Path Planning of Follow-up Transport Robot Based on Machine Vision

With the upgrading and innovation of the logistics industry, the requirements for the level of transportation smart technologies continue to increase. The outbreak of the COVID-19 has further promoted the development of unmanned transportation machines. Aimed at the requirements of intelligent following and automatic obstacle avoidance of mobile robots in dynamic and complex environments, this paper uses machine vision to realize the visual perception function, and studies the real-time path planning of robots in complicated environment. And this paper proposes the Dijkstra-ant colony optimization (ACO) fusion algorithm, the environment model is established by the link viewable method, the Dijkstra algorithm plans the initial path. The introduction of immune operators improves the ant colony algorithm to optimize the initial path. Finally, the simulation experiment proves that the fusion algorithm has good reliability in a dynamic environment. © 2022 IEEE.

Coronavirus Disease 2019 and Thyroid Diseases.

Since the outbreak of coronavirus disease 2019 (COVID-19), a number of COVID-19 related thyroid disorders have been reported, including subacute thyroiditis, autoimmune thyroid disease, non-thyroidal illness syndrome and some unexplained thyroid dysfunction. This review aimed to summarize clinical characteristics of COVID-19 related thyroid disorders and to discuss some possible mechanisms.Copyright © 2022, Peking Union Medical College Hospital. All rights reserved.

Impact of the COVID-19 pandemic on youth delinquency: A discontinuous growth analysis

IntroductionThis study aimed to investigate (a) the immediate and long-term changes in youth offending rates among 138 neighborhoods within a large metropolitan area in the context of COVID-19 and (b) the extent to which the socioeconomic composition of the neighborhoods accounted for variations of the changes. MethodsDiscontinuous growth models were applied to demonstrate the changes in offenses against a person, property offenses, and drug-related offenses one-year prior to, at (March 2020), and one-year following the pandemic. ResultsAt the onset of the pandemic, we registered an immediate reduction in offenses against a person and property offenses but not in drug-related offenses. There was a steeper declining trend for property offenses one-year following the pandemic as compared with that one-year prior to the pandemic. The neighborhood concentration of affluence and poverty was not related to the immediate reduction in any type of delinquency. DiscussionWe conclude that the COVID-19 pandemic not only had an abrupt but also an enduring impact on youth delinquency.

Examining gains and pains of a new virtual internship design

This study investigated how student effort and the course design influenced an online internship in China. A cohort of 95 postgraduate students became distance learners in a credit-bearing internship course due to COVID-19. The course leader applied the action learning framework to prompt student online collaboration and group inquiry. The framework assumes the importance of self-reliant learner autonomy in virtual internships. After the course, researchers analyzed the effects of self-directed learning with technology on a multidimensional community of inquiry in a virtual environment. The study also identified students' narratives that explain how self-directed learning with technology interacted with three elements of virtual communities of inquiry: social, cognitive, and teaching. Findings explain how virtual internships can be facilitated through a community of inquiry model. Educators and practitioners may consider the model to demonstrate student-staff partnerships (Fitzgerald et al., 2020) to achieve quality transformation of internships from face-to-face mode to distance education. © 2023 Open and Distance Learning Association of Australia, Inc.

Quantifying Mobility Perturbation in America's Cities during COVID-19: A Network-Based Approach

The pandemic of COVID-19 has caused severe disruptions in urban lives. Understanding and quantifying these disruptions is important to inform the development of targeted and effective measures to control the pandemic and its impact. One way of achieving this object is to measure the urban mobility perturbation caused by the pandemic. In this study, we built mobility-based networks for seven major metropolitan statistical areas (MSAs) across the United States in the years of 2019 and 2020, respectively. We quantified the disruptions of urban mobility by computing and comparing a set of network-based metrics before and during the pandemic. The proposed approach is able to uncover the impact of COVID-19 in cities and provides new insights into the resilience of cities when facing large-scale disasters.

Using H5P to Enrich Online Learning Engagement in a Postgraduate Certificate Program Teaching

The COVID-19 pandemic has accelerated the educational transition from traditional face-to-face to entirely online or hybrid learning. Online learning engagement has long been considered essential to effective learning and teaching, but with many challenges (e.g., learner autonomy, cyber distraction, and digital competence) in higher education, especially post-pandemic. This chapter aims to present empirical innovative practices and experiences in using H5P, a free and open-source content collaboration platform, to enrich online learning engagement in a hybrid mode teaching of the Post Graduate Certificate in Teaching and Supporting Learning in Higher Education (PGCert) in a Sino-British international university in China. The authors introduce the technological features and demonstrate how the H5P based educational technologies enhance online learning rather than just a substitute. For reflective rethinking, the challenges, barriers, and practical implications in developing the curriculum, designing the learning activities, and delivering the course using the H5P in an online learning environment. © 2023 selection and editorial matter, Areej ElSayary and Abdulrasheed Olowoselu;individual chapters, the contributors.

A data sharing method for remote medical system based on federated distillation learning and consortium blockchain

With the development of Medical Internet of Things (MIoT) technology and the global COVID-19 pandemic, hospitals gain access to patients' health data from remote wearable medical equipment. Federated learning (FL) addresses the difficulty of sharing data in remote medical systems. However, some key issues and challenges persist, such as heterogeneous health data stored in hospitals, which leads to high communication cost and low model accuracy. There are many approaches of federated distillation (FD) methods used to solve these problems, but FD is very vulnerable to poisoning attacks and requires a centralised server for aggregation, which is prone to single-node failure. To tackle this issue, we combine FD and blockchain to solve data sharing in remote medical system called FedRMD. FedRMD use reputation incentive to defend against poisoning attacks and store reputation values and soft labels of FD in Hyperledger Fabric. Experimenting on COVID-19 radiography and COVID-Chestxray datasets shows our method can reduce communication cost, and the performance is higher than FedAvg, FedDF, and FedGen. In addition, the reputation incentive can reduce the impact of poisoning attacks. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

FedTD: Efficiently Share Telemedicine Data with Federated Distillation Learning

With the Internet of Things and medical technology development, patients use wearable telemedicine devices to transmit health data to hospitals. The need for data sharing for public health has become more urgent under the COVID-19 pandemic. Previously, security protection technology was difficult to solve the increasing security risks and challenges of telemedicine. To address the above hindrances, Federated learning (FL) solves the difficulty for companies and institutions to share user data securely. The global server iterative aggregates the model parameters from the local server instead of uploading the user's data directly to the cloud server. We propose a new model of federated distillation learning called FedTD, which allows the different models between local hospital servers and global servers. Unlike traditional federated learning, we combine the knowledge distillation method to solve the non-Independent Identically Distribution (non-IID) problem of patient medical data. It provides a security solution for sharing patients' medical information among hospitals. We tested our approach on the COVID-19 Radiography and COVID-Chestxray datasets to improve the model performance and reduce communication costs. Extensive experiments show that our FedTD significantly outperforms the state-of-the-art. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research on the development strategy of interactive courseware based on data mining mode during the epidemic

Since the outbreak of the novel coronavirus pneumonia, teachers in schools all over the country have been learning to use various network resources to carry out online teaching and launch a new Internet teaching model based on diversified online classrooms. Under such extreme circumstances, teachers and students were forced to collide with online teaching for the first time, and an unprecedented large-scale online teaching practice kicked off. A full range of online teaching activities and the use of online teaching platforms have changed the way of teaching interaction between teachers and students. The article first analyzes the situation of education and teaching during the epidemic, introduces the application of association rule technology, DBSCAN algorithm, etc. in teaching interactive courseware, studies the interactive courseware development strategy based on data mining mode, provides online teaching system for teachers, intelligent monitoring system and human nature. It is convenient for students to use instant messaging software, online discussion and online answering for interactive learning. © 2022 IEEE.

The Psychological Impacts of COVID-19 on Breast Cancer Patients in China

Background: Approximately 30% to 50% of breast cancer patients experienced mental distress prior to the advent of COVID.The delayed access to cancer treatment due to the outbreak of COVID -19 pandemic posed a unique challenge to breast cancer patients and caused a significant level of mental distress among them. In the current research, we examined the psychological impacts of COVID on breast cancer patients in China using Symptom Checklist-90-R (SCL-90-R). Method(s): Participants were breast cancer patients at the outpatient clinic of Xijing hospital. The study was conducted virtually, and the questionnaires were distributed via Wenjuanxing, the Chinese alternative of Qualtrics. The researchers were healthcare workers affiliated with Xijing hospital, and the survey was sent to a breast cancer patient support group which included 1399 cancer patients and 6 healthcare workers. The initial sample consisted of 199 participants who signed an informed consent form to participate in the study. The inclusion criteria were as follows: 1) diagnosed with breast cancer, 2) aged 18 years or above, and 3) had no history of cognitive impairment or previous diagnosis of psychiatric disorders. The validated Mandarin version of the SCL-90-R (Wang, 1984) was then given to the participants to evaluate their psychological status.Categorical variables were summarized as numbers and percentages;continuous variables were described as mean (M) +/- standard deviation (SD). Data were analyzed using IBM SPSS Statistics Version 26. Result(s): Participants (N = 195) filled out the SCL-90 questionnaire in February, 2020. All participants were female breast cancer patients treated at Xijing hospital, among which 16.41%, 36.41%, 19.49%, and 28.21% had respectively received treatment for less than a year, 1-3 years, 3-5 years, and 5 years or more. 64.62% of the patients were at stage I;0.77% were at stage II and III;4.62% were at stage IV according to TNM classification. The molecular type of participants is as follows: 47.2% of ER+ HER2-, 31.8% of HER2+, and 21.0% of Triple negative.Participants whose treatments continued to be delayed, on average, reported an elevated general psychopathology score (M = 1.48, SD = 0.47) compared to participants whose treatments were resumed (M = 1.30, SD = 0.34), and the difference was statistically significant, t(193) = 2.96, p = .003, d = 0.44, 95%Cl [0.06, 0.30]. The one-way ANOVA revealed a marginally significant effect of length of treatment delay on general psychopathology score, F(4, 190) = 2.09, p = .08, eta2 = .04. Follow-up multiple comparison analysis showed that participants who had their treatment delayed for 3 weeks to 1 month (M = 1.70, SD = 0.70) reported significantly higher general psychopathology scores than participants whose delay in treatment was less than 1 week (M = 1.34, SD = 0.40), p = .05. General health status (p < .001) and current treatment status (p = .02) are the only two variables that were statistically associated with general psychopathology score.Poorer perceived health status and current delay in treatment were associated with higher general psychopathology score, Additionally, younger age was associated with higher interpersonal sensitivity (p = .01) and hostility (p = .006). Conclusion(s): We found that breast cancer patients at an advanced stage were more likely to experience psychological symptoms with longer treatment delay, and whose treatments continued to be delayed reported elevated psychological symptoms than individuals whose treatment were resumed, regardless of treatment type. Additionally, a treatment delay of more than three weeks might have exacerbated breast cancer patients' psychological symptoms, whereas a short-term delay of less than three weeks was less likely to have a significant effect on one's mental wellbeing.

How Do Logistics Disruptions Affect Rural Households? Evidence from COVID-19 in China

Using a comprehensive survey of rural households during the early stage of the COVID-19 pandemic in China, we find that logistics disruptions due to the lockdown have resulted in severe economic losses for rural households. Insufficient production inputs and perishable outputs can aggravate the impact of logistics disruptions on losses, while the purchase of agriculture insurance and higher regional GDP can mitigate this effect. We further examine the mechanisms by which logistics disruptions affect rural households, including both sales and production channels in agricultural supply chains. The former includes changes in product prices and reduced sales, while the latter includes changes in input prices and shortages of raw materials, capital, and labor. Of these channels, logistics has the most severe impact on sales. Opening up the logistics of sales channels is the primary policy choice. More storage warehouses and insurance are also important preemptive measures. Building stable and sustainable agricultural supply chains can ensure rural household viability during the pandemic. © 2022 by the authors.

Impact of lockdown and government subsidies on rural households at early COVID-19 pandemic in China

Purpose: Given the scarcity of data during the early stages of the COVID-19 pandemic in China, the decision-making for non-pharmaceutical policies was mostly based on insufficient evidence. The purpose of this study is to assess the effectiveness of these policies, such as lockdown and government subsidies, on rural households and identify policy implications for China and other countries in dealing with pandemics. Design/methodology/approach: The authors survey 2,408 rural households by telephone from 101 counties across 17 provinces in China during the first stage of the pandemic (March 2020). The authors use the ordered probit model and linear regression model to study the overall impact of policies and then use the quantile regression model and sub-sample regression method to study the heterogeneity of the effects of government policies. Findings: The authors find that logistics disruption due to lockdown negatively affected rural households. Obstructed logistics is associated with a more significant loss for high-income households, while its impact on the loss expectation of low-income households is more severe. Breeding and other industries such as transport and sales suffer more from logistics than cultivation. The impact of logistics on intensive agricultural entities is more serious than that on professional farms. The government subsidy is more effective at reducing loss for low-income households. Lockdown and government subsidies have shown heterogeneous impacts on rural households. Practical implications: The overall economic losses experienced by rural households in the early stages of the pandemic are controllable. The government policies of logistics and subsidies should target specific groups. Originality/value: The authors evaluate the economic impacts of lockdown and government subsidies on rural households and show their heterogeneity among different groups. The authors further demonstrate the policy effectiveness in supporting rural households during the early stages of the pandemic and provide future policy guidance on major public health event. © 2022, Emerald Publishing Limited.

The reliability and integrity of overall survival data based on follow-up records only and potential solutions to the challenges

Overall survival (OS) is considered the standard clinical endpoint to support effectiveness claims in new drug applications globally, particularly for lethal conditions such as cancer. However, the source and reliability of OS in the setting of clinical trials have seldom been doubted and discussed. This study first raised the common issue that data integrity and reliability are doubtful when we collect OS information or other time-to-event endpoints based solely on simple follow-up records by investigators without supporting material, especially since the 2019 COVID-19 pandemic. Then, two rounds of discussions with 30 Chinese experts were held and 12 potential source scenarios of three methods for obtaining the time of death of participants, including death certificate, death record and follow-up record, were sorted out and analysed. With a comprehensive assessment of the 12 scenarios by legitimacy, data reliability, data acquisition efficiency, difficulty of data acquisition, and coverage of participants, both short-term and long-term recommended sources, overall strategies and detailed measures for improving the integrity and reliability of death date are presented. In the short term, we suggest integrated sources such as public security systems made available to drug inspection centres appropriately as soon as possible to strengthen supervision. Death certificates provided by participants' family members and detailed standard follow-up records are recommended to investigators as the two channels of mutual compensation, and the acquisition of supporting materials is encouraged as long as it is not prohibited legally. Moreover, we expect that the sharing of electronic medical records and the legal disclosure of death records in established health registries can be realized with the joint efforts of the whole industry in the long-term. The above proposed solutions are mainly based on the context of China and can also provide reference for other countries in the world. © 2022 The Authors

Intelligent unmanned medicine cabinet based on 51 single-chip microcomputer

With the relatively uneven distribution of medical resources in China and a new outbreak of COVID-19 at the end of 2019, we developed an intelligent medicine cabinet to alleviate the problem of high pressure and difficulty in accessing medical care in hospitals around the country. The medicine cabinet has a signal transmission circuit system based on 51 microcontroller and a new X-Y trajectory control module, which controls the Pulse Width Modulation (PWM) signal by Proportion Integration Differentiation (PID) algorithm to improve the accuracy of the DC motor. It has the functions of online drug selection, drug sales, drug transmission, etc. Meanwhile, the online drug purchase system based on the WeChat applet can reduce the probability of infection by a new coronavirus. And the new X-Y cargo track will significantly improve the safety of fragile drugs while ensuring their delivery. The development of this medicine cabinet will greatly reduce the operating cost of pharmacies and meet the demand of people to purchase medicine at night. © 2022 SPIE.

Design and Implementation of a Robot System for High Efficiency Automatic Oropharyngeal Swab Sampling

For normalized prevention and control of novel corona virus disease 2019 (COVID-19) pandemic, a robot system is desired to assist in performing large numbers of oropharyngeal (OP) swab sampling. However, reliability and efficiency are still challenges for the practical application of existing robot systems. In this paper, a robot system and related implementation scheme for high efficiency automatic OP swab sampling are developed. A novel robot end-effector with a disposable protective cover is designed, that testee keeps biting on its terminal during sampling. The main steps of the sampling procedure, including sterilizing, recycling, swab mounting and collection, are realized automatically. The effectiveness and efficiency of the proposed robot system are validated through experiment on human subjects. The whole sampling procedure takes about 80 to 90 seconds. © 2022 IEEE.

PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols

Many proximity-based tracing (PCT) protocols have been proposed and deployed to combat the spreading of COVID-19. In this article, we take a systematic approach to analyze PCT protocols. We identify a list of desired properties of a contact tracing design from the four aspects of Privacy, Utility, Resiliency, and Efficiency (PURE). We also identify two main design choices for PCT protocols: what information patients report to the server and which party performs the matching. These two choices determine most of the PURE properties and enable us to conduct a comprehensive analysis and comparison of the existing protocols.

Efficacy and safety of traditional Chinese medicine and antiviral antibody therapies for COVID-19: a network meta-analysis

Objective To systematically review the efficacy and safety of traditional Chinese medicine (TCM) and antiviral antibody therapy in the treatment of COVID-19. Methods PubMed, EMbase, The Cochrane Library, Web of Science, CNKI, WanFang Data, VIP and SinoMED databases were electronically searched to collect randomized controlled trials (RCTs) on efficacy and safety of traditional Chinese medicine and antiviral antibody therapies for COVID-19 from inception to June 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies;then, network meta-analysis was performed by using Stata 14.0 software. Results A total of 44 RCTs were included. The results of network meta-analysis showed that, for mortality rate, the rank of cumulative probability was: TCM+ standard care (SC) (100%)>convalescent plasma (CP)+SC (42%)>SC (8%). In terms of hospital stay time, the rank of cumulative probability was: TCM+SC (95.5%)>SC (31.4%)>CP+SC (23.2%). In terms of time to viral clearance, the rank of cumulative probability was: TCM+SC (97.4%)>SC (37.4%)>CP+SC (15.2%). In the aspect of mechanical ventilation rate, the rank of cumulative probability was: TCM+SC (98.9%)>CP+SC (42.9%)>SC (8.3%). In the aspect of adverse reactions/events, the rank of cumulative probability was: TCM+SC (99.9%)>SC (47.9%)>CP+SC (2.2%). Conclusion The current evidence shows that TCM combined with SC is the most effective treatment in reducing mortality, shortening hospitalization time and viral negative conversion time, reducing mechanical ventilation rate, and the incidence of adverse reactions/events is low. Due to limited quality and quantity of the included studies, more high quality studies are needed to verify above conclusion. Copyright © 2022 West China University of Medical Science. All rights reserved.

A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study

COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant. As deep learning and convolutional neural network (CNN) has been widely utilized and been verified in analyzing medical images, it has become a powerful tool for computer-assisted diagnosis. However, there are two most significant challenges in medical image classification with the help of deep learning and neural networks, one of them is the difficulty of acquiring enough samples, which may lead to model overfitting. Privacy concerns mainly bring the other challenge since medical-related records are often deemed patients’ private information and protected by laws such as GDPR and HIPPA. Federated learning can ensure the model training is decentralized on different devices and no data is shared among them, which guarantees privacy. However, with data located on different devices, the accessible data of each device could be limited. Since transfer learning has been verified in dealing with limited data with good performance, therefore, in this paper, We made a trial to implement federated learning and transfer learning techniques using CNNs to classify COVID-19 using lung CT scans. We also explored the impact of dataset distribution at the client-side in federated learning and the number of training epochs a model is trained. Finally, we obtained very high performance with federated learning, demonstrating our success in leveraging accuracy and privacy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.