ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning

With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose ELASTIC, which is the first study that leverages a cloud-edge collaborative paradigm for edge workload forecasting with multi-view graphs. Specifically, at the global stage, we design a learnable aggregation layer on each edge site to reduce the time consumption while capturing the inter-site correlation. Additionally, at the local stage, we design a disaggregation layer combining both the intra-site correlation and inter-site correlation to improve the prediction accuracy. Extensive experiments on realistic edge workload datasets collected from China's largest edge service provider show that ELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication cost. © 2023 ACM.

Guidance on strengthening the management processes of children's fever in outpatient department during the novel coronavirus pneumonia epidemic period (First Edition).

Novel Coronavirus Pneumonia (NCP) is a class B infectious disease, which is prevented and controlled according to class A infectious diseases. Recently, children's NCP cases have gradually increased, and children's fever outpatient department has become the first strategic pass to stop the epidemic.Strengthening the management of the fever diagnosis process is very important for early detection of suspected children, early isolation, early treatment and prevention of cross-infection. This article proposes prevention and control strategies for fever diagnosis, optimizes processes, prevents cross-infection, health protection and disinfection of medical staff, based on the relevant diagnosis, treatment, prevention and control programs of the National Health and Health Commission and on the diagnosis and treatment experience of experts in various provinces and cities. The present guidance summarizes current strategies on pre-diagnosis;triage, diagnosis, treatment, and prevention of 2019-nCoV infection in common fever, suspected and confirmed children, which provide practical suggestions on strengthening the management processes of children's fever in outpatient department during the novel coronavirus pneumonia epidemic period.Copyright © 2020 by the Chinese Medical Association.

Research on factors of marine ranch leisure fisheries development based on multiple orderly logit model

Based on the survey of 43 Marine ranches and 260 consumers, this paper uses the diversified and orderly Logit model to study the significant factors affecting the development of recreational fishery in Marine ranches.The study found that six factors, including consumer gender, individual economic strength, consumer demand for Marine pasture products, brand construction level of Marine pasture recreational fishery, online channel promotion level of Marine pasture recreational fishery and the impact of COVID-19 epidemic, had a significant impact on the development of Marine pasture recreational fishery.This paper divides tower into product development and brand building, marketing and daily operation three dimensions, suggest operators improve the level of recreational fishery product development and brand building, develop differentiation price, develop online channels, strengthen the whole process communication, innovation under the outbreak of daily operation mode, expand the market share, so as to enhance the competitiveness of Marine pasture recreational fishery. © 2022 SPIE.

Critique of “ Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team from Peking University

Ankit Srivastava et al. [1] proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely, GS, IAMB and Inter-IAMB. As part of the Student Cluster Competition at SC21, we reproduce the computational efficiency of ramBLe on our assigned Oracle cluster. The cluster has 4x36 cores in total with 100 Gbps RoCE v2 support and is equipped with Centos-compatible Oracle Linux. Our experiments, covering the same three algorithms of ramBLe, evaluate its strong and weak scalability of the algorithms using real COVID-19 data sets. We verify part of the conclusions in the paper and propose our explanation of the differences. IEEE

Recent Advances in Metal Chalcogenide Quantum Dots: From Material Design to Biomedical Applications

With the rapid progress in nanomaterials and biochemistry, there has been an explosion of interest in biomolecule-modified quantum dots (QDs) for biomedical applications. Metal chalcogenide quantum dots (MCQDs), as the most widely studied QDs, have attracted tremendous attention in the biomedical field on account of their unique and excellent optical properties and the ease of biomolecular modifications. Herein, important advances in MCQDs over recent years are reviewed, from materials design to biomedical applications. Especially, this review focuses on the challenges encountered in the applications of MCQDs in biomedical fields and how these problems can be solved by rational design of synthesis methods and modifications, which have opened a universal route to develop the functionalized MCQDs. Moreover, recent processes in bioimaging, biosensing, and cancer therapy based on MCQDs are examined, including the rapid detection and diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This review provides broad insights into MCQDs in the biomedical field and will inspire material researchers to develop MCQDs in the future.

Research on Universities’ Control of Online Discourse Power in the Period of COVID-19: A Case Study of Shanghai Universities

Under the situation of the normalization of the prevention and control of COVID-19, related online public opinion occurs from time to time. University administrators must grasp the right of online discourse to guide the direction of online public opinion and ensure the stability of campus order. This paper analyzes the necessity and feasibility of university administrators to grasp the right of online discourse from the basis of reality, compares two kinds of measures and their combinations through questionnaires and computer simulation experiments: publishing authoritative information and focusing on opinion leaders, argues the effectiveness of these two types of measures, and puts forward specific countermeasure suggestions on this basis. © 2022, The Author(s).

Explore Alternative Future: A Case Study of Cultivating Designers’ Future Literacy via Hybrid Collaboration

The epidemic has made it necessary to face the challenges of an uncertain future on the one hand, and to adapt to new ways of working remotely on the other. For designers, a focus on the future helps them step outside of their inherited framework and explore alternative futures. However, research on the design future education is still lacking in Chinese context. Therefore, in the summer of 2021, an initiative called future pioneer was launched, it aims to explore a process model for developing designers’ future literacy. Which can assist designers envision alternative future and influence present through the integration of design and futurology tools. By combining the designer's familiar double diamond model with the process of future scenario planning, the steps are gradually dismantled, and futurological tools are added in stages, from first understanding the future to discovering the future, selecting the direction, and then finally, finally developing the vision. Participants are guided to move from the figurative “real world” to ion and then refine and analyze the future vision to form a complete “future world view” and realize a complete narrative through the future vision map. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Predicting the Length of Stay of Patients in Hospitals

Due to the intensive treatment process of coronavirus pneumonia cases, it is important to predict the Length of Stay (LOS) of patients at the hospital to allow better management of resources and increase the efficiency of hospital services to provide improved healthcare. To predict LOS, we used four artificial neural network models namely the Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Multilayer Perceptron with PCA (PCA+MLP), and the Bidirectional Long Short Term Memory (BiLSTM) model to analyze the advantages and disadvantages of the different models using the Microsoft Hospital Length of Stay data. The proposed method is compared with the state-of-the-art models and a simple MLP model. Our models achieved an accuracy between 73% and 88% with the CNN model providing the highest accuracy. © 2021 IEEE.

[Preparation and identification of rat polyclonal antibody against SARS-CoV-2 main protease (Mpro)]

Objective To investigate the immunological functions of SARS-CoV-2 main protease (Mpro) in coronavirus disease 2019 (COVID-19), polyclonal antibody against Mpro was developed. Methods A codon-optimized SARS-CoV-2 Mpro gene was synthesized and ligated into a pET-28a vector for construction of a recombinant plasmid named by pET-28a-Mpro. Subsequently, this plasmid was transformed into E.coli Rosetta (DE3) competent cells for Mpro expression in an optimized condition, and then Mpro was purified using a HisTrap chelating column. The purified Mpro was used as immunogen to inoculate rats and the serum was collected after third immunization cycle. The titer, selectivity and sensitivity of polyclonal antibody against Mpro were analyzed using the ELISA and Western blot analysis. Results An optimized expression condition in E.coli cells for Mpro was determined, and the recombinant Mpro was purified by a HisTrap chelating column. The ELISA and Western blot analysis demonstrated that the highly sensitive polyclonal antibody against Mpro specially recognized the recombinant Mpro, and the titer reached 1:256 000. Conclusion The highly specific polyclonal antibody against SARS-CoV-2 Mpro is successfully prepared, which lays an experimental foundation for investigating the immunological function of Mpro in COVID-19.

Opinion Mining about Online Education Basing on GDELT and Twitter Data

This public health incident transformed teaching activities from offline to online. The media content and comments on social media provide a dataset for digging the public opinion on online learning. This study uses the GDELT and TWITTER platforms' data, searching "COVID" and "online education" as keywords;the relevant information is collected and analyzed in python. The results of this public opinion mining will play an essential role in discovering the problem of online teaching in this pandemic.

Research progress in anti-coronavirus effect and safety of chloroquine

Coronavirus, identified as a zoonotic pathogen, can cause pulmonary infections and even a pandemic. Corona Virus Disease 2019 (COVID-19), which was induced by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), struck the world in December 2019. There is no specific drug for coronavirus. As a classical antimalarial drug, chloroquine has been proved to have antiviral activity by changing the pH of endocytosis, via autophagy reactions and by changing the glycosylation mode of the virus envelope. At the cellular level, chloroquine has inhibitory effects on SARS-CoV, Middle East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV-2. Recent clinical research results are beginning to show that chloroquine can improve the success rate and prognosis. Chloroquine has been used less clinically in recent years due to the many adverse reactions it causes. The safety of chloroquine used in coronavirus infection requires in-depth evaluation. This article summarizes research progress in the anti-coronavirus effect and safety of chloroquine in order to provide reference for its clinical application. © 2020 Chinese Journal of Pharmacology and Toxicology. All rights reserved.

A Network for Detecting Facial Features during the COVID-19 Epidemic

The new coronavirus can spread through respiratory droplets, and wearing a mask correctly can effectively prevent the virus from spreading. However, the current detection algorithms are based on unobstructed faces, which affects the detection task when wearing a mask. To solve these problems, a facial feature detection algorithm based on Mtcnn+Mobilenet+GDBT in complex scenes is proposed. First, it can detect whether to wear a mask and the fatigue state of the face. Second, it can set different thresholds according to the facial characteristics of different people, and initialize the characteristics of different frames in 5 seconds. The innovation of our paper: the self-adaption characteristics for every person, it avoids measuring everyone by one standard, which is of great significance to the popularization of the product. Then train a dataset of masks and feature points containing 708 images. The experimental results show that compared with the traditional detection network, the new network can effectively detect facial features in the context of the epidemic. The loss we adopt is Focal loss. The lowest loss of net is 0.01 nearly. The feature of this paper is that before the outbreak of the new crown epidemic, the relevant model only designed the detection method of whether or not to wear the mask. On the basis of wearing the mask on the face, there is no research on the algorithm for judging the facial movements such as fatigue and drowsiness. This work adopts a two-step method, using the mature face detection network mtcnn, first detects the face, and then sends it into the mobilenet network for classification and monitoring status. This makes the model parameters smaller and the accuracy higher. Even if it is divided into two steps, the lightweight network of mobilenet can run smoothly on terminal devices, especially mobile devices. As for the fatigue detection system, the extraction of fatigue features with occlusions on the face has not been circulated on the network. Therefore, our team's innovation in fatigue detection based on facial occlusions such as masks has realized more dimensional detection, with stronger robustness and smaller limitations. In the future, it can be used in areas with severe influenza virus, factories, and sterile environments. It will be able to make judgments on the mask wearing conditions of on-site personnel through real-time images collected by the camera and remind people to wear masks correctly. During the epidemic, it is equipped with an on-board video system, which automatically records the driver's facial state. If fatigue performance such as frequent blinking is detected, the monitoring system will immediately issue an alarm to ensure safety. © 2021 ACM.

A preliminary analysis of remote monitoring practice in clinical trials

Objective: This study summarizes the practical experience of remote monitoring in clinical trials, preliminarily discusses the advantages and consideration factors, and provides reference for monitoring and management of clinical trials. Methods: In view of the problem that on-site monitor cannot be conducted on time during the COVID-19, our hospital launched the online remote monitoring system in time relying on the DPAP technology for the use of sponsor and CRO. The application of remote monitoring by September 15, 2020 were counted through the system backstage. The effect of application was objectively evaluated by comparing the defect detection rate per subject found by remote monitoring and on-site monitoring with chi-square. The user experience, advantages and disadvantages of remote monitoring system were investigated through homemade questionnaire and in-depth interview. The method of health economics evaluation was used to estimate the cost of simple on-site monitoring mode, remote monitoring and on-site monitoring combined mode. Results: The hospital officially launched the remote monitoring system on February 10, 2020. Until September 15, 2020, 176 CRAs from 76 sponsors/CRO have used the remote monitoring system for totally 10 470 times to conduct remote monitoring, involving 228 projects, 1 318 subjects, 16 departments in our hospital. A total of 3 820 findings have been found during the remote monitoring of 228 trials. Compared with the on-site monitoring of the same project in 2019, the remote monitoring achieved the on-site monitoring effect in terms of checking protocol deviation, case report form (CRF), combined drug use and biological samples;however, it is not as good as the on-site monitoring in terms of original records, informed consent, adverse events and experimental drugs, which involves more paper materials. 72.61% of CRAs believed that remote monitoring system effectively supported the completion of monitoring work, and 62.50% of CRAs thought that it reduced the time of on-site monitoring. The in-depth interviews with 16 CRAs showed that remote monitoring had advantages and was highly praised. The cost of the combination of routine on-site monitoring visit, remote monitoring and on-site monitoring was calculated, resulting that on-site monitoring visit frequency was reduced by 16 times, saving 73 600 RMB of travel expenses and 624 hours of travel time. But remote monitoring should be considered and standardized in system verification, subject informed consent monitoring and privacy protection, data security, implementation and other aspects. Conclusion: The remote monitoring mode made the monitoring free from location and time constraints, timely found the problems in the trials, greatly improved the efficiency of the monitoring, and reduced the risk and cost of clinical trials. In the future, it can be applied and explored in more aspects, but still needs to be further supplemented with data sources and standardized in the aspects of system verification.

Be Aware of the Hot Zone: A Warning System of Hazard Area Prediction to Intervene Novel Coronavirus COVID-19 Outbreak

Dating back from late December 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia, now known as lung inflammation caused by novel coronavirus (COVID-19). Cases have spread to other cities in China and more than 180 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency is perhaps one of the top concerns in the year of 2020 for governments all over the world. Till today, the coronavirus outbreak is still raging and has no sign of being under control in many countries. In this paper, we aim at drawing lessons from the COVID-19 outbreak process in China and using the experiences to help the interventions against the coronavirus wherever in need. To this end, we have built a system predicting hazard areas on the basis of confirmed infection cases with location information. The purpose is to warn people to avoid of such hot zones and reduce risks of disease transmission through droplets or contacts. We analyze the data from the daily official information release which are publicly accessible. Based on standard classification frameworks with reinforcements incrementally learned day after day, we manage to conduct thorough feature engineering from empirical studies, including geographical, demographic, temporal, statistical, and epidemiological features. Compared with heuristics baselines, our method has achieved promising overall performance in terms of precision, recall, accuracy, F1 score, and AUC. We expect that our efforts could be of help in the battle against the virus, the common opponent of human kind. © 2020 ACM.

Network video technologies and applications in education and research

The COVID-19 pandemic changes the mode of life, and forces people to keep social distance or even separate from each other. Education and research, as a type of activities that usually needs many people gather at the same space, need network video technologies to keep participants separating from each other. In this paper, we analyze three different types of education and research activities, and try to give the appropriate solutions of the network video technologies for each particular activity. © 2020, Science Press. All right reserved.

Prevention and control strategies of coronavirus disease 2019 in oral and maxillofacial surgery wards

Coronavirus disease 2019 (COVID-19) has been confirmed to transmit by contact and respiratory droplets, and possibly by aerosols. Medical staff of oral and maxillofacial surgery departments need close contact with head and face of patients during the diagnosis and treatment, and aerosols may be generated during the operation. The risk of infection exposure is high, and the ward management is facing severe challenges. During the COVID-19 epidemic, the diagnosis and treatment in oral and maxillofacial surgery wards should be carried out reasonably after comprehensive assessment of patient condition. We put forward relevant prevention and control measures during the epidemic from the aspects of patient treatment, ward infection prevention and control, supply management, emergency plan, and training of medical staff, so as to provide references for prevention and control of the epidemic in oral and maxillofacial surgery wards.