Event region detection is the important application for wireless sensor networks（WSNs）, where the existing faulty sensors would lead to drastic deterioration of network quality of service.Considering single-moment n...Event region detection is the important application for wireless sensor networks（WSNs）, where the existing faulty sensors would lead to drastic deterioration of network quality of service.Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance（DFWD） is proposed, which exploits the spatial correlation among sensor nodes and their redundant information.In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node.Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node.Simultaneously, readings of faulty sensors would be corrected during this process.Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.展开更多
目的探讨C反应蛋白/白蛋白比值(C-reactive protein to albumin ratio,CAR)与维持性血液透析(maintenance hemodialysis,MHD)患者出现心血管事件风险的相关性。方法选择2016年8月至2019年12月在广州医科大学附属第二医院血液净化中心进...目的探讨C反应蛋白/白蛋白比值(C-reactive protein to albumin ratio,CAR)与维持性血液透析(maintenance hemodialysis,MHD)患者出现心血管事件风险的相关性。方法选择2016年8月至2019年12月在广州医科大学附属第二医院血液净化中心进行治疗的MHD患者为研究对象,随访截止时间为2021年3月31日。收集符合研究纳入标准的患者的人口学资料、合并症、原发病因、规律透析治疗3个月后的生化指标及随访截止时间内心血管事件发生情况。采用Kaplan-Meier法估计MHD患者出现心血管事件的概率。基于广义倾向性得分加权(GPSW)的Cox比例风险回归模型评估CAR水平与MHD患者出现心血管事件风险的关联。结果共纳入符合标准的研究对象170例,其中64例患者出现心血管事件(占37.6%)。基于GPSW的Cox比例风险回归模型提示(HR_(CAR)=2.087,95%CI:1.085~4.015,P=0.028),说明MHD患者的CAR平均每增加一个单位,出现心血管事件的风险比为2.087。结论CAR与MHD患者的心血管事件风险存在显著的正相关关系,这将有助于临床工作者识别具有高心血管事件风险的MHD患者并及时干预。展开更多
Weighted priority queueing is a modification of priority queueing that eliminates the possibility of blocking lower priority traffic. The weights assigned to priority classes determine the fractions of the bandwith th...Weighted priority queueing is a modification of priority queueing that eliminates the possibility of blocking lower priority traffic. The weights assigned to priority classes determine the fractions of the bandwith that are guaranteed for individual traffic classes, similarly as in weighted fair queueing. The paper describes a timed Petri net model of weighted priority queueing and uses discrete-event simulation of this model to obtain performance characteristics of simple queueing systems. The model is also used to analyze the effects of finite queue capacity on the performance of queueing systems.展开更多
为提高水产动物疾病防治事件抽取的准确性,有效解决抽取过程中出现的专有名词边界模糊和事件实体过长等问题,本研究将动态权重思想引入多模型集成的事件抽取方法中。改进后的方法利用百度自然语言理解开放平台(enhanced representation ...为提高水产动物疾病防治事件抽取的准确性,有效解决抽取过程中出现的专有名词边界模糊和事件实体过长等问题,本研究将动态权重思想引入多模型集成的事件抽取方法中。改进后的方法利用百度自然语言理解开放平台(enhanced representation through knowledge integration,ERNIE)和澎湃BERT(MLM as correction BERT,MacBERT)2个预训练模型来学习文本语义信息;采用动态权重的gate模块融合特征;将学习到的语义信息传入双向长短时记忆网络(bi-directional long shortterm memory,BiLSTM)中,并通过条件随机场(conditional random field,CRF)对输出标签序列进行约束。选取ERNIE⊕MacBERT-CRF模型和ERNIE⊕MacBERT-BiLSTM-CRF模型(⊕代表简单相加求平均的融合方法)作为对照模型对提出的方法进行融合性能对比试验验证,结果显示,该方法 F1值达74.15%,比经典模型BiLSTM-CRF提高了20.02个百分点。结果表明,该方法用于水产动物疾病防治事件抽取具有更好的效果。展开更多
基金supported by the National Science Foundation for Outstanding Young Scientists (60425310)the Science Foundation for Post-doctoral Scientists of Central South University (2008)
文摘Event region detection is the important application for wireless sensor networks（WSNs）, where the existing faulty sensors would lead to drastic deterioration of network quality of service.Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance（DFWD） is proposed, which exploits the spatial correlation among sensor nodes and their redundant information.In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node.Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node.Simultaneously, readings of faulty sensors would be corrected during this process.Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.
文摘目的探讨C反应蛋白/白蛋白比值(C-reactive protein to albumin ratio,CAR)与维持性血液透析(maintenance hemodialysis,MHD)患者出现心血管事件风险的相关性。方法选择2016年8月至2019年12月在广州医科大学附属第二医院血液净化中心进行治疗的MHD患者为研究对象,随访截止时间为2021年3月31日。收集符合研究纳入标准的患者的人口学资料、合并症、原发病因、规律透析治疗3个月后的生化指标及随访截止时间内心血管事件发生情况。采用Kaplan-Meier法估计MHD患者出现心血管事件的概率。基于广义倾向性得分加权(GPSW)的Cox比例风险回归模型评估CAR水平与MHD患者出现心血管事件风险的关联。结果共纳入符合标准的研究对象170例,其中64例患者出现心血管事件(占37.6%)。基于GPSW的Cox比例风险回归模型提示(HR_(CAR)=2.087,95%CI:1.085~4.015,P=0.028),说明MHD患者的CAR平均每增加一个单位,出现心血管事件的风险比为2.087。结论CAR与MHD患者的心血管事件风险存在显著的正相关关系,这将有助于临床工作者识别具有高心血管事件风险的MHD患者并及时干预。
文摘Weighted priority queueing is a modification of priority queueing that eliminates the possibility of blocking lower priority traffic. The weights assigned to priority classes determine the fractions of the bandwith that are guaranteed for individual traffic classes, similarly as in weighted fair queueing. The paper describes a timed Petri net model of weighted priority queueing and uses discrete-event simulation of this model to obtain performance characteristics of simple queueing systems. The model is also used to analyze the effects of finite queue capacity on the performance of queueing systems.
文摘为提高水产动物疾病防治事件抽取的准确性,有效解决抽取过程中出现的专有名词边界模糊和事件实体过长等问题,本研究将动态权重思想引入多模型集成的事件抽取方法中。改进后的方法利用百度自然语言理解开放平台(enhanced representation through knowledge integration,ERNIE)和澎湃BERT(MLM as correction BERT,MacBERT)2个预训练模型来学习文本语义信息;采用动态权重的gate模块融合特征;将学习到的语义信息传入双向长短时记忆网络(bi-directional long shortterm memory,BiLSTM)中,并通过条件随机场(conditional random field,CRF)对输出标签序列进行约束。选取ERNIE⊕MacBERT-CRF模型和ERNIE⊕MacBERT-BiLSTM-CRF模型(⊕代表简单相加求平均的融合方法)作为对照模型对提出的方法进行融合性能对比试验验证,结果显示,该方法 F1值达74.15%,比经典模型BiLSTM-CRF提高了20.02个百分点。结果表明,该方法用于水产动物疾病防治事件抽取具有更好的效果。
