The impact of COVID-19 epidemic on the control of important parasitic diseases.

The spread of COVID-19 has increased the risk of contact transmission between people, and imposes challenges and opportunities for the prevention and control of important parasitic diseases. There may be risks of COVID-19 infection during the implementation of the virus control, including in field investigation and intervention, contacting with fecal samples, or with asymptomatic infected people. Therefore, corresponding prevention and control measures should be taken before and during field investigation, and in sample transportation and detection to prevent the risks effectively. In this paper, the risks and corresponding countermeasures in the above processes are discussed in detail, and the opportunities that COVID-19 brought to the disease control system are also analyzed.Copyright © 2021, National Institute of Parasitic Diseases. All rights reserved.

Mental health status survey in the first⁃line healthcare workers of designated hospitals for COVID⁃19 under the delta strain outbreak

Objective: To investigate mental health status of first-line healthcare workers in designated hospitals for coronavirus disease 2019 (COVID-19) under the Delta strain outbreak, so as to provide a basis for the implementation of effective countermeasures. Methods: We conducted an online survey on mental health status among 227 first-line medical staff treating COVID-19 patients in Wuhan Jinyintan Hospital during the period from September 4 to 6, 2021 using Self-Rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS). Results：The surveyed first-line medical staff had significantly higher levels of anxiety and depression than the national norms (P<0. 01), and among them, 54 (23. 70%) individuals had anxiety, 33(14. 5%) had depressive emotions, and 26 (11. 5%) had both. Nurses or female staff scored significantly higher than clinicians or male staff for anxiety and depression (P<0. 01). Married or middle-aged and young (30 to 45 years old) first-line medical staff had the relatively high scores of depression and anxiety (P<0. 01). Anxiety and depression of medical staff were associated with the time of working in the first-line, and those who continued to work 10-20 days had the relatively high scores of depression and anxiety (P<0. 05). Conclusion: In the face of SARS-CoV -2 Delta variant outbreak, the first-line medical staff still had negative mentality, such as anxiety and depression, psychological intervention support system should be established in order to help improve the level of the first-line medical staff's mental health and improve the working condition. © 2022 Editorial Board of Medical Journal of Wuhan University. All rights reserved.

ENHANCING ONLINE LEARNING FOCUSING ON THE RELATIONSHIP BETWEEN GAZE AND BROWSING MATERIALS

Online synchronous classes and seminars are increasing in universities along with the outbreak of COVID-19. Since the online classes are not in-person condition, it is difficult for a teacher to monitor the learners. In the field of distance education, the need to support learner's concentration has been identified. More effective methods adapted to online classes in universities should be discussed. In this paper, we focus on the teacher's and learners' attention to the material in online real-time classes. We then propose a system that provides feedback on the differences in learners' gaze in order to improve the followability of learners. The proposed system assumes an online environment using Microsoft Teams PowerPointLive and acquires the "gazing point" at every slide-material and "page change history" of the teacher and learners. The results of using the proposed system suggest that it can improve the learner's followability to the teacher. © 2022 Proceedings of the 19th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2022. All rights reserved.

COVID-19 Image Classification Method Based on Model Fusion

In 2020, the world witnessed a new and severe global health crisis: the outbreak of Covid-19 and the number of positive cases and deaths around the world rose at a frightening rate throughout 2021. Given its highly contagious, convenient and efficient detection means are significant. At present, RT-PCR testing is the common diagnostic method for COVID-19 cases, but the process is time-consuming and inefficient. The recent COVID-19 radiology literature has focused on CT imaging because of its higher sensitivity, but it leads to high costs compared to X-ray imaging. Nowadays, many AI applications are focused on quantification and identification of infections to fully automate diagnoses to assist medical experts. Therefore, we compared seven classic network models including ResNet50, VGG16, VGG19, InceptionV3, InceptionResNetV2, MobileNetV1, DenseNet169 by the diversity measure. DenseNet169 performed pretty well with an accuracy of 97.5% on the training set and 96.58% on the test set. After comparing the results of different model fusion methods, stacking these models by four folds and selecting the tree classifier as second layer models outweighed other methods which reach 100% on the test set, which is helpful in the diagnosis of COVID19. © 2022 IEEE.

ENHANCING BATHYMETRIC MAPPING CAPABILITY USING MULTI-ZONE ENSEMBLE FITTING: FACILITATING EMERGING POST COVID-19 DEMANDS

Determination of hydrospatial information across the marine environment has conventionally appeased by vessel-based acoustic surveys. For the first time in history, the unpredictability COVID-19 health crisis has shut down the entire social and economic sectors across the globe. The continuous nationwide lockdown has made it very difficult to mobilize vessels and survey crews for bathymetric mapping. With the emerging remote sensing technology, hydrospatial specialists today have too accustomed to live and work in the new normality. Apparently, hydrography is clearly undergoing dramatic change which an expanded role to serve an increasing number of stakeholders in the blue economy. In order to seek the maximum benefits from the adoption of forth industrial revolution (IR4.0) paradigms, utilization of high-technology sensors and various unmanned autonomous crafts for bathymetric data acquisition to generate actionable data and information to serve the hydrographic communities. In response to the COVID-19 outbreak, hydrographic communities have been forced to accelerate the adoption of emerging technologies to mitigate its impact. Indeed, satellite derived bathymetry (SDB) has become a recognized tool for acquisition to generate actionable hydrospatial data that can alleviate future economic upheavals. Stakeholders are able to extract the bathymetric depth information from the remotely sensed imagery in a split second without physical mobilization and on-site survey. In this paper a new proposed methodology using multi-zone ensemble fitting is introduced for bathymetric determination across the coastal region from high resolution satellite images. By segmentizing the training sets to fit into several designated depth zones, this sequential ensemble fitting approach demonstrates better performance if compares to the traditional single regression algorithm. Derived conclusion points out that newly proposed method can enhance the current bathymetric mapping capability and deliver precise and accurate actionable hydrospatial information in facilitating the emerging demands, in the post COVID-19 era. © ACRS 2021.All right reserved.

A Stochastic Model with Optimal Control Strategy of the Transmission of Covid-19

In this work, a stochastic differential equation model about the novel Coronavirus 2019 (COVID-19) is introduced to describe the transmission dynamics of that disease among the susceptible person. By taking the social distance, musk wearing, and other human behavior as a control strategy and introducing an objective function which both considers the limitation of social distance and minimizes the infection population, an optimal control strategy is given numerically. This result gives a new numerical method to simulate the epidemic model and make a new insight into the control strategy choice of the pandemic control under the environments and conditions of different countries. © 2021 IEEE.

Observation of Unconventional Charge Density Wave without Acoustic Phonon Anomaly in Kagome Superconductors AV(3)Sb(5) (A = Rb, Cs)

The combination of nontrivial band topology and symmetry-breaking phases gives rise to novel quantum states and phenomena such as topological superconductivity, quantum anomalous Hall effect, and axion electrodynamics. Evidence of intertwined charge density wave (CDW) and superconducting order parameters has recently been observed in a novel kagome material AV(3)Sb(5) (A = K, Rb, Cs) that features a Z(2) topological invariant in the electronic structure. However, the origin of the CDW and its intricate interplay with the topological state has yet to be determined. Here, using hard-x-ray scattering, we demonstrate a three-dimensional CDW with 2 x 2 x 2 superstructure in (Rb, Cs)V3Sb5. Unexpectedly, we find that the CDW fails to induce acoustic phonon anomalies at the CDW wave vector but yields a novel Raman mode that quickly damps into a broad continuum below the CDW transition temperature. Our observations exclude strong electron-phonon-coupling-driven CDW in AV(3)Sb(5) and support an unconventional CDW that was proposed in the kagome lattice at van Hove filling.

Numerical investigation on the origin of virus-laden droplets in the respiratory tract

There have been over 17 million COVID-19 infections worldwide up to July 31, 2020, but the exact transmission route remains a subject for debate. Virus load in different-sized respiratory droplets is the key to address the above question. Investigation on the droplet deposition characteristics during exhalation inside the respiratory tract will facilitate the understanding of their origin and importance in transmitting the respiratory infection. Based on a realistic respiratory model, this study utilized the computational fluid dynamic (CFD) simulation to investigate the deposition of droplets originating from infection sites like pharynx, larynx and trachea under three flow conditions, namely 30 L/min (representing the normal breathing condition), 60 L/min (speaking) and 180 L/min (coughing). The SST k-ω turbulence model integrated in ANSYS Fluent software was utilized to obtain the flow field inside the respiratory tract by solving the continuity and momentum equations, and the Lagrangian approach was used to calculate droplet motion and deposition with the discrete random walk model to account for the turbulence fluctuation effect. Evaporation of droplets was not considered inside the saturated respiratory tract. Droplet diameter, flow rate of the exhaling air, and complexity of the respiratory tract geometry are the most important factors in determining the deposition pattern of respiratory droplets. It is revealed that the largest droplet that originates from the joint of pharynx and larynx and is able to escape out of the respiratory tract is about 20 µm (30 L/min) or 10 µm(60-180 L/min) in diameter;the so-called cut-off sizes for vocal cord and trachea originated droplets are 7 µm (30 L/min), 5 µm (60 L/min) or 3 µm (180 L/min). Larynx and joint of larynx and pharynx are the most important deposition sites due to their complexity in geometry and the existence of the laryngeal jet, whose velocity is up to 102 m/s during coughing and32 m/s during speaking;the nasal cavity is also effective in trapping relatively small droplets as the airflow has a sudden change in direction before entering the oral cavity. Under the investigated speaking scenario (60 L/min), the escape rate of1 µm droplets is around 50%, and the maximum escape rates of 5 and 10 µm droplets are respectively 13.1% and 1.3% (originating from the joint of pharynx and larynx). Although large droplets up to several hundred micrometers can be produced inside the oral cavity due to the atomization mechanism, they seldom carry pathogen. Thus, for COVID-19 patents in the early stage of infection who show upper respiratory symptoms, the cut-off size of virus-laden droplets released into the environment is about 20 µm;with the shifting of infection to the lower respiratory tract in the later stage, the cut-off size decreases to 7 µm. Respiratory droplets evaporate immediately after escaping into the indoor environments and shrink to about one third of their initial size, so airborne route is speculated to be important in the transmission of COVID-19. Further investigations considering realistic flow conditions together with droplet sampling experiments on human volunteers are necessary to confirm these cut-off sizes. © 2021, Science Press. All right reserved.

Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China.

OBJECTIVE: In December 2019, coronavirus disease (COVID-19) emerged in Wuhan. However, the characteristics and risk factors associated with disease severity, unimprovement and mortality are unclear and our objective is to throw some light on these. METHODS: All consecutive patients diagnosed with COVID-19 admitted to the Renmin Hospital of Wuhan University from January 11 to February 6, 2020, were enrolled in this retrospective cohort study. RESULTS: A total of 663 COVID-19 patients were included in this study. Among these, 247 (37.3%) had at least one kind of chronic disease; 0.5% of the patients (n = 3) were diagnosed with mild COVID-19, while 37.8% (251/663), 47.5% (315/663), and 14.2% (94/663) were in moderate, severe, and critical conditions, respectively. In our hospital, during follow-up 251 of 663 patients (37.9%) improved and 25 patients died, a mortality rate of 3.77%. Older patients (>60 years old) and those with chronic diseases were prone to have a severe to critical COVID-19 condition, to show unimprovement, and to die (p <0.001, <0.001). Multivariate logistic regression analysis identified being male (OR = 0.486, 95%CI 0.311-0.758; p 0.001), having a severe COVID-19 condition (OR = 0.129, 95%CI 0.082-0.201; p <0.001), expectoration (OR = 1.796, 95%CI 1.062-3.036; p 0.029), muscle ache (OR = 0.309, 95%CI 0.153-0.626; p 0.001), and decreased albumin (OR = 1.929, 95%CI 1.199-3.104; p 0.007) as being associated with unimprovement in COVID-19 patients. CONCLUSION: Male sex, a severe COVID-19 condition, expectoration, muscle ache, and decreased albumin were independent risk factors which influence the improvement of COVID-19 patients.