In view of the problem that the multimodal transport network is vulnerable to attack and faces the risk of cascading failure,three low polarization linking strategies considering the characteristics of the multimodal ...In view of the problem that the multimodal transport network is vulnerable to attack and faces the risk of cascading failure,three low polarization linking strategies considering the characteristics of the multimodal transport network are proposed to optimize network robustness.They are the low polarization linking strategy based on the degree of nodes(D_LPLS),low polarization linking strategy based on the betweenness of nodes(B_LPLS),and low polarization linking strategy based on the closeness of nodes(C_LPLS).The multimodal transport network in the Sichuan-Tibet region is analyzed,and the optimization effects of these three strategies are compared with the random linking strategy under random attacks and intentional attacks.The results show that C_LPLS can effectively optimize the robustness of the network.Under random attacks,the advantages of C_LPLS are obvious when the ratio of increased links is less than 15%,but it has fewer advantages compared with B_LPLS when the ratio of increased links is 15%to 30%.Under intentional attacks,as the ratio of increased links goes up,the advantages of C_LPLS become more obvious.Therefore,the increase of links by C_LPLS is conducive to the risk control of the network,which can provide theoretical support for the optimization of future multimodal transport network structures.展开更多
In the current society, based on the growing development of network information technology, the teaching in many colleges and universities has also introduced it to adapt to the situation. This trend can provide more ...In the current society, based on the growing development of network information technology, the teaching in many colleges and universities has also introduced it to adapt to the situation. This trend can provide more useful conditions for students to learn, which requires students to master enough self-learning abilities to adapt to this model. The study in the paper shows that students are usually interested in autonomous learning in a multimodal environment, but the degree of strategy choice is relatively low, and the learning process is blind and passive with the lack of self-confidence. Facing the future, schools should actively integrate into network thinking, and teachers should change their roles and train and guide students’ learning strategies and learning motivations, so as to achieve better teaching results.展开更多
To the Editor:Basal cell carcinoma(BCC)is the most prevalent skin malignancy,with an increasing incidence and economic burden worldwide.[1]Various histopathological subtypes of BCC have been well described,and subtype...To the Editor:Basal cell carcinoma(BCC)is the most prevalent skin malignancy,with an increasing incidence and economic burden worldwide.[1]Various histopathological subtypes of BCC have been well described,and subtype confirmation is essential for BCC classification according to the risk of recurrence.[2]Early diagnosis and intervention are important,especially considering that the incidence of aggressive subtypes of BCC is increasing faster than that of indolent subtypes.展开更多
Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moist...Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moisture-driven weather events such as fog,rain,and snow.To address this challenge,we propose a dual-branch neural architecture that synergistically processes optical imagery and multi-source meteorological data(temperature,humidity,and wind speed).The framework employs a convolutional neural network(CNN)branch to extract visibility-related visual features from video imagery sequences,while a parallel artificial neural network(ANN)branch decodes nonlinear relationships among the meteorological factors.Cross-modal feature fusion is achieved through an adaptive weighting layer.To validate the framework,multimodal Backpropagation-VGG(BP-VGG)and Backpropagation-ResNet(BP-ResNet)models are developed and trained/tested using historical imagery and meteorological observations from Nanjing Lukou International Airport.The results demonstrate that the multimodal networks reduce retrieval errors by approximately 8%–10%compared to unimodal networks relying solely on imagery.Among the multimodal models,BP-ResNet exhibits the best performance with a mean absolute percentage error(MAPE)of 8.5%.Analysis of typical case studies reveals that visibility fluctuates rapidly while meteorological factors change gradually,highlighting the crucial role of high-frequency imaging data in intelligent visibility retrieval models.The superior performance of BP-ResNet over BP-VGG is attributed to its use of residual blocks,which enables BP-ResNet to excel in multimodal processing by effectively leveraging data complementarity for synergistic improvements.This study presents an end-to-end intelligent visibility inversion framework that directly retrieves visibility values,enhancing its applicability across industries.However,while this approach boosts accuracy and applicability,its performance in critical low-visibility scenarios remains suboptimal,necessitating further research into more advanced retrieval techniques—particularly under extreme visibility conditions.展开更多
The reflection and transmission characteristics of the guided modes in parallel-plate waveguides partially filled with one or multi chiral rods have been investigated by a method, which combines the multi- mode networ...The reflection and transmission characteristics of the guided modes in parallel-plate waveguides partially filled with one or multi chiral rods have been investigated by a method, which combines the multi- mode network theory with a rigorous mode matching procedure. The formulas of the reflection and transmis- sion coefficient matrix are derived. The numerical results for different cases are presented and have indicated that the chirality parameters and the geometrical dimensions of the chiral rods have significant influence on the reflection and transmission characteristics of the guided modes.展开更多
Traditional system optimization models for traffic network focus on the treatment of congestion, which usually have an objective of minimizing the total travel time. However, the negative externality of congestion, su...Traditional system optimization models for traffic network focus on the treatment of congestion, which usually have an objective of minimizing the total travel time. However, the negative externality of congestion, such as environment pollution, is neglected in most cases. Such models fall short in taking Greenhouse Gas （GHG） emissions and its impact on climate change into consideration. In this paper, a social-cost based system optimization （SO） model is proposed for the multimodal traffic network considering both traffic congestion and corresponding vehicle emission. Firstly, a variation inequality model is developed to formulate the equilibrium problem for such network based on the analysis of travelers＇ combined choices. Secondly, the computational models of traffic congestion and vehicle emission of whole multimodal network are proposed based on the equilibrium link-flows and the corresponding travel times. A bi-level programming model, in which the social-cost based SO model is treated as the upper-level problem and the combined equilibrium model is processed as the lower-level problem, is then presented with its solution algorithm. Finally, the proposed models are illustrated through a simple numerical example. The study results confirm and support the idea of giving the priority to the development of urban public transport, which is an effective way to achieve a sustainable urban transportation.展开更多
Image question answering (IQA) has emerged as a promising interdisciplinary topic in computer vision and natural language processing fields. In this paper, we propose a contextually guided recurrent attention model fo...Image question answering (IQA) has emerged as a promising interdisciplinary topic in computer vision and natural language processing fields. In this paper, we propose a contextually guided recurrent attention model for solving the IQA issues. It is a deep reinforcement learning based multimodal recurrent neural network. Based on compositional contextual information, it recurrently decides where to look using reinforcement learning strategy. Different from traditional 'static' soft attention, it is deemed as a kind of 'dynamic' attention whose objective is designed based on reinforcement rewards purposefully towards IQA. The finally learned compositional information incorporates both global context and local informative details, which is demonstrated to benefit for generating answers. The proposed method is compared with several state-of-the-art methods on two public IQA datasets, including COCO-QA and VQA from dataset MS COCO. The experimental results demonstrate that our proposed model outperforms those methods and achieves better performance.展开更多
Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA...Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA)is critical in improving content delivered to end users.Convolutional neural networks(CNNs)used in IQA face two common challenges.One issue is that these methods fail to provide the best representation of the image.The other issue is that the models have a large number of parameters,which easily leads to overfitting.To address these issues,the dense convolution network(DSC-Net),a deep learning model with fewer parameters,is proposed for no-reference image quality assessment(NR-IQA).Moreover,it is obvious that the use of multimodal data for deep learning has improved the performance of applications.As a result,multimodal dense convolution network(MDSC-Net)fuses the texture features extracted using the gray-level co-occurrence matrix(GLCM)method and spatial features extracted using DSC-Net and predicts the image quality.The performance of the proposed framework on the benchmark synthetic datasets LIVE,TID2013,and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.展开更多
This study presents a descriptive and prescriptive analysis of rail service subsidies for China Railway Express(CRE)in the China-Europe freight transportation market.The analysis is conducted by advanced mathematical ...This study presents a descriptive and prescriptive analysis of rail service subsidies for China Railway Express(CRE)in the China-Europe freight transportation market.The analysis is conducted by advanced mathematical modeling and programming methods.Specifically,we implemented a multicommodity multimodal freight transportation network equilib-rium model that can be used for predicting the commodity-specific mode-route cargo flow pattern and hence for assessing the effectiveness and limitations of the current CRE subsidy scheme.To properly quantify the impact of subsidies on individual shippers’decision mak-ing,the model explicitly characterizes individual shippers’mode-route choice behavior and takes into account shipping cost,transit time,capacity-induced congestion surcharge,and unobserved transportation impedances as shippers’disutility.The solution of the net-work equilibrium model resorts to a disaggregate simplicial decomposition(DSD)algo-rithm within the well-known Lagrangian relaxation framework.A bi-level network-based subsidy optimization model is constructed,in which the upper level aims at mini-mizing the sum of revenue loss and congestion charge,and the lower level is the aforemen-tioned freight transportation network equilibrium model.A tabu search procedure is proposed and implemented to derive the solution of the bi-level model.The above models and algorithms are then applied to the China-Europe containerized freight transportation network,which comprises all China-Europe liner shipping lines,all CRE service lines,and the highway networks in China and Europe.The evaluation and optimization results show that the current subsidy scheme creates an imbalanced capacity utilization pattern across CRE service lines while an optimized line-specific subsidy solution can yield note-worthy improvements in the service utilization and economic efficiency of CRE.展开更多
A weighted edge-coloured graph is a graph for which each edge is assigned both a positive weight and a discrete colour, and can be used to model transportation and computer networks in which there are multiple transpo...A weighted edge-coloured graph is a graph for which each edge is assigned both a positive weight and a discrete colour, and can be used to model transportation and computer networks in which there are multiple transportation modes. In such a graph paths are compared by their total weight in each colour, resulting in a Pareto set of minimal paths from one vertex to another. This paper will give a tight upper bound on the cardinality of a minimal set of paths for any weighted edge-coloured graph. Additionally, a bound is presented on the expected number of minimal paths in weighted edge-bicoloured graphs. These bounds indicate that despite weighted edge-coloured graphs are theoretically intractable, amenability to computation is typically found in practice.展开更多
基金The National Key Research and Development Program of China(No.2018YFB1601400)。
文摘In view of the problem that the multimodal transport network is vulnerable to attack and faces the risk of cascading failure,three low polarization linking strategies considering the characteristics of the multimodal transport network are proposed to optimize network robustness.They are the low polarization linking strategy based on the degree of nodes(D_LPLS),low polarization linking strategy based on the betweenness of nodes(B_LPLS),and low polarization linking strategy based on the closeness of nodes(C_LPLS).The multimodal transport network in the Sichuan-Tibet region is analyzed,and the optimization effects of these three strategies are compared with the random linking strategy under random attacks and intentional attacks.The results show that C_LPLS can effectively optimize the robustness of the network.Under random attacks,the advantages of C_LPLS are obvious when the ratio of increased links is less than 15%,but it has fewer advantages compared with B_LPLS when the ratio of increased links is 15%to 30%.Under intentional attacks,as the ratio of increased links goes up,the advantages of C_LPLS become more obvious.Therefore,the increase of links by C_LPLS is conducive to the risk control of the network,which can provide theoretical support for the optimization of future multimodal transport network structures.
文摘In the current society, based on the growing development of network information technology, the teaching in many colleges and universities has also introduced it to adapt to the situation. This trend can provide more useful conditions for students to learn, which requires students to master enough self-learning abilities to adapt to this model. The study in the paper shows that students are usually interested in autonomous learning in a multimodal environment, but the degree of strategy choice is relatively low, and the learning process is blind and passive with the lack of self-confidence. Facing the future, schools should actively integrate into network thinking, and teachers should change their roles and train and guide students’ learning strategies and learning motivations, so as to achieve better teaching results.
基金This work was supported by grants from CAMS Innovation Fund for Medical Sciences(CIFMS)(No.2022-I2M-C&T-A-007)the National Natural Science Foundation of China(No.92354307)the Fundamental Research Funds for the Central Universities(No.2023RC09).
文摘To the Editor:Basal cell carcinoma(BCC)is the most prevalent skin malignancy,with an increasing incidence and economic burden worldwide.[1]Various histopathological subtypes of BCC have been well described,and subtype confirmation is essential for BCC classification according to the risk of recurrence.[2]Early diagnosis and intervention are important,especially considering that the incidence of aggressive subtypes of BCC is increasing faster than that of indolent subtypes.
基金Foundation of Key Laboratory of Smart Earth(KF2023ZD03-02)China Meteorological Administration Innovation development project(CXFZ2025J116)+1 种基金National Natural Science Foundation of China(42205197)Basic Research Fund of CAMS(2022Y023,2022Y025)。
文摘Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moisture-driven weather events such as fog,rain,and snow.To address this challenge,we propose a dual-branch neural architecture that synergistically processes optical imagery and multi-source meteorological data(temperature,humidity,and wind speed).The framework employs a convolutional neural network(CNN)branch to extract visibility-related visual features from video imagery sequences,while a parallel artificial neural network(ANN)branch decodes nonlinear relationships among the meteorological factors.Cross-modal feature fusion is achieved through an adaptive weighting layer.To validate the framework,multimodal Backpropagation-VGG(BP-VGG)and Backpropagation-ResNet(BP-ResNet)models are developed and trained/tested using historical imagery and meteorological observations from Nanjing Lukou International Airport.The results demonstrate that the multimodal networks reduce retrieval errors by approximately 8%–10%compared to unimodal networks relying solely on imagery.Among the multimodal models,BP-ResNet exhibits the best performance with a mean absolute percentage error(MAPE)of 8.5%.Analysis of typical case studies reveals that visibility fluctuates rapidly while meteorological factors change gradually,highlighting the crucial role of high-frequency imaging data in intelligent visibility retrieval models.The superior performance of BP-ResNet over BP-VGG is attributed to its use of residual blocks,which enables BP-ResNet to excel in multimodal processing by effectively leveraging data complementarity for synergistic improvements.This study presents an end-to-end intelligent visibility inversion framework that directly retrieves visibility values,enhancing its applicability across industries.However,while this approach boosts accuracy and applicability,its performance in critical low-visibility scenarios remains suboptimal,necessitating further research into more advanced retrieval techniques—particularly under extreme visibility conditions.
基金Supported by the National Natural Science Foundation of China (No.60307003, No.60371010) and the Natural Science Foundation of Zhejiang Province (No.602153).
文摘The reflection and transmission characteristics of the guided modes in parallel-plate waveguides partially filled with one or multi chiral rods have been investigated by a method, which combines the multi- mode network theory with a rigorous mode matching procedure. The formulas of the reflection and transmis- sion coefficient matrix are derived. The numerical results for different cases are presented and have indicated that the chirality parameters and the geometrical dimensions of the chiral rods have significant influence on the reflection and transmission characteristics of the guided modes.
基金supported by National Natural Science Foundation of China under Grant Nos.71071016,71131001National Basic Research Program of China under Grant No.2012CB725400supported by Fundamental Research Funds for the Central Universities under Grant Nos.2012JBM056,2012JBZ005
文摘Traditional system optimization models for traffic network focus on the treatment of congestion, which usually have an objective of minimizing the total travel time. However, the negative externality of congestion, such as environment pollution, is neglected in most cases. Such models fall short in taking Greenhouse Gas （GHG） emissions and its impact on climate change into consideration. In this paper, a social-cost based system optimization （SO） model is proposed for the multimodal traffic network considering both traffic congestion and corresponding vehicle emission. Firstly, a variation inequality model is developed to formulate the equilibrium problem for such network based on the analysis of travelers＇ combined choices. Secondly, the computational models of traffic congestion and vehicle emission of whole multimodal network are proposed based on the equilibrium link-flows and the corresponding travel times. A bi-level programming model, in which the social-cost based SO model is treated as the upper-level problem and the combined equilibrium model is processed as the lower-level problem, is then presented with its solution algorithm. Finally, the proposed models are illustrated through a simple numerical example. The study results confirm and support the idea of giving the priority to the development of urban public transport, which is an effective way to achieve a sustainable urban transportation.
文摘Image question answering (IQA) has emerged as a promising interdisciplinary topic in computer vision and natural language processing fields. In this paper, we propose a contextually guided recurrent attention model for solving the IQA issues. It is a deep reinforcement learning based multimodal recurrent neural network. Based on compositional contextual information, it recurrently decides where to look using reinforcement learning strategy. Different from traditional 'static' soft attention, it is deemed as a kind of 'dynamic' attention whose objective is designed based on reinforcement rewards purposefully towards IQA. The finally learned compositional information incorporates both global context and local informative details, which is demonstrated to benefit for generating answers. The proposed method is compared with several state-of-the-art methods on two public IQA datasets, including COCO-QA and VQA from dataset MS COCO. The experimental results demonstrate that our proposed model outperforms those methods and achieves better performance.
文摘Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA)is critical in improving content delivered to end users.Convolutional neural networks(CNNs)used in IQA face two common challenges.One issue is that these methods fail to provide the best representation of the image.The other issue is that the models have a large number of parameters,which easily leads to overfitting.To address these issues,the dense convolution network(DSC-Net),a deep learning model with fewer parameters,is proposed for no-reference image quality assessment(NR-IQA).Moreover,it is obvious that the use of multimodal data for deep learning has improved the performance of applications.As a result,multimodal dense convolution network(MDSC-Net)fuses the texture features extracted using the gray-level co-occurrence matrix(GLCM)method and spatial features extracted using DSC-Net and predicts the image quality.The performance of the proposed framework on the benchmark synthetic datasets LIVE,TID2013,and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.
基金sponsored by National Natural Science Foundation of China(Nos.72171175 and 71771150)the Open Foundation of Key Laboratory of Transport Industry of Comprehensive Transportation Theory,and the Fundamental Research Funds of Central Universities at Tongji University.
文摘This study presents a descriptive and prescriptive analysis of rail service subsidies for China Railway Express(CRE)in the China-Europe freight transportation market.The analysis is conducted by advanced mathematical modeling and programming methods.Specifically,we implemented a multicommodity multimodal freight transportation network equilib-rium model that can be used for predicting the commodity-specific mode-route cargo flow pattern and hence for assessing the effectiveness and limitations of the current CRE subsidy scheme.To properly quantify the impact of subsidies on individual shippers’decision mak-ing,the model explicitly characterizes individual shippers’mode-route choice behavior and takes into account shipping cost,transit time,capacity-induced congestion surcharge,and unobserved transportation impedances as shippers’disutility.The solution of the net-work equilibrium model resorts to a disaggregate simplicial decomposition(DSD)algo-rithm within the well-known Lagrangian relaxation framework.A bi-level network-based subsidy optimization model is constructed,in which the upper level aims at mini-mizing the sum of revenue loss and congestion charge,and the lower level is the aforemen-tioned freight transportation network equilibrium model.A tabu search procedure is proposed and implemented to derive the solution of the bi-level model.The above models and algorithms are then applied to the China-Europe containerized freight transportation network,which comprises all China-Europe liner shipping lines,all CRE service lines,and the highway networks in China and Europe.The evaluation and optimization results show that the current subsidy scheme creates an imbalanced capacity utilization pattern across CRE service lines while an optimized line-specific subsidy solution can yield note-worthy improvements in the service utilization and economic efficiency of CRE.
基金supported by Católica del Maule University Through the Project MECESUP–UCM0205
文摘A weighted edge-coloured graph is a graph for which each edge is assigned both a positive weight and a discrete colour, and can be used to model transportation and computer networks in which there are multiple transportation modes. In such a graph paths are compared by their total weight in each colour, resulting in a Pareto set of minimal paths from one vertex to another. This paper will give a tight upper bound on the cardinality of a minimal set of paths for any weighted edge-coloured graph. Additionally, a bound is presented on the expected number of minimal paths in weighted edge-bicoloured graphs. These bounds indicate that despite weighted edge-coloured graphs are theoretically intractable, amenability to computation is typically found in practice.
