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    Collection of COVID-19 Prevention and Control highlights articles from the Journal of Shanghai Jiao Tong University (Science)
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    1. Input-Constrained Hybrid Control of a Hyper-Redundant Mobile Medical Manipulator
    输入受限的超冗余移动医疗机械臂混合控制
    摘要:为降低医务人员在传染病区域工作的感染风险,提出了采用超冗余移动医疗机械臂(HRMMM)代替医务人员在医疗服务中执行接触性任务。设计了一种基于运动学的姿态跟踪算法,以实现高精度的位姿跟踪。建立了HRMMM的运动学模型,推导了其整体雅可比矩阵。为了保证准确的目标跟踪,设计了基于罗德里格斯旋转公式的跟踪误差,并推导了跟踪误差与夹爪速度之间的关系。考虑到物理的输入约束,建立了HRMMM的关节约束模型,采用变量替换法将非对称约束转换为对称约束,所有约束都通过除以其最大值进行无量纲化。为了满足医疗事件中的实时运动控制要求,设计了一种基于伪逆(PI)和二次规划(QP)的混合控制器,无输入约束饱和时采用PI方法,出现约束饱和时采用QP方法。设计二次性能指标保证PI和QP之间的平滑切换。仿真结果表明HRMMM可以在满足在不同类型的输入约束情况下,以平滑的运动轨迹接近目标姿态。
    关键词:输入受限的混合控制,超冗余移动医疗机械臂,伪逆,二次规划,位姿跟踪
    原文链接:https://doi.org/10.1007/s12204-023-2580-4



    2. Random Regret Minimization Model of Carpool Travel Choice for Urban Residents Considering Perceived Heterogeneity and Psychological Distance

    考虑感知异质性和心理距离的随机后悔最小化城市居民合乘出行选择模型
    摘要:合乘是一种可持续、经济和环保的解决方案,可以有效减少城市地区的空气污染和缓解交通拥堵。然而现有的后悔理论缺少考虑不同方式属性感知异质性以及影响后悔的心理因素,不能对城市居民合乘出行决策进行准确刻画,也不能对真实合乘选择行为做出正确的解释。本文在分析经典随机后悔最小化模型和考虑异质性随机后悔最小化模型的基础上,针对现有模型的不足,引入心理距离概念,构建了考虑异质性和心理距离的随机后悔最小化改进模型。结果表明,本文提出的改进模型的拟合度和解释效果相较于其他两种模型均较优,出行居民在2019冠状病毒病疫情期间的心理距离会影响出行决策的预期后悔值和合乘意愿。该模型可以更好地描述出行居民合乘出行选择机理,有效地解释出行居民的合乘选择行为。
    关键词:合乘出行行为,随机后悔最小化理论,预期后悔值,感知异质性,心理距离
    原文链接:https://doi.org/10.1007/s12204-023-2588-9


    3. Visual Positioning of Nasal Swab Robot Based on Hierarchical Decision
    基于层次决策网络的鼻拭子采样机器人视觉定位方法
    摘要:本文主要研究一种用于鼻拭子机器人自动采样操作的视觉定位方法。使用机器人完成鼻拭子采样任务可以减少医务人员对新型冠状病毒病(COVID-19)患者的直接接触,从而减小COVID-19带来的负面影响,对COVID-19的检测和防疫具有重要意义。该方法根据COVID-19的传播特点使用层次决策网络来处理机器人的行为约束条件,并结合医务人员的采样动作特点设计了使用单臂机器人进行鼻拭子采样操作的视觉导航定位方法。该方法所使用的决策网络综合考虑了人工采样操作中引起潜在接触感染风险的影响因素,以尽可能降低病毒在人员之间的传播概率。进一步形成具有人工智能特征的视觉伺服控制策略,并完成稳定、安全的鼻拭子机器人采样操作。实验证明,该方法能够实现良好的机器人视觉系统的定位,可以为公共卫生防控提供必要的技术支持。 
    关键词:医疗机器人,鼻拭子采样,视觉伺服,层次决策
    原文链接:https://doi.org/10.1007/s12204-023-2581-3

    4. Psychological Impact of the 2022 Round COVID-19 Pandemic on China's College Students
    2022年新一轮新冠疫情对中国大学生心理的影响
    Abstract: In response to the new round of COVID-19 outbreaks since March 2022, universities with high outbreak rates around the country have taken quarantine measures to contain the epidemic. Evidence from previous coronavirus outbreaks has shown that people under quarantine are at risk for mental health disorders. To better understand the impacts of this round of COVID-19 quarantine on domestic college students and their responses, we conducted a systematic survey to assess the stress and anxiety, and to evaluate effective measurements in this population. We searched relevant documents and literature, and designed a questionnaire from six aspects, including psychological status, epidemic situation, study, daily life, sports, and interpersonal communication, with 51 items in total. We sent the questionnaire on the Wenjuanxing Web platform, from April 2 to 8, 2022. We evaluated the mental status according to parts of the Generalized Anxiety Disorder-7 (GAD-7) and Depression Anxiety Stress Scales-21 (DASS-21), and investigated the influencing risk factors and countermeasures. Statistical analysis was performed by using the Chi-square test and multi-variable logistic regression. In total, 508 college respondents were recruited in our survey, and the pooled prevalence of mild anxiety (GAD score ≥ 5, or DASS-21 anxiety score ≥ 8) or stress (DASS-21 pressure score ≥ 14) caused by the new round of COVID-19 pandemic quarantine was 19.69% (100/508). The prevalence of the anxiety or stress in college students with COVID-19 quarantine between different genders, regions, and majors was not significantly different. Independent risk factors for the mild anxiety or stress of undergraduates by COVID-19 quarantine included learning efficiency or duration [OR = 1.36, 95%CI (1.14—1.62), P = 0.001], based on the combined analysis of Chi-square test analysis with multi-variable logistic regression analysis.  Interestingly, the mental well-beings before COVID-19 epidemic quarantine [OR = 0.22, 95%CI (0.13—0.36), P < 0.000 1], more low-intensity exercise [OR = 0.36, 95%CI (0.15—0.87), P = 0.02, high-intensity exercise as reference], and good sleep quality [OR = 0.14, 95%CI (0.07—0.30), P < 0.000 1: OR = 0.42, 95%CI (0.30—0.59), P < 0.000 1] are protective factors for alleviating the quarantinecaused anxiety or stress in Chinese college students for this round of COVID-19 epidemic quarantine. During the round of COVID-19 epidemic quarantine in 2022, a small number of college students have mild anxiety, affected by decreased learning efficiency or duration, which could be mitigated with low-intensity exercise and good sleep quality.
    Key words: COVID-19, psychological investigation, college students, anxiety, stress
    原文链接:https://doi.org/10.1007/s12204-022-2557-8


    5. Social Network Analysis of COVID-19 Research and the Changing International Collaboration Structure
    COVID-19 社会网络分析与国际合作结构变化
    Abstract:  Research in Information Science and interdisciplinary areas suggested the formation of a growing network of international research collaboration. The massive transmission of COVID-19 worldwide especially after the identification of the Omicron variant could fundamentally alter the factors shaping the network's development. This study employs network analysis methods to analyze the structure of the COVID-19 research collaboration from 2020 to 2022, using two major academic publication databases and the VOSviewer software. A novel temporal view is added by examining the dynamic changes of the network, and a fractional counting method is adopted as methodological improvements to previous research. Analysis reveals that the COVID-19 research network structure has undergone substantial changes over time, as collaborating countries and regions form and re-form new clusters. Transformations in the network can be partly explained by key developments in the pandemic and other social-political events. China as one of the largest pivots in the network formed a relatively distinct cluster, with potential to develop a larger Asia-Pacific collaboration cluster based on its research impact.
    Key words: COVID-19, international research collaboration, network analysis, high interdisciplinary research
    原文链接:https://doi.org/10.1007/s12204-022-2558-7


    6. 低呼吸阻力COVID-19口罩设计研究
    Design of a Low Respiratory Resistance Mask for COVID-19
    原文链接:https://doi.org/10.1007/s12204-022-2434-5
    建模多孔材料,CFD模型网格尺寸、介质参数、计算收敛控制参数;试验数据分析验证。针对COVID-19的设计及口罩相关使用性能研究有较大的参考价值。

    7. 供应链视角下COVID-19 对制造业的影响及对策

    Influence of COVID-19 on Manufacturing Industry and Corresponding Countermeasures from Supply Chain Perspective

    原文链接: http://https://doi.org/10.1007/s12204-020-2206-z

    本文研究了COVID-19 在全球范围内传播所造成的初始影响,分析了后COVID-19(疫情时代)的经济与未来,并提出帮助制造业复元两个阶段的策略。

     

    8. CIRD-F模型:COVID-19在中国的传播和影响

    CIRD-F: Spread and Influence of COVID-19 in China

    原文链接: https://doi.org/10.1007/s12204-020-2168-1

    团队基于SEIR模型加入量化的公众对COVID-19疫情的警惕性。根据模型预测结果预计了疫情结束时间以及最终累积感染人数。

     

    9. 基于病毒传播动态的COVID-19疫情预测及大学生最佳返校期研究

    Prediction of COVID-19 Outbreak in China and Optimal Return Date for University Students Based on Propagation Dynamics

    原文链接: https://doi.org/10.1007/s12204-020-2167-2

    团队首先在SEIR模型四类人群的基础上,建立了4+1五类人群模型,并提出BAT模型。预测本次疫情最佳复工复学时间。讨论了未来可能发生的一些影响因素,如二次感染、有效药物开发以及国际间人口流向。

     

    10. 3阶段e-ISHR模型对COVID-19暴发的评估

    Preliminary Assessment of the COVID-19 Outbreak Using 3-Staged Model e-ISHR

    原文链接: https://doi.org/10.1007/s12204-020-2169-0

    基于我国政府强大的干预能力和人们的自觉意识,团队将传统的SEIR模型、分级机制、时滞机制和医院系统相结合,建立了e-ISHR模型并预测武汉的疫情在202038日出现拐点,全国(非湖北)在227日出现拐点。

     

    11. D2EA模型:预测COVID-19的流行

    D2EA: Depict the Epidemic Picture of COVID-19

    原文链接: https://doi.org/10.1007/s12204-020-2170-7

    团队建立的D2EA模型基于SEIR模型,引入一组隔离者的数据,并预测湖北累计确诊的感染病例,预测疫情将在2月底出现拐点,并建议武汉市最迟在3月底解除封锁。

     

    12. 需求分析与管理建议:特大城市医疗机构流行病学数据共享与防疫工作

    Demand Analysis and Management Suggestion: Sharing Epidemiological Data Among Medical Institutions in Megacities for Epidemic Prevention and Control

    原文链接: https://doi.org/10.1007/s12204-020-2166-3

    共享“核心”信息,凸显精准防疫。上海市第六人民医院由党委书记陈方教授牵头,首家试点将门诊预约与上海市大数据中心实时对接,实现患者近14天的入沪道口信息自动校验,确保患者流调信息的及时更新,为超大城市医疗机构精准防疫提供新的经验。

     

    13. 一种使用胸部 CT 图像诊断 COVID-19 的新型多模型集成深度学习方法

    Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images

    原文链接:https://doi.org/10.1007/s12204-021-2392-3

    本文使用集成学习方法结合多个神经网络,对同一样本进行预测,并与使用单一模型方法的类似研究进行比较。将使用单一模型方法的类似研究与该研究集成模型的性能进行了比较。两种集成策略对比结果表明,本文的集成学习方法在预测COVID-19方面具有明显的优势。

     

    14. 基于少量CXR样本的COVID-19智能诊断解释算法

    COVID-19 Interpretable Diagnosis Algorithm Based on a Small Number of Chest X-Ray Samples

    原文链接:https://doi.org/10.1007/s12204-021-2393-2

    针对新冠疫情前中期CXR样本数据稀少的情况进行研究,探究少样本情况下的人工智能与医学诊断结合方案,研究成功适用于多数突发少样本状况。

  • Pubdate: 2022-02-16    Viewed: 1147