complete the implementation of vehicle detection. © 2022 IEEE.

An Epistemic Trend or a Digital Pitfall? De-Westernizing Media and Communication Studies in Digital China

Governments hiding facts and truth from the public seems to have become a common phenomenon, especially during the social crisis in China. The practice of the public using various media to express dissent and opinions, to overcome government censorship, appears to contribute to freedom of speech. Inspired by widespread online articles during the COVID-19 pandemic in 2020, this paper argues that the flaws in this logic are the dualism, which digital media created (pro-democracy vs authoritarian;freedom vs control), in understanding media in China. By borrowing the discussion of the de-westernization of media and communication studies, the paper argues that the introduc-tion of digital media makes de-westernized studies in China harder because it prompts us to think "digitally."

FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction

Many efforts have been exerted toward screening potential drugs for targets, and conducting wet experiments remains a laborious and time-consuming approach. Artificial intelligence methods, such as Convolutional Neural Network (CNN), are widely used to facilitate new drug discovery. Owing to the structural limitations of CNN, features extracted from this method are local patterns that lack global information. However, global information extracted from the whole sequence and local patterns extracted from the special domain can influence the drugtarget affinity. A fusion of global information and local patterns can construct neural network calculations closer to actual biological processes. This paper proposes a Fingerprint-embedding framework for Drug-Target binding Affinity prediction (FingerDTA), which uses CNN to extract local patterns and utilize fingerprints to characterize global information. These fingerprints are generated on the basis of the whole sequence of drugs or targets. Furthermore, FingerDTA achieves comparable performance on Davis and KIBA data sets. In the case study of screening potential drugs for the spike protein of the coronavirus disease 2019 (COVID-19), 7 of the top 10 drugs have been confirmed potential by literature. Ultimately, the docking experiment demonstrates that FingerDTA can find novel drug candidates for targets. All codes are available at http://lanproxy.biodwhu.cn:9099/mszjaas/FingerDTA.git. © 2018 Tsinghua University Press.