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  • Predicting marine litter ac... Predicting marine litter accumulation patterns in the Mediterranean basin: Spatio-temporal variability and comparison with empirical data
    Mansui, J.; Darmon, G.; Ballerini, T. ... Progress in oceanography, March 2020, 2020-03-00, 2020-03, Volume: 182
    Journal Article
    Peer reviewed
    Open access

    •The spatio-temporal distribution of floating litter was predicted at the Mediterranean level with a model coupled with a literature review.•The main litter accumulation patterns are located in the ...
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  • A critical evaluation of th... A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset
    Coifman, Benjamin; Li, Lizhe Transportation research. Part B: methodological, 11/2017, Volume: 105
    Journal Article
    Peer reviewed
    Open access

    •NGSIM has become the de facto empirical microscopic traffic data set.•This study manually re-extracts vehicle trajectories from NGSIM video.•The raw NGSIM data exhibit trends not evident in the ...
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  • The Matthew effect in empir... The Matthew effect in empirical data
    Perc, Matjaž Journal of the Royal Society interface, 09/2014, Volume: 11, Issue: 98
    Journal Article
    Peer reviewed
    Open access

    The Matthew effect describes the phenomenon that in societies, the rich tend to get richer and the potent even more powerful. It is closely related to the concept of preferential attachment in ...
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  • Empirical observations of m... Empirical observations of multi-modal network-level models: Insights from the pNEUMA experiment
    Paipuri, Mahendra; Barmpounakis, Emmanouil; Geroliminis, Nikolas ... Transportation research. Part C, Emerging technologies, 10/2021, Volume: 131
    Journal Article
    Peer reviewed
    Open access

    •Multimodal regressions are defined for mean speed with respect to accumulations or stop durations (two-fluid models).•A Macroscopic traffic states analysis comes from an unprecedent complete trips ...
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  • AI algorithms for fitting G... AI algorithms for fitting GARCH parameters to empirical financial data
    De Clerk, Luke; Savel’ev, Sergey Physica A, 10/2022, Volume: 603
    Journal Article
    Peer reviewed
    Open access

    We use Deep Artificial Neural Networks (ANNs) to estimate GARCH parameters for empirical financial time series. The algorithm we develop, allows us to fit autocovariance of squared returns of ...
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  • Inequalities, chance and su... Inequalities, chance and success in sport competitions: Simulations vs empirical data
    Sobkowicz, Pawel; Frank, Robert H.; Biondo, Alessio E. ... Physica A, 11/2020, Volume: 557
    Journal Article
    Peer reviewed
    Open access

    We present a new way of estimation of the role of chance in achieving success, by comparing the empirical data from 100-m dash competitions (one of the sports disciplines with the most stringent ...
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  • Disentangling the city traf... Disentangling the city traffic rhythms: A longitudinal analysis of MFD patterns over a year
    Ambühl, Lukas; Loder, Allister; Leclercq, Ludovic ... Transportation research. Part C, Emerging technologies, 05/2021, Volume: 126
    Journal Article
    Peer reviewed
    Open access

    Urban road transportation performance is the result of a complex interplay between the network supply and the travel demand. Fortunately, the framework around the macroscopic fundamental diagram ...
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