The microstructural evolution of a thermoplastic polyurethane(TPU)with low hard segment content has been monitored utilizing in situ real-time synchrotron small angle X-ray scattering(SAXS)and time-domain nuclear magn...The microstructural evolution of a thermoplastic polyurethane(TPU)with low hard segment content has been monitored utilizing in situ real-time synchrotron small angle X-ray scattering(SAXS)and time-domain nuclear magnetic resonance(NMR)measurements.The TPU is composed of 23 wt% of[4,4-methylenediphenyl diisocyanate(MDI)]-[1,4-butanediol(BD)]chain segments,which form hard domains,as[polytetrahydrofuran(PTHF)]forming soft domains.The number and distribution of monomer units in hard blocks is determined by the successive self-nucleation and annealing thermal fractionation technique.In situ SAXS method reveals heating-induced increase in the spacing of hard and soft domains,while time-domain ^(1)H-NMR characterizes the changes in the phase composition and chain dynamics in these domains.A glassy fraction of short MDI-BD chain segments in hard domains passes through T_(g) above ambient temperature.At higher temperatures,MDI-BD nanocrystals start to melt.Sequence length distribution of MDI-BD chain segments causes a distribution in crystal sizes and wide melting temperature range.The melting is accompanied by the mixing of MDI-BD with PTHF segments in soft domains,and by increase in segmental mobility in these domains.Above 180℃,the TPU melt is homogeneous on the scale above nanometers according to SAXS data.展开更多
Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in futu...Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model has very goodpredictive performance when predicting missing edges of isolated nodes.展开更多
基金financially supported by the National Natural Science Foundation of China (No. 22161132007)BASF within the framework of NAO (Network for Advanced Materials Open Research)。
文摘The microstructural evolution of a thermoplastic polyurethane(TPU)with low hard segment content has been monitored utilizing in situ real-time synchrotron small angle X-ray scattering(SAXS)and time-domain nuclear magnetic resonance(NMR)measurements.The TPU is composed of 23 wt% of[4,4-methylenediphenyl diisocyanate(MDI)]-[1,4-butanediol(BD)]chain segments,which form hard domains,as[polytetrahydrofuran(PTHF)]forming soft domains.The number and distribution of monomer units in hard blocks is determined by the successive self-nucleation and annealing thermal fractionation technique.In situ SAXS method reveals heating-induced increase in the spacing of hard and soft domains,while time-domain ^(1)H-NMR characterizes the changes in the phase composition and chain dynamics in these domains.A glassy fraction of short MDI-BD chain segments in hard domains passes through T_(g) above ambient temperature.At higher temperatures,MDI-BD nanocrystals start to melt.Sequence length distribution of MDI-BD chain segments causes a distribution in crystal sizes and wide melting temperature range.The melting is accompanied by the mixing of MDI-BD with PTHF segments in soft domains,and by increase in segmental mobility in these domains.Above 180℃,the TPU melt is homogeneous on the scale above nanometers according to SAXS data.
基金supported by the Teaching Reform Research Project of Qinghai Minzu University,China(2021-JYYB-009)the“Chunhui Plan”Cooperative Scientific Research Project of the Ministry of Education of China(2018).
文摘Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model has very goodpredictive performance when predicting missing edges of isolated nodes.
