423 research outputs found
Recommended from our members
Air itinerary shares estimation using multinomial logit models
The main goal of this study is the development of an aggregate air itinerary market share model. In order to achieve this, multinomial logit models are applied to distribute the city-pair passenger demand across the available itineraries. The models are developed at an aggregate level using open-source booking data for a large group of city-pairs within the US air transport system. Although there is a growing trend in the use of discrete choice models in the aviation industry, existing air itinerary share models are mostly focused on supporting carrier decision-making. Consequently, those studies define itineraries at a more disaggregate level using variables describing airlines and time preferences. In this study, we define itineraries at a more aggregate level, i.e. as a combination of flight segments between an origin and destination, without further insight into service preferences. Although results show some potential for this approach, there are challenges associated with prediction performance and computational intensity
Theoretical approach and impact of correlations on the critical packet generation rate in traffic dynamics on complex networks
Using the formalism of the biased random walk in random uncorrelated networks
with arbitrary degree distributions, we develop theoretical approach to the
critical packet generation rate in traffic based on routing strategy with local
information. We explain microscopic origins of the transition from the flow to
the jammed phase and discuss how the node neighbourhood topology affects the
transport capacity in uncorrelated and correlated networks.Comment: 6 pages, 5 figure
Depositional and structural controls on a fault-related dolostone formation (Maestrat Basin, E Spain)
Acknowledgments This research was funded by the Natural Environment Research Council (NERC) Centre for Doctoral Training (CDT) in Oil & Gas, through a PhD grant to EH. Equinor ASA are thanked for providing additional support. Additional funding was provided by the Grup Consolidat de Recerca “Geologia Sedimentària” (2017SGR-824) and DGICYT Spanish Projects CGL2017-85532-P, PGC2018-093903-B-C22 and PID2020-118999GB-I00, all funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER). EGR acknowledges the Spanish Ministry of Science, Innovation and Universities for the “Ramón y Cajal” fellowship RYC2018-026335-I. EH, EGR, JDM and JN conceived the idea and provided funding whilst field data was collected by EH, EGR, and JDM. EH organised the sampling for geochemical analysis (supervised by JDM) and RS and JG provided the regional stratigraphic context and structural cross-section. Petrographic data was collected by EH (supervised by JN). EH wrote the manuscript with edits and contributions provided by all co authors.Peer reviewedPublisher PD
Systems modelling predicts chronic inflammation and genomic instability prevent effective mitochondrial regulation during biological ageing
The regulation of mitochondrial turnover under conditions of stress occurs partly through the AMPK-NAD+-PGC1α-SIRT1 signalling pathway. This pathway can be affected by both genomic instability and chronic inflammation since these will result in an increased rate of NAD+ degradation through PARP1 and CD38 respectively. In this work we develop a computational model of this signalling pathway, calibrating and validating it against experimental data. The computational model is used to study mitochondrial turnover under conditions of stress and how it is affected by genomic instability, chronic inflammation and biological ageing in general. We report that the AMPK-NAD+-PGC1α-SIRT1 signalling pathway becomes less responsive with age and that this can prime for the accumulation of dysfunctional mitochondria
eBay users form stable groups of common interest
Market segmentation of an online auction site is studied by analyzing the
users' bidding behavior. The distribution of user activity is investigated and
a network of bidders connected by common interest in individual articles is
constructed. The network's cluster structure corresponds to the main user
groups according to common interest, exhibiting hierarchy and overlap. Key
feature of the analysis is its independence of any similarity measure between
the articles offered on eBay, as such a measure would only introduce bias in
the analysis. Results are compared to null models based on random networks and
clusters are validated and interpreted using the taxonomic classifications of
eBay categories. We find clear-cut and coherent interest profiles for the
bidders in each cluster. The interest profiles of bidder groups are compared to
the classification of articles actually bought by these users during the time
span 6-9 months after the initial grouping. The interest profiles discovered
remain stable, indicating typical interest profiles in society. Our results
show how network theory can be applied successfully to problems of market
segmentation and sociological milieu studies with sparse, high dimensional
data.Comment: Major revision of the manuscript. Methodological improvements and
inclusion of analysis of temporal development of user interests. 19 pages, 12
figures, 5 table
Network Topology of an Experimental Futures Exchange
Many systems of different nature exhibit scale free behaviors. Economic
systems with power law distribution in the wealth is one of the examples. To
better understand the working behind the complexity, we undertook an empirical
study measuring the interactions between market participants. A Web server was
setup to administer the exchange of futures contracts whose liquidation prices
were coupled to event outcomes. After free registration, participants started
trading to compete for the money prizes upon maturity of the futures contracts
at the end of the experiment. The evolving `cash' flow network was
reconstructed from the transactions between players. We show that the network
topology is hierarchical, disassortative and scale-free with a power law
exponent of 1.02+-0.09 in the degree distribution. The small-world property
emerged early in the experiment while the number of participants was still
small. We also show power law distributions of the net incomes and
inter-transaction time intervals. Big winners and losers are associated with
high degree, high betweenness centrality, low clustering coefficient and low
degree-correlation. We identify communities in the network as groups of the
like-minded. The distribution of the community sizes is shown to be power-law
distributed with an exponent of 1.19+-0.16.Comment: 6 pages, 12 figure
Statistical Analysis of the Road Network of India
In this paper we study the Indian Highway Network as a complex network where
the junction points are considered as nodes, and the links are formed by an
existing connection. We explore the topological properties and community
structure of the network. We observe that the Indian Highway Network displays
small world properties and is assortative in nature. We also identify the most
important road-junctions (or cities) in the highway network based on the
betweenness centrality of the node. This could help in identifying the
potential congestion points in the network. Our study is of practical
importance and could provide a novel approach to reduce congestion and improve
the performance of the highway networ
Second-Order Assortative Mixing in Social Networks
In a social network, the number of links of a node, or node degree, is often
assumed as a proxy for the node's importance or prominence within the network.
It is known that social networks exhibit the (first-order) assortative mixing,
i.e. if two nodes are connected, they tend to have similar node degrees,
suggesting that people tend to mix with those of comparable prominence. In this
paper, we report the second-order assortative mixing in social networks. If two
nodes are connected, we measure the degree correlation between their most
prominent neighbours, rather than between the two nodes themselves. We observe
very strong second-order assortative mixing in social networks, often
significantly stronger than the first-order assortative mixing. This suggests
that if two people interact in a social network, then the importance of the
most prominent person each knows is very likely to be the same. This is also
true if we measure the average prominence of neighbours of the two people. This
property is weaker or negative in non-social networks. We investigate a number
of possible explanations for this property. However, none of them was found to
provide an adequate explanation. We therefore conclude that second-order
assortative mixing is a new property of social networks.Comment: Cite as: Zhou S., Cox I.J., Hansen L.K. (2017) Second-Order
Assortative Mixing in Social Networks. In: Goncalves B., Menezes R., Sinatra
R., Zlatic V. (eds) Complex Networks VIII. CompleNet 2017. Springer
Proceedings in Complexity. Springer, Cham.
https://doi.org/10.1007/978-3-319-54241-6_
- …