The quantitative structure-activity relationship(QSAR) of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives was studied.Three different alignment methods were used to get the models of the comparative molecular field...The quantitative structure-activity relationship(QSAR) of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives was studied.Three different alignment methods were used to get the models of the comparative molecular field analysis(CoMFA),the comparative molecular similarity indices analysis(CoMSIA),and the hologram quantitative structure?activity relationship(HQSAR).The statistical results from the established models show believable predictivity based on the cross-validated value(q2>0.5) and the non-validated value(r2>0.9),The analysis on contour maps of CoMFA and CoMSIA models suggests that hydrophobic and hydrogen-bond acceptor fields are important factors that affect the AT1 antagonistic activity of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives besides the steric and electrostatic fields,The structural modification information from different atom contributions in the HQSAR model is in agreement with that in the 3D-QSAR models.展开更多
Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship am...Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.展开更多
The understanding of crack propagation characteristics and law of rocks during the loading process is of great significance for the exploitation and support of rock engineering.In this study,the crack propagation beha...The understanding of crack propagation characteristics and law of rocks during the loading process is of great significance for the exploitation and support of rock engineering.In this study,the crack propagation behavior of rocks in triaxial compression tests was investigated in detail.The main conclusions were as follows:1)According to the evolution characteristics of crack axial strain,the differential stress?strain curve of rocks under triaxial compressive condition can be divided into three phases which are linear elastic phase,crack propagation phase,post peak phase,respectively;2)The proposed models are applied to comparison with the test data of rocks under triaxial compressive condition and different temperatures.The theoretical data calculated by the models are in good agreement with the laboratory data,indicating that the proposed model can be applied to describing the crack propagation behavior and the nonlinear properties of rocks under triaxial compressive condition;3)The inelastic compliance and crack initiation strain in the proposed model have a decrease trend with the increase of confining pressure and temperature.Peak crack axial strain increases nonlinearly with the inelastic compliance and the increase rate increases gradually.Crack initiation strain has a linear relation with peak crack axial strain.展开更多
A kind of hybrid reliability model is presented to solve the fatigue reliability problems of steel bridges. The cumulative damage model is one kind of the models used in fatigue reliability analysis. The parameter cha...A kind of hybrid reliability model is presented to solve the fatigue reliability problems of steel bridges. The cumulative damage model is one kind of the models used in fatigue reliability analysis. The parameter characteristics of the model can be described as probabilistic and interval. The two-stage hybrid reliability model is given with a theoretical foundation and a solving algorithm to solve the hybrid reliability problems. The theoretical foundation is established by the consistency relationships of interval reliability model and probability reliability model with normally distributed variables in theory. The solving process is combined with the definition of interval reliability index and the probabilistic algorithm. With the consideration of the parameter characteristics of the S-N curve, the cumulative damage model with hybrid variables is given based on the standards from different countries. Lastly, a case of steel structure in the Neville Island Bridge is analyzed to verify the applicability of the hybrid reliability model in fatigue reliability analysis based on the AASHTO.展开更多
阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood...阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood AgGrEgation,GraphNAGE)的AD分类新方法.首先进行图数据建模,将AD数据样本表示为图数据.采用基于互信息(Mutual Information,MI)的特征选择方法,从样本的114维大脑皮层与皮层下感兴趣区域(Cerebral Cortex and Subcortical Regions Of Interest,CCS-ROI)的体积特征中选取重要性高的体积特征,并将其用于节点建模.提出基于相似性度量的关系建模方法,利用重要性高的体积特征、遗传基因、人口统计信息和认知评分对样本之间的关系进行建模.进而构建GraphNAGE,针对每个节点,基于与该节点相关的边的权重进行最近邻域采样,然后使用均值聚合方法对采样得到的邻居节点和中心节点的数据进行聚合,最后通过一个全连接层和一个Softmax层实现AD分类.在TADPOLE(The Alzheimer’s Disease Prediction Of Longitudinal Evolution)数据集上进行实验,结果表明:本文提出的AD分类方法的准确率(ACCuracy,ACC)为98.20%,F_(1)分数为97.34%,曲线下面积(Area Under Curve,AUC)为97.80%.实验结果表明:本文提出的AD分类方法充分利用了AD数据样本之间的相关性,其性能优于传统的基于机器学习、深度学习和图神经网络(Graph Neural Network,GNN)的AD分类方法.展开更多
基金Project(20876180) supported by the National Natural Science Foundation of China
文摘The quantitative structure-activity relationship(QSAR) of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives was studied.Three different alignment methods were used to get the models of the comparative molecular field analysis(CoMFA),the comparative molecular similarity indices analysis(CoMSIA),and the hologram quantitative structure?activity relationship(HQSAR).The statistical results from the established models show believable predictivity based on the cross-validated value(q2>0.5) and the non-validated value(r2>0.9),The analysis on contour maps of CoMFA and CoMSIA models suggests that hydrophobic and hydrogen-bond acceptor fields are important factors that affect the AT1 antagonistic activity of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives besides the steric and electrostatic fields,The structural modification information from different atom contributions in the HQSAR model is in agreement with that in the 3D-QSAR models.
基金the National Natural Science Foundation of China(71871121).
文摘Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.
基金Project(51622404)supported by Outstanding Youth Science Foundation of the National Natural Science Foundation of ChinaProjects(51374215,11572343,51904092)supported by the National Natural Science Foundation of China+2 种基金Project(2016YFC0801404)supported by the State Key Research Development Program of ChinaProject(KCF201803)supported by Henan Key Laboratory for Green and Efficient Mining&Comprehensive Utilization of Mineral Resources,Henan Polytechnic University,ChinaProject supported by Beijing Excellent Young Scientists,China
文摘The understanding of crack propagation characteristics and law of rocks during the loading process is of great significance for the exploitation and support of rock engineering.In this study,the crack propagation behavior of rocks in triaxial compression tests was investigated in detail.The main conclusions were as follows:1)According to the evolution characteristics of crack axial strain,the differential stress?strain curve of rocks under triaxial compressive condition can be divided into three phases which are linear elastic phase,crack propagation phase,post peak phase,respectively;2)The proposed models are applied to comparison with the test data of rocks under triaxial compressive condition and different temperatures.The theoretical data calculated by the models are in good agreement with the laboratory data,indicating that the proposed model can be applied to describing the crack propagation behavior and the nonlinear properties of rocks under triaxial compressive condition;3)The inelastic compliance and crack initiation strain in the proposed model have a decrease trend with the increase of confining pressure and temperature.Peak crack axial strain increases nonlinearly with the inelastic compliance and the increase rate increases gradually.Crack initiation strain has a linear relation with peak crack axial strain.
基金Projects(51178042,51578047)supported by the National Natural Science Foundation of ChinaProject(C14JB00340)supported by the Innovative Research Fund in Beijing Jiaotong University,ChinaProject(2014-ZJKJ-03)supported by Science and Technology Research and Development Fund of the China Communications Construction Co.,LTD
文摘A kind of hybrid reliability model is presented to solve the fatigue reliability problems of steel bridges. The cumulative damage model is one kind of the models used in fatigue reliability analysis. The parameter characteristics of the model can be described as probabilistic and interval. The two-stage hybrid reliability model is given with a theoretical foundation and a solving algorithm to solve the hybrid reliability problems. The theoretical foundation is established by the consistency relationships of interval reliability model and probability reliability model with normally distributed variables in theory. The solving process is combined with the definition of interval reliability index and the probabilistic algorithm. With the consideration of the parameter characteristics of the S-N curve, the cumulative damage model with hybrid variables is given based on the standards from different countries. Lastly, a case of steel structure in the Neville Island Bridge is analyzed to verify the applicability of the hybrid reliability model in fatigue reliability analysis based on the AASHTO.
文摘阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood AgGrEgation,GraphNAGE)的AD分类新方法.首先进行图数据建模,将AD数据样本表示为图数据.采用基于互信息(Mutual Information,MI)的特征选择方法,从样本的114维大脑皮层与皮层下感兴趣区域(Cerebral Cortex and Subcortical Regions Of Interest,CCS-ROI)的体积特征中选取重要性高的体积特征,并将其用于节点建模.提出基于相似性度量的关系建模方法,利用重要性高的体积特征、遗传基因、人口统计信息和认知评分对样本之间的关系进行建模.进而构建GraphNAGE,针对每个节点,基于与该节点相关的边的权重进行最近邻域采样,然后使用均值聚合方法对采样得到的邻居节点和中心节点的数据进行聚合,最后通过一个全连接层和一个Softmax层实现AD分类.在TADPOLE(The Alzheimer’s Disease Prediction Of Longitudinal Evolution)数据集上进行实验,结果表明:本文提出的AD分类方法的准确率(ACCuracy,ACC)为98.20%,F_(1)分数为97.34%,曲线下面积(Area Under Curve,AUC)为97.80%.实验结果表明:本文提出的AD分类方法充分利用了AD数据样本之间的相关性,其性能优于传统的基于机器学习、深度学习和图神经网络(Graph Neural Network,GNN)的AD分类方法.
