Restoring force correction based on online discrete tangent stiffness estimation method for real-time hybrid simulation

In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation（online DTSE） to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that：（1） the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and（2） the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.

Direct immune-SCIR public-opinion propagation model based on real-time online users

Current public-opinion propagation research usually focused on closed network topologies without considering the fluctuation of the number of network users or the impact of social factors on propagation. Thus, it remains difficult to accurately describe the public-opinion propagation rules of social networks. In order to study the rules of public opinion spread on dynamic social networks, by analyzing the activity of social-network users and the regulatory role of relevant departments in the spread of public opinion, concepts of additional user and offline rates are introduced, and the direct immune-susceptible, contacted, infected, and refractory (DI-SCIR) public-opinion propagation model based on real-time online users is established. The interventional force of relevant departments, credibility of real information, and time of intervention are considered, and a public-opinion propagation control strategy based on direct immunity is proposed. The equilibrium point and the basic reproduction number of the model are theoretically analyzed to obtain boundary conditions for public-opinion propagation. Simulation results show that the new model can accurately reflect the propagation rules of public opinion. When the basic reproduction number is less than 1, public opinion will eventually disappear in the network. Social factors can significantly influence the time and scope of public opinion spread on social networks. By controlling social factors, relevant departments can analyze the rules of public opinion spread on social networks to suppress the propagate of negative public opinion and provide a powerful tool to ensure security and stability of society.

A Non-Contact Original-State Online Real-Time Monitoring Method for Complex Liquids in Industrial Processes

Failures are very common during the online real-time monitoring of large quantities of complex liquids in industrial processes, and can result in excessive resource consumption and pollution. In this study, we introduce a monitoring method capable of non-contact original-state online real-time monitoring for strongly coated, high-salinity, and multi-component liquids. The principle of the method is to establish the relationship among the concentration of the target substance in the liquid （C）, the color space coor- dinates of the target substance at different concentrations （L＊, a＊, b＊）, and the maximum absorption wave- length （λmax）; subsequently, the optimum wavelength λT of the liquid is determined by a high-precision scanning-type monitoring system that is used to detect the instantaneous concentration of the target substance in the flowing liquid. Unlike traditional monitoring methods and existing online monitoring methods, the proposed method does not require any pretreatment of the samples （i.e., filtration, dilution, oxidation/reduction, addition of chromogenic agent, constant volume, etc.）, and it is capable of original- state online real-time monitoring. This method is employed at a large electrolytic manganese plant to monitor the Fe3. concentration in the colloidal process of the plant＇s aging liquid （where the concentra- tions of Fe3＋, Mn2＋, and （NH4）2SO4 are 0.5-18 mg.L 1, 35-39 g.L 1, and 90-110 g.L 1, respectively）. The relative error of this monitoring method compared with an off-line laboratory monitoring is less than 2%.

How reliable is online diffusion of medical information targeting patients and families?

AIM: To determine whether online diffusion of the "Ten Warning Signs of Primary Immunodeficiency Diseases(PID)'' adheres to accepted scientific standards.METHODS: We analyzed how reproducible is online diffusion of a unique instrument, the "Ten Warning Signs of PID", created by the Jeffrey Modell Foundation(JMF),by Google-assisted searches among highly visited sites from professional, academic and scientific organizations;governmental agencies; and patient support/advocacy organizations. We examined the diffusion, consistency of use and adequate referencing of this instrument.Where applicable, variant versions of the instrument were examined for changes in factual content that would have practical impact on physicians or on patients and their families.RESULTS: Among the first 100 sites identified by Google search, 85 faithfully reproduced the JMF model, and correctly referenced to its source. By contrast, the other15 also referenced the JMF source but presented one or more changes in content relative to their purported model and therefore represent uncontrolled variants, of unknown origin. Discrepancies identified in the latter included changes in factual content of the original JMF list(C), as well as removal(R) and introduction(I) of novel signs(Table 2), all made without reference to any scientific publications that might account for the drastic changes in factual content. Factual changes include changes inthe number of infectious episodes considered necessary to raise suspicion of PID, as well as the inclusion of various medical conditions not mentioned in the original.Together, these changes will affect the way physicians use the instrument to consult or to inform patients,and the way patients and families think about the need for specialist consultation in view of a possible PID diagnosis.CONCLUSION: The retrieved adaptations and variants,which significantly depart from the original instrument,raise concerns about standards for scientific information provided online to physicians, patients and families.

Online Induction of Probabilistic Real-Time Automata

The probabilistic real-time automaton （PRTA） is a representation of dynamic processes arising in the sciences and industry. Currently, the induction of automata is divided into two steps： the creation of the prefix tree acceptor （PTA） and the merge procedure based on clustering of the states. These two steps can be very time intensive when a PRTA is to be induced for massive or even unbounded datasets. The latter one can be efficiently processed, as there exist scalable online clustering algorithms. However, the creation of the PTA still can be very time consuming. To overcome this problem, we propose a genuine online PRTA induction approach that incorporates new instances by first collapsing them and then using a maximum frequent pattern based clustering. The approach is tested against a predefined synthetic automaton and real world datasets, for which the approach is scalable and stable. Moreover, we present a broad evaluation on a real world disease group dataset that shows the applicability of such a model to the analysis of medical processes.

两性健康互联网医院的建设探讨

通过百度搜索引擎、苹果和安卓应用商店,搜索2018年8月至2022年9月我国已建成的两性健康互联网医院,并以隶属四川省人民医院的两性健康互联网专科医院为例,详细分析其构架体系。两性健康互联网医院充分展示了“医-患-药-险-送”的医疗服务体系闭环,极大地保护了患者的隐私,并提供了全面的服务,兼具社会化、工具化和商业化三方面价值体现。中国互联网医疗行业的蓬勃发展是由国家政策推动的,专科互联网医院的快速崛起很大程度上源于国家对监管合理化和运营合规化的更明确要求。关于两性健康领域,特别是在新冠肺炎全球蔓延的情况下,在COVID-19已被证明与两性健康密切相关的背景下,只有进一步保证合理及规范的管理,两性健康互联网医院才能获得安全、高效的发展,患者的健康需求才能更有效的被满足。

基于移动医疗的新生儿护理线上咨询门诊的建立与实施

目的建立并实施新生儿护理线上咨询门诊,让群众得到“指尖上的医疗便利”,及时解决家长的育儿问题,识别疾病的风险。方法在医院微信公众号设立互联网医院之新生儿护理线上咨询门诊;选拔出诊护士、建立互联网专科护理服务制度、工作流程;监测互联网护理门诊工作量、接诊反应时长、服务时长和满意度。结果共选拔16名护士出诊,2020年2月—2023年8月新生儿护理线上咨询门诊共接诊9212例次,咨询条目145598次,足月儿8489人次(92.2%),早产儿723人次(7.8%),新生儿喂养咨询最多,为62799条(43.14%),新生儿黄疸咨询44778条(30.75%),皮肤问题咨询26297例次(18.06%),其他11724条(8.05%),判断需要转诊至线下护理或者医疗门诊者337人次(3.66%)。患者挂号到护士接诊的反应时长中位数为33(0,162)分钟。护士服务时长中位数为46(17,1440)min。进行了满意度评价的为6427人次(69.77%),其中服务质量、服务态度、系统使用3个维度均获得5星好评的为6362人次(98.99%)。结论开设新生儿护理线上咨询门诊可有效帮助家长足不出户解决育儿问题,满足新生儿延续性护理的需求,节约了家长就医时间,提升了家长就医体验感,也体现了新生儿专科护士的专业价值。

基于生成式预训练语言模型的在线问诊平台 青少年抑郁症患者需求分析

[目的]深化对青少年抑郁症群体需求的认知,为推动青少年抑郁症的有效预防和干预提供参考。[方法]选取“好大夫在线”平台的青少年抑郁症问诊记录作为数据来源,利用生成式预训练模型的文本生成和文本向量化功能,提取问诊文本中的关键词,利用关键词语义相似性和词频分析以总结患者的问诊需求维度。[结果]通过4258名患者的有效问诊文本记录,基于ERG需求理论识别出青少年抑郁症患者的7种需求维度:就诊需求、治疗需求、用药需求、家庭支持、社会支持、学业发展、心理成长。[结论]研究结果提示青少年抑郁症群体问诊需求强烈,且具有多样性,在线问诊需求主要聚焦在身体健康改善、疾病知识获取、症状管理、用药安全、家庭关系改善、社交支持获取、教育与学业支持及心理成长等方面。未来研究应扩大到更多平台和文化环境,持续更新对青少年抑郁症患者需求的识别,用以优化在线医疗服务。

Online learning method for predicting air environmental information used in agricultural robots

Air environmental information plays an important role during plant growth and reproduction, prompt and accurate prediction of atmospheric environmental data is helpful for agricultural robots to make a timely decision. For efficiency, an online learning method for predicting air environmental information was presented in this work. This method combines the advantages of convolutional neural network (CNN) and experience replay technique: CNN is used to extract features from raw data and predict atmospheric environmental information, experience replay technique can store environmental data over some time and update the hyperparameters of CNN. To validate the effects of this method, this online method was compared with three different predictive methods (including random forest, multi-layer perceptron, and support vector regression) using a public dataset (Jena). According to results, a suitable sample sequence size (e.g., 16) has a smaller number of training sessions and stable results, a larger replay memory size (e.g., 200) can provide enough samples to capture useful features, and 6 d of historical information is the best setting for training predictor. Compared with traditional methods, the method proposed in this study is the only method applied for various conditions.

融合ERNIE和双向注意力流的医生推荐算法

医生推荐是在线问诊平台的研究热点之一,为了向患者提供更加精准、个性化的医生推荐,提出一种融合ERNIE和双向注意力流的医生推荐算法。首先,利用ERNIE预训练模型对患者疾病描述进行词向量编码,通过弱监督的方式自定义标签生成医生擅长文本训练数据,再将医生职称信息与擅长文本进行拼接输入到ERNIE模型中;其次,采用双向注意力流(bi-directional attention flow)捕捉医生文本中的关键信息,学习患者疾病描述文本和医生文本的关系,获得融合患者信息的医生综合特征向量;最后,将医生综合特征和患者特征进行相似度计算产生推荐结果。利用来自“好大夫在线”的真实数据进行了不同算法实验对比,结果表明所提算法在推荐性能上有明显提升。

广州市大学生线上问诊意愿调查研究

目的了解大学生线上问诊意愿,为线上问诊可持续健康发展提供建议。方法采用问卷调查和非正式访谈法,通过问卷星发放问卷,回收有效问卷191份,采用SPSS 22.0进行数据分析。结果研究发现:70%的广州市大学生能够接受并且乐于进行线上问诊,其中治疗效果、个人隐私安全、医患沟通以及医生的专业水平是影响问诊意愿的重要因素。结论大学生线上问诊意愿受多种因素影响,应继续推广线上问诊,提高线上问诊可靠度、线上医生的可信度,推动线上线下问诊一体化发展。

组态视角下患者在线问诊选择的驱动因素

在线健康社区拥有大量医生及多种来源的医生在线评价信息,患者面临选择过载问题并不确定如何根据各种信息选择合适的医生。基于整体论的组态视角,采集好大夫在线平台中的二手数据,利用fsQCA方法,探究多种来源信息的协同对患者在线问诊选择的影响。研究结果表明:各种医生在线评价信息均无法单独构成高或非高患者在线问诊选择的必要条件;存在4种产生高患者在线问诊选择的驱动模式以及4种产生非高患者在线问诊选择的驱动模式,两者存在非对称关系。首次从整体论的组态视角对患者在线问诊选择的驱动因素进行分析,拓展了患者在线问诊选择的研究范畴,为医患在线匹配以及提高医生在线影响力提供了重要的实践价值。

在线问诊用户知识需求的演化

文章揭示在线问诊平台交互过程中的用户知识需求演化特征,为提高医患交互效率、深化健康信息学研究提供参考。通过爬取在线问诊平台C平台中消化内科板块医患交互对话记录,设计用户知识编码体系,分析用户表达的知识类型总体特征、知识类型转化概率和知识需求演化路径。结果发现:在线问诊平台医患交互过程中用户表达的知识类型多但趋于集中,主动型知识占比高于被动型知识,用户知识需求演化过程呈现三个主要特征。

医疗评论内容特征挖掘及其对患者在线问诊服务利用的影响

目的/意义阐述在线医疗评论的内容特征及其对患者在线问诊服务利用的影响,为在线医疗平台信息管理提供参考。方法/过程构建基于TF-IDF的LDA主题模型,采用基于情感词典的语义分析方法,挖掘在线医疗评论的内容特征,应用主成分分析法对其进行分类探索,并利用二元logistic进行回归分析。结果/结论将在线医疗评论内容特征分为3大类别,包括9个指标,其中评论者属性、时效性、问诊流程、服务态度和平台操作反馈对患者高效在线问诊服务利用存在显著影响,建议通过优化在线医疗平台评论板块相关服务提高患者对在线问诊服务的利用水平。

在线医疗咨询会话的情感模式与话语策略研究

在线医疗咨询是后疫情时代世界各国关注的热点话题。本文借鉴系统功能语言学的及物性和评价理论、修辞论辩理论和话语行为分析框架,爬取www.haodf.com互联网医疗网站上的医患会话自建小型语料库,考察语料中情感词汇的语义类型分布,比较医患双方消极情感的经验意义建构差异;描述、分析和阐释OMC医患情感互动过程,并分析“年度好大夫”回应患者消极情感的话语策略。本研究将有助于推动构建OMC话语治理体系,为提升远程医疗服务人员的人际沟通能力和医学人文素养提供实践指导。

在线健康社区中睡眠障碍疾病描述文本主题特征研究

目的/意义通过挖掘睡眠障碍疾病描述文本,深入了解睡眠障碍线上问诊的现状与睡眠障碍用户的在线问诊信息主题特征。方法/过程以“好大夫在线”平台为数据源,利用网络爬虫获取睡眠障碍相关医患信息,使用隐含狄利克雷分布模型识别患者疾病描述的主题。结果/结论睡眠障碍涉及科室较分散、治疗方式以药物为主,线上问诊能改善83.2%患者病情。用户疾病描述主题包括用药情况与咨询、外界环境、症状描述、代问与病因。建议平台与医生关注患者用药预后情况、心理健康状况,注重共病科普工作。

商业平台在线问诊服务使用意愿的影响因素研究

目前学界对在线问诊服务已展开多方面的研究,但在线问诊服务使用意愿的研究并不丰富,尤其是商业平台在线问诊服务使用意愿的研究。为探究商业平台在线问诊服务使用意愿的影响因素,采用半结构化访谈的方式对22位受访者进行深度访谈,并运用扎根理论对原始访谈资料进行分析。通过对访谈资料进行开放式编码、主轴式编码和选择性编码,得到了平台因素、个人特征、医生因素、患者感知四个主要影响维度,并基于SR和SOR理论构建了理论模型。最后从平台、医生和患者的角度就提高在线问诊服务使用意愿提出了相关建议。

智慧移动医疗App的设计与实现

现在一些大医院门诊拥挤、热门科室一号难求,因此有些人利用医院管理的漏洞,倒买倒卖“专家号”从中获利。通过实现一款移动医疗通在线问诊App以缓解以上情况。系统基于前后端分离架构进行设计开发,前端采用VUE框架实现,后台利用Spring Boot框架,结合JDBC、MyBatis等技术。整套系统分为三端:患者端、医生端、管理员端。用户端:用户通过手机访问App,可以查看医生信息,在线问诊,还可以查询自己的处方、订单等信息。医生端:选择患者在线交流,添加处方和修改处方等。管理员端:主要功能是对用户、医生、药品、科室、订单等进行管理。该系统大大降低了患者在线下医院就诊所需要的时间,简化了操作流程。

基于改进YOLOv3的街道行人检测与跟踪方法

针对室外街道的行人检测与跟踪,提出一种改进YOLOv3与简单在线实时跟踪(simple online and real-time tracking,SORT)算法相结合的检测及跟踪方法。首先,引入距离和比例交并比(distance and proportional-IOU,DPIOU)损失,将原有的损失函数中的均方误差(mean square error,MSE)部分进行变化,从而得到更精确的检测框;其次,将网络结构中的RestNet进行优化,改变下采样区域,增加池化层,进而减少特征信息的丢失;最后将检测结果输入SORT算法进行建模和匹配。实验结果表明,在室外街道的场景下,改进的算法与YOLOv3相比较,损失值收敛更快,平均准确率高出4.85%,跟踪准确率上升3.4%,同时,模型的速度有所提高,最快可达14.39 FPS。

网络支持项目对乳腺癌患者抑郁和疾病知识水平的影响

目的探讨网络支持项目对乳腺癌术后患者抑郁和疾病知识水平的影响,以提供减轻乳腺癌患者抑郁水平、提高相关知识的有效途径。方法将110例乳腺癌手术出院患者按照时间顺序分为对照组(59例)和干预组(51例)。对照组患者出院后给予乳腺癌术后常规随访,干预组在此基础上给予12周依托网络的干预,提供网络平台的信息资源,邀请专家为患者提供在线咨询,组织患者进行乳腺癌康复相关主题讨论。分别于基线调查时,干预6周、12周采用流调中心用抑郁量表评估患者的抑郁程度;于基线调查时、干预12周采用自行设计的问卷了解患者的疾病相关知识掌握情况。结果两组抑郁评分比较,差异有统计学意义(P<0.05),干预组抑郁评分下降幅度显著大于对照组(P<0.05)。干预12周后干预组疾病知识得分显著高于对照组(P<0.01)。结论运用网络提供信息和交流平台,可以降低乳腺癌术后患者的抑郁水平,提高其疾病知识水平。