We fabricate and characterize Au nanoparticle-aggregated nanowires by using the nano meniscus-induced colloidal stacking method. The Au nanoparticle solution ejects with guidance of nanopipette/quartz tuning fork-base...We fabricate and characterize Au nanoparticle-aggregated nanowires by using the nano meniscus-induced colloidal stacking method. The Au nanoparticle solution ejects with guidance of nanopipette/quartz tuning fork-based atomic force microscope in ambient conditions, and the stacking particles form Au nanoparticle-aggregated nanowire while the nozzle retracts from the surface. Their mechanical properties with relatively low elastic modulus are in situ investigated by using the same apparatus.展开更多
The quality of the low frequency electromagnetic data is affected by the spike and the trend noises.Failure in removal of the spikes and the trends reduces the credibility of data explanation.Based on the analyses of ...The quality of the low frequency electromagnetic data is affected by the spike and the trend noises.Failure in removal of the spikes and the trends reduces the credibility of data explanation.Based on the analyses of the causes and characteristics of these noises,this paper presents the results of a preset statistics stacking method(PSSM)and a piecewise linear fitting method(PLFM)in de-noising the spikes and trends,respectively.The magnitudes of the spikes are either higher or lower than the normal values,which leads to distortion of the useful signal.Comparisons have been performed in removing of the spikes among the average,the statistics and the PSSM methods,and the results indicate that only the PSSM can remove the spikes successfully.On the other hand,the spectrums of the linear and nonlinear trends mainly lie in the low frequency band and can change the calculated resistivity significantly.No influence of the trends is observed when the frequency is higher than a certain threshold value.The PLSM can remove effectively both the linear and nonlinear trends with errors around 1% in the power spectrum.The proposed methods present an effective way for de-noising the spike and the trend noises in the low frequency electromagnetic data,and establish a research basis for de-noising the low frequency noises.展开更多
Surface modification may have important influences on the penetration behavior of nanoscale drug delivery system. In the present study, we mainly focused on whether cell targeting or cell penetration could affect pene...Surface modification may have important influences on the penetration behavior of nanoscale drug delivery system. In the present study, we mainly focused on whether cell targeting or cell penetration could affect penetration abilities of nanostructured lipid carriers（NLC）. Real--time penetration of folate--or cell penetrating peptide（CPP）-modified NLC was evaluated using a multicellular tumor spheroid（MTS） established by stacking culture method as an in vitro testing platform. The results suggested that CPP modification had a better penetration behavior both on penetration depth and intensity compared with folate-modified NLC at the early stage of penetration process.展开更多
Dielectric elastomers(DEs)have emerged as one of the most promising artificial muscle technologies,due to their exceptional properties such as large actuation strain,fast response,high energy density,and flexible proc...Dielectric elastomers(DEs)have emerged as one of the most promising artificial muscle technologies,due to their exceptional properties such as large actuation strain,fast response,high energy density,and flexible processibility for various configurations.Over the past two decades,researchers have been working on developing DE materials with improved properties and exploring innovative applications of dielectric elastomer actuators(DEAs).This review article focuses on two main topics:recent material innovation of DEs and development of multilayer stacking processes for DEAs,which are important to promoting commercialization of DEs.It begins by explaining the working principle of a DEA.Then,recently developed strategies for preparing new DE materials are introduced,including reducing mechanical stiffness,increasing dielectric permittivity,suppressing viscoelasticity loss,and mitigating electromechanical instability without pre-stretching.In the next section,different multilayer stacking methods for fabricating multilayer DEAs are discussed,including conventional dry stacking,wet stacking,a novel dry stacking method,and micro-fabrication-enabled stacking techniques.This review provides a comprehensive and up-to-date overview of recent developments in high-performance DE materials and multilayer stacking methods.It highlights the progress made in the field and also discusses potential future directions for further advancements.展开更多
In this study, we collected 1156 broadband vertical components records at 22 digital seismic stations in Xinjiang region, Urumqi station, and 7 stations in the adjacent regions during the period of 1999-2003. The reco...In this study, we collected 1156 broadband vertical components records at 22 digital seismic stations in Xinjiang region, Urumqi station, and 7 stations in the adjacent regions during the period of 1999-2003. The records were firstly processed by the stacked spectral ratio method to obtain Q0 (Q at 1 Hz) and the frequency correlation factor η corresponding to each path. Based on the results, the distribution images of Q0 and η in 1°×1° grids for Xinjiang region were gained by the back-projection technique. The results indicate that Q0 is high (300-450) in the Tarim platform and marginal Siberian platform, while Q0 is low (150-250) in the southern regions as west Kunlun fold system and Songpan-Ganzi fold system. In the northern regions as Junggar fold system and Tianshan fold system, Q0 is also low (250-300) and η varies between 0.5 and 0.9.展开更多
Different types of landslides exhibit distinct relationships with environmental conditioning factors.Therefore,in regions where multiple types of landslides coexist,it is required to separate landslide types for lands...Different types of landslides exhibit distinct relationships with environmental conditioning factors.Therefore,in regions where multiple types of landslides coexist,it is required to separate landslide types for landslide susceptibility mapping(LSM).In this paper,a landslideprone area located in Chongqing Province within the middle and upper reaches of the Three Gorges Reservoir area(TGRA),China,was selected as the study area.733 landslides were classified into three types:reservoir-affected landslides,non-reservoir-affected landslides,and rockfalls.Four landslide inventory datasets and 15 landslide conditional factors were trained by three Machine Learning models(logistic regression,random forest,support vector machine),and a Deep Learning(DL)model.After comparing the models using receiver operating characteristics(ROC),the landslide susceptibility indexes of three types landslides were acquired by the best performing model.These indexes were then used as input to generate the final map based on the Stacking method.The results revealed that DL model showed the best performance in LSM without considering landslide types,achieving an area under the curve(AUC)of 0.854 for testing and 0.922 for training.Moreover,when we separated the landslide types for LSM,the AUC improved by 0.026 for testing and 0.044 for training.Thus,this paper demonstrates that considering different landslide types in LSM can significantly improve the quality of landslide susceptibility maps.These maps in turn,can be valuable tools for evaluating and mitigating landslide hazards.展开更多
This research presents a novel approach to pipeline Structure Health Monitoring(SHM)by utilizing frequency response function signals and integrating advanced data-driven techniques to detect and evaluate vibration res...This research presents a novel approach to pipeline Structure Health Monitoring(SHM)by utilizing frequency response function signals and integrating advanced data-driven techniques to detect and evaluate vibration responses regarding loose bolts,scale deposits within pipelines,and cracks at pipeline supports,aiming to determine the effectiveness of utilizing artificial neural networks(ANN)and an ensemble learning approach in detecting the aforementioned damages through a data-driven approach.The research starts by recording 6500 samples captured by two accelerometers,related to 11 replicated pipeline structural scenarios.The research demonstrated the potential of principal component analysis(PCA)in dimensionality reduction,achieving approximately 81%reduction in data set 1 acquired by accelerometer 1 and around 79.5%in data set 2 acquired by accelerometer 2,without significant loss of information.Additionally,two ANN base models were employed for fault recognition and classification,achieving over 99.88%accuracy and mean squared error values ranging from 0.00006 to 0.00019.A significant innovation of this work lies in the implementation of an ensemble learning approach,which integrates the strengths of the base models,showcasing outstanding performance that was proved consistent across multiple iterations,effectively mitigating the weaknesses of the base models and providing a reliable fault classification and prediction system.This research underscores the effectiveness of combining PCA,ANN,k-fold cross-validation,and ensemble learning techniques in pipeline SHM for improved reliability and safety.The findings highlight the potential for broader applications of this methodology in real-world scenarios,addressing urgent challenges faced by infrastructure owners and operators.展开更多
基金supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (No. 200983512)Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2013R1A6A3A03063900)the Brain Korea 21
文摘We fabricate and characterize Au nanoparticle-aggregated nanowires by using the nano meniscus-induced colloidal stacking method. The Au nanoparticle solution ejects with guidance of nanopipette/quartz tuning fork-based atomic force microscope in ambient conditions, and the stacking particles form Au nanoparticle-aggregated nanowire while the nozzle retracts from the surface. Their mechanical properties with relatively low elastic modulus are in situ investigated by using the same apparatus.
文摘The quality of the low frequency electromagnetic data is affected by the spike and the trend noises.Failure in removal of the spikes and the trends reduces the credibility of data explanation.Based on the analyses of the causes and characteristics of these noises,this paper presents the results of a preset statistics stacking method(PSSM)and a piecewise linear fitting method(PLFM)in de-noising the spikes and trends,respectively.The magnitudes of the spikes are either higher or lower than the normal values,which leads to distortion of the useful signal.Comparisons have been performed in removing of the spikes among the average,the statistics and the PSSM methods,and the results indicate that only the PSSM can remove the spikes successfully.On the other hand,the spectrums of the linear and nonlinear trends mainly lie in the low frequency band and can change the calculated resistivity significantly.No influence of the trends is observed when the frequency is higher than a certain threshold value.The PLSM can remove effectively both the linear and nonlinear trends with errors around 1% in the power spectrum.The proposed methods present an effective way for de-noising the spike and the trend noises in the low frequency electromagnetic data,and establish a research basis for de-noising the low frequency noises.
基金National key Basic Research Program(Grant No.2013CB932501)National Natural Science Foundation of China(Grant No.81273454 and 81473156)+1 种基金Beijing National Science Foundation(Grant No.7132113)Doctoral Foundation of the Ministry of Education(Grant No.20130001110055)
文摘Surface modification may have important influences on the penetration behavior of nanoscale drug delivery system. In the present study, we mainly focused on whether cell targeting or cell penetration could affect penetration abilities of nanostructured lipid carriers（NLC）. Real--time penetration of folate--or cell penetrating peptide（CPP）-modified NLC was evaluated using a multicellular tumor spheroid（MTS） established by stacking culture method as an in vitro testing platform. The results suggested that CPP modification had a better penetration behavior both on penetration depth and intensity compared with folate-modified NLC at the early stage of penetration process.
基金This work is supported by the National Natural Science Foundation of China(No.T229722).
文摘Dielectric elastomers(DEs)have emerged as one of the most promising artificial muscle technologies,due to their exceptional properties such as large actuation strain,fast response,high energy density,and flexible processibility for various configurations.Over the past two decades,researchers have been working on developing DE materials with improved properties and exploring innovative applications of dielectric elastomer actuators(DEAs).This review article focuses on two main topics:recent material innovation of DEs and development of multilayer stacking processes for DEAs,which are important to promoting commercialization of DEs.It begins by explaining the working principle of a DEA.Then,recently developed strategies for preparing new DE materials are introduced,including reducing mechanical stiffness,increasing dielectric permittivity,suppressing viscoelasticity loss,and mitigating electromechanical instability without pre-stretching.In the next section,different multilayer stacking methods for fabricating multilayer DEAs are discussed,including conventional dry stacking,wet stacking,a novel dry stacking method,and micro-fabrication-enabled stacking techniques.This review provides a comprehensive and up-to-date overview of recent developments in high-performance DE materials and multilayer stacking methods.It highlights the progress made in the field and also discusses potential future directions for further advancements.
基金National Natural Science Foundation of China (49974012) and Joint Seismological Science Foundation of China (604004).
文摘In this study, we collected 1156 broadband vertical components records at 22 digital seismic stations in Xinjiang region, Urumqi station, and 7 stations in the adjacent regions during the period of 1999-2003. The records were firstly processed by the stacked spectral ratio method to obtain Q0 (Q at 1 Hz) and the frequency correlation factor η corresponding to each path. Based on the results, the distribution images of Q0 and η in 1°×1° grids for Xinjiang region were gained by the back-projection technique. The results indicate that Q0 is high (300-450) in the Tarim platform and marginal Siberian platform, while Q0 is low (150-250) in the southern regions as west Kunlun fold system and Songpan-Ganzi fold system. In the northern regions as Junggar fold system and Tianshan fold system, Q0 is also low (250-300) and η varies between 0.5 and 0.9.
文摘Different types of landslides exhibit distinct relationships with environmental conditioning factors.Therefore,in regions where multiple types of landslides coexist,it is required to separate landslide types for landslide susceptibility mapping(LSM).In this paper,a landslideprone area located in Chongqing Province within the middle and upper reaches of the Three Gorges Reservoir area(TGRA),China,was selected as the study area.733 landslides were classified into three types:reservoir-affected landslides,non-reservoir-affected landslides,and rockfalls.Four landslide inventory datasets and 15 landslide conditional factors were trained by three Machine Learning models(logistic regression,random forest,support vector machine),and a Deep Learning(DL)model.After comparing the models using receiver operating characteristics(ROC),the landslide susceptibility indexes of three types landslides were acquired by the best performing model.These indexes were then used as input to generate the final map based on the Stacking method.The results revealed that DL model showed the best performance in LSM without considering landslide types,achieving an area under the curve(AUC)of 0.854 for testing and 0.922 for training.Moreover,when we separated the landslide types for LSM,the AUC improved by 0.026 for testing and 0.044 for training.Thus,this paper demonstrates that considering different landslide types in LSM can significantly improve the quality of landslide susceptibility maps.These maps in turn,can be valuable tools for evaluating and mitigating landslide hazards.
文摘This research presents a novel approach to pipeline Structure Health Monitoring(SHM)by utilizing frequency response function signals and integrating advanced data-driven techniques to detect and evaluate vibration responses regarding loose bolts,scale deposits within pipelines,and cracks at pipeline supports,aiming to determine the effectiveness of utilizing artificial neural networks(ANN)and an ensemble learning approach in detecting the aforementioned damages through a data-driven approach.The research starts by recording 6500 samples captured by two accelerometers,related to 11 replicated pipeline structural scenarios.The research demonstrated the potential of principal component analysis(PCA)in dimensionality reduction,achieving approximately 81%reduction in data set 1 acquired by accelerometer 1 and around 79.5%in data set 2 acquired by accelerometer 2,without significant loss of information.Additionally,two ANN base models were employed for fault recognition and classification,achieving over 99.88%accuracy and mean squared error values ranging from 0.00006 to 0.00019.A significant innovation of this work lies in the implementation of an ensemble learning approach,which integrates the strengths of the base models,showcasing outstanding performance that was proved consistent across multiple iterations,effectively mitigating the weaknesses of the base models and providing a reliable fault classification and prediction system.This research underscores the effectiveness of combining PCA,ANN,k-fold cross-validation,and ensemble learning techniques in pipeline SHM for improved reliability and safety.The findings highlight the potential for broader applications of this methodology in real-world scenarios,addressing urgent challenges faced by infrastructure owners and operators.
