TY - JOUR A1 - Niven, Robert K. A1 - Abel, Markus A1 - Schlegel, Michael A1 - Waldrip, Steven H. T1 - Maximum Entropy Analysis of Flow Networks: Theoretical Foundation and Applications JF - Entropy N2 - The concept of a "flow network"-a set of nodes and links which carries one or more flows-unites many different disciplines, including pipe flow, fluid flow, electrical, chemical reaction, ecological, epidemiological, neurological, communications, transportation, financial, economic and human social networks. This Feature Paper presents a generalized maximum entropy framework to infer the state of a flow network, including its flow rates and other properties, in probabilistic form. In this method, the network uncertainty is represented by a joint probability function over its unknowns, subject to all that is known. This gives a relative entropy function which is maximized, subject to the constraints, to determine the most probable or most representative state of the network. The constraints can include "observable" constraints on various parameters, "physical" constraints such as conservation laws and frictional properties, and "graphical" constraints arising from uncertainty in the network structure itself. Since the method is probabilistic, it enables the prediction of network properties when there is insufficient information to obtain a deterministic solution. The derived framework can incorporate nonlinear constraints or nonlinear interdependencies between variables, at the cost of requiring numerical solution. The theoretical foundations of the method are first presented, followed by its application to a variety of flow networks. KW - maximum entropy analysis KW - flow network KW - probabilistic inference Y1 - 2019 U6 - https://doi.org/10.3390/e21080776 SN - 1099-4300 VL - 21 IS - 8 SP - 776 PB - MDPI CY - Basel ER - TY - GEN A1 - Waldrip, Steven H. A1 - Niven, Robert K. A1 - Abel, Markus A1 - Schlegel, Michael T1 - Consistent maximum entropy representations of pipe flow networks T2 - AIP conference proceedings N2 - The maximum entropy method is used to predict flows on water distribution networks. This analysis extends the water distribution network formulation of Waldrip et al. (2016) Journal of Hydraulic Engineering (ASCE), by the use of a continuous relative entropy defined on a reduced parameter set. This reduction in the parameters that the entropy is defined over ensures consistency between different representations of the same network. The performance of the proposed reduced parameter method is demonstrated with a one-loop network case study. Y1 - 2017 SN - 978-0-7354-1527-0 U6 - https://doi.org/10.1063/1.4985365 SN - 0094-243X VL - 1853 IS - 1 PB - American Institute of Physics CY - Melville ER - TY - GEN A1 - Waldrip, Steven H. A1 - Niven, Robert K. A1 - Abel, Markus A1 - Schlegel, Michael T1 - Maximum entropy analysis of transport networks T2 - AIP conference proceedings N2 - The maximum entropy method is used to derive an alternative gravity model for a transport network. The proposed method builds on previous methods which assign the discrete value of a maximum entropy distribution to equal the traffic flow rate. The proposed method however, uses a distribution to represent each flow rate. The proposed method is shown to be able to handle uncertainty in a more elegant way and give similar results to traditional methods. It is able to incorporate more of the observed data through the entropy function, prior distribution and integration limits potentially allowing better inferences to be made. Y1 - 2017 SN - 978-0-7354-1527-0 U6 - https://doi.org/10.1063/1.4985364 SN - 0094-243X VL - 1853 IS - 1 PB - American Institute of Physics CY - Melville ER - TY - JOUR A1 - Schmidt, Sabrina A1 - Saxenhofer, Moritz A1 - Drewes, Stephan A1 - Schlegel, Mathias A1 - Wanka, Konrad M. A1 - Frank, Raphael A1 - Klimpel, Sven A1 - von Blanckenhagen, Felix A1 - Maaz, Denny A1 - Herden, Christiane A1 - Freise, Jona A1 - Wolf, Ronny A1 - Stubbe, Michael A1 - Borkenhagen, Peter A1 - Ansorge, Hermann A1 - Eccard, Jana A1 - Lang, Johannes A1 - Jourdain, Elsa A1 - Jacob, Jens A1 - Marianneau, Philippe A1 - Heckel, Gerald A1 - Ulrich, Rainer Günter T1 - High genetic structuring of Tula hantavirus JF - Archives of virology N2 - Tula virus (TULV) is a vole-associated hantavirus with low or no pathogenicity to humans. In the present study, 686 common voles (Microtus arvalis), 249 field voles (Microtus agrestis) and 30 water voles (Arvicola spec.) were collected at 79 sites in Germany, Luxembourg and France and screened by RT-PCR and TULV-IgG ELISA. TULV-specific RNA and/or antibodies were detected at 43 of the sites, demonstrating a geographically widespread distribution of the virus in the studied area. The TULV prevalence in common voles (16.7 %) was higher than that in field voles (9.2 %) and water voles (10.0 %). Time series data at ten trapping sites showed evidence of a lasting presence of TULV RNA within common vole populations for up to 34 months, although usually at low prevalence. Phylogenetic analysis demonstrated a strong genetic structuring of TULV sequences according to geography and independent of the rodent species, confirming the common vole as the preferential host, with spillover infections to co-occurring field and water voles. TULV phylogenetic clades showed a general association with evolutionary lineages in the common vole as assessed by mitochondrial DNA sequences on a large geographical scale, but with local-scale discrepancies in the contact areas. Y1 - 2016 U6 - https://doi.org/10.1007/s00705-016-2762-6 SN - 0304-8608 SN - 1432-8798 VL - 161 SP - 1135 EP - 1149 PB - Springer CY - Wien ER -