Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiologi...The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiological measurements,including electroencephalograph(EEG)and functional magnetic resonance imaging(fMRI),and are crucial for insights into physiological phenomena.This study introduces a novel method,the baseline perspective visibility graph(BPVG),which can analyze time series by accurately capturing connectivity across data points both above and below the baseline.We present the BPVG construction process and validate its performance using simulated signals.Results demonstrate that BPVG accurately translates periodic,random,and fractal signals into regular,random,and scale-free networks respectively,exhibiting diverse degree distribution traits.Furthermore,we apply BPVG to classify Alzheimer’s disease(AD)patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI(rs-fMRI)data.Utilizing degree distribution entropy derived from BPVG networks,our results exceed the best accuracy benchmark(77.01%)in EEG analysis,especially at channels F4(78.46%)and O1(81.54%).Additionally,our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%.These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment.In conclusion,BPVG’s validation across both simulated and real data confirms its capability to capture comprehensive information from time series,irrespective of baseline constraints,providing a novel method for studying neural physiological signals.展开更多
The present study was aimed to evaluate restingstate functional connectivity and topological properties of brain networks in narcolepsy patients compared with healthy controls.Resting-state fMRI was performed in 26 ad...The present study was aimed to evaluate restingstate functional connectivity and topological properties of brain networks in narcolepsy patients compared with healthy controls.Resting-state fMRI was performed in 26 adult narcolepsy patients and 30 matched healthy controls.MRI data were first analyzed by group independent component analysis,then a graph theoretical method was applied to evaluate the topological properties in the whole brain.Small-world network parameters and nodal topological properties were measured.Altered topological properties in brain areas between groups were selected as regionof-interest seeds,then the functional connectivity among these seeds was compared between groups.Partial correlation analysis was performed to evaluate the relationship between the severity of sleepiness and functional connectivity or topological properties in the narcolepsy patients.Twenty-one independent components out of 48 were obtained.Compared with healthy controls,the narcolepsy patients exhibited significantly decreased functional connectivity within the executive and salience networks,along with increased functional connectivity in the bilateral frontal lobes within the executive network.There were no differences in small-world network properties between patients and controls.The altered brain areas in nodal topological properties between groups were mainly in the inferior frontal cortex,basal ganglia,anterior cingulate,sensory cortex,supplementary motor cortex,and visual cortex.In the partial correlation analysis,nodal topological properties in the putamen,anterior cingulate,and sensory cortex as well as functional connectivity between these regions were correlated with the severity of sleepiness(sleep latency,REM sleep latency,and Epworth sleepiness score)among narcolepsy patients.Altered connectivity within the executive and salience networks was found in narcolepsy patients.Functional connection changes between the left frontal cortex and left caudate nucleus may be one of the parameters describing the severity of narcolepsy.Changes in the nodal topological properties in the left putamen and left posterior cingulate,changes in functional connectivity between the left supplementary motor area and right occipital as well as in functional connectivity between the left anterior cingulate gyrus and bilateral postcentral gyrus can be considered as a specific indicator for evaluating the severity of narcolepsy.展开更多
In the past 30 years,signed directed graph(SDG) ,one of the qualitative simulation technologies,has been widely applied for chemical fault diagnosis.However,SDG based fault diagnosis,as any other qualitative method,ha...In the past 30 years,signed directed graph(SDG) ,one of the qualitative simulation technologies,has been widely applied for chemical fault diagnosis.However,SDG based fault diagnosis,as any other qualitative method,has poor diagnostic resolution.In this paper,a new method that combines SDG with qualitative trend analysis(QTA) is presented to improve the resolution.In the method,a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model.Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consis-tent paths found in the previous step.New consistency rules based on qualitative trends are used to find the real causes from the candidate causes.The resolution can be improved.This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA.The implementation of SDG-QTA based fault diagno-sis is done using the integrated SDG modeling,inference and post-processing software platform.Its application is illustrated on an atmospheric distillation tower unit of a simulation platform.The result shows its good applicability and efficiency.展开更多
Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.展开更多
Qualitative algebraic equations are the basis of qualitative simulation,which are used to express the dynamic behavior of steady-state continuous processes.When the values and operation of qualitative variables are re...Qualitative algebraic equations are the basis of qualitative simulation,which are used to express the dynamic behavior of steady-state continuous processes.When the values and operation of qualitative variables are redefined,qualitative algebraic equations can be transformed into signed direct graphs,which are frequently used to predict the trend of dynamic changes.However,it is difficult to use traditional qualitative algebra methods based on artificial trial and error to solve a complex problem for dynamic trends.An important aspect of modern qualitative algebra is to model and characterize complex systems with the corresponding computer-aided automatic reasoning.In this study,a qualitative affection equation based on multiple conditions is proposed,which enables the signed di-rect graphs to describe complex systems better and improves the fault diagnosis resolution.The application to an industrial case shows that the method performs well.展开更多
Graphical methods are used for construction.Data analysis and visualization are an important area of applications of big data.At the same time,visual analysis is also an important method for big data analysis.Data vis...Graphical methods are used for construction.Data analysis and visualization are an important area of applications of big data.At the same time,visual analysis is also an important method for big data analysis.Data visualization refers to data that is presented in a visual form,such as a chart or map,to help people understand the meaning of the data.Data visualization helps people extract meaning from data quickly and easily.Visualization can be used to fully demonstrate the patterns,trends,and dependencies of your data,which can be found in other displays.Big data visualization analysis combines the advantages of computers,which can be static or interactive,interactive analysis methods and interactive technologies,which can directly help people and effectively understand the information behind big data.It is indispensable in the era of big data visualization,and it can be very intuitive if used properly.Graphical analysis also found that valuable information becomes a powerful tool in complex data relationships,and it represents a significant business opportunity.With the rise of big data,important technologies suitable for dealing with complex relationships have emerged.Graphics come in a variety of shapes and sizes for a variety of business problems.Graphic analysis is first in the visualization.The step is to get the right data and answer the goal.In short,to choose the right method,you must understand each relative strengths and weaknesses and understand the data.Key steps to get data:target;collect;clean;connect.展开更多
Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at ...Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.展开更多
Limit equilibrium method （LEM） and strength reduction method （SRM） are the most widely used methods for slope stability analysis. However, it can be noted that they both have some limitations in practical applicat...Limit equilibrium method （LEM） and strength reduction method （SRM） are the most widely used methods for slope stability analysis. However, it can be noted that they both have some limitations in practical application. In the LEM, the constitutive model cannot be considered and many assumptions are needed between slices of soil/rock. The SRM requires iterative calculations and does not give the slip surface directly. A method for slope stability analysis based on the graph theory is recently developed to directly calculate the minimum safety factor and potential critical slip surface according to the stress results of numerical simulation. The method is based on current stress state and can overcome the disadvantages mentioned above in the two traditional methods. The influences of edge generation and mesh geometry on the position of slip surface and the safety factor of slope are studied, in which a new method for edge generation is proposed, and reasonable mesh size is suggested. The results of benchmark examples and a rock slope show good accuracy and efficiency of the presented method.展开更多
[Objectives]Based on bibliometric methods,the evolution path and knowledge structure of shampoo product research in China were systematically analyzed,with a focus on the composition,efficacy,and mechanisms of traditi...[Objectives]Based on bibliometric methods,the evolution path and knowledge structure of shampoo product research in China were systematically analyzed,with a focus on the composition,efficacy,and mechanisms of traditional Chinese medicine shampoos,aiming to reveal the shifting patterns of core technological focuses at different stages and identify key future research directions.[Methods]Using a sample of 515 publications from CNKI journals between 2005 and 2025,CiteSpace 6.3.R1 was employed to construct multi-dimensional networks of authors,institutions,and keywords.Burst detection,keyword and cluster analyses,and time zone mapping were applied to track disciplinary dynamics.[Results]The analysis on the annual number of publications indicated that research on shampoo products underwent multiple phases including initial development,growth,fluctuation,peak,and adjustment from 2005 to 2025,with an overall upward trend.The significant differences between the collaboration network density of core authors and the strength of institutional cooperation indicated a need for academia to enhance cross-institutional collaborative innovation mechanisms.Keyword clustering analysis and co-occurrence mapping revealed that the research on traditional Chinese medicine shampoo products,known for their natural,mild,and non-irritating characteristics,demonstrates notable advantages in dandruff removal,anti-hair loss,hair growth,and scalp health management,which have become current research hotspots,providing data-driven decision-making support for technological upgrading and industry-academia-research integration in the field.[Conclusions]The bibliometric analysis based on 515 publications indicates that the overall development direction of shampoo products will comprehensively advance toward refined efficacy and personalized customization,greener and more natural ingredients,technological innovation and industry-academia-research collaboration,market segmentation and diversified development,as well as safety and efficacy evaluation.This study provides theoretical support for the innovation of specialized traditional Chinese medicine shampoo products.展开更多
With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extract...With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis.To address these challenges,this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception(TGICP).Specifically,we utilize a Inter-sample Commonality Perception(ICP)module to extract common features from similar samples within the same modality,and use these common features to enhance the original features of each modality,thereby obtaining a richer and more complete multimodal sentiment representation.Subsequently,in the cross-modal interaction stage,we design a Text-Gated Interaction(TGI)module,which is text-driven.By calculating the mutual information difference between the text modality and nonverbal modalities,the TGI module dynamically adjusts the influence of emotional information from the text modality on nonverbal modalities.This helps to reduce modality information asymmetry while enabling full cross-modal interaction.Experimental results show that the proposed model achieves outstanding performance on both the CMU-MOSI and CMU-MOSEI baseline multimodal sentiment analysis datasets,validating its effectiveness in emotion recognition tasks.展开更多
Due to limitations in geometric representation and semantic description, the current pedestrian route analysis models are inadequate. To express the geometry of geographic entities in a micro-spatial environment accur...Due to limitations in geometric representation and semantic description, the current pedestrian route analysis models are inadequate. To express the geometry of geographic entities in a micro-spatial environment accurately, the concept of a grid is presented, and grid-based methods for modeling geospatial objects are described. The semantic constitution of a building environment and the methods for modeling rooms, corridors, and staircases with grid objects are described. Based on the topology relationship between grid objects, a grid-based graph for a building environment is presented, and the corresponding route algorithm for pedestrians is proposed. The main advantages of the graph model proposed in this paper are as follows： 1） consideration of both semantic and geometric information, 2） consideration of the need for accurate geometric representation of the micro-spatial environment and the efficiency of pedestrian route analysis, 3） applicability of the graph model to route analysis in both static and dynamic environments, and 4） ability of the multi-hierarchical route analysis to integrate the multiple levels of pedestrian decision characteristics, from the high to the low, to determine the optimal path.展开更多
In this paper, a new method has been introduced to find the most vulnerable lines in the system dynamically in an interconnected power system to help with the security and load flow analysis in these networks. Using t...In this paper, a new method has been introduced to find the most vulnerable lines in the system dynamically in an interconnected power system to help with the security and load flow analysis in these networks. Using the localization of power networks, the power grid can be divided into several divisions of sub-networks in which, the connection of the elements is stronger than the elements outside of that division. By using our proposed method, the probable important lines in the network can be identified to do the placement of the protection apparatus and planning for the extra extensions in the system. In this paper, we have studied the pathfinding strategies in most vulnerable line detection in a partitioned network. The method has been tested on IEEE39-bus system which is partitioned using hierarchical spectral clustering to show the feasibility of the proposed method.展开更多
In this work a method called “signal flow graph (SFG)” is presented. A signal-flow graph describes a system by its signal flow by directed and weighted graph;the signals are applied to nodes and functions on edges. ...In this work a method called “signal flow graph (SFG)” is presented. A signal-flow graph describes a system by its signal flow by directed and weighted graph;the signals are applied to nodes and functions on edges. The edges of the signal flow graph are small processing units, through which the incoming signals are processed in a certain form. In this case, the result is sent to the outgoing node. The SFG allows a good visual inspection into complex feedback problems. Furthermore such a presentation allows for a clear and unambiguous description of a generating system, for example, a netview. A Signal Flow Graph (SFG) allows a fast and practical network analysis based on a clear data presentation in graphic format of the mathematical linear equations of the circuit. During creation of a SFG the Direct Current-Case (DC-Case) was observed since the correct current and voltage directions was drawn from zero frequency. In addition, the mathematical axioms, which are based on field algebra, are declared. In this work we show you in addition: How we check our SFG whether it is a consistent system or not. A signal flow graph can be verified by generating the identity of the signal flow graph itself, illustrated by the inverse signal flow graph (SFG−1). Two signal flow graphs are always generated from one circuit, so that the signal flow diagram already presented in previous sections corresponds to only half of the solution. The other half of the solution is the so-called identity, which represents the (SFG−1). If these two graphs are superposed with one another, so called 1-edges are created at the node points. In Boolean algebra, these 1-edges are given the value 1, whereas this value can be identified with a zero in the field algebra.展开更多
The development and the revolution of nanotechnology require more and effective methods to accurately estimating the timing analysis for any CMOS transistor level circuit. Many researches attempted to resolve the timi...The development and the revolution of nanotechnology require more and effective methods to accurately estimating the timing analysis for any CMOS transistor level circuit. Many researches attempted to resolve the timing analysis, but the best method found till the moment is the Static Timing Analysis (STA). It is considered the best solution because of its accuracy and fast run time. Transistor level models are mandatory required for the best estimating methods, since these take into consideration all analysis scenarios to overcome problems of multiple-input switching, false paths and high stacks that are found in classic CMOS gates. In this paper, transistor level graph model is proposed to describe the behavior of CMOS circuits under predictive Nanotechnology SPICE parameters. This model represents the transistor in the CMOS circuit as nodes in the graph regardless of its positions in the gates to accurately estimating the timing analysis rather than inaccurate estimating which caused by the false paths at the gate level. Accurate static timing analysis is estimated using the model proposed in this paper. Building on the proposed model and the graph theory concepts, new algorithms are proposed and simulated to compute transistor timing analysis using RC model. Simulation results show the validity of the proposed graph model and its algorithms by using predictive Nano-Technology SPICE parameters for the tested technology. An important and effective extension has been achieved in this paper for a one that was published in international conference.展开更多
Taking CNKI database as the data source and measuring visual map analysis tool Citespace as auxiliary tool,domestic 843 articles in recent 15 years are analyzed,and visualization maps such as time line analysis,instit...Taking CNKI database as the data source and measuring visual map analysis tool Citespace as auxiliary tool,domestic 843 articles in recent 15 years are analyzed,and visualization maps such as time line analysis,institutional cooperation network analysis,author collaboration network analysis,keyword co-occurrence,and emergent words analysis are drawn.Combined with the literature content analysis,four hot spots in the research field of landscape architecture microclimate in China are obtained,namely ENVI-MET,comfort,design strategy and urban green space.The research trend is thermal comfort,human body comfort and winter city.The research results can provide reference for the research on domestic garden microclimate.展开更多
For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural net...For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.展开更多
The concepts of complementary cofactor pairs, normal double-graphs and feasible torn vertex seta are introduced. By using them a decomposition theorem for first-order cofactor C(Y) is derived. Combining it with the mo...The concepts of complementary cofactor pairs, normal double-graphs and feasible torn vertex seta are introduced. By using them a decomposition theorem for first-order cofactor C(Y) is derived. Combining it with the modified double-graph method, a new decomposition analysis-modified double-graph decomposition analysis is presented for finding symbolic network functions. Its advantages are that the resultant symbolic expressions are compact and contain no cancellation terms, and its sign evaluation is very simple.展开更多
Objective To study the research status,research hotspots and development trends in the field of real-world data(RWD)through social network analysis and knowledge graph analysis.Methods RWD of the past 10 years were re...Objective To study the research status,research hotspots and development trends in the field of real-world data(RWD)through social network analysis and knowledge graph analysis.Methods RWD of the past 10 years were retrieved,and literature metrological analysis was made by using UCINET and CiteSpace from CNKI.Results and Conclusion The frequency and centrality of related keywords such as real-world study,hospital information system(HIS),drug combination,data mining and TCM are high.The clusters labeled as clinical medication and RWD contain more keywords.In recent 4 years,there are more articles involving the keywords of data specification,data authenticity,data security and information security.Among them,compound Kushen injection,HIS database and RWD are the top three keywords.It is a long-term research hotspot for Chinese and western medicine to use HIS to study clinical medication,clinical characteristics,diseases and injections.Besides,the research of RWD database has changed from construction to standardized collection and governance,which can make RWD effective.Data authenticity,data security and information security will become the new hotspots in the research of RWD.展开更多
Tourist trails as a linear form of tourist infrastructure fulfill various functions(i.e. recreational, ecological, economic, social, ensuring safety). They are especially important in national parks, where in selected...Tourist trails as a linear form of tourist infrastructure fulfill various functions(i.e. recreational, ecological, economic, social, ensuring safety). They are especially important in national parks, where in selected areas tourist penetration is allowed only along specially designed, official routes. A well-planned layout of tourist trails with appropriate facilities can help to limit the negative consequences of tourist pressure on protected natural areas. The aim of the article is a comparison of offers for active tourists in two mountain national parks(the Krkono?e National Park in the Czech Republic and the Peneda-Gerês National Park in Portugal), taking into consideration the marked hiking trails – the most frequently used type of tourist trails. As a result the level of area coverage by the networks of hiking trails was assessed, as well as their adequateness towards the needs of tourists. The descriptive analysis was based on author's personal observations. In the examination of hiking trails as part of a system, some elements of the graph theory were used, especially coefficients for topologic analysis of spatial structure. This method enables simplification of a network, comparison of various areas and making some assumptions concerning tourist infrastructure, which is a crucial factor while analyzing trails from a tourists' point of view. In both analyzed national parks the relief is quite similar, as well as their locations near national borders, what justifies the choice of the areas scrutinized in the paper. What differ them are patterns of tourism development and the current ways of undertaking active tourism. Not similarities but the two latter factors resulted in a distinct character of the two compared networks of trails and facilities connected with them. The system of hiking trails and tourist infrastructure seem better developed in the Krkono?e National Park, what can be explained by historical and social conditions, especially the adopted model of hiking. In the article some disadvantages of tourist infrastructure in both protected areas were presented, as well as some suggestions in terms of its development, resulting from the analysis of networks of hiking trails.展开更多
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFF1204803)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20190736)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.NJ2024029)the National Natural Science Foundation of China(Grant Nos.81701346 and 62201265).
文摘The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiological measurements,including electroencephalograph(EEG)and functional magnetic resonance imaging(fMRI),and are crucial for insights into physiological phenomena.This study introduces a novel method,the baseline perspective visibility graph(BPVG),which can analyze time series by accurately capturing connectivity across data points both above and below the baseline.We present the BPVG construction process and validate its performance using simulated signals.Results demonstrate that BPVG accurately translates periodic,random,and fractal signals into regular,random,and scale-free networks respectively,exhibiting diverse degree distribution traits.Furthermore,we apply BPVG to classify Alzheimer’s disease(AD)patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI(rs-fMRI)data.Utilizing degree distribution entropy derived from BPVG networks,our results exceed the best accuracy benchmark(77.01%)in EEG analysis,especially at channels F4(78.46%)and O1(81.54%).Additionally,our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%.These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment.In conclusion,BPVG’s validation across both simulated and real data confirms its capability to capture comprehensive information from time series,irrespective of baseline constraints,providing a novel method for studying neural physiological signals.
基金supported by the National Natural Science Foundation of China (81700088 and 81671765)the Key International (Regional) Cooperation Program of the National Natural Science Foundation of China (81420108002)+1 种基金the National Basic Research Development Program (973 Program) of China (2015CB856405)the Beijing Municipal Natural Science Foundation (7172121)
文摘The present study was aimed to evaluate restingstate functional connectivity and topological properties of brain networks in narcolepsy patients compared with healthy controls.Resting-state fMRI was performed in 26 adult narcolepsy patients and 30 matched healthy controls.MRI data were first analyzed by group independent component analysis,then a graph theoretical method was applied to evaluate the topological properties in the whole brain.Small-world network parameters and nodal topological properties were measured.Altered topological properties in brain areas between groups were selected as regionof-interest seeds,then the functional connectivity among these seeds was compared between groups.Partial correlation analysis was performed to evaluate the relationship between the severity of sleepiness and functional connectivity or topological properties in the narcolepsy patients.Twenty-one independent components out of 48 were obtained.Compared with healthy controls,the narcolepsy patients exhibited significantly decreased functional connectivity within the executive and salience networks,along with increased functional connectivity in the bilateral frontal lobes within the executive network.There were no differences in small-world network properties between patients and controls.The altered brain areas in nodal topological properties between groups were mainly in the inferior frontal cortex,basal ganglia,anterior cingulate,sensory cortex,supplementary motor cortex,and visual cortex.In the partial correlation analysis,nodal topological properties in the putamen,anterior cingulate,and sensory cortex as well as functional connectivity between these regions were correlated with the severity of sleepiness(sleep latency,REM sleep latency,and Epworth sleepiness score)among narcolepsy patients.Altered connectivity within the executive and salience networks was found in narcolepsy patients.Functional connection changes between the left frontal cortex and left caudate nucleus may be one of the parameters describing the severity of narcolepsy.Changes in the nodal topological properties in the left putamen and left posterior cingulate,changes in functional connectivity between the left supplementary motor area and right occipital as well as in functional connectivity between the left anterior cingulate gyrus and bilateral postcentral gyrus can be considered as a specific indicator for evaluating the severity of narcolepsy.
基金Supported by the Science and Technological Tackling Project of Heilongjiang Province(GB06A106)
文摘In the past 30 years,signed directed graph(SDG) ,one of the qualitative simulation technologies,has been widely applied for chemical fault diagnosis.However,SDG based fault diagnosis,as any other qualitative method,has poor diagnostic resolution.In this paper,a new method that combines SDG with qualitative trend analysis(QTA) is presented to improve the resolution.In the method,a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model.Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consis-tent paths found in the previous step.New consistency rules based on qualitative trends are used to find the real causes from the candidate causes.The resolution can be improved.This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA.The implementation of SDG-QTA based fault diagno-sis is done using the integrated SDG modeling,inference and post-processing software platform.Its application is illustrated on an atmospheric distillation tower unit of a simulation platform.The result shows its good applicability and efficiency.
基金supported by the Science and Technology Project of Henan Province(No.222102210081).
文摘Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
基金Supported by the National High Technology Research and Development Program of China(2009AA04Z133)
文摘Qualitative algebraic equations are the basis of qualitative simulation,which are used to express the dynamic behavior of steady-state continuous processes.When the values and operation of qualitative variables are redefined,qualitative algebraic equations can be transformed into signed direct graphs,which are frequently used to predict the trend of dynamic changes.However,it is difficult to use traditional qualitative algebra methods based on artificial trial and error to solve a complex problem for dynamic trends.An important aspect of modern qualitative algebra is to model and characterize complex systems with the corresponding computer-aided automatic reasoning.In this study,a qualitative affection equation based on multiple conditions is proposed,which enables the signed di-rect graphs to describe complex systems better and improves the fault diagnosis resolution.The application to an industrial case shows that the method performs well.
基金This research work is supported by Hunan Provincial Education Science 13th Five Year Plan(Grant No.XJK016BXX001)Social Science Foundation of Hunan Province(Grant No.17YBA049)+2 种基金Hunan Provincial Natural Science Foundation of China(Grant No.2017JJ2016)National Students’platform for innovation and entrepreneurship training(Grant No.201811532010)The work is also supported by Open foundation for University Innovation Platform from Hunan Province,China(Grand No.16K013)and the 2011 Collaborative Innovation Center of Big Data for Financial and Economical Asset Development and Utility in Universities of Hunan Province.We also thank the anonymous reviewers for their valuable comments and insightful suggestions.
文摘Graphical methods are used for construction.Data analysis and visualization are an important area of applications of big data.At the same time,visual analysis is also an important method for big data analysis.Data visualization refers to data that is presented in a visual form,such as a chart or map,to help people understand the meaning of the data.Data visualization helps people extract meaning from data quickly and easily.Visualization can be used to fully demonstrate the patterns,trends,and dependencies of your data,which can be found in other displays.Big data visualization analysis combines the advantages of computers,which can be static or interactive,interactive analysis methods and interactive technologies,which can directly help people and effectively understand the information behind big data.It is indispensable in the era of big data visualization,and it can be very intuitive if used properly.Graphical analysis also found that valuable information becomes a powerful tool in complex data relationships,and it represents a significant business opportunity.With the rise of big data,important technologies suitable for dealing with complex relationships have emerged.Graphics come in a variety of shapes and sizes for a variety of business problems.Graphic analysis is first in the visualization.The step is to get the right data and answer the goal.In short,to choose the right method,you must understand each relative strengths and weaknesses and understand the data.Key steps to get data:target;collect;clean;connect.
基金This work has been supported by.Central University Research Fund(No.2016MS116,No.2016MS117,No.2018MS074)the National Natural Science Foundation(51677072).
文摘Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.
基金support of the National Natural Science Foundation of China (Grant No. 41130751)China Scholarship Council, Research Program for Western China Communication (Grant No. 2011ZB04)China Central University Funding
文摘Limit equilibrium method （LEM） and strength reduction method （SRM） are the most widely used methods for slope stability analysis. However, it can be noted that they both have some limitations in practical application. In the LEM, the constitutive model cannot be considered and many assumptions are needed between slices of soil/rock. The SRM requires iterative calculations and does not give the slip surface directly. A method for slope stability analysis based on the graph theory is recently developed to directly calculate the minimum safety factor and potential critical slip surface according to the stress results of numerical simulation. The method is based on current stress state and can overcome the disadvantages mentioned above in the two traditional methods. The influences of edge generation and mesh geometry on the position of slip surface and the safety factor of slope are studied, in which a new method for edge generation is proposed, and reasonable mesh size is suggested. The results of benchmark examples and a rock slope show good accuracy and efficiency of the presented method.
基金Supported by Undergraduate Innovation and Entrepreneurship Program of Faculty of Chinese Medicine Science of Guangxi University of Chinese Medicine (Autonomous Region Level) (S202413643051)Guangxi First-class Discipline Construction Project (GJKY[2022]1).
文摘[Objectives]Based on bibliometric methods,the evolution path and knowledge structure of shampoo product research in China were systematically analyzed,with a focus on the composition,efficacy,and mechanisms of traditional Chinese medicine shampoos,aiming to reveal the shifting patterns of core technological focuses at different stages and identify key future research directions.[Methods]Using a sample of 515 publications from CNKI journals between 2005 and 2025,CiteSpace 6.3.R1 was employed to construct multi-dimensional networks of authors,institutions,and keywords.Burst detection,keyword and cluster analyses,and time zone mapping were applied to track disciplinary dynamics.[Results]The analysis on the annual number of publications indicated that research on shampoo products underwent multiple phases including initial development,growth,fluctuation,peak,and adjustment from 2005 to 2025,with an overall upward trend.The significant differences between the collaboration network density of core authors and the strength of institutional cooperation indicated a need for academia to enhance cross-institutional collaborative innovation mechanisms.Keyword clustering analysis and co-occurrence mapping revealed that the research on traditional Chinese medicine shampoo products,known for their natural,mild,and non-irritating characteristics,demonstrates notable advantages in dandruff removal,anti-hair loss,hair growth,and scalp health management,which have become current research hotspots,providing data-driven decision-making support for technological upgrading and industry-academia-research integration in the field.[Conclusions]The bibliometric analysis based on 515 publications indicates that the overall development direction of shampoo products will comprehensively advance toward refined efficacy and personalized customization,greener and more natural ingredients,technological innovation and industry-academia-research collaboration,market segmentation and diversified development,as well as safety and efficacy evaluation.This study provides theoretical support for the innovation of specialized traditional Chinese medicine shampoo products.
基金supported by the Natural Science Foundation of Henan under Grant 242300421220the Henan Provincial Science and Technology Research Project under Grants 252102211047 and 252102211062+3 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126.
文摘With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis.To address these challenges,this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception(TGICP).Specifically,we utilize a Inter-sample Commonality Perception(ICP)module to extract common features from similar samples within the same modality,and use these common features to enhance the original features of each modality,thereby obtaining a richer and more complete multimodal sentiment representation.Subsequently,in the cross-modal interaction stage,we design a Text-Gated Interaction(TGI)module,which is text-driven.By calculating the mutual information difference between the text modality and nonverbal modalities,the TGI module dynamically adjusts the influence of emotional information from the text modality on nonverbal modalities.This helps to reduce modality information asymmetry while enabling full cross-modal interaction.Experimental results show that the proposed model achieves outstanding performance on both the CMU-MOSI and CMU-MOSEI baseline multimodal sentiment analysis datasets,validating its effectiveness in emotion recognition tasks.
基金supported by National Natural Science Foundation of China(Nos.41571387,41201375 and 41501440)Tianjin Research Program of Application Foundation and Advanced Technology(No.14JCQNJC07900)+1 种基金Tianjin Science and Technology Planning Project(Nos.15ZCZDSF00390 and 14TXGCCX00015)Opening Fund of Tianjin Engineering Research Center of Geospatial Information Technology"Modeling and analysis of path graph in 3D indoor spatial environment"
文摘Due to limitations in geometric representation and semantic description, the current pedestrian route analysis models are inadequate. To express the geometry of geographic entities in a micro-spatial environment accurately, the concept of a grid is presented, and grid-based methods for modeling geospatial objects are described. The semantic constitution of a building environment and the methods for modeling rooms, corridors, and staircases with grid objects are described. Based on the topology relationship between grid objects, a grid-based graph for a building environment is presented, and the corresponding route algorithm for pedestrians is proposed. The main advantages of the graph model proposed in this paper are as follows： 1） consideration of both semantic and geometric information, 2） consideration of the need for accurate geometric representation of the micro-spatial environment and the efficiency of pedestrian route analysis, 3） applicability of the graph model to route analysis in both static and dynamic environments, and 4） ability of the multi-hierarchical route analysis to integrate the multiple levels of pedestrian decision characteristics, from the high to the low, to determine the optimal path.
文摘In this paper, a new method has been introduced to find the most vulnerable lines in the system dynamically in an interconnected power system to help with the security and load flow analysis in these networks. Using the localization of power networks, the power grid can be divided into several divisions of sub-networks in which, the connection of the elements is stronger than the elements outside of that division. By using our proposed method, the probable important lines in the network can be identified to do the placement of the protection apparatus and planning for the extra extensions in the system. In this paper, we have studied the pathfinding strategies in most vulnerable line detection in a partitioned network. The method has been tested on IEEE39-bus system which is partitioned using hierarchical spectral clustering to show the feasibility of the proposed method.
文摘In this work a method called “signal flow graph (SFG)” is presented. A signal-flow graph describes a system by its signal flow by directed and weighted graph;the signals are applied to nodes and functions on edges. The edges of the signal flow graph are small processing units, through which the incoming signals are processed in a certain form. In this case, the result is sent to the outgoing node. The SFG allows a good visual inspection into complex feedback problems. Furthermore such a presentation allows for a clear and unambiguous description of a generating system, for example, a netview. A Signal Flow Graph (SFG) allows a fast and practical network analysis based on a clear data presentation in graphic format of the mathematical linear equations of the circuit. During creation of a SFG the Direct Current-Case (DC-Case) was observed since the correct current and voltage directions was drawn from zero frequency. In addition, the mathematical axioms, which are based on field algebra, are declared. In this work we show you in addition: How we check our SFG whether it is a consistent system or not. A signal flow graph can be verified by generating the identity of the signal flow graph itself, illustrated by the inverse signal flow graph (SFG−1). Two signal flow graphs are always generated from one circuit, so that the signal flow diagram already presented in previous sections corresponds to only half of the solution. The other half of the solution is the so-called identity, which represents the (SFG−1). If these two graphs are superposed with one another, so called 1-edges are created at the node points. In Boolean algebra, these 1-edges are given the value 1, whereas this value can be identified with a zero in the field algebra.
文摘The development and the revolution of nanotechnology require more and effective methods to accurately estimating the timing analysis for any CMOS transistor level circuit. Many researches attempted to resolve the timing analysis, but the best method found till the moment is the Static Timing Analysis (STA). It is considered the best solution because of its accuracy and fast run time. Transistor level models are mandatory required for the best estimating methods, since these take into consideration all analysis scenarios to overcome problems of multiple-input switching, false paths and high stacks that are found in classic CMOS gates. In this paper, transistor level graph model is proposed to describe the behavior of CMOS circuits under predictive Nanotechnology SPICE parameters. This model represents the transistor in the CMOS circuit as nodes in the graph regardless of its positions in the gates to accurately estimating the timing analysis rather than inaccurate estimating which caused by the false paths at the gate level. Accurate static timing analysis is estimated using the model proposed in this paper. Building on the proposed model and the graph theory concepts, new algorithms are proposed and simulated to compute transistor timing analysis using RC model. Simulation results show the validity of the proposed graph model and its algorithms by using predictive Nano-Technology SPICE parameters for the tested technology. An important and effective extension has been achieved in this paper for a one that was published in international conference.
基金Sponsored by the National Natural Science Foundation of China(Youth Program)(51908063)。
文摘Taking CNKI database as the data source and measuring visual map analysis tool Citespace as auxiliary tool,domestic 843 articles in recent 15 years are analyzed,and visualization maps such as time line analysis,institutional cooperation network analysis,author collaboration network analysis,keyword co-occurrence,and emergent words analysis are drawn.Combined with the literature content analysis,four hot spots in the research field of landscape architecture microclimate in China are obtained,namely ENVI-MET,comfort,design strategy and urban green space.The research trend is thermal comfort,human body comfort and winter city.The research results can provide reference for the research on domestic garden microclimate.
基金Supported by the National Key Research and Development Program of China(No.2018YFB1702601).
文摘For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.
文摘The concepts of complementary cofactor pairs, normal double-graphs and feasible torn vertex seta are introduced. By using them a decomposition theorem for first-order cofactor C(Y) is derived. Combining it with the modified double-graph method, a new decomposition analysis-modified double-graph decomposition analysis is presented for finding symbolic network functions. Its advantages are that the resultant symbolic expressions are compact and contain no cancellation terms, and its sign evaluation is very simple.
文摘Objective To study the research status,research hotspots and development trends in the field of real-world data(RWD)through social network analysis and knowledge graph analysis.Methods RWD of the past 10 years were retrieved,and literature metrological analysis was made by using UCINET and CiteSpace from CNKI.Results and Conclusion The frequency and centrality of related keywords such as real-world study,hospital information system(HIS),drug combination,data mining and TCM are high.The clusters labeled as clinical medication and RWD contain more keywords.In recent 4 years,there are more articles involving the keywords of data specification,data authenticity,data security and information security.Among them,compound Kushen injection,HIS database and RWD are the top three keywords.It is a long-term research hotspot for Chinese and western medicine to use HIS to study clinical medication,clinical characteristics,diseases and injections.Besides,the research of RWD database has changed from construction to standardized collection and governance,which can make RWD effective.Data authenticity,data security and information security will become the new hotspots in the research of RWD.
文摘Tourist trails as a linear form of tourist infrastructure fulfill various functions(i.e. recreational, ecological, economic, social, ensuring safety). They are especially important in national parks, where in selected areas tourist penetration is allowed only along specially designed, official routes. A well-planned layout of tourist trails with appropriate facilities can help to limit the negative consequences of tourist pressure on protected natural areas. The aim of the article is a comparison of offers for active tourists in two mountain national parks(the Krkono?e National Park in the Czech Republic and the Peneda-Gerês National Park in Portugal), taking into consideration the marked hiking trails – the most frequently used type of tourist trails. As a result the level of area coverage by the networks of hiking trails was assessed, as well as their adequateness towards the needs of tourists. The descriptive analysis was based on author's personal observations. In the examination of hiking trails as part of a system, some elements of the graph theory were used, especially coefficients for topologic analysis of spatial structure. This method enables simplification of a network, comparison of various areas and making some assumptions concerning tourist infrastructure, which is a crucial factor while analyzing trails from a tourists' point of view. In both analyzed national parks the relief is quite similar, as well as their locations near national borders, what justifies the choice of the areas scrutinized in the paper. What differ them are patterns of tourism development and the current ways of undertaking active tourism. Not similarities but the two latter factors resulted in a distinct character of the two compared networks of trails and facilities connected with them. The system of hiking trails and tourist infrastructure seem better developed in the Krkono?e National Park, what can be explained by historical and social conditions, especially the adopted model of hiking. In the article some disadvantages of tourist infrastructure in both protected areas were presented, as well as some suggestions in terms of its development, resulting from the analysis of networks of hiking trails.
