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Structural network alignment utilizes the topological structure information to find correspondences between nodes of two networks. Researchers have proposed a line of useful algorithms which usually require a prior mapping of seed...
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Structural network alignment utilizes the topological structure information to find correspondences between nodes of two networks. Researchers have proposed a line of useful algorithms which usually require a prior mapping of seeds acting as landmark points to align the rest nodes. Several seed-free algorithms are developed to solve the cold-start problem. However, existing approaches suffer high computational cost and low reliability, limiting their applications to large-scale network alignment. Moreover, there is a lack of useful metrics to quantify the credibility of seed mappings. To address these issues, we propose a credible seed identification framework and develop a metric to assess the reliability of a mapping. To tackle the cold-start problem, we employ graph embedding techniques to represent nodes by structural feature vectors in a latent space. We then leverage point set registration algorithms to match nodes algebraically and obtain an initial mapping of nodes. Besides, we propose a heuristic algorithm to improve the credibility of the initial mapping by filtering out mismatched node pairs. To tackle the computational problem in large-scale network alignment, we propose a divide-and-conquer scheme to divide large networks into smaller ones and then match them individually. It significantly improves the recall of mapping results. Finally, we conduct extensive experiments to evaluate the effectiveness and efficiency of our new approach. The results illustrate that the proposed method outperforms the state-of-the-art approaches in terms of both effectiveness and efficiency.
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Spatial data infrastructures, which are characterized by multi-represented datasets, are prevalent throughout the world. The multi-represented datasets contain different representations for identical real-world entities. Therefore...
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Spatial data infrastructures, which are characterized by multi-represented datasets, are prevalent throughout the world. The multi-represented datasets contain different representations for identical real-world entities. Therefore, update propagation is useful and required for maintaining multi-represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real-world entities and the detection of changes in updated datasets. Using polygon features of settlements as examples, this article addresses these key problems and proposes an approach for multi-represented feature matching based on spatial similarity and a back-propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in contrast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one-to-one matching but also to one-to-many and many-to-many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to demonstrate the weight differences in different data contexts and to measure the performance of the BPNN-based feature matching approach.
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Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow pattern...
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Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow patterns between days, and it is difficult to consider the impact of dynamic changes in daily travel demand analysis when supporting related decision making. Taxi trajectory data have been widely used in urban taxi travel-pattern analysis. This paper uses the taxi datasets of Shenzhen and New York to analyze and compare the interday stability of the taxi travel spatial structure and the flow volume based on the improved Levenshtein algorithm and geographic flow theory. The results show that (1) interday differences in taxi travel flow are obvious in both spatial structure and flow volume, high-frequency origin–destination (OD) trips are relatively stable; (2) the ODs between the central urban area and surrounding areas exhibit high traffic volume and high interday stability, and the ODs starting or ending at an airport exhibit high traffic stability; (3) one week’s data can describe 86% of the overall travel structure and 84% of travel flow in Shenzhen, and one week’s New York data can describe 73% of travel structure and 76% of travel flow. There are differences in the travel patterns of people in different cities, and the representativeness of datasets in different cities will be different. These findings can help to better understand the outcomes of taxi travel patterns derived from a relatively short period of data to avoid potential misuse in related decision making.
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This study proposes a novel space station remote manipulator system (SSRMS)-type reconfig-urable space manipulator to enhance the adaptability of the traditional SSRMS-type manipulators in complex on-orbit tasks. The proposed mani...
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This study proposes a novel space station remote manipulator system (SSRMS)-type reconfig-urable space manipulator to enhance the adaptability of the traditional SSRMS-type manipulators in complex on-orbit tasks. The proposed manipulator has two lockable passive telescopic links (LPTLs) and allows changing their lengths to achieve reconfigurability. The inverse kinematics (IK) and the workspace for this manipulator are minutely analyzed. We develop a new CCDJAP-IK method to solve the IK problem by combining the cyclic coordinate descent (CCD) and joint angle parameterization (JAP) methods. This new method addresses the CCD method's convergence instability and the JAP method's high dependence on the pre-set value of the redundancy parameter. In particular, our approach is insensitive to the initial and singular configurations and can generate multiple exact solutions that satisfy the joint limit constraints. A reachability sphere map captures the proposed manipulator's kinematic capability for the workspace analysis. Then the effects of the two LPTLs on this capability are deeply explored. Based on the results, we propose an information-driven optimal configuration search method. Several typical simulations validate the proposed methods' effectiveness.
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In this study, a lithium-ion battery hybrid cooling system is designed to cool the battery using nanofluid and extended surfaces (microchannels). The battery connected to a solar system is evaluated during charging mode. Initially...
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In this study, a lithium-ion battery hybrid cooling system is designed to cool the battery using nanofluid and extended surfaces (microchannels). The battery connected to a solar system is evaluated during charging mode. Initially, according to topology optimization based on the maximum heat transfer and minimum pressure drop, the model with maximum efficiency is proposed. A non-Newtonian hybrid nanofluid containing carbon nanotubes flows inside it. Numerical simulations are performed for this system. The Galerkin finite element algorithm is used to perform the calculations and the simulated results are performed to analyze the flow behavior and heat transfer of non-Newtonian nanofluid around the battery. The results demonstrate that the use of the A-3 model at the Reynolds number of 100 creates the lowest maximum temperature in the battery cell, which is 0.4 degrees C lower than that in the classic model. Also, at a Reynolds number of 1000, model A-2 has the lowest maximum temperature among different models, which is 0.11 degrees C lower than the classic model. The use of hybrid nanoparticles in all models reduces initial temperature value and enhances the amount of pressure drop at all Reynolds numbers. Among these, the classic model has the maximum and the A-4 model has the minimum pressure drop among different models at different Reynolds numbers, especially Re =1000. Finally, it is revealed that adding carbon nanotubes to the base fluid and using the nanofluids in microchannels to cool the battery improves battery performance and enhances battery efficiency.
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Mobile phone location data have been extensively used to understand human mobility patterns through the employment of mobility indicators. The temporal sampling interval (TSI), which is measured by the temporal interval between co...
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Mobile phone location data have been extensively used to understand human mobility patterns through the employment of mobility indicators. The temporal sampling interval (TSI), which is measured by the temporal interval between consecutive records, determines how well such data can describe human activities and influence the values of human mobility indicators. However, systematic investigations of how the TSI affects human mobility indicators remain scarce, and characterizing those relationships is a fundamental research question for many related studies. This study uses a mobile phone location dataset containing 19,370 intensively sampled individual trajectories (TSI < 5 minutes) to systematically assess the impacts of the TSI on four typical mobility indicators that describe human mobility patterns from different aspects, which are movement entropy, radius of gyration, eccentricity, and daily travel frequency. We find that different TSIs have complex impacts on the values of different mobility indicators. Specifically, (1) coarser TSIs tend to underestimate the values of the four selected indicators with different degrees; (2) the degrees of underestimation vary significantly among users for eccentricity and daily travel frequency but exhibit high inter-user consistency for radius of gyration and movement entropy. The above findings can help better understand the variations among human mobility studies.
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Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger s...
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Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.
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Mg-based hydrogen storage alloys have become a research hotspot in recent years owing to their high hydrogen storage capacity, good reversibility of hydrogen absorption/desorption, low cost, and abundant resources. However, its hi...
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Mg-based hydrogen storage alloys have become a research hotspot in recent years owing to their high hydrogen storage capacity, good reversibility of hydrogen absorption/desorption, low cost, and abundant resources. However, its high thermodynamic stability and slow kinetics limit its application, so the modification of Mg-based hydrogen storage alloys has become the development direction of Mg-based alloys. Transition metals can be used as catalysts for the dehydrogenation of hydrogen storage alloys due to their excellent structural, electrical, and magnetic properties. Graphene, because of its unique sp(2) hybrid structure, excellent chemical stability, and a specific surface area of up to 2600 m(2)/g, can be used as a support for transition metal catalysts. In this paper, the internal mechanism of graphene as a catalyst for the catalysis of Mg-based hydrogen storage alloys was analyzed, and the hydrogen storage properties of graphene-catalyzed Mg-based hydrogen storage alloys were reviewed. The effects of graphene-supported different catalysts (transition metal, transition metal oxides, and transition metal compounds) on the hydrogen storage properties of Mg-based hydrogen storage alloys were also reviewed. The results showed that graphene played the roles of catalysis, co-catalysis, and inhibition of grain aggregation and growth in Mg-based hydrogen storage materials. (C) 2021 Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC.
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Turbine cooling is an effective way to improve the comprehensive performance and service life of gas turbines. In recent decades, there has been rapid growth in research into external cooling and internal cooling methods. As a res...
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Turbine cooling is an effective way to improve the comprehensive performance and service life of gas turbines. In recent decades, there has been rapid growth in research into external cooling and internal cooling methods. As a result, there is a significant amount of experimental and numerical data. However, due to their multi-source nature, the datasets have different degrees of fidelity and different data structures, which hinder the effective use of the data. Besides, high-fidelity (HF) data often have high acquisition costs, which hinder their application in aerospace. A novel form of data fusion is introduced in this paper. We integrate multi-source data using special algorithms to produce more reliable data. A deep-learning neural network with the PointNet architecture is designed to establish two surrogate models: a high-fidelity model (HF model) trained by experimental data and a low-fidelity model (LF model) based on Reynolds-averaged Navier-Stokes simulation data. Both models predict results with less than 1% reference errors compared to their respective ground truth at most data points. In addition, we explore the role of transfer learning in multi-fidelity modeling. A fusion algorithm based on a Gaussian function and a weighted average strategy is proposed to combine the values from the HF model and the LF model. The presented results show that the fusion data are more accurate than computational fluid dynamics data, successfully meeting the goal of reducing the cost of data acquisition.
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The pyrolysis of polymers infiltrated with combustible liquids is one common behavior in the fire accidents resulted by the leakage and overflow of combustible liquids over polymers. The present study may provide a theoretical bas...
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The pyrolysis of polymers infiltrated with combustible liquids is one common behavior in the fire accidents resulted by the leakage and overflow of combustible liquids over polymers. The present study may provide a theoretical basis and guidance for the prediction of fire development, the selection or design of fire detection and the design of ventilation and fire extinguishing systems. In the present study, the thermal decomposition be-haviors, kinetics, thermodynamics, volatiles and chemical reactions of micron polypropylene (typical polymer) infiltrated with kerosene (typical combustible liquid) are investigated. It is concluded that the thermal decom-position process of micron PP with kerosene can be divided into two stages, and all stages can be considered as one-step reaction. As the kerosene content increases, the average and peak reaction rates of the first stage in-crease, while they decrease for the second stage and the whole process. As the heating rate increases, the average and peak reaction rates of the whole process decrease. In addition, increasing the heating rate or kerosene content may result in better combustion performance compared with that of pure PP. The calculated average activation energy, pre-exponential factor and global reaction model of the second stage can well predict the thermal decomposition behaviors of this stage. Thermodynamic analysis shows that the thermal decomposition of micron PP with kerosene is an endothermic and non-spontaneous reaction. The main volatile products contain olefins, alkynes, alkanes, hydrogen, carbon dioxide, carbon monoxide and aromatic compounds. The possible chemical reactions generating the above-mentioned products are deduced.
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