Abstract:
This paper presents an alternative approach for time-dependent multimodal transport problem. We describe a new graph structure to abstract multimodal networks, called transfer graph, which adapts to the distributed nature of real information sources of transportation networks. A decomposition of the Shortest Path Problem in transfer graph is proposed to optimize the computation time. This approach was computationally tested in several experimental multimodal networks having different size and complexity. The approach was integrated in the multimodal transport service of the European Carlink platform, where it has been validated in real scenarios. Comparision with other related works is provided.

Abstract:
Maintaining a sustainable socio-ecological state of a river delta requires delivery of material and energy fluxes to its body and coastal zone in a way that avoids malnourishment that would compromise system integrity. We present a quantitative framework for studying delta topology and transport based on representation of a deltaic system by a rooted directed acyclic graph. Applying results from spectral graph theory allows systematic identification of the upstream and downstream subnetworks for a given vertex, computing steady flux propagation in the network, and finding partition of the flow at any channel among the downstream channels. We use this framework to construct vulnerability maps that quantify the relative change of sediment and water delivery to the shoreline outlets in response to possible perturbations in hundreds of upstream links. This enables us to evaluate which links hotspots and what management scenarios would most influence flux delivery to the outlets. The results can be used to examine local or spatially distributed delta interventions and develop a system approach to delta management.

Abstract:
Discrete dynamic models are a powerful tool for the understanding and modeling of large biological networks. Although a lot of progress has been made in developing analysis tools for these models, there is still a need to find approaches that can directly relate the network structure to its dynamics. Of special interest is identifying the stable patterns of activity, i.e., the attractors of the system. This is a problem for large networks, because the state space of the system increases exponentially with network size. In this work we present a novel network reduction approach that is based on finding network motifs that stabilize in a fixed state. Notably, we use a topological criterion to identify these motifs. Specifically, we find certain types of strongly connected components in a suitably expanded representation of the network. To test our method we apply it to a dynamic network model for a type of cytotoxic T cell cancer and to an ensemble of random Boolean networks of size up to 200. Our results show that our method goes beyond reducing the network and in most cases can actually predict the dynamical repertoire of the nodes (fixed states or oscillations) in the attractors of the system.

Abstract:
Models describing transport and diffusion processes occurring along the edges of a graph and interlinked by its vertices have been recently receiving a considerable attention. In this paper we generalize such models and consider a network of transport or diffusion operators defined on one dimensional domains and connected through boundary conditions linking the end-points of these domains in an arbitrary way (not necessarily as the edges of a graph are connected). We prove the existence of $C_0$-semigroups solving such problems and provide conditions fully characterizing when they are positive.

Abstract:
The true slime mould Physarum polycephalum is a recent well studied example of how complex transport networks emerge from simple auto-catalytic and self- organising local interactions, adapting structure and function against changing environmental conditions and external perturbation. Physarum networks also exhibit computationally desirable measures of transport efficiency in terms of overall path length, minimal connectivity and network resilience. Although significant progress has been made in mathematically modelling the behaviour of Physarum networks (and other biological transport networks) based on observed features in experimental settings, their initial emergence - and in particular their long-term persistence and evolution - is still poorly understood. We present a low-level, bottom-up, approach to the modelling of emergent transport networks. A population of simple particle-like agents coupled with paracrine chemotaxis behaviours in a dissipative environment results in the spontaneous emergence of persistent, complex structures. Second order emergent behaviours, in the form of network surface minimisation, are also observed contributing to the long term evolution and dynamics of the networks. The framework is extended to allow data presentation and the population is used to perform a direct (spatial) approximation of network minimisation problems. Three methods are employed, loosely relating to behaviours of Physarum under different environmental conditions. Finally, the low-level approach is summarised with a view to further research.

Abstract:
In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains to be an outstanding problem. We develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability (multiple coexisting final states or attractors), which are representative of, e.g., gene regulatory networks (GRNs). The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically useful, we consider RESTRICTED parameter perturbation by imposing the following two constraints: (a) it must be experimentally realizable and (b) it is applied only temporarily. We introduce the concept of ATTRACTOR NETWORK, in which the nodes are the distinct attractors of the system, and there is a directional link from one attractor to another if the system can be driven from the former to the latter using restricted control perturbation. Introduction of the attractor network allows us to formulate a controllability framework for nonlinear dynamical networks: a network is more controllable if the underlying attractor network is more strongly connected, which can be quantified. We demonstrate our control framework using examples from various models of experimental GRNs. A finding is that, due to nonlinearity, noise can counter-intuitively facilitate control of the network dynamics.

Abstract:
Carbon nanotube networks are one of the candidate materials to function as malleable, transparent, conducting films, with the technologically promising application of being used as flexible electronic displays. Nanotubes disorderly distributed in a film offers many possible paths for charge carriers to travel across the entire system, but the theoretical description of how this charge transport occurs is rather challenging for involving a combination of intrinsic nanotube properties with network morphology aspects. Here we attempt to describe the transport properties of such films in two different length scales. Firstly, from a purely macroscopic point of view we carry out a geometrical analysis that shows how the network connectivity depends on the nanotube concentration and on their respective aspect ratio. Once this is done, we are able to calculate the resistivity of a heavily disordered networked film. Comparison with experiment offers us a way to infer about the junction resistance between neighbouring nanotubes. Furthermore, in order to guide the frantic search for high-conductivity films of nanotube networks, we turn to the microscopic scale where we have developed a computationally efficient way for calculating the ballistic transport across these networks. While the ballistic transport is probably not capable of describing the observed transport properties of these films, it is undoubtedly useful in establishing an upper value for their conductivity. This can serve as a guideline in how much room there is for improving the conductivity of such networks.

Abstract:
From mathematical model, this paper deals with the characters of sediments transport and deposits of the Lower Yellow River on the average of water and sediments of multiple years and obtains the quantities of sediments reduced when the equilibrium of scour and deposit of sediments is reached in the Lower Yellow River. The results are got as follows.1. It is Possible to use the movable-bed mathematical model of nonequi-librium transport of nonuniform sediment in order to inquire into the characters of sediments transport in the Lower Yellow River. The numerical results conform with the field investigations fundamentally.2. The nonuniformity of longitudinal deposits of sediment in the Lower Yellow River is caused by the nonuniformity of longitudinal deposits of coarse and middle sediments. This c onclusion shows that the relationship of contrast of sediments and capacity of sediments transport is different in the sections of river. Generally, sediments conform with the capacity of sediments transport better nearer to the mouth of river.3. The policy of primary and secondary may be taken to bring the Lower Yellow River under control according to the character of sediments deposit. The Coarse and middle sediments are 79.5% in all the sediment deposits, but only 43.3% in the sediments. The river channel has lower ability to transport the coarse and middle sediments. So the coarse and middle sediment must be reduced in the sediments.4. The numerical results show that if the quantities of sediments can be reduced to 42. 6%, the equilibrium of scour and deposit of sediments will be readied in the Lower Yellow River

Abstract:
Noise is generally thought as detrimental for energy transport in coupled oscillator networks. However, it has been shown that for certain coherently evolving systems, the presence of noise can enhance, somehow unexpectedly, their transport efficiency; a phenomenon called environment-assisted quantum transport (ENAQT) or dephasing-assisted transport. Here, we report on the experimental observation of such effect in a network of coupled electrical oscillators. We demonstrate that by introducing stochastic fluctuations in one of the couplings of the network, a relative enhancement in the energy transport efficiency of $22.5 \pm 3.6\,\%$ can be observed.

Abstract:
We study the kinetic and chemical equilibration in "infinite" parton matter within the parton-hadron-string dynamics off-shell transport approach, which is based on a dynamical quasiparticle model (DQPM) for partons matched to reproduce lattice QCD results-including the partonic equation of state-in thermodynamic equilibrium. The "infinite" parton matter is simulated by a system of quarks and gluons within a cubic box with periodic boundary conditions, at different energy densities, initialized slightly out of kinetic and chemical equilibrium. We investigate the approach of the system to equilibrium and the time scales for the equilibration of different observables. We, furthermore, study particle distributions in the strongly interacting quark-gluon plasma (sQGP) including partonic spectral functions, momentum distributions, abundances of the different parton species and their fluctuations (scaled variance, skewness, and kurtosis) in equilibrium. We also compare the results of the microscopic calculations with the ansatz of the DQPM. It is found that the results of the transport calculations are in equilibrium well matched by the DQPM for quarks and antiquarks, while the gluon spectral function shows a slightly different shape due to the explicit interaction of partons. The time scales for the relaxation of fluctuation observables are found to be shorter than those for the average values. Furthermore, in the local subsystem, a strong change of the fluctuation observables with the size of the local volume is observed. These fluctuations no longer correspond to those of the full system and are reduced to Poissonian distributions when the volume of the local subsystem becomes much smaller than the total volume.