Steady-state Network Research Articles

A Pore-Scale Investigation of Low-Salinity Waterflooding in Porous Media: Uniformly Wetted Systems

The potential of low-salinity (LS) water injection as an oil recovery technique has been the source of much recent debate within the petroleum industry. Evidence from both laboratory and field-level studies has indicated significant benefits compared to conventional high-salinity (HS) waterflooding, but many conflicting results have also been reported and, to date, the underlying mechanisms remain poorly understood. In this paper, we aim to address this uncertainty by developing a novel, steady-state pore network model in which LS brine displaces oil from a HS-bearing network. The model allows systematic investigation of the crude oil/brine/rock parameter space, with the goal of identifying features that may be critical to the production of incremental oil following LS brine injection. By coupling the displacement model to a salinity-tracking tracer algorithm, and assuming that a reduction of water salinity within the pore network leads to localised wettability alteration, substantial perturbations to standard pore filling sequences are predicted. The results clearly point to two principal effects of dynamic contact angle modification at the pore scale: a “pore sequence” effect, characterised by an alteration to the distribution of displaced pore sizes, and a “sweep efficiency” effect, demonstrated by a change in the overall fraction of pores invaded. Our study indicates that any LS effect will depend on the relative (scenario-dependent) influence of each mechanism, where factors such as the initial wettability state of the system and the pore size distribution of the underlying network are found to play crucial roles. In addition, we highlight the important role played by end-point capillary pressure in determining LS efficacy.

A Multitask Diffusion Strategy With Optimized Inter-cluster Cooperation

We consider a multitask estimation problem where nodes in a network are divided into several connected clusters, with each cluster performing a least-mean-squares estimation of a different random parameter vector. Inspired by the adapt-then-combine diffusion strategy, we propose a multitask diffusion strategy whose mean stability can be ensured whenever individual nodes are stable in the mean, regardless of the inter-cluster cooperation weights. In addition, the proposed strategy is able to achieve an asymptotically unbiased estimation, when the parameters have same mean. We also develop an inter-cluster cooperation weights selection scheme that allows each node in the network to locally optimize its inter-cluster cooperation weights. Numerical results demonstrate that our approach leads to a lower average steady-state network mean-square deviation, compared with using weights selected by various other commonly adopted methods in the literature.

Steady-state and dynamic network modes for perceptual expectation

Perceptual expectation can attenuate repetition suppression, the stimulus-induced neuronal response generated by repeated stimulation, suggesting that repetition suppression is a top-down modulatory phenomenon. However, it is still unclear which high-level brain areas are involved and how they interact with low-level brain areas. Further, the temporal range over which perceptual expectation can effectively attenuate repetition suppression effects remains unclear. To elucidate the details of this top-down modulatory process, we used two short and long inter-stimulus intervals for a perceptual expectation paradigm of paired stimulation. We found that top-down modulation enhanced the response to the unexpected stimulus when repetition suppression was weak and that the effect disappeared at 1,000 ms prior to stimulus exposure. The high-level areas involved in this process included the left inferior frontal gyrus (IFG_L) and left parietal lobule (IPL_L). We also found two systems providing modulatory input to the right fusiform face area (FFA_R): one from IFG_L and the other from IPL_L. Most importantly, we identified two states of networks through which perceptual expectation modulates sensory responses: one is a dynamic state and the other is a steady state. Our results provide the first functional magnetic resonance imaging (fMRI) evidence of temporally nested networks in brain processing.

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

BackgroundComparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison.ResultsIn this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow – or the long-term relative frequency of the transitions that the random walker makes – between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment.ConclusionsThrough evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID.

Analytical and numerical modeling of non-isothermal and steady-state gas transportation network and the comparison with the results of artificial neural network(ANN) and fuzzy inference system (FIS)

This study was conducted for modeling gas transmission network under non-isothermal condition and steady-state for real gas. By considering these circumstances, as well as length of gas pipeline, the dependent and independent variables, and multiple unknowns such as changes in compressibility factor, the gas density, viscosity, etc, which in turn vary according to changes in temperature and pressure along the pipe; these conditions increases the time in which analytical and numerical equations are solved. Therefore, intelligent systems are used to reduction the long process of analytical and numerical calculations of compressor's energy consumption, their compression ratio and destination point pressure by considering compressor rotation velocity variations which are leading to changes in temperature and pressure as well. Intelligent systems are able to predict the model without need to boundary conditions and constraints of analytical and numerical solution. Eventually, the results of each modeling approach compared with each other; the results suggest that the simulation conducted using artificial intelligence techniques has high accuracy.

ACCURATE CHEMICAL MASTER EQUATION SOLUTION USING MULTI-FINITE BUFFERS.

The discrete chemical master equation (dCME) provides a fundamental framework for studying stochasticity in mesoscopic networks. Because of the multi-scale nature of many networks where reaction rates have large disparity, directly solving dCMEs is intractable due to the exploding size of the state space. It is important to truncate the state space effectively with quantified errors, so accurate solutions can be computed. It is also important to know if all major probabilistic peaks have been computed. Here we introduce the Accurate CME (ACME) algorithm for obtaining direct solutions to dCMEs. With multi-finite buffers for reducing the state space by O(n!), exact steady-state and time-evolving network probability landscapes can be computed. We further describe a theoretical framework of aggregating microstates into a smaller number of macrostates by decomposing a network into independent aggregated birth and death processes, and give an a priori method for rapidly determining steady-state truncation errors. The maximal sizes of the finite buffers for a given error tolerance can also be pre-computed without costly trial solutions of dCMEs. We show exactly computed probability landscapes of three multi-scale networks, namely, a 6-node toggle switch, 11-node phage-lambda epigenetic circuit, and 16-node MAPK cascade network, the latter two with no known solutions. We also show how probabilities of rare events can be computed from first-passage times, another class of unsolved problems challenging for simulation-based techniques due to large separations in time scales. Overall, the ACME method enables accurate and efficient solutions of the dCME for a large class of networks.

Analysis of Asymmetrical Modes in Medium Voltage Electrical Grids with Compensated Neutral

A mathematical model of steady-state radial medium voltage electrical network with earthed neutral via arc-suppression coils has been carried out. A detailed equivalent circuit of the examined network using the completed equivalent circuit of phase coordinates methods and Kirchhoff’s laws has been developed. The system of mathematical models equations has been completed. The advantage of the created mathematical model is the ability to increase the accuracy of the simulation by a detailed account of features of a network with arc-suppression coils. The mathematical model considers the load currents, the non-linear nature of consumers, the impact of the power lines asymmetry. Using the developed mathematical model there are presented results of the carried out researches of three power lines with different cross sections with neutral grounding via arc-suppression coils. The analysis of asymmetry parameters impact of power lines on the value of the neutral displacement voltage and on the voltage value at the end of the lines at different values of the reactor inductance has been completed. The conclusion about the model possibility to perform validation of the reactor tuning, to evaluate voltage quality parameters, to carry out the formation of reliable initial conditions for calculating the overload in the single-phase earth faults. In conclusion the model performs validation of the reactor tuning and it evaluates voltage quality parameters in order to carry out the formation of reliable initial conditions. It also calculates the overload in the single-phase earth faults.

Dynamic Management of Electrical Power Distribution Networks

This paper considers the problem of optimal dynamic management of electrical power distribution networks with distributed generation and storage. Initially, analysis is performed of the Optimal Power Flow (OPF) problem—a paramount optimization problem that needs to be solved to ensure optimal steady-state power network operation. In the rest of the paper we present a hierarchical control structure for solving the considered optimal control problem in a dynamical framework. At the upper level a dynamic OPF solver computes the optimal power references for distributed generators and storages at slow rate. These references are then transmitted to the intermediate level, where a faster Model Predictive Control algorithm computes small deviations from power references given by the OPF solver to take into account the variability of load profiles that was neglected at the upper layer. Finally, the power references are forwarded to the primary level where local controllers track these power reference values. A realistic simulation case study of a Croatian power distribution grid is used for testing purposes and to demonstrate the applicability and usefulness of the proposed control strategy.

Formulation and steady‐state analysis of diffusion mobile adaptive networks with noisy links

In this study, the effects of noisy links are investigated on the steady-state performance of mobile adaptive networks with diffusion least mean-squares strategies. The authors derive theoretical relations which explain how the steady-state performance metrics, including the steady-state network mean-square deviation and steady-state velocity mean-square-error is affected by noisy links. The provided analysis relies on the spatial–temporal energy conservation argument. The proposed simulation results reveal that although the noisy links degrade the performance of mobile adaptive networks; however, for suitably chosen combination coefficients the mobile adaptive network with noisy links provides a bounded estimation error. Finally, the proposed simulations verify that the derived theoretical analysis closely matches the actual steady-state performance observed in a network.

Time-Dependent Gene Network Modelling by Sequential Monte Carlo.

Most existing methods used for gene regulatory network modeling are dedicated to inference of steady state networks, which are prevalent over all time instants. However, gene interactions evolve over time. Information about the gene interactions in different stages of the life cycle of a cell or an organism is of high importance for biology. In the statistical graphical models literature, one can find a number of methods for studying steady-state network structures while the study of time varying networks is rather recent. A sequential Monte Carlo method, namely particle filtering (PF), provides a powerful tool for dynamic time series analysis. In this work, the PF technique is proposed for dynamic network inference and its potentials in time varying gene expression data tracking are demonstrated. The data used for validation are synthetic time series data available from the DREAM4 challenge, generated from known network topologies and obtained from transcriptional regulatory networks of S. cerevisiae. We model the gene interactions over the course of time with multivariate linear regressions where the parameters of the regressive process are changing over time.

Stability of basic steady states of networks in bounded domains

In this article, we study the problem of a network of curves in a planar domain with the normal velocity proportional to each curvature and fixed angle conditions at the points at which the curves intersect. We introduce the evolution problem of networks, identify two cases of basic steady states in bounded and smooth domains and study their stability in terms of the geometry of the boundary.

Link travel time inference using entry/exit information of trips on a network

This paper studies link travel time estimation using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation–Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times.

A ride-through power structure for CFETR

The China fusion engineering test reactor (CFETR) is an ITER-like tokamak system for fusion energy development. It could output 50-200 MW fusion power in phase I for demonstrating power generation with complete structure of fusion power plant, and 1 GW output power can be expected in phase II. Its ITER-like HV substation receives power from the 500 kV transmission grid for powering its pulsed power electric network (PPEN) and steady-state electric power network (SSEN). This paper describes the concept design of the electric power system, with emphasis on its turbine generator implemented with flexible doubly fed induction generator (DFIG). DFIG has ride through ability during the plasma-off period. Its capability diagram is configured to analyze its operation range. Similar to ITER, the CFETR also requires about 1 GVar reactive power to be compensated. Based on this requirement, the generator is expected to generate part of reactive power to compensate and stabilize the local power network.

Multi-Hop Diffusion LMS for Energy-Constrained Distributed Estimation

We propose a multi-hop diffusion strategy for a sensor network to perform distributed least mean-squares (LMS) estimation under local and network-wide energy constraints. At each iteration of the strategy, each node can combine intermediate parameter estimates from nodes other than its physical neighbors via a multi-hop relay path. We propose a rule to select combination weights for the multi-hop neighbors, which can balance between the transient and the steady-state network mean-square deviations (MSDs). We study two classes of networks: simple networks with a unique transmission path from one node to another, and arbitrary networks utilizing diffusion consultations over at most two hops. We propose a method to optimize each node's information neighborhood subject to local energy budgets and a network-wide energy budget for each diffusion iteration. This optimization requires the network topology, and the noise and data variance profiles of each node, and is performed offline before the diffusion process. In addition, we develop a fully distributed and adaptive algorithm that approximately optimizes the information neighborhood of each node with only local energy budget constraints in the case where diffusion consultations are performed over at most a predefined number of hops. Numerical results suggest that our proposed multi-hop diffusion strategy achieves the same steady-state MSD as the existing one-hop adapt-then-combine diffusion algorithm but with a lower energy budget.

Commentary: “Comparison of spike parameters from optically identified GABAergic and glutamatergic neurons in sparse cortical cultures”

GENERAL COMMENTARY article Front. Cell. Neurosci., 22 April 2015Sec. Cellular Neurophysiology https://doi.org/10.3389/fncel.2015.00157

Implications of network topology on stability.

In analogy to chemical reaction networks, I demonstrate the utility of expressing the governing equations of an arbitrary dynamical system (interaction network) as sums of real functions (generalized reactions) multiplied by real scalars (generalized stoichiometries) for analysis of its stability. The reaction stoichiometries and first derivatives define the network’s “influence topology”, a signed directed bipartite graph. Parameter reduction of the influence topology permits simplified expression of the principal minors (sums of products of non-overlapping bipartite cycles) and Hurwitz determinants (sums of products of the principal minors or the bipartite cycles directly) for assessing the network’s steady state stability. Visualization of the Hurwitz determinants over the reduced parameters defines the network’s stability phase space, delimiting the range of its dynamics (specifically, the possible numbers of unstable roots at each steady state solution). Any further explicit algebraic specification of the network will project onto this stability phase space. Stability analysis via this hierarchical approach is demonstrated on classical networks from multiple fields.

Error-based extraction of states and energy landscapes from experimental single-molecule time-series.

Characterization of states, the essential components of the underlying energy landscapes, is one of the most intriguing subjects in single-molecule (SM) experiments due to the existence of noise inherent to the measurements. Here we present a method to extract the underlying state sequences from experimental SM time-series. Taking into account empirical error and the finite sampling of the time-series, the method extracts a steady-state network which provides an approximation of the underlying effective free energy landscape. The core of the method is the application of rate-distortion theory from information theory, allowing the individual data points to be assigned to multiple states simultaneously. We demonstrate the method's proficiency in its application to simulated trajectories as well as to experimental SM fluorescence resonance energy transfer (FRET) trajectories obtained from isolated agonist binding domains of the AMPA receptor, an ionotropic glutamate receptor that is prevalent in the central nervous system.

A new second-order method for branch contingency analysis and static voltage security

This work presents a method for static contingency analysis. The complex bus voltages after the outage of a transmission line (or a transformer) are estimated from one base case power flow solution and sensitivity relationships. As the steady-state power network equations are expressed in rectangular coordinates, the linearity of the Jacobian and the invariance of the Hessian allow the computation of sensitivity relationships with extra second-order information. This increases considerably the accuracy of the estimates of the power system variables and makes easier to handle the reactive power generation constraints, thus resulting in higher accuracy in the voltage estimation with limited additional computational effort. Numerical results with both test systems of different sizes and a real system illustrate the characteristics of the proposed methodology.

System Identification and Control of a Biotrickling Filter

Abstract This paper studies empirical modeling and control of a biotrickling filter (BTF) used for air pollution control. Step response transfer function (TF) with first-order-plus-time-delay model and steady-state artificial neural network (NN) model were developed for BTF based on input–output (I/O) data obtained from simulation of a rigorous model. These simple models offer fast predictions compared to the rigorous model and render control implementation for BTF feasible. Gas velocity and inlet concentration of hydrogen sulfide (H2S) (target pollutant) were considered as the main process inputs while outlet concentration of H2S was selected as the BTF performance variable (output). The TF and NN models fitted well with the I/O data and the resulting regression coefficient values were above 0.97. Different simulations with the fitted NN model were performed and compared with the rigorous model data at steady state. The NN model perfectly captured the steady-state behavior of the BTF process. Two control strategies were implemented, namely proportional–integral/feedback control and model predictive control, also known as receding-horizon control. The controllers were based on the fitted TF model representation of BTF under study. For the control structure, gas velocity, inlet concentration, and outlet concentration were selected as manipulated, disturbance and controlled variables, respectively. Through set-point and disturbance change tests, it was observed that the model predictive controller offered superior set-point tracking capabilities while the feedback controller showed better control in dealing with disturbances. However, both controllers provided adequate control in general.

S100A4 and its role in metastasis – computational integration of data on biological networks.

Characterising signal transduction networks is fundamental to our understanding of biology. However, redundancy and different types of feedback mechanisms make it difficult to understand how variations of the network components contribute to a biological process. In silico modelling of signalling interactions therefore becomes increasingly useful for the development of successful therapeutic approaches. Unfortunately, quantitative information cannot be obtained for all of the proteins or complexes that comprise the network, which limits the usability of computational models. We developed a flexible computational framework for the analysis of biological signalling networks. We demonstrate our approach by studying the mechanism of metastasis promotion by the S100A4 protein, and suggest therapeutic strategies. The advantage of the proposed method is that only limited information (interaction type between species) is required to set up a steady-state network model. This permits a straightforward integration of experimental information where the lack of details are compensated by efficient sampling of the parameter space. We investigated regulatory properties of the S100A4 network and the role of different key components. The results show that S100A4 enhances the activity of matrix metalloproteinases (MMPs), causing higher cell dissociation. Moreover, it leads to an increased stability of the pathological state. Thus, avoiding metastasis in S100A4-expressing tumours requires multiple target inhibition. Moreover, the analysis could explain the previous failure of MMP inhibitors in clinical trials. Finally, our method is applicable to a wide range of biological questions that can be represented as directional networks.