Topology Property Based on Network Tomography for Wireless Mobile Multihop Communication Network by Danyang Qin, Lin Ma, Qun Ding

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Pulmonary Disease due to Mycobacterium tuberculosis in a Horse: Zoonotic Concerns and Limitations of Antemortem Testing by Konstantin P. Lyashchenko, Rena Greenwald, Javan Esfandiari, Alexis Lecu, W. Ray Waters, Horst Posthaus, Thomas Bodmer, Jean-Paul Janssens, Fabio Aloisio, Claudia Graubner, Eléonore Grosclaude, Alessandra Piersigilli, Irene Schiller

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Post disaster repair strategy for distribution network based on hybrid power supply mode by Shaofan Zhang, Yanchun Cai, Yanhong Li, Yingjie Huang, Zhengtian Li, Ran Lv

“…Furthermore, on the basis of fully considering the constraints of power grid, road network and the logic of emergency repair task, the work plan of road emergency repair and power emergency repair is arranged as a whole with the objective of minimizing the weight of power loss of all load nodes. …”
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Solving Minimum Cost Three-Dimensional Localization Problem in Ocean Sensor Networks by Chao Zhang, Yingjian Liu, Zhongwen Guo, Yu Wang

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Total neoadjuvant therapy integrating long-course radiotherapy with trifluridine/tipiracil for locally advanced rectal cancer: a single-arm, multicenter phase 2 trialResearch in co... by Xue Dou, Lina Zhao, Zhenyu Ma, Lichun Wei, Xin Jiang, Yi Ding, Shichang Sun, Yanlai Sun, Minghuan Li, Rui Feng, Kunli Zhu, Fang Shi, Kai Liu, Minghao Li, Yongsheng Gao, Peng Su, Shanshan Li, Jinming Yu, Jinbo Yue

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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Predictive Analysis of Settlement Risk in Tunnel Construction: A Bow-Tie-Bayesian Network Approach by Wen Liu, Shihong Zhai, Wenli Liu

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Neuromorphic Sensor Network Platform: A Bioinspired Tool to Grow Applications in Wireless Sensor Networks by Mark Richard Wilby, Ana Belén Rodríguez González, Juan José Vinagre Díaz, Jesús Requena Carrión

“…Finally, we describe a real implementation of the NSN platform in a road traffic monitoring and information system currently in operation in the cities of Madrid and Seville (Spain).…”
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Research on optimal control design of displaced left turn signal at one two way and three one way traffic intersections by Ning Han, Guozhu Cheng, Jiadong Lin, Zhiyun Tang, Fei Xie

“…Abstract As the nodes of urban road traffic network, intersections serve as an effective means to improve traffic flow efficiency and alleviate traffic congestion through the optimization of reasonable traffic organization and signal schemes for intersections. …”
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Prevalence of Clinically Significant Extraosseous Findings on Unenhanced CT Portions of 18F-Fluoride PET/CT Bone Scans by Chao-Jung Chen, Shih-Ya Ma

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Emergent Self‐Adaptation in an Integrated Photonic Neural Network for Backpropagation‐Free Learning by Alessio Lugnan, Samarth Aggarwal, Frank Brückerhoff‐Plückelmann, C. David Wright, Wolfram H. P. Pernice, Harish Bhaskaran, Peter Bienstman

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Power-Management Techniques for Wireless Sensor Networks and Similar Low-Power Communication Devices Based on Nonrechargeable Batteries by Agnelo Silva, Mingyan Liu, Mahta Moghaddam

“…However, two trade-offs are identified: a significant increase of both data latency and hardware/software complexity. Unattended nodes deployed in outdoors under extreme temperatures, buried sensors (underground communication), and nodes embedded in the structure of buildings, bridges, and roads are some of the target scenarios for this work. …”
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Pancreatic Tuberculosis with Vascular Involvement and Peritoneal Dissemination in a Young Man by Meng Zhu, Ning Zhang, Wei Tao, Zhitao Wang, Shuixiang He

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Assessing Urban Land Parcel Dynamics Driven by Bus Rapid Transit (BRT) as an Exclusive Transit Route by Rana Tahir Mehmood, Muhammad Zaly Shah, Mehdi Moeinaddini, Muhammad Mashhood Arif, Ramine Chuhdary, Mufeeza Tahira

“…This study applies the C5.0 decision tree algorithm, a non-parametric model that creates a decision tree with leaf nodes that predict the relationship. Using the BRT Lahore case study, the time series data of parcel variables in the 2 km circle of five transit stations before BRT 2010 and after BRT 2018, as well as transit route characteristics including feeder routes and road infrastructure, were collected and analyzed. …”
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Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction by Song Zhang, Yanbing Liu, Yunpeng Xiao, Rui He

“…Specifically, in spatial dimension, we construct an upsampled road network by adding virtual nodes to the original road network to capture local and global spatial correlations. …”
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Construction and Optimization of Ecological Network in Guanzhong Region of Shaanxi Province Based on PLUS-InVEST-Linkage Mapper by Mingming Zhuge, Yuandong Hu

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Multiple Signals Can Be Integrated into Pathways of Blue-Light-Mediated Floral Transition: Possible Explanations on Diverse Flowering Responses to Blue Light Manipulation by Yun Kong, Youbin Zheng

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Urban Functional Zone Mapping by Integrating Multi-Source Data and Spatial Relationship Characteristics by Daoyou Zhu, Xu Dang, Wenjia Shi, Yixiang Chen, Wenmei Li

“…This framework leverages the OpenStreetMap (OSM) road network to partition the study area into functional units, employs a graph model to represent urban functional nodes and their intricate spatial topological relationships, and harnesses the capabilities of Graph Convolutional Network (GCN) to fuse these multi-dimensional features through end-to-end learning for accurate urban function discrimination. …”
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Enhancing security in IIoT: RFID authentication protocol for edge computing and blockchain-enabled supply chain by Vikash Kumar, Santosh Kumar Das

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