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  • 1
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    Stochastic resonance in genetic regulatory networks under Lévy noise (2019)
    In:  Europhysics Letters (epl)
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    Publikationsdatum: 2022-03-21
    Materialart: info:eu-repo/semantics/article
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
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    Performance evaluation of groundwater overdraft recovery units in North and Coastal China based on DEA models (2019)
    In:  Journal of Coastal Research
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    Publikationsdatum: 2022-03-21
    Beschreibung: Groundwater overdraft has affected sustainable development, especially in North and Coastal China, since the 1960s. The Chinese government instituted the Pilot Project of Groundwater Overexploitation Control (PPGOC) in Hebei Province during 2014 to 2016. This project introduced a set of hydrological, agricultural and administrative activities to recover the aquifer in the pilot area. In order to evaluate the effects of these activities on the groundwater status, a series of Data Envelopment Analysis (DEA) models are assembled as a model group and applied to calculate the relative performance of groundwater recovery units, i.e. the recovery efficiency in 49 counties or Decision-Making Units (DMUs). It is shown that the DEA model group can be used to evaluate the recovery efficiency, improve the performance of units not on the DEA frontier via radial and slack movement, and study the possibility of cost reduction. The result shows that 20 DMUs formed the frontier, which is the collective of the efficient DMUs, and that another 29 DMUs require efficiency improvement. The high efficiency of certain DMUs is related to the location and farmers' responses, which indicates that groundwater overdraft recovery is a technical problem that also has something to do with social and economic development and comprehensive governance. The model group can be used as a reference in the forthcoming implementation of aquifer recovery in groundwater overdraft zones in North and Coastal China.
    Materialart: info:eu-repo/semantics/article
    Standort Signatur Erwartet Verfügbarkeit
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  • 3
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    A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C (2017)
    In:  Climatic Change
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    Publikationsdatum: 2022-03-21
    Beschreibung: We present one of the first climate change impact assessments on river runoff that utilises an ensemble of global hydrological models (Glob-HMs) and an ensemble of catchment-scale hydrological models (Cat-HMs), across multiple catchments: the upper Amazon, Darling, Ganges, Lena, upper Mississippi, upper Niger, Rhine and Tagus. Relative changes in simulated mean annual runoff (MAR) and four indicators of high and low extreme flows are compared between the two ensembles. The ensemble median values of changes in runoff with three different scenarios of global-mean warming (1, 2 and 3 °C above pre-industrial levels) are generally similar between the two ensembles, although the ensemble spread is often larger for the Glob-HM ensemble. In addition the ensemble spread is normally larger than the difference between the two ensemble medians. Whilst we find compelling evidence for projected runoff changes for the Rhine (decrease), Tagus (decrease) and Lena (increase) with global warming, the sign and magnitude of change for the other catchments is unclear. Our model results highlight that for these three catchments in particular, global climate change mitigation, which limits global-mean temperature rise to below 2 °C above preindustrial levels, could avoid some of the hydrological hazards that could be seen with higher magnitudes of global warming.
    Materialart: info:eu-repo/semantics/article
    Format: application/pdf
    Standort Signatur Erwartet Verfügbarkeit
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  • 4
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    Dynamical network size estimation from local observations (2020)
    In:  New Journal of Physics
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    Publikationsdatum: 2022-03-21
    Beschreibung: Here we present a method to estimate the total number of nodes of a network using locally observed response dynamics. The algorithm has the following advantages: (a) it is data-driven. Therefore it does not require any prior knowledge about the model; (b) it does not need to collect measurements from multiple stimulus; and (c) it is distributed as it uses local information only, without any prior information about the global network. Even if only a single node is measured, the exact network size can be correctly estimated using a single trajectory. The proposed algorithm has been applied to both linear and nonlinear networks in simulation, illustrating the applicability to real-world physical networks.
    Materialart: info:eu-repo/semantics/article
    Format: application/pdf
    Standort Signatur Erwartet Verfügbarkeit
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  • 5
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    Analysis of the dynamic characteristics of combustion instabilities in a pre-mixed lean-burn natural gas engine (2016)
    In:  Applied Energy
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    Publikationsdatum: 2022-03-21
    Beschreibung: The cyclic combustion instabilities in a pre-mixed lean-burn natural gas engine have been studied. Using non-linear embedding theory, recurrence plots (RPs) and recurrence qualification analysis (RQA), the hidden rhythms and dynamic complexity of a combustion system in high dimensional phase space for each gas injection timing (GIT) have been examined, and the possible source of combustion instabilities has been identified based on 3-D computational fluid dynamics (CFD) simulation. The results reveal that for lower engine load, with the decrease of mixture concentration, the combustion instability and complexity of combustion system become more sensitive to the variation of GITs. Richer mixture and earlier (GIT 〈 30°CA ATDC) or delayed (GIT 〉 90°CA ATDC) gas injection will lead to more stable combustion, regular oscillatory and low complexity of combustion system, while leaner mixture together with the medium GITs (from 30 to 90°CA ATDC) easily leads to increase of combustion fluctuations, time irreversibility and dynamic complexity of combustion system. When GITs are changed, the combustion instabilities of pre-mixed lean-burn natural gas engines are from in-cylinder unreasonable stratification of mixture concentration and turbulent motion.
    Materialart: info:eu-repo/semantics/article
    Standort Signatur Erwartet Verfügbarkeit
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  • 6
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    Data-Driven Discovery of Stochastic Differential Equations (2022)
    In:  Engineering
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    Publikationsdatum: 2023-01-17
    Beschreibung: Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.
    Sprache: Englisch
    Materialart: info:eu-repo/semantics/article
    Format: application/pdf
    Standort Signatur Erwartet Verfügbarkeit
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  • 7
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    Machine discovery of partial differential equations from spatiotemporal data: A sparse Bayesian learning framework (2023)
    In:  Chaos
    Details
    Publikationsdatum: 2024-01-17
    Beschreibung: This study presents a general framework, namely, Sparse Spatiotemporal System Discovery (S3d⁠), for discovering dynamical models given by Partial Differential Equations (PDEs) from spatiotemporal data. S3d is built on the recent development of sparse Bayesian learning, which enforces sparsity in the estimated PDEs. This approach enables a balance between model complexity and fitting error with theoretical guarantees. The proposed framework integrates Bayesian inference and a sparse priori distribution with the sparse regression method. It also introduces a principled iterative re-weighted algorithm to select dominant features in PDEs and solve for the sparse coefficients. We have demonstrated the discovery of the complex Ginzburg–Landau equation from a traveling-wave convection experiment, as well as several other PDEs, including the important cases of Navier–Stokes and sine-Gordon equations, from simulated data.
    Sprache: Englisch
    Materialart: info:eu-repo/semantics/article
    Format: application/pdf
    Standort Signatur Erwartet Verfügbarkeit
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