An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and ...An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.展开更多
An analysis of a 68-year monthly hindcast output from an eddy-resolving ocean general circulation model reveals the relationship between the interannual variability of the Kerama Gap transport(KGT)and the Kuroshio/Ryu...An analysis of a 68-year monthly hindcast output from an eddy-resolving ocean general circulation model reveals the relationship between the interannual variability of the Kerama Gap transport(KGT)and the Kuroshio/Ryukyu Current system.The study found a significant difference in the interannual variability of the upstream and downstream transports of the East China Sea-(ECS-)Kuroshio and the Ryukyu Current.The interannual variability of the KGT was found to be of paramount importance in causing the differences between the upstream and downstream ECS-Kuroshio.Additionally,it contributed approximately 37%to the variability of the Ryukyu Current.The interannual variability of the KGT was well described by a two-layer rotating hydraulic theory.It was dominated by its subsurface-intensified flow core,and the upper layer transport made a weaker negative contribution to the total KGT.The subsurface flow core was found to be mainly driven by the subsurface pressure head across the Kerama Gap,and the pressure head was further dominated by the subsurface density anomalies on the Pacific side.These density anomalies could be traced back to the eastern open ocean,and their propagation speed was estimated to be about 7.4 km/d,which is consistent with the speed of the local first-order baroclinic Rossby wave.When the negative(positive)density anomaly signal reached the southern region of the Kerama Gap,it triggered the increase(decrease)of the KGT towards the Pacific side and the formation of an anticyclonic(cyclonic)vortex by baroclinic adjustment.Meanwhile,there is an increase(decrease)in the upstream transport of the entire Kuroshio/Ryukyu Current system and an offshore flow that decreases(increases)the downstream Ryukyu Current.展开更多
Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook se...Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.展开更多
Nanocomposite ZrCN films consisting of nanocrystalline ZrCN grains embedded in nitrogen-doped amorphous carbon film are deposited by filtered cathodic vacuum arc technology under different bias voltages ranging from 5...Nanocomposite ZrCN films consisting of nanocrystalline ZrCN grains embedded in nitrogen-doped amorphous carbon film are deposited by filtered cathodic vacuum arc technology under different bias voltages ranging from 50to 400 V.The influence of bias voltage on the characterization and the mechanical properties of the ZrCN films are investigated by x-ray diffraction,x-ray photoelectron spectroscopy,scanning electron microscopy,transmission electron microscopy,Raman spectroscopy and nano-indentation.The bias voltage has a subtle effect on the ZrCN grain size,which is around 9.5 nm and keeps almost constant.A slight increase of the bias voltage induces a relatively high sp;fraction about 40%in N-doped amorphous C films but leads to the graphitization of the films under a higher voltage.The best mechanical property of the ZrCN film with the hardness of 41 GPa is obtained under the bias voltage of 200 V,indicating the positive effect of slight increase of ion bombardment on the hardness of the films.展开更多
As key components of artificial afferent nervous systems,synaptic devices can mimic the physiological synaptic behaviors,which have attracted extensive attentions.Here,a flexible tribotronic artificial synapse(TAS)wit...As key components of artificial afferent nervous systems,synaptic devices can mimic the physiological synaptic behaviors,which have attracted extensive attentions.Here,a flexible tribotronic artificial synapse(TAS)with bioinspired neurosensory behavior is developed.The triboelectric potential generated by the external contact electrification is used as the ion-gel-gate voltage of the organic thin film transistor,which can tune the carriers transport through the migration/accumulation of ions.The TAS successfully demonstrates a series of synaptic behaviors by external stimuli,such as excitatory postsynaptic current,paired-pulse facilitation,and the hierarchical memory process from sensory memory to short-term memory and long-term memory.Moreover,the synaptic behaviors remained stable under the strain condition with a bending radius of 20 mm,and the TAS still exhibits excellent durability after 1000 bending cycles.Finally,Pavlovian conditioning has been successfully mimicked by applying force and vibration as food and bell,respectively.This work demonstrates a bioinspired flexible artificial synapse that will help to facilitate the development of artificial afferent nervous systems,which is great significance to the practical application of artificial limbs,robotics,and bionics in future.展开更多
Drug target relationship(DTR)prediction is a rapidly evolving area of research in com-putational drug discovery.Despite recent advances in computational solutions that have overcome the challenges of in vitro and in v...Drug target relationship(DTR)prediction is a rapidly evolving area of research in com-putational drug discovery.Despite recent advances in computational solutions that have overcome the challenges of in vitro and in vivo experiments,most computational methods still focus on binary classification.They ignore the importance of binding affinity,which correctly distinguishes between on-targets and off-targets.In this study,we propose a deep learning model based on the microstruc-ture of compounds and proteins to predict drug-target binding affinity(DTA),which utilizes topo-logical structure information of drug molecules and sequence semantic information of proteins.In this model,graph attention network(GAT)is used to capture the deep features of the compound molecular graph,and bidirectional long short-term memory(BiLSTM)network is used to extract the protein sequence features,and the pharmacological context of DTA is obtained by combining the two.The results show that the proposed model has achieved superior performance in both cor-rectly predicting the value of interaction strength and correctly discriminating the ranking of bind-ing strength compared to the state-of-the-art baselines.A case study experiment on COVID-19 con-firms that the proposed DTA model can be used as an effective pre-screening tool in drug discovery.展开更多
The current digital educational resources are of many kinds and large quantities, to solve the problems existing in the existing dynamic resource selection methods, a dynamic resource selection method based on machine...The current digital educational resources are of many kinds and large quantities, to solve the problems existing in the existing dynamic resource selection methods, a dynamic resource selection method based on machine learning is proposed. Firstly, according to the knowledge structure and concepts of mathematical resources, combined with the basic components of dynamic mathematical resources, the knowledge structure graph of mathematical resources is constructed;according to the characteristics of mathematical resources, the interaction between users and resources is simulated, and the graph of the main body of the resources is identified, and the candidate collection of mathematical knowledge is selected;finally, according to the degree of matching between mathematical literature and the candidate collection, machine learning is utilized, and the mathematical resources are screened.展开更多
基金the support of the National Natural Science Foundation of China(22278234,21776151)。
文摘An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.
基金The Fundamental Research Funds for the Central Universities under contract No.B220201024.
文摘An analysis of a 68-year monthly hindcast output from an eddy-resolving ocean general circulation model reveals the relationship between the interannual variability of the Kerama Gap transport(KGT)and the Kuroshio/Ryukyu Current system.The study found a significant difference in the interannual variability of the upstream and downstream transports of the East China Sea-(ECS-)Kuroshio and the Ryukyu Current.The interannual variability of the KGT was found to be of paramount importance in causing the differences between the upstream and downstream ECS-Kuroshio.Additionally,it contributed approximately 37%to the variability of the Ryukyu Current.The interannual variability of the KGT was well described by a two-layer rotating hydraulic theory.It was dominated by its subsurface-intensified flow core,and the upper layer transport made a weaker negative contribution to the total KGT.The subsurface flow core was found to be mainly driven by the subsurface pressure head across the Kerama Gap,and the pressure head was further dominated by the subsurface density anomalies on the Pacific side.These density anomalies could be traced back to the eastern open ocean,and their propagation speed was estimated to be about 7.4 km/d,which is consistent with the speed of the local first-order baroclinic Rossby wave.When the negative(positive)density anomaly signal reached the southern region of the Kerama Gap,it triggered the increase(decrease)of the KGT towards the Pacific side and the formation of an anticyclonic(cyclonic)vortex by baroclinic adjustment.Meanwhile,there is an increase(decrease)in the upstream transport of the entire Kuroshio/Ryukyu Current system and an offshore flow that decreases(increases)the downstream Ryukyu Current.
基金National Natural Science Foundation of China(Grant Nos.62171130,62172197,61972093)the Natural Science Foundation of Fujian Province(Grant Nos.2020J01573,2022J01131257,2022J01607)+3 种基金Fujian University Industry University Research Joint Innovation Project(No.2022H6006)in part by the Fund of Cloud Computing and BigData for SmartAgriculture(GrantNo.117-612014063)NationalNatural Science Foundation of China(Grant No.62301160)Nature Science Foundation of Fujian Province(Grant No.2022J01607).
文摘Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.
基金Supported by the National Natural Science Foundation of China under Grant No 51171028
文摘Nanocomposite ZrCN films consisting of nanocrystalline ZrCN grains embedded in nitrogen-doped amorphous carbon film are deposited by filtered cathodic vacuum arc technology under different bias voltages ranging from 50to 400 V.The influence of bias voltage on the characterization and the mechanical properties of the ZrCN films are investigated by x-ray diffraction,x-ray photoelectron spectroscopy,scanning electron microscopy,transmission electron microscopy,Raman spectroscopy and nano-indentation.The bias voltage has a subtle effect on the ZrCN grain size,which is around 9.5 nm and keeps almost constant.A slight increase of the bias voltage induces a relatively high sp;fraction about 40%in N-doped amorphous C films but leads to the graphitization of the films under a higher voltage.The best mechanical property of the ZrCN film with the hardness of 41 GPa is obtained under the bias voltage of 200 V,indicating the positive effect of slight increase of ion bombardment on the hardness of the films.
基金supported by the National Natural Science Foundation of China(No.22178392)the Fundamental Research Funds for the Central Universities of Central South University,China(No.2022ZZTS0493)。
基金supported by the National Natural Science Foundation of China(Grant Nos.51922023,61874011)the China Postdoctoral Science Foundation(Grant No.2021M703159)Fundamental Research Funds for the Central Universities(Grant No.E1EG6804).
文摘As key components of artificial afferent nervous systems,synaptic devices can mimic the physiological synaptic behaviors,which have attracted extensive attentions.Here,a flexible tribotronic artificial synapse(TAS)with bioinspired neurosensory behavior is developed.The triboelectric potential generated by the external contact electrification is used as the ion-gel-gate voltage of the organic thin film transistor,which can tune the carriers transport through the migration/accumulation of ions.The TAS successfully demonstrates a series of synaptic behaviors by external stimuli,such as excitatory postsynaptic current,paired-pulse facilitation,and the hierarchical memory process from sensory memory to short-term memory and long-term memory.Moreover,the synaptic behaviors remained stable under the strain condition with a bending radius of 20 mm,and the TAS still exhibits excellent durability after 1000 bending cycles.Finally,Pavlovian conditioning has been successfully mimicked by applying force and vibration as food and bell,respectively.This work demonstrates a bioinspired flexible artificial synapse that will help to facilitate the development of artificial afferent nervous systems,which is great significance to the practical application of artificial limbs,robotics,and bionics in future.
文摘Drug target relationship(DTR)prediction is a rapidly evolving area of research in com-putational drug discovery.Despite recent advances in computational solutions that have overcome the challenges of in vitro and in vivo experiments,most computational methods still focus on binary classification.They ignore the importance of binding affinity,which correctly distinguishes between on-targets and off-targets.In this study,we propose a deep learning model based on the microstruc-ture of compounds and proteins to predict drug-target binding affinity(DTA),which utilizes topo-logical structure information of drug molecules and sequence semantic information of proteins.In this model,graph attention network(GAT)is used to capture the deep features of the compound molecular graph,and bidirectional long short-term memory(BiLSTM)network is used to extract the protein sequence features,and the pharmacological context of DTA is obtained by combining the two.The results show that the proposed model has achieved superior performance in both cor-rectly predicting the value of interaction strength and correctly discriminating the ranking of bind-ing strength compared to the state-of-the-art baselines.A case study experiment on COVID-19 con-firms that the proposed DTA model can be used as an effective pre-screening tool in drug discovery.
文摘The current digital educational resources are of many kinds and large quantities, to solve the problems existing in the existing dynamic resource selection methods, a dynamic resource selection method based on machine learning is proposed. Firstly, according to the knowledge structure and concepts of mathematical resources, combined with the basic components of dynamic mathematical resources, the knowledge structure graph of mathematical resources is constructed;according to the characteristics of mathematical resources, the interaction between users and resources is simulated, and the graph of the main body of the resources is identified, and the candidate collection of mathematical knowledge is selected;finally, according to the degree of matching between mathematical literature and the candidate collection, machine learning is utilized, and the mathematical resources are screened.
