针对作物病虫害领域存在实体关系交叉关联、多源异构数据聚合能力差、知识共享困难等问题,利用知识图谱以结构化的形式描述实体间复杂关系的优势,该研究提出了一种基于深度学习的作物病虫害知识图谱构建方法。该方法在领域本体的基础上...针对作物病虫害领域存在实体关系交叉关联、多源异构数据聚合能力差、知识共享困难等问题,利用知识图谱以结构化的形式描述实体间复杂关系的优势,该研究提出了一种基于深度学习的作物病虫害知识图谱构建方法。该方法在领域本体的基础上,以一种与领域语料相适应的新标注模式实现实体和关系的联合抽取。将实体和关系抽取任务转化为序列标注问题,对实体和关系进行同步标注,有效提高标注效率;为了解决重叠关系抽取问题,直接对三元组建模而不是分别对实体和关系建模,通过标签匹配和映射即可获得三元组数据。利用来自转换器的双向编码器表征量(Bidirectional Encoder Representations from Transformers,BERT)-双向长短期记忆网络(Bi-directional Long-Short Term Memory,BiLSTM)+条件随机场(Conditional Random Field,CRF)端到端模型进行试验,结果表明效果优于基于普通标注方式的流水线方法和联合学习方法中的卷积神经网络(ConvolutionalNeuralNetworks,CNN)+BiLSTM+CRF、BiLSTM+CRF等经典模型,F1得分为91.34%。最后,将抽取到的知识存储到Neo4j图数据库中,直观地反映知识图谱的内部结构,实现知识可视化和知识推理。该研究构建的知识图谱可为作物病虫害智能问答系统、推荐系统、智能搜索等下游应用提供高质量的知识库基础。展开更多
A comprehensive understanding of the dynamic frictional characteristics in rock joints under high normal load and strong confinement is essential for ensuring the safety of deep engineering construction and mitigating...A comprehensive understanding of the dynamic frictional characteristics in rock joints under high normal load and strong confinement is essential for ensuring the safety of deep engineering construction and mitigating geological disasters.This study conducted shear experiments on rough rock joints under displacement-controlled dynamic normal loads,investigating the shear behaviors of joints across varying initial normal loads,normal loading frequencies,and normal loading amplitudes.Experimental results showed that the peak/valley shear force values increased with initial normal loads and normal loading frequencies but showed an initial increase followed by a decrease with normal loading amplitudes.Dynamic normal loading can either increase or decrease shear strength,while this study demonstrates that higher frequencies lead to enhanced friction.Increased initial normal loading and normal loading frequency result in a gradual decrease in joint roughness coefficient(JRC)values of joint surfaces after shearing.Positive correlations existed between frictional energy dissipation and peak shear forces,while post-shear joint surface roughness exhibited a negative correlation with peak shear forces through linear regression analysis.This study contributes to a better understanding of the sliding responses and shear mechanical characteristics of rock joints under dynamic disturbances.展开更多
目的分析ADOPT[态度(attitude,A)、定义(definition,D)、开放思维(open mind,O)、计划(planning,P)、实施(try it out,T)]模式在妊娠期糖尿病产妇中的应用效果。方法以南通大学附属妇幼保健院于2022年1月至2023年2月收治的120例妊娠期...目的分析ADOPT[态度(attitude,A)、定义(definition,D)、开放思维(open mind,O)、计划(planning,P)、实施(try it out,T)]模式在妊娠期糖尿病产妇中的应用效果。方法以南通大学附属妇幼保健院于2022年1月至2023年2月收治的120例妊娠期糖尿病产妇为研究对象,根据入院顺序将其分为对照组与观察组,每组60例,其中对照组产妇年龄24~35岁[(28.35±5.06)岁],观察组产妇年龄23~36岁[(28.29±5.33)岁],对照组实行常规护理,观察组实行ADOPT模式。比较2组产妇干预前与干预7 d时的心理弹性量表评分、糖尿病自我效能量表评分以及血糖水平,并对比2组产妇不良妊娠结局发生率。结果干预前,2组产妇的心理弹性量表评分、糖尿病自我效能量表评分及血糖水平差异无统计学意义(P>0.05),干预7 d时,观察组产妇心理弹性量表评分、糖尿病自我效能量表评分均高于对照组,且观察组产妇的空腹血糖、餐后2 h血糖水平均低于对照组(P<0.05)。干预后,观察组产妇(6.67%)、新生儿(5.00%)的不良妊娠结局发生率均低于对照组产妇(20.00%)、新生儿(16.67%),差异有统计学意义(P<0.05)。结论ADOPT模式能够改善妊娠期糖尿病产妇的心理弹性,强化自我效能,有效控制其血糖水平,同时降低不良妊娠结局发生率。展开更多
文摘针对作物病虫害领域存在实体关系交叉关联、多源异构数据聚合能力差、知识共享困难等问题,利用知识图谱以结构化的形式描述实体间复杂关系的优势,该研究提出了一种基于深度学习的作物病虫害知识图谱构建方法。该方法在领域本体的基础上,以一种与领域语料相适应的新标注模式实现实体和关系的联合抽取。将实体和关系抽取任务转化为序列标注问题,对实体和关系进行同步标注,有效提高标注效率;为了解决重叠关系抽取问题,直接对三元组建模而不是分别对实体和关系建模,通过标签匹配和映射即可获得三元组数据。利用来自转换器的双向编码器表征量(Bidirectional Encoder Representations from Transformers,BERT)-双向长短期记忆网络(Bi-directional Long-Short Term Memory,BiLSTM)+条件随机场(Conditional Random Field,CRF)端到端模型进行试验,结果表明效果优于基于普通标注方式的流水线方法和联合学习方法中的卷积神经网络(ConvolutionalNeuralNetworks,CNN)+BiLSTM+CRF、BiLSTM+CRF等经典模型,F1得分为91.34%。最后,将抽取到的知识存储到Neo4j图数据库中,直观地反映知识图谱的内部结构,实现知识可视化和知识推理。该研究构建的知识图谱可为作物病虫害智能问答系统、推荐系统、智能搜索等下游应用提供高质量的知识库基础。
基金Projects(52174092,51904290)supported by the National Natural Science Foundation,ChinaProject(BK20220157)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Project(232102321009)supported by Henan Province Science and Technology Key Project,ChinaProject(2022YCPY0202)supported by Fundamental Research Funds for the Central Universities,China。
文摘A comprehensive understanding of the dynamic frictional characteristics in rock joints under high normal load and strong confinement is essential for ensuring the safety of deep engineering construction and mitigating geological disasters.This study conducted shear experiments on rough rock joints under displacement-controlled dynamic normal loads,investigating the shear behaviors of joints across varying initial normal loads,normal loading frequencies,and normal loading amplitudes.Experimental results showed that the peak/valley shear force values increased with initial normal loads and normal loading frequencies but showed an initial increase followed by a decrease with normal loading amplitudes.Dynamic normal loading can either increase or decrease shear strength,while this study demonstrates that higher frequencies lead to enhanced friction.Increased initial normal loading and normal loading frequency result in a gradual decrease in joint roughness coefficient(JRC)values of joint surfaces after shearing.Positive correlations existed between frictional energy dissipation and peak shear forces,while post-shear joint surface roughness exhibited a negative correlation with peak shear forces through linear regression analysis.This study contributes to a better understanding of the sliding responses and shear mechanical characteristics of rock joints under dynamic disturbances.
