This paper reviews the important role of vehicle platooning in road transportation, analyzing key aspects including modeling, communication, and control strategies. It provides a detailed examination of following and ...
This paper reviews the important role of vehicle platooning in road transportation, analyzing key aspects including modeling, communication, and control strategies. It provides a detailed examination of following and lane-changing models, emphasizing their significance in autonomous platooning control, while also identifying potential future directions for model development. The paper explores key communication technologies like Vehicle-to-Vehicle (V2V) communication and 5 G, discussing their applications in platooning systems. In terms of control strategies, the paper delves into both vertical and horizontal control methods for platooning, evaluating the stability of control mechanisms. This review aims to offer a comprehensive understanding of platooning technology and presents valuable insights to guide future research and development in this field.
Histopathology and transcriptomics are fundamental modalities in cancer diagnostics, encapsulating the morphological and molecular characteristics of the disease. Multi-modal self-supervised learning has demonstrated ...
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Histopathology and transcriptomics are fundamental modalities in cancer diagnostics, encapsulating the morphological and molecular characteristics of the disease. Multi-modal self-supervised learning has demonstrated remarkable potential in learning pathological representations by integrating diverse data sources. Conventional multi-modal integration methods primarily emphasize modality alignment, while paying insufficient attention to retaining the modality-specific intrinsic structures. However, unlike conventional scenarios where multi-modal inputs often share highly overlapping features, histopathology and transcriptomics exhibit pronounced heterogeneity, offering orthogonal yet complementary insights. Histopathology data provides morphological and spatial context, elucidating tissue architecture and cellular topology, whereas transcriptomics data delineates molecular signatures through quantifying gene expression patterns. This inherent disparity introduces a major challenge in aligning these modalities while maintaining modality-specific fidelity. To address these challenges, we present MIRROR, a novel multi-modal representation learning framework designed to foster both modality alignment and retention. MIRROR employs dedicated encoders to extract comprehensive feature representations for each modality, which is further complemented by a modality alignment module to achieve seamless integration between phenotype patterns and molecular profiles. Furthermore, a modality retention module safeguards unique attributes from each modality, while a style clustering module mitigates redundancy and enhances disease-relevant information by modeling and aligning consistent pathological signatures within a clustering space. Extensive evaluations on The Cancer Genome Atlas (TCGA) cohorts for cancer subtyping and survival analysis highlight MIRROR’s superior performance, demonstrating its effectiveness in constructing comprehensive oncological feature representations and be
The scheduling problem in Hurdle (1973) was formulated in a general form that simultaneously concerned the vehicle dispatching, circulating, fleet sizing, and patron queueing. As a constrained variational problem, it ...
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Based on the characteristics of the air alliance environment saving transport mileage,the hub location problem of the air cargo network was ***,the air alliance selection probability model was introduced to determine ...
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Based on the characteristics of the air alliance environment saving transport mileage,the hub location problem of the air cargo network was ***,the air alliance selection probability model was introduced to determine the alliance self-operation or outsourcing probability in different ***,according to the location center rule,with the goal of minimizing the total cost,the hub location model was *** improved immune chaos genetic algorithm was used to solve this *** results show that the improved algorithm has stronger convergence and better effect than the immune genetic *** the number of hubs increases,the fixed cost increases,but the transportation cost *** greater the discount factor,the fixed cost,and the self operating cost sharing coefficient,the higher the total network *** airline which joins the air alliance can greatly reduce the operating cost of ***,airlines should consider joining the alliance.
Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing ...
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This paper develops a heterogeneous graph neural network (HetGNN) model, which can dispose of dissimilarly attributed nodes (i.e., nodes with different input features), to investigate the evolution of railway network ...
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This paper develops a heterogeneous graph neural network (HetGNN) model, which can dispose of dissimilarly attributed nodes (i.e., nodes with different input features), to investigate the evolution of railway network delay by predicting the delays of running trains (RTs). A graph architecture combining the HetGNN model and the GraphSAGE homogeneous graph neural network (HomoGNN), called SAGE-Het, is proposed. This architecture considers four kinds of nodes, namely RTs, terminated trains (TTs), passing stations (PSs), and terminated stations (TSs), and can capture the effects of the interactions between trains and other trains (e.g., the train headways), trains and stations (e.g., the scheduled remaining train running times to the downstream station), and stations and other stations (e.g., the minimum running times) on delay evolution. In contrast to the traditional machine learning methods that require the inputs to have the same formats (e.g., in rectangular or grid-like arrays) and the HomoGNNs that do not allow for dissimilarly attributed nodes in the graph, SAGE-Het allows for flexible inputs. The data from two areas of the China railway network, namely the Guangzhou South network (GZS-Net) and the Changsha South network (CSS-Net), are applied to test the performance and robustness of the proposed SAGE-Het model. The experimental results show that SAGE-Het exhibits better performance than the existing delay prediction methods and some advanced HetGNNs used for other prediction tasks for both short and long delays. Subsequently, the influences of train interactions on delay propagation are investigated. The results show that train interactions are insignificant when the train headways are long (e.g., when the headways are over 20 min, cutting the edges does not decrease the prediction performance). Ultimately, it is found that the predictive performances of SAGE-Het under different prediction time horizons (10/20/30 min ahead) all outperform the performances of o
Elderly pedestrians are one of the groups that are most vulnerable to traffic accidents among all ages. Exploring the safety awareness of elderly pedestrians is of significant importance in enacting countermeasures fo...
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Accurate modeling of lower-level controller plays an important role in the traffic flow of automated vehicles (AVs). However, there lacks enough attention with this respect. To address this issue, we conduct a field e...
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Elderly pedestrians are one of the groups that are most vulnerable to traffic accidents among all ages. Exploring the safety awareness of elderly pedestrians is of significant importance in enacting countermeasures fo...
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The Driver Behavior Questionnaire (DBQ) is one of the most widely used tool for measuring self-reported driving style and behavior. Nigeria, which is among the top countries with high rate of accident is lacking in be...
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