1,722,158 research outputs found
Resilience Evaluation of Urban Bus-Subway Traffic Networks for Potential Applications in IoT-based Smart Transportation
No full textIn megacities, Internet of Things (IoT) based smart transportation is an effective solution to dealing with traffic congestions. Resilience evaluation of traffic networks can help enhance the management of IoT-based smart transportation. Currently, most of the research work on resilience evaluation is devoted to a single type of traffic network. However, in reality, urban traffic networks are complex, and various types of traffic networks are often highly interrelated and need to be evaluated concurrently. To address the above problem, this paper proposed a method for resilience evaluation of urban bus-subway traffic networks for potential applications in IoT-based smart transportation in megacities. The essential idea behind the proposed method is to combine the urban bus network with the subway network and present a model of an urban bus-subway hybrid traffic network, rather than a single type of traffic network. In the proposed method, (1) an urban bus-subway hybrid traffic networks model is established; (2) four metrics such as the degree centrality and clustering coefficient are used to identify vital nodes in the hybrid traffic network; and (3) four indicators such as the network efficiency and sensitivity are used to analyze the network resilience. The effectiveness of the proposed method is verified by the application of urban bus-subway hybrid traffic networks in Beijing. The proposed method can be potentially applied to optimize the resource allocation of important nodes in an urban hybrid traffic network, which is of great interest for enhancing the management of IoT-based smart transportation in megacities
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A network-based method with privacy-preserving for identifying influential providers in large healthcare service systems
No full textIn data science, networks provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and physical systems. Healthcare service systems are one of the main social systems that can also be understood using network-based approaches, for example, to identify and evaluate influential providers. In this paper, we propose a network-based method with privacy-preserving for identifying influential providers in large healthcare service systems. First, the provider-interacting network is constructed by employing publicly available information on locations and types of healthcare services of providers. Second, the ranking of nodes in the generated provider-interacting network is conducted in parallel on the basis of four nodal influence metrics. Third, the impact of the top-ranked influential nodes in the provider-interacting network is evaluated using three indicators. Compared with other research work based on patient-sharing networks, in this paper, the provider-interacting network of healthcare service providers can be roughly created according to the locations and the publicly available types of healthcare services, without the need for personally private electronic medical claims, thus protecting the privacy of patients. The proposed method is demonstrated by employing Physician and Other Supplier Data CY 2017, and can be applied to other similar datasets to help make decisions for the optimization of healthcare resources in the response to public health emergencies
Temporal-frequency distribution and multi-fractal characterization of acoustic emission of rock materials containing two parallel pre-existing flaws
No full textTo explore the temporal-frequency distribution and multi-fractal characterization of acoustic emission (AE) signals, a series of uniaxial compressive tests on flawed sandstone containing different flaw geometric arrangements were conducted. The results show that there are primarily low-frequency and low-amplitude signals at relatively low stress levels. With the increase of stress level, the components of high-frequency and high-amplitude signals increase remarkably. Spectrum width ( increment alpha) follows an approximate trend of first decreasing and then increasing with increasing stress levels. When the stress level increases to 0.8 sigma(c), spectrum morphology ( increment alpha(0)) changes from a positive value to a negative value, indicating that the failure mechanism in rock transforms from microcrack damage to large-scale shear rupture. Additionally, spectrum measure subset ( increment f) and increment alpha(0) present an opposite trend. With regard to the flawed sandstone, the fracture mechanism is predominately dominated by the microscopic tensile cracks, whereas the microscopic shear cracks in intact sandstone account for a large proportion
A Deep Learning Approach for Long-Term Traffic Flow Prediction With Multifactor Fusion Using Spatiotemporal Graph Convolutional Network
No full textAs a vital research subject in the field of intelligent transportation systems (ITSs), traffic flow prediction using deep learning methods has attracted much attention in recent years. However, numerous existing studies mainly focus on short-term traffic flow predictions and fail to consider the influence of external factors. Effective long-term traffic flow prediction has become a challenging issue. As a solution to these challenges, this paper proposes a deep learning approach based on a spatiotemporal graph convolutional network for long-term traffic flow prediction with multiple factors. In the proposed method, our innovative idea is to introduce an attribute feature unit (AF-unit) to fuse external factors into a spatiotemporal graph convolutional network. The proposed method consists of (1) constructing a weighted adjacency matrix using Gaussian similarity functions; (2) assembling a feature matrix to store time-series traffic flow; (3) building an external attribute matrix composed of external factors, including temperature, visibility, and weather conditions; and (4) building a spatiotemporal graph convolutional network based on a deep learning architecture (i.e., T-GCN). The experimental results indicate that (1) the performance of our method considering spatiotemporal dependence has better prediction capability than baseline models; (2) the fusion of meteorological factors can reduce the inaccuracy of traffic prediction; and (3) our method has high accuracy and stability in long-term traffic flow prediction
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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