112 research outputs found

    A Cloud-Computing-Based Data Placement Strategy in High-Speed Railway

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    As an important component of China’s transportation data sharing system, high-speed railway data sharing is a typical application of data-intensive computing. Currently, most high-speed railway data is shared in cloud computing environment. Thus, there is an urgent need for an effective cloud-computing-based data placement strategy in high-speed railway. In this paper, a new data placement strategy named hierarchical structure data placement strategy is proposed. The proposed method combines the semidefinite programming algorithm with the dynamic interval mapping algorithm. The semi-definite programming algorithm is suitable for the placement of files with various replications, ensuring that different replications of a file are placed on different storage devices, while the dynamic interval mapping algorithm ensures better self-adaptability of the data storage system. A hierarchical data placement strategy is proposed for large-scale networks. In this paper, a new theoretical analysis is provided, which is put in comparison with several other previous data placement approaches, showing the efficacy of the new analysis in several experiments

    The Analysis and Calculation Method of Urban Rail Transit Carrying Capacity Based on Express-Slow Mode

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    Urban railway transport that connects suburbs and city areas is characterized by uneven temporal and spatial distribution in terms of passenger flow and underutilized carrying capacity. This paper aims to develop methodologies to measure the carrying capacity of the urban railway by introducing a concept of the express-slow mode. We first explore factors influencing the carrying capacity under the express-slow mode and the interactive relationships among these factors. Then we establish seven different scenarios to measure the carrying capacity by considering the ratio of the number of the express trains and the slow trains, the station where overtaking takes place, and the number of overtaking maneuvers. Taking Shanghai Metro Line 16 as an empirical study, the proposed methods to measure the carrying capacity under different express-slow mode are proved to be valid. This paper contributes to the literature by remodifying the traditional methods to measure the carrying capacity when different express-slow modes are applied to improve the carrying capacity of the suburban railway

    Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model

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    Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation

    Efficient Processing of Continuous Skyline

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    The analyzing and processing of multisource real-time transportation data stream lay a foundation for the smart transportation's sensibility, interconnection, integration, and real-time decision making. Strong computing ability and valid mass data management mode provided by the cloud computing, is feasible for handling Skyline continuous query in the mass distributed uncertain transportation data stream. In this paper, we gave architecture of layered smart transportation about data processing, and we formalized the description about continuous query over smart transportation data Skyline. Besides, we proposed mMR-SUDS algorithm (Skyline query algorithm of uncertain transportation stream data based on micro-batchinMap Reduce) based on sliding window division and architecture

    Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

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    Most existing machine learning approaches for analysing thermograms mainly focus on either thermal images or pixel-wise temporal profiles of specimens. To fully leverage useful information in thermograms, this article presents a novel spatiotemporal-based deep learning model incorporating an attention mechanism. Using captured thermal image sequences, the model aims to better characterise barely visible impact damages (BVID) in composite materials caused by different impact energy levels. This model establishes the relationship between patterns of BVID in thermography and their corresponding impact energy levels by learning from spatial and temporal information simultaneously. Validation of the model using 100 composite specimens subjected to five different low-velocity impact forces demonstrates its superior performance with a classification accuracy of over 95%. The proposed approach can contribute to Structural Health Monitoring (SHM) community by enabling cause analysis of impact incidents based on predicting the potential impact energy levels. This enables more targeted predictive maintenance, which is especially significant in the aviation industry, where any impact incidents can have catastrophic consequences

    Impact of multidisciplinary team on the pattern of care for brain metastasis from breast cancer

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    PurposeThe aim of this study was to explore how a multidisciplinary team (MDT) affects patterns of local or systematic treatment.MethodsWe retrospectively reviewed the data of consecutive patients in the breast cancer with brain metastases (BCBM) database at our institution from January 2011 to April 2021. The patients were divided into an MDT group and a non-MDT group.ResultsA total of 208 patients were analyzed, including 104 each in the MDT and non-MDT groups. After MDT, 56 patients (53.8%) were found to have intracranial “diagnosis upgrade”. In the matched population, patients in the MDT group recorded a higher proportion of meningeal metastases (14.4% vs. 4.8%, p = 0.02), symptomatic tumor progression (11.5% vs. 5.8%, p = 0.04), and an increased number of occurrences of brain metastases (BM) progression (p < 0.05). Attending MDT was an independent factor associated with ≥2 courses of intracranial radiotherapy (RT) [odds ratio (OR) 5.4, 95% confidence interval (CI): 2.7–10.9, p < 0.001], novel RT technique use (7.0, 95% CI 3.5–14.0, p < 0.001), and prospective clinical research (OR 5.7, 95% CI 2.4–13.4, p < 0.001).ConclusionPatients with complex conditions are often referred for MDT discussions. An MDT may improve the qualities of intracranial RT and systemic therapy, resulting in benefits of overall survival for BC patients after BM. This encourages the idea that treatment recommendations for patients with BMBC should be discussed within an MDT

    Simultaneously enhancing adsorbed hydrogen and dinitrogen to enable efficient electrochemical NH3 synthesis on Sm(OH)3

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    The electrochemical N2 reduction reaction (ENRR), driven by renewable electricity and run under ambient conditions, offers a promising sustainable avenue for carbon-neutral NH3 production. Yet, to efficiently bind and activate the inert N2 remains challenge. Herein, effective and stable electrochemical NH3 synthesis on Sm(OH)3 via enhanced adsorption of hydrogen and dinitrogen by dual integration of sulfur dopants and oxygen vacancies (VO) is reported. The resulting S-doped lanthanide electrocatalyst attains both a good NH3 yield rate, exceeding 21 μgNH3 h−1 mgcat.−1, and an NH3 faradaic efficiency of over 29% at −0.3 V (vs reversible hydrogen electrode) in an H-type cell using a neutral electrolyte, figures of merit that are largely maintained after 2 days of consecutive polarization. Density functional theory calculations show that the adsorption energy barrier of N2 on S-Sm(OH)3(VO) is greatly lowered by the introduction of VO. In addition, the S sites improve the adsorption of hydrogen produced via the Volmer reaction, which is conducive to the formation of the *N–NH intermediate (i.e., the potential determining step, PDS) on adjacent Sm sites, and thereby significantly promotes the reaction kinetics of ENRR. The PDS free energy for the catalyst is comparable with the values at the peak of the ENRR volcano plots of leading transition metal catalyst surfaces

    Public Expenditure and Green Total Factor Productivity: Evidence from Chinese Prefecture-Level Cities

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    Whilst effective public expenditure policies are essential for transforming the traditional factor-driven economy into a green and innovation-driven economy, the impacts of public expenditure’s size and composition on green economic development have not been comprehensively investigated. This paper attempts to fill this research gap. Based on the data of Chinese prefecture-level cities from 2010 to 2018, we first measure green total factor productivity (GTFP), the proxy variable for green development, and briefly analyze its spatial-temporal trends. Then, using the dynamic panel models, dynamic panel mediation models, and dynamic panel threshold models, we evaluate how public expenditure affects GTFP. The main findings are fourfold: (1) there is a significant inverted U-shaped relationship between the expenditure size and GTFP. (2) The expansion of social expenditures and science and technology (S&T) and environmental protection expenditures play an important role in stimulating green growth, while economic expenditures and administrative expenditures have adverse effects. (3) Public expenditure mainly promotes green development through four channels: human capital accumulation, technological innovation, environmental quality improvement, and labor productivity increase. (4) The expenditure composition influences the turning point of the inverted U-shaped relationship. Based on these findings, we propose some targeted policy suggestions to promote green development