Person Detection Based Adaptive Video Synthesis: A Fusion Framework for Ultra HD Panorama and Green Screen Videos

The past two years have witnessed the increasing prevalence of metaverse, while the COVID-19 pandemic has accelerated the formation of a non-contact culture. Under this circumstance, virtual reality once again attracts the public attention. Panorama video, as one of the most important forms of virtual reality, provides users with excellent immersion experience. This paper has proposed a fusion framework for Ultra HD panorama videos and green screen videos. In this framework, panorama videos are set as the virtual background layer on which the user-defined real foreground layer of portrait obtained by green screen matting is superimposed. During video fusion process, the portrait size is adaptively determined by parameters provided by a person detection algorithm running on panorama videos. Therefore, a more natural video synthesis result can be achieved, and presented on a head-mounted display or a flat screen device to provide an indistinguishable visual experience to the users. © 2022 IEEE.

Research on Blended Learning Evaluation Method of Overseas Chinese Education in the Post-epidemic Era

The spread of COVID-19 in the world has changed the way of life, economy, society, learning, and work around the world. Under the background of normalization of epidemic prevention and control, how to carry out overseas Chinese education and how to affect overseas Chinese education is an important issue with scholars in China and abroad and overseas Chinese. How to effectively deal with the international situation after the outbreak of the epidemic and how to determine the future developmental direction of overseas Chinese education is an important goal and direction that researchers need to study in depth. Based on the perspective of teaching effect evaluation of blended learning mode under the background of artificial intelligence, this article takes ASEAN Phuket Thai Chinese Schools as an example, collects relevant research data through a grounded survey method, adopts a quantitative analysis method, comprehensively judges the impact of overseas Chinese education after the outbreak of the epidemic, and puts forward systematic, comprehensive, and scientific suggestions. It is hoped that the decision-making mechanism and method innovation of overseas Chinese education in response to COVID-19 will be effectively promoted, and the effective development of overseas Chinese education will be actively promoted.

A Close Contact Tracing Method Based on Bluetooth Signals Applicable to Ship Environments

There are still outbreaks of COVID-19 across the world. Ships increase the risk of worldwide transmission of the virus. Close contact tracing remains as an effective method of reducing the risk of virus transmission. Therefore, close contact tracing in ship environments becomes a research topic. Exposure Notifications API (Application Programming Interface) can be used to determine the encountered location points of close contacts on ships. Location points of close contact are estimated by the encountered location points. Risky areas in ships can be calculated based on the encountered location points. The tracking of close contacts is possible with Bluetooth technology without the Internet. The Bluetooth signal can be used to judge the proximity among detecting devices by using the feature that Bluetooth has a strong signal at close range. This Bluetooth feature makes it possible to trace close contacts in ship environments. In this paper, we propose a method for close contact tracing and showing the risky area in a ship environment by combining beacon and Exposure Notification API using Bluetooth technology. This method does not require an Internet connection for tracing close contacts and can protect the personal information of close contacts. Copyright © 2023 KSII.

ACE2 in pulmonary diseases

Angiotensin converting enzyme 2 (ACE2), a relatively new member of the RAS, has drawn extensive attention since 2003, because of the findings that ACE2 is the receptor for SARS-CoV 1 and 2, and that maintenance of normal ACE2 levels in the lung is beneficial for the host to combat inflammatory lung disease. Given that ACE2 has vasodilatory, anti-inflammation, anti-fibrotic, and pro-regenerative activities, the involvement of this enzyme in many aspects of physiology and pathophysiology, especially in cardiopulmonary diseases, is rapidly evolving in the literature. In this chapter, we summarize the contribution of ACE2 signalling to the initiation and progression of lung diseases such as pulmonary hypertension, asthma, fibrosis, tumors, COPD, and other acute lung injuries as well as COVID-19. A better understanding of the role of ACE2 signalling in the pathogenesis of pulmonary diseases would offer novel approaches for the development of therapeutics to treat a variety of inflammatory disorders in respiratory and other systems. © 2023 Elsevier Inc. All rights reserved.

An Effective COVID-19 CT Image Denoising Method Based on a Deep Convolutional Neural Network

Background: Faced with the global threat posed by SARS-CoV-2 (COVID-19), low-dose computed tomography (LDCT), as the primary diagnostic tool, is often accompanied by high levels of noise. This can easily interfere with the radiologist's assessment. Convolutional neural networks (CNN), as a method of deep learning, have been shown to have excellent effects in image denoising. Objective: The objective of the study was to use modified convolutional neural network algorithm to train the denoising model. The purpose was to make the model extract the highlighted features of the lesion region better and ensure its effectiveness in removing noise from COVID-19 lung CT images, preserving more important detail information of the images and reducing the adverse effects of denoising. Methods: We propose a CNN-based deformable convolutional denoising neural network (DCDNet). By combining deformable convolution methods with residual learning on the basis of CNN structure, more image detail features are retained in CT image denoising. Results: According to the noise reduction evaluation index of PSNR, SSIM and RMSE, DCDNet shows excellent denoising performance for COVID-19 CT images. From the visual effect of denoising, DCDNet can effectively remove image noise and preserve more detailed features of lung lesions. Conclusion: The experimental results indicate that the DCDNet-trained model is more suitable for image denoising of COVID-19 than traditional image denoising algorithms under the same training set. © 2023 Bentham Science Publishers.

Different associations of parental involvement with children's learning of Chinese, English, and math: a three-wave longitudinal study

Due to the impact of COVID-19, children and their parents are spending more time at home, which increases parent–child interactions. The goals of the present study were to examine the mediating effects of children's learning engagement on the relationships of parental involvement in Chinese, English, and math performance and to investigate whether parent-perceived parental involvement and child-perceived parental involvement consistently affected children's academic performance. Data were collected from 253 Chinese primary school students (117 boys, Mage = 10.53) during the COVID-19 pandemic. We included parental involvement perceived by the parents and by the children to comprehensively describe parental involvement (in wave 2);we collected children's learning engagement (wave 2);and we compared children's Chinese, English and math academic performances before (wave 1) and after (wave 3) China's first wave of COVID-19 in 2020. The results showed that after controlling for gender, age, and SES, the parental involvement perceived by parents could be directly and positively related to children's learning engagement, and it also indirectly influenced children's learning engagement through the children's perceived parental involvement. Learning engagement was a mediator of the relationship between parental involvement and children's academic performance. Parental involvement significantly predicted children's Chinese and English performances through their learning engagement, while parental involvement failed to predict children's mathematics performances during the COVID-19 pandemic. The current research provides insights into the underlying mechanisms of how parental involvement affects children's academic performances during school closures and hopes to guide parents and schools to consider how to cooperate and continue to use rapidly developing digital education resources amid the long-term impact of COVID-19 to provide children using more effective and suitable guidance in the future. © 2022, Instituto Universitário de Ciências Psicológicas, Sociais e da Vida.

A Self-Learning Mean Optimization Filter to Improve Bluetooth 5.1 AoA Indoor Positioning Accuracy for Ship Environments

As COVID-19 is still spreading globally, the narrow ship space makes COVID-19 easier for the virus to infect ship passengers. Tracking close contacts remains an effective way to reduce the risk of virus transmission. Therefore, indoor positioning technology should be developed for ship environments. Today, almost all smart devices are equipped with Bluetooth. The Angle of Arrival (AoA) using Bluetooth 5.1 indoor positioning technology is well suited for ship environments. But the narrow ship space and steel walls make the multipath effect more pronounced in ship environments. This also means that more noises are included in the signal. In the Uniform Rectangular Array (URA) type receiving antenna array, elevation and azimuth angles are two important parameters for the AoA indoor positioning technology. Elevation and azimuth angles are unstable because of the influence of noise. In this paper, a Self-Learning Mean Optimization Filter (SLMOF) is proposed. The goal of SLMOF is to find the optimal elevation and azimuth angles as a way to improve the Bluetooth 5.1 AoA indoor positioning accuracy. The experimental results show that the Root Mean Square Error (RMSE) of SLMOF is 0.44 m, which improves the accuracy by 72% compared to Kalman Filter (KF). This method can be applied to find the optimal average in every dataset.

AutoDock Koto: A Gradient Boosting Differential Evolution for Molecular Docking

Molecular docking plays a vital role in modern drug discovery, by supporting predictions of the binding modes and affinities of ligands at the binding site of target proteins. Several docking programs have been developed for both commercial and academic applications. Typically, a docking program’s performance depends on the sampling algorithm used to generate the ligand’s potential conformations and the scoring function applied to evaluate and rank these conformations. Evolutionary algorithms are widely used as sampling algorithms in docking programs. However, both the linkage problem and the dimensionality degenerate the search ability of evolutionary algorithms in the docking process. Therefore, a newly designed docking program named AutoDock Koto was developed in this study, which adopts a novel gradient boosting differential evolution algorithm to effectively address these issues. Experimental results show that compared with commonly used docking programs, AutoDock Koto yields dramatic improvements in docking performance based on an extensive dataset of 285 protein-ligand complexes. In addition, due to its strong docking ability, AutoDock Koto was used to identify potential drugs for COVID-19 based on a virtual screening of all approved drugs in our experiments. Sixteen drugs are found to possess low binding energy to the main target protease of SARS-CoV-2, and thus have the potential to treat COVID-19 as antiviral drugs. The source code of AutoDock Koto can be downloaded for free from. https://github.com/codezhouj/Molecular_Docking. IEEE

A Mean Optimization Filter to Improve Bluetooth AoA Indoor Positioning Accuracy for Ship Environments

Currently, the most effective way to reduce transmission of COVID-19 is to differentiate between close contacts. Location points of close contact are essential for differentiation. As a major mode of transportation, ships provide a vehicle for virus transmission. Timely detection location of close contacts inside a ship can prevent the spread of viruses. Location-based services can be provided for ship passengers. Bluetooth is widely available in many wearable devices. The Bluetooth 5.1 angle of arrival (AoA) indoor positioning algorithms can provide a certain indoor positioning accuracy for ship passengers. The two most essential parameters in Bluetooth 5.1 AoA indoor positioning are elevation angle and azimuth angle. Elevation and azimuth are often not accurate enough due to noise, which increases indoor positioning errors. As a result, this paper proposes a mean optimization filter for ship environments, which combines the box plot method to improve Bluetooth 5.1 AoA indoor positioning accuracy, with an RMSE of 0.34 m. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

A smartphone-assisted high-throughput integrated color-sensing platform for the rapid detection of Campylobacter coli

Campylobacter is one of the most common foodborne pathogens worldwide. A new smartphone-assisted high-throughput integrated color-sensing platform, called the HICS platform, was developed for the rapid detection of Campylobacter coli. This platform was based on the visual loop-mediated isothermal amplification method. Using this system, as many as 64 samples could be assessed in less than an hour after enrichment. 60 meat samples were tested to compare the performance of the HICS platform and other methods. Having the consistent detection result with TaqMan qPCR (Quantitative Real-time Polymerase Chain Reaction), the HICS platform was able to reliably detect C. coli in meat samples, and its limit of detection is 550 CFU/mL and 120 copies/mu L, which was 10-fold higher than that of the PCR (Polymerase Chain Reaction) method. To conclude, considering that our platform showed robust performance and does not require any expensive equipment, it can also be reliably applied for the high-throughput detection of other pathogens.

Chinese EdTech Economy in the COVID-19 era

This chapter discusses the Chinese EdTech economy in the COVID-19 era. After studying the characteristics of the Chinese online education market and its market size, this research focuses on the differences in business strategies and market responses among online education applications during the COVID-19 era and the subsequent institutional changes, as well as analyzing the factors that cause such differences. For the analysis, a few cases of successful online education companies in China are selected, and their business models and technology applications are discussed. The authors conclude this chapter by addressing the challenges that China's online education faces and providing corresponding suggestions. © 2022, IGI Global. All rights reserved.

A Clustering Mechanism to Identify Close Contact for the Ship Passenger Health

COVID-19 is spreading globally, and this spread is continuous. Ships have become the leading platform for virus transmission as a means of transportation. The small space of ships makes the possibility of virus outbreaks highly increased. The current way to effectively interrupt the spread of the virus is to track close contacts and physically isolate them. Therefore, the identification of close contacts becomes critical. This paper proposes a close contact identification algorithm applicable to the ship environment. The user ID is creatively proposed as the initialized location point cluster in this algorithm. And the KDE is introduced into the clustering process of the algorithm, and the center of the cluster is calculated by using the KDE of the location points as weights. The threshold value is used as the criterion for merging the clusters. Finally, the correct cluster result is obtained. This algorithm can provide technical support for ship companies to sustainably manage ships in the post-epidemic era, thus serving the purpose of maximizing the protection of ship passengers' health. © 2022 IEEE.

AN IN-SHIP LOCALIZATION ALGORITHM FOR CLOSE CONTACT IDENTIFICATION

Concern about the health of people who traveled on board was raised in the COVID-19 outbreak on the Diamond Princess cruise ship. The ship narrow space offers an environment conducive to the virus spread. Close contact isolation remains one of the most important current measures to stop the rapid spread of the virus. Contacts can be identified efficiently by detecting smart devices nearby. The smartphone Bluetooth RSSI signal is significant data for positioning. The traditional indoor positioning algorithm cannot be directly applied in the mobile ship environment. It is necessary to study the indoor positioning algorithm which applies to the mobile ship environment. In this paper, we propose an in-ship localization algorithm, which can achieve indoor positioning without the fingerprint map, with an RMSE of 1.63 m. © 2022 Little Lion Scientific

Human Nasal Epithelial Cells Sustain Persistent SARS-CoV-2 Infection In Vitro, despite Eliciting a Prolonged Antiviral Response

The dynamics of SARS-CoV-2 infection in COVID-19 patients are highly variable, with a subset of patients demonstrating prolonged virus shedding, which poses a significant challenge for disease management and transmission control. In this study, the long-term dynamics of SARS-CoV-2 infection were investigated using a human well-differentiated nasal epithelial cell (NEC) model of infection. NECs were observed to release SARS-CoV-2 virus onto the apical surface for up to 28 days post-infection (dpi), further corroborated by viral antigen staining. Single-cell transcriptome sequencing (sc-seq) was utilized to explore the host response from infected NECs after short-term (3-dpi) and long-term (28-dpi) infection. We identified a unique population of cells harboring high viral loads present at both 3 and 28 dpi, characterized by expression of cell stress-related genes DDIT3 and ATF3 and enriched for genes involved in tumor necrosis factor alpha (TNF-alpha) signaling and apoptosis. Remarkably, this sc-seq analysis revealed an antiviral gene signature within all NEC cell types even at 28 dpi. We demonstrate increased replication of basal cells, absence of widespread cell death within the epithelial monolayer, and the ability of SARS-CoV-2 to replicate despite a continuous interferon response as factors likely contributing to SARS-CoV-2 persistence. This study provides a model system for development of therapeutics aimed at improving viral clearance in immunocompromised patients and implies a crucial role for immune cells in mediating viral clearance from infected epithelia. IMPORTANCE Increasing medical attention has been drawn to the persistence of symptoms (long-COVID syndrome) or live virus shedding from subsets of COVID-19 patients weeks to months after the initial onset of symptoms. In vitro approaches to model viral or symptom persistence are needed to fully dissect the complex and likely varied mechanisms underlying these clinical observations. We show that in vitro differentiated human NECs are persistently infected with SARS-CoV-2 for up to 28 dpi. This viral replication occurred despite the presence of an antiviral gene signature across all NEC cell types even at 28 dpi. This indicates that epithelial cell intrinsic antiviral responses are insufficient for the clearance of SARS-CoV-Z implying an essential role for tissue-resident and infiltrating immune cells for eventual viral clearance from infected airway tissue in COVID-19 patients.

Quantifying the robustness of empirical inferences in strategic management: The impact threshold of a confounding variable and robustness of inference to replacement

In this chapter, we explicate two related techniques that help quantify the sensitivity of a given causal inference to potential omitted variables and/or other sources of unexplained heterogeneity. In particular, we describe the Impact Threshold of a Confounding Variable (ITCV) and the Robustness of Inference to Replacement (RIR). The ITCV describes the minimum correlation necessary between an omitted variable and the focal parameters of a study to have created a spurious or invalid statistical inference. The RIR is a technique that quantifies the percentage of observations with nonzero effects in a sample that would need to be replaced with zero effects in order to overturn a given causal inference at any desired threshold. The RIR also measures the percentage of a given parameter estimate that would need to be biased in order to overturn an inference. Each of these procedures is critical to help establish causal inference, perhaps especially for research urgently studying the COVID-19 pandemic when scholars are not afforded the luxury of extended time periods to determine precise magnitudes of relationships between variables. Over the course of this chapter, we define each technique, illustrate how they are applied in the context of seminal strategic management research, offer guidelines for interpreting corresponding results, and delineate further considerations. © 2021 Emerald Publishing Limited.

Nurses' Emotional Stress Levels When Caring for COVID-19 Patients in an Intensive Care Unit

CONTEXT: The COVID-19 pandemic has had an effect on many communities' physical and mental well-being, especially that of healthcare workers. During the pandemic, health workers have shown signs of depression and anxiety and have experienced sleep disturbances. Few studies have examined health workers' resilience during the pandemic. OBJECTIVE: The current study intended to examine the job stress and mental well-being of nurses who have supported, worked with, and cared for COVID-19 patients in an intensive care unit. DESIGN: The research team performed a narrative review by searching the Mendeley, ScienceDirect, Medline, PubMed, Google Scholar, and Springer databases. The search used many keywords, both alone and in combination, such as COVID-19, pandemic, nurses, healthcare professionals, stress, and frontline workers. The review considered only English journals. SETTING: This study was take place in Second Affiliated Hospital of Hainan Medical University, Hainan Province, China. RESULTS: During the current pandemic, COVID-19 prevention in social settings, governmental regulation during the pandemic, and provision of frontline care have faced notable challenges. In general, nurses who have assisted during the COVID-19 pandemic have been under severe strain. The key factors that influenced nurses' stress were being only children, their working time per week, and their levels of anxiety. CONCLUSIONS: COVID-19 has posed a vast threat to public health worldwide. The psychological stress of nurses should be managed in public-health emergencies.

Application of the standardized nasopharyngeal swab sampling for the nucleic acid detection of COVID-19

Introduction: The aim of the present study was to investigate a relatively convenient, safe, and sensitive sampling method in the nucleic acid detection of the 2019-novel coronavirus (2019-nCoV). Materials and method: The nasopharyngeal swab samples of patients admitted to the 13 inpatient areas of the Tumor Center, Xiehe Hospital of the affiliated Tongji Medical College of Huazhong University of Science and Technology (temporarily transformed into a designated hospital for critical patients with COVID-19) from February 21 to 23, 2020 were used for the nucleic acid detection analysis of 2019-nCoV. The nasopharyngeal swab samples in the present inpatient area were obtained by a standardized sampling method. Results: A total of 663 samples were collected from the tumor center with 125 positive ones. Among these samples, 33 samples were collected from the present inpatient area, and 11 cases (33%) were positive. A further 630 samples were collected from other inpatient areas, in which 114 samples (18%) were positive. The difference in the positivity between the present inpatient area and other areas was statistically significant. Conclusion: The standardized nasopharyngeal swabs sampling had a high positive detection rate in the nucleic acid detection of 2019-nCoV and was safer and more convenient for medical staff and worthy of wider clinical use.

Analysis Based on the Data of 2660 College Students During COVID-19 Epidemic: Emotional State and Influence Factors

As a public health emergency, the COVID-19 epidemic has caused a strong psychological impact on college students. This paper conducted an online survey of 2660 college students to explore the relationship between anxiety, depressive emotional state and psychological resilience and social support by using Symptom Check List-90 (SCL-90), Connor- Davidson Resilience Scale (CD-RISC), Perceived Social Support Scale (PSSS), and self-compiled questionnaires. The results of the research show that: 1. The levels of females’ anxiety and depression are significantly higher than those of males (p <0.01). There is no significant influence in anxiety and depression between college students who live in one-child families and those who live in non-only-child families;2. Anxiety, depression and psychological resilience of college students are all negatively correlated with the tenacity, strength and optimism (p <0.01);3. Anxiety and depression of college students are significantly negatively correlated with social support, family support, friend support, and other support (p <0.01). © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.