The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard...The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.展开更多
Objective Using graph theory analysis,this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties bet...Objective Using graph theory analysis,this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties between bipolar depression(BD)patients with and without obsessive-compulsive symptoms(OCS).Methods A total of 90 BD patients(27 males,63 females;median age 19.0(22.0,25.0)years)were recruited from the psychiatric outpatient and inpatient departments of the First Affiliated Hospital of Jinan University between March 2018 and December 2022.Fifty healthy controls(19 males,31 females;median age:23.0(20.0,27.0)years)were also enrolled.The BD patients were divided into two groups based on the presence of OCS:53 with OCS(OCS group)and 37 without OCS(NOCS group).Resting-state structural and functional MRI data were collected for all participants to construct gray matter structural and functional networks.Graph therory analysis was aapplied to calculate network topological metrics such as small-world properties.The structural and functional network topological properties were compared among the BD-OCS,BD-nOCS,and control groups.Partial correlation analysis was conducted to examine the association between network topological metrics with significant group differences and Yale-Brown Obsessive-Compulsive Scale(Y-BOCS)scores.Support vector machines(SVM)were used with these metrics as classificationfeaturevalues toimproveediagnostic accuracy through pairwise group classification.Results Structural network analysis of gray matter:compared to HC group,both OCS group and NOCS group showed increasedshortesttpathlengthand standardized characteristic path length(shortest path length:0.78 and 0.80 vs.0.69;normalized characteristic path length:0.48 and 0.49 vs.0.43),and decreased global efficiency(0.21 and 0.21 vs.0.24)compared to the HC group(permutation test,all P<0.05).Compared to NOCS and HC groups,the OCS group showed increased nodal centrality and betweenness centrality in the right rolandic operculum and left superior occipital gyrus(permutation test,all P<0.05).Functional network analysis of gray matter:compared to the NOCS group,the OCS group showed increased node efficiency and decreased betweenness centrality in the cerebellum(t=2.15,-3.04;all P<0.05);compared to HC groups,the OCS group showed decreased betweenness centrality in the cerebellum and left inferior frontal gyrus,along with increased node centrality and nodal efficiency in the right transverse temporal gyrus(t=-2.99,-3.61,3.06,3.10;all P<0.05).In the 0CS group,betweenness centrality in the left inferior frontal gyrus positively correlated with Y-BOCS scale obsessive thinking score(r=0.303,P=0.034).Nodal centrality and node efficiency of the right transverse temporal gyrus negatively correlated with Y-BOCS total score(r=-0.301,-0.311)and Y-BOCS obsessional thinking scores(r=-0.385,-0.380)separately(all P<0.05).SVM classification:the combined network features achieved an area under the curve of 0.80 in distinguising OCS from NOCS patients.Conclusion BDOCS and BD-nOCS patients both exhibit consistent changes in gray matter structural network topology,with theOCSSgroup displaying more pronounced nodal topological abnormalities.Multi-network feature integration demostrates potential for diagnostic classfication.展开更多
Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly fav...Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.展开更多
Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convol...Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.展开更多
Several meta-analyses were recently conducted in attempts to identify the core brain regions exhibiting pathological changes in schizophrenia,which could potentially act as disease markers.Based on the findings of the...Several meta-analyses were recently conducted in attempts to identify the core brain regions exhibiting pathological changes in schizophrenia,which could potentially act as disease markers.Based on the findings of these meta-analyses,we developed a multivariate pattern analysis method to classify schizophrenic patients and healthy controls using structural magnetic resonance imaging(sMRI)data.Independent component analysis(ICA)was used to decompose gray matter density images into a set of spatially independent components.Spatial multiple regression of a region of interest(ROI)mask with each of the components was then performed to determine pathological patterns,in which the voxels were taken as features for classification.After dimensionality reduction using principal component analysis(PCA),a nonlinear support vector machine(SVM)classifier was trained to discriminate schizophrenic patients from healthy controls.The performance of the classifier was tested using a 10-fold cross-validation strategy.Experimental results showed that two distinct spatial patterns displayed discriminative power for schizophrenia,which mainly included the prefrontal cortex(PFC)and subcortical regions respectively.It was found that simultaneous usage of these two patterns improved the classification performance compared to using either of them alone.Moreover,the two pathological patterns constitute a prefronto-subcortical network,suggesting that schizophrenia involves abnormalities in networks of brain regions.展开更多
基金Project(61673399)supported by the National Natural Science Foundation of ChinaProject(2017JJ2329)supported by the Natural Science Foundation of Hunan Province,ChinaProject(2018zzts550)supported by the Fundamental Research Funds for Central Universities,China
文摘The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.
文摘Objective Using graph theory analysis,this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties between bipolar depression(BD)patients with and without obsessive-compulsive symptoms(OCS).Methods A total of 90 BD patients(27 males,63 females;median age 19.0(22.0,25.0)years)were recruited from the psychiatric outpatient and inpatient departments of the First Affiliated Hospital of Jinan University between March 2018 and December 2022.Fifty healthy controls(19 males,31 females;median age:23.0(20.0,27.0)years)were also enrolled.The BD patients were divided into two groups based on the presence of OCS:53 with OCS(OCS group)and 37 without OCS(NOCS group).Resting-state structural and functional MRI data were collected for all participants to construct gray matter structural and functional networks.Graph therory analysis was aapplied to calculate network topological metrics such as small-world properties.The structural and functional network topological properties were compared among the BD-OCS,BD-nOCS,and control groups.Partial correlation analysis was conducted to examine the association between network topological metrics with significant group differences and Yale-Brown Obsessive-Compulsive Scale(Y-BOCS)scores.Support vector machines(SVM)were used with these metrics as classificationfeaturevalues toimproveediagnostic accuracy through pairwise group classification.Results Structural network analysis of gray matter:compared to HC group,both OCS group and NOCS group showed increasedshortesttpathlengthand standardized characteristic path length(shortest path length:0.78 and 0.80 vs.0.69;normalized characteristic path length:0.48 and 0.49 vs.0.43),and decreased global efficiency(0.21 and 0.21 vs.0.24)compared to the HC group(permutation test,all P<0.05).Compared to NOCS and HC groups,the OCS group showed increased nodal centrality and betweenness centrality in the right rolandic operculum and left superior occipital gyrus(permutation test,all P<0.05).Functional network analysis of gray matter:compared to the NOCS group,the OCS group showed increased node efficiency and decreased betweenness centrality in the cerebellum(t=2.15,-3.04;all P<0.05);compared to HC groups,the OCS group showed decreased betweenness centrality in the cerebellum and left inferior frontal gyrus,along with increased node centrality and nodal efficiency in the right transverse temporal gyrus(t=-2.99,-3.61,3.06,3.10;all P<0.05).In the 0CS group,betweenness centrality in the left inferior frontal gyrus positively correlated with Y-BOCS scale obsessive thinking score(r=0.303,P=0.034).Nodal centrality and node efficiency of the right transverse temporal gyrus negatively correlated with Y-BOCS total score(r=-0.301,-0.311)and Y-BOCS obsessional thinking scores(r=-0.385,-0.380)separately(all P<0.05).SVM classification:the combined network features achieved an area under the curve of 0.80 in distinguising OCS from NOCS patients.Conclusion BDOCS and BD-nOCS patients both exhibit consistent changes in gray matter structural network topology,with theOCSSgroup displaying more pronounced nodal topological abnormalities.Multi-network feature integration demostrates potential for diagnostic classfication.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61332003 and 61303068)the Natural Science Foundation of Hunan Province,China(Grant No.2015JJ3024)
文摘Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.
文摘Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.61003202,90820304,and 60835005)the National Basic Research Program of China(No.2011CB707802).
文摘Several meta-analyses were recently conducted in attempts to identify the core brain regions exhibiting pathological changes in schizophrenia,which could potentially act as disease markers.Based on the findings of these meta-analyses,we developed a multivariate pattern analysis method to classify schizophrenic patients and healthy controls using structural magnetic resonance imaging(sMRI)data.Independent component analysis(ICA)was used to decompose gray matter density images into a set of spatially independent components.Spatial multiple regression of a region of interest(ROI)mask with each of the components was then performed to determine pathological patterns,in which the voxels were taken as features for classification.After dimensionality reduction using principal component analysis(PCA),a nonlinear support vector machine(SVM)classifier was trained to discriminate schizophrenic patients from healthy controls.The performance of the classifier was tested using a 10-fold cross-validation strategy.Experimental results showed that two distinct spatial patterns displayed discriminative power for schizophrenia,which mainly included the prefrontal cortex(PFC)and subcortical regions respectively.It was found that simultaneous usage of these two patterns improved the classification performance compared to using either of them alone.Moreover,the two pathological patterns constitute a prefronto-subcortical network,suggesting that schizophrenia involves abnormalities in networks of brain regions.
