Discrete Network Research Articles

Asymptotic, second-order homogenization of linear elastic beam networks

We propose a general approach to the higher-order homogenization of discrete elastic networks made up of linear elastic beams or springs in dimension 2 or 3. The network may be nearly (rather than exactly) periodic: its elastic and geometric properties are allowed to vary slowly in space, in addition to being periodic at the scale of the unit cell. The reference configuration may be prestressed. A homogenized strain energy depending on both the macroscopic strain ɛ and its gradient ∇ɛ is obtained by means of a two-scale expansion. The homogenized energy is asymptotically exact two orders beyond that obtained by classical homogenization. The homogenization method is implemented in a symbolic calculation language and applied to various types of networks, such as a 2D honeycomb, a 2D Kagome lattice, a 3D truss and a 1D pantograph. It is validated by comparing the predictions of the microscopic displacement to that obtained by full, discrete simulations. This second-order method remains highly accurate even when the strain gradient effects are significant, such as near the lips of a crack tip or in regions where a gradient of pre-strain is imposed.

Prediction of constrained modulus for granular soil using 3D discrete element method and convolutional neural networks

To efficiently predict the mechanical parameters of granular soil based on its random micro-structure, this study proposed a novel approach combining numerical simulation and machine learning algorithms. Initially, 3500 simulations of one-dimensional compression tests on coarse-grained sand using the three-dimensional (3D) discrete element method (DEM) were conducted to construct a database. In this process, the positions of the particles were randomly altered, and the particle assemblages changed. Interestingly, besides confirming the influence of particle size distribution parameters, the stress-strain curves differed despite an identical gradation size statistic when the particle position varied. Subsequently, the obtained data were partitioned into training, validation, and testing datasets at a 7:2:1 ratio. To convert the DEM model into a multi-dimensional matrix that computers can recognize, the 3D DEM models were first sliced to extract multi-layer two-dimensional (2D) cross-sectional data. Redundant information was then eliminated via gray processing, and the data were stacked to form a new 3D matrix representing the granular soil's fabric. Subsequently, utilizing the Python language and Pytorch framework, a 3D convolutional neural networks (CNNs) model was developed to establish the relationship between the constrained modulus obtained from DEM simulations and the soil's fabric. The mean squared error (MSE) function was utilized to assess the loss value during the training process. When the learning rate (LR) fell within the range of 10-5–10-1, and the batch sizes (BSs) were 4, 8, 16, 32, and 64, the loss value stabilized after 100 training epochs in the training and validation dataset. For BS = 32 and LR = 10-3, the loss reached a minimum. In the testing set, a comparative evaluation of the predicted constrained modulus from the 3D CNNs versus the simulated modulus obtained via DEM reveals a minimum mean absolute percentage error (MAPE) of 4.43% under the optimized condition, demonstrating the accuracy of this approach. Thus, by combining DEM and CNNs, the variation of soil's mechanical characteristics related to its random fabric would be efficiently evaluated by directly tracking the particle assemblages.

Investigating and monitoring central nave vaults of the Turin Cathedral with Acoustic Emissions and Thrust Network Analysis

Ancient masonry constructions and historical buildings, such as cathedrals, are exposed to considerable risks attributed to factors like ageing and long-term exposure to both dynamic and static variations in loading conditions. In this study, an innovative and promising monitoring approach was applied to assess the structural integrity of the vault in the central nave of the Turin Cathedral. Specifically, the outcomes obtained from Acoustic Emissions (AE) are correlated with the insights derived from the Thrust Network Analysis (TNA) conducted on the structure. This analysis considers the structural elements introduced early in the twentieth century to mitigate horizontal forces. Acoustic Emission (AE) is a commonly employed technique in structural monitoring to detect and analyze elastic waves generated by crack formation, providing valuable information about structural damage. The Thrust Network Analysis (TNA) is an approach that applies Heyman's principles to represent stress in masonry vaults. This method models the stresses as a discrete network of forces, achieving equilibrium with gravitational loads. In this context, the results obtained by TNA analysis are strictly correlated with AE localization results.

Mechanical Models of Collagen Networks for Understanding Changes in the Failure Properties of Aging Skin.

Skin undergoes mechanical alterations due to changes in the composition and structure of the collagenous dermis with aging. Previous studies have conflicting findings, with both increased and decreased stiffness reported for aging skin. The underlying structure-function relationships that drive age-related changes are complex and difficult to study individually. One potential contributor to these variations is the accumulation of nonenzymatic crosslinks within collagen fibers, which affect dermal collagen remodeling and mechanical properties. Specifically, these crosslinks make individual fibers stiffer in their plastic loading region and lead to increased fragmentation of the collagenous network. To better understand the influence of these changes, we investigated the impact of nonenzymatic crosslink changes on the dermal microstructure using discrete fiber networks representative of the dermal microstructure. Our findings suggest that stiffening the plastic region of collagen's mechanical response has minimal effects on network-level stiffness and failure stresses. Conversely, simulating fragmentation through a loss of connectivity substantially reduces network stiffness and failure stress, while increasing stretch ratios at failure.

Rock Mass Structure Characterization Considering Finite and Folded Discontinuities: A Parametric Study

The quantification of a rock mass’s internal structure and description of discontinuity properties is imperative for modern rock mass characterization. This study builds on decades of rock engineering development by reviewing and revising different parameters such as the rock quality designation (RQD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{RQD}}$$\\end{document}), volumetric joint count (Jv\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${J}_{v}$$\\end{document}), the Pij system and others. Analyses of these parameters are done by means of a Monte Carlo simulation that generated 5000 samples of discrete discontinuity networks including finite, folded and very low to very high discontinuity densities (Jv\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${J}_{v}$$\\end{document} range: 0.5–117 discontinuities/m3), thus representing a wide range of possible geological scenarios. P10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P10$$\\end{document}, P20\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P20$$\\end{document}, P21\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P21$$\\end{document}, P30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P30$$\\end{document}, P32\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P32$$\\end{document}, RQD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$RQD$$\\end{document} and the fractal dimension of the rock mass are virtually measured on these samples and a range of higher level parameters that are used in practical rock engineering computed and their relationships investigated. It is concluded that parameters which are based on subjective estimations of discontinuity spacing, the number of discontinuity sets or RQD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$RQD$$\\end{document} are not suited to describe the rock mass structure in cases of demanding geological scenarios featuring many discontinuities, weak and anisotropic rock masses, metamorphic rock masses, folded rock masses, etc. By revising classical parameters and their relationships, this study contributes to basic rock mass characterization and furthermore paves the way for future developments by making the developed discrete discontinuity network dataset and all included codes openly accessible to the rock mechanics community.

A novel associative memory model based on semi-tensor product (STP).

A good intelligent learning model is the key to complete recognition of scene information and accurate recognition of specific targets in intelligent unmanned system. This study proposes a new associative memory model based on the semi-tensor product (STP) of matrices, to address the problems of information storage capacity and association. First, some preliminaries are introduced to facilitate modeling, and the problem of information storage capacity in the application of discrete Hopfield neural network (DHNN) to associative memory is pointed out. Second, learning modes are equivalently converted into their algebraic forms by using STP. A memory matrix is constructed to accurately remember these learning modes. Furthermore, an algorithm for updating the memory matrix is developed to improve the association ability of the model. And another algorithm is provided to show how our model learns and associates. Finally, some examples are given to demonstrate the effectiveness and advantages of our results. Compared with mainstream DHNNs, our model can remember learning modes more accurately with fewer nodes.

HP3O algorithm-based all electric ship energy management strategy integrating demand-side adjustment

To tackle the energy management challenge that integrates power generation scheduling and demand-side adjustment for all-electric ship in uncertain marine environment, a hybrid penalized proximal policy optimization algorithm (HP3O)-based energy management strategy is proposed. First, demand-side adjustment, which involves adjusting the power of the ship's electric propulsion motors and flexible service loads, is integrated into the energy management problem. Second, HP3O algorithm is employed to obtain both continuous and discrete variables simultaneously. It utilizes a continuous actor network to obtain continuous variables, such as the generator's power and ship cruising speed, while employing a discrete actor network to determine discrete variables, i.e., the on/off status of the generators. Third, to handle complex constraints reasonably, the energy management problem is formulated as a constrained Markov decision process (CMDP), and an action mask mechanism is also integrated into the energy management framework to make agent's actions more reliable. The simulation results of an all-electric cruise ship validate the effectiveness and superiority of the proposed strategy in achieving near-optimal scheduling while satisfying operation constraints. Furthermore, a case study on a hybrid diesel-electric ferry confirms its generalization performance.

Indirect: invertible and discrete noisy image rescaling with enhancement from case-dependent textures

Rescaling digital images for display on various devices, while simultaneously removing noise, has increasingly become a focus of attention. However, limited research has been done on a unified framework that can efficiently perform both tasks. In response, we propose INDIRECT (INvertible and Discrete noisy Image Rescaling with Enhancement from Case-dependent Textures), a novel method designed to address image denoising and rescaling jointly. INDIRECT leverages a jointly optimized framework to produce clean and visually appealing images using a lightweight model. It employs a discrete invertible network, DDR-Net, to perform rescaling and denoising through its reversible operations, efficiently mitigating the quantization errors typically encountered during downscaling. Subsequently, the Case-dependent Texture Module (CTM) is introduced to estimate missing high-frequency information, thereby recovering a clean and high-resolution image. Experimental results demonstrate that our method achieves competitive performance across three tasks: noisy image rescaling, image rescaling, and denoising, all while maintaining a relatively small model size.

Discrete wavelet transform assisted convolutional neural network equalizer for PAM VLC system.

With the deepening of research and the further differentiation of damage types, and to compensate for both linear and nonlinear damage in visible light communication systems (VLCs), we propose a novel discrete wavelet transform-assisted convolutional neural network (DWTCNN) equalizer that combines the advantages of wavelet transform and deep learning methods. More specifically, wavelet transform is used in DWTCNN to decompose the signal into diverse coefficient series and employ an adaptive soft-threshold method to eliminate redundant information in the signal. The coefficients are then reconstructed to achieve complete signal compensation. The experimental results show that the proposed DWTCNN equalizer can significantly reduce nonlinear impairment and improve system performance with the bit error rate (BER) under the 7% hard-decision forward error correction (HD-FEC) limit of 3.8 × 10-3. We also experimentally compared DWTCNN with the Long Short-Term Memory (LSTM) and entity extraction neural network (EXNN) equalizer, the Q factor has been improved by 0.76 and 0.53 dB, and the operating ranges of the direct current (DC) bias have increased by 4.76% and 23.5%, respectively.

Spatial-Temporal Attention TCN-Based Link Prediction for Opportunistic Network

Link prediction for opportunistic networks faces the challenges of frequent changes in topology and complex and variable spatial-temporal information. Most existing studies focus on temporal or spatial features, ignoring ample potential information. In order to better capture the spatial-temporal correlations in the evolution of networks and explore their potential information, a link prediction method based on spatial-temporal attention and temporal convolution network (STA-TCN) is proposed. It slices opportunistic networks into discrete network snapshots. A state matrix based on topology information and attribute information is constructed to represent snapshots. Time convolutional networks and spatial-temporal attention mechanisms are employed to learn spatial-temporal information. Furthermore, to better improve link prediction performance, the proposed method converts the auto-correlation error into non-correlation error. On three real opportunistic network datasets, ITC, MIT, and Infocom06, experimental results demonstrate the superior predictive performance of the proposed method compared to baseline models, as shown by improved AUC and F1-score metrics.

MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network

The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order to fill in this gap and improve the efficiency of the metaheuristic algorithm in systematic logic, we proposed a metaheuristic algorithm based on mutation tabu search and embedded it in probabilistic satisfiability logic in discrete Hopfield neural networks. Based on the traditional tabu search algorithm, the mutation operators of the genetic algorithm were combined to improve its global search ability during the learning phase and ensure that the cost function of the systematic logic converged to zero at different proportions of positive literals. Additionally, further optimization was carried out in the retrieval phase to enhance the diversity of solutions. Compared with nine other metaheuristic algorithms and exhaustive search algorithms, the proposed algorithm was superior to other algorithms in terms of time complexity and global convergence, and showed higher efficiency in the search solutions at the binary search space, consolidated the efficiency of systematic logic in the learning phase, and significantly improved the diversity of the global solution in the retrieval phase of systematic logic.

Effect of compaction degree on the topological characteristics of force chain network (FCN) in aggregate blend

Aggregates are the main carrier to resist external loads for asphalt pavement, and the characteristics of force chain network (FCN) in aggregate blend are closely related to the skeleton performance of asphalt mixture. This study analyzed the effect of compaction degree of aggregate blend on the topological characteristics of FCN using discrete element (DEM) and complex network theory. Six types of aggregate blends with different gradations were generated and compacted using DEM software EDEM. The compaction degree was evenly divided into 7 parts from the loose to the compacted states. FCN topology graph (FCNTG) of aggregate blend at each compaction degree was built according to the contact information between aggregates and the definition of complex network. The features of each FCNTG were quantified using the indicators characterizing complex network (including degree, vertex strength, clustering coefficient, and path length). The effect of compaction degree on the characteristics of FCN was analyzed. The results showed that complex network theory is a feasible method for investigating aggregate blend FCN. Each topological feature indicator in complex network method can be used to analyze the internal structural features of aggregate blend. With increasing compaction degree, the average degree and the average vertex strength of aggregate blend FCN and different size particles increase gradually, the clustering performance of particles becomes better gradually, and the average path length of FCN decreases gradually. There is a good linear relationship between the compaction degree and each topology indicator except the vertex strength. The compaction can reduce the dispersion degree of the contact forces between particles in aggregate blend and make it more uniform. It can also shorten the path length between two particles, which is important to improve the efficiency of transferring the external load.

Complex regulatory networks influence pluripotent cell state transitions in human iPSCs

Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discover 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which are highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlie the coordinated expression of genes in the GNMs. Epigenetic analyses reveal that regulatory networks underlying self-renewal and pluripotency are more complex than previously realized. Genetic analyses identify thousands of regulatory variants that overlapped predicted transcription factor binding sites and are associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network are significantly enriched for regulatory variants with large effects, suggesting that they play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work bins tens of thousands of regulatory elements in hiPSCs into discrete regulatory networks, shows that pluripotency and self-renewal processes have a surprising level of regulatory complexity, and suggests that genetic factors may contribute to cell state transitions in human iPSC lines.

Effectiveness of Diethanolamine (DEA) Addition on Band Gap Value of SnO2 by Using Sol-Gel Methods

The need for electrical energy is increasing with the increase in the economy and population in Indonesia. Fossil energy sources are used as fuel to produce electrical energy and will run out if used continuously. Fossil energy sources can be replaced by using New Renewable Energy (NRE) to meet national electrical energy needs. The purpose of adding additives in this study is to observe the effectiveness of the addition of DEA on the band gap value, crystal phase, and surface morphology on SnO2. In this study using the sol-gel method for the synthesis of SnO2. The sol-gel method is the conversion of monomers into colloidal solutions (sol) which serve as precursors for integrated networks (gels) either discrete particles or network polymers. SnO2 nanomaterials will be characterized by UV-DRS Spectrophotometer. The results of characterization of SnO2 nanomaterials with the addition of Diethanolamine additives as much as 1.5 mL have obtained a band gap value of 3.60 eV.

Micromechanics of fibrous scaffolds and their stiffness sensing by cells

Mechanical properties of the tissue engineering scaffolds are known to play a crucial role in cell response. Therefore, an understanding of the cell-scaffold interactions is of high importance. Here, we have utilized discrete fiber network model to quantitatively study the micromechanics of fibrous scaffolds with different fiber arrangements and cross-linking densities. We observe that localized forces on the scaffold result in its anisotropic deformation even for isotropic fiber arrangements. We also see an exponential decay of the displacement field with distance from the location of applied force. This nature of the decay allows us to estimate the characteristic length for force transmission in fibrous scaffolds. Furthermore, we also looked at the stiffness sensing of fibrous scaffolds by individual cells and its dependence on the cellular sensing mechanism. For this, we considered two conditions- stress-controlled, and strain-controlled application of forces by a cell. With fixed strain, we find that the stiffness sensed by a cell is proportional to the scaffold’s ‘macroscopic’ elastic modulus. However, under fixed stress application by the cell, the stiffness sensed by the cell also depends on the cell’s own stiffness. In fact, the stiffness values for the same scaffold sensed by the stiff and soft cells can differ from each other by an order of magnitude. The insights from this work will help in designing tissue engineering scaffolds for applications where mechanical stimuli are a critical factor.

Discrete network models of endothelial cells and their interactions with the substrate

Endothelial cell monolayers line the inner surfaces of blood and lymphatic vessels. They are continuously exposed to different mechanical loads, which may trigger mechanobiological signals and hence play a role in both physiological and pathological processes. Computer-based mechanical models of cells contribute to a better understanding of the relation between cell-scale loads and cues and the mechanical state of the hosting tissue. However, the confluency of the endothelial monolayer complicates these approaches since the intercellular cross-talk needs to be accounted for in addition to the cytoskeletal mechanics of the individual cells themselves. As a consequence, the computational approach must be able to efficiently model a large number of cells and their interaction. Here, we simulate cytoskeletal mechanics by means of molecular dynamics software, generally suitable to deal with large, locally interacting systems. Methods were developed to generate models of single cells and large monolayers with hundreds of cells. The single-cell model was considered for a comparison with experimental data. To this end, we simulated cell interactions with a continuous, deformable substrate, and computationally replicated multistep traction force microscopy experiments on endothelial cells. The results indicate that cell discrete network models are able to capture relevant features of the mechanical behaviour and are thus well-suited to investigate the mechanics of the large cytoskeletal network of individual cells and cell monolayers.

KarstNSim: A graph-based method for 3D geologically-driven simulation of karst networks

In karst aquifers, groundwater flow is highly influenced by the interconnected underground cavities and conduits that form the karst network. Modeling karst flows requires the use of spatially distributed approaches accounting for these networks. Their exploration is, however, often complex, and mapping them using indirect methods such as geophysical ones has proven challenging. To overcome these limitations, stochastically simulating discrete karst networks should account for the uncertainties on conduit position and geometry. Only a few existing methods can reproduce realistic and diverse karst morphologies. In this work, we propose a new algorithm, KarstNSim, for simulating discrete karst networks, that incorporates field data to generate a range of possible karst network geometries. It relies on the computation of the shortest path between the inlets and outlets of the network with the use of an anisotropic cost function defined on an n-nearest neighbor graph conformal to geological and structural heterogeneities. This cost function represents the physico-chemical processes that govern speleogenesis – such as erosion and chemical weathering – providing simplified control over the geometry of the generated networks. Our approach reproduces the vadose-phreatic partition visible in the karst networks, by generating sub-vertical conduits in the unsaturated zone and sub-horizontal ones in the saturated part. It encompasses geological parameters such as inception surfaces, fractures, permeability, and solubility of layers, along with considering the hydrological context of recharge by assigning relative weights to the inlets. We simulate various synthetic models to demonstrate the influence of different input parameters on the spatial organization of the past and present karst flows. We also apply KarstNSim to a real case study – the Ribeaucourt system (Meuse, France) – and use available data to generate realistic karst networks. The lack of knowledge on the Ribeaucourt network geometry is alleviated by the use of multiple scenarios corresponding to different sets of weights in the cost function, resulting in a variety of network morphologies which represent the uncertainty on the real geometry. The geometrical and topological metrics computed on the simulated networks generally overlap with those of real ones, suggesting the reliability of the method.

Conduction Modulation of Solution‐Processed 2D Materials

AbstractSolution‐processed 2D materials hold promise for their scalable applications. However, the random, fragmented nature of the solution‐processed nanoflakes and the poor percolative conduction through their discrete networks limit the performance of the enabled devices. To overcome the problem, conduction modulation of the solution‐processed 2D materials is reported via Stark effect. Using liquid‐phase exfoliated molybdenum disulfide (MoS2) as an example, nonlinear conduction switching with a ratio of >105 is demonstrated by the local fields from the interfacial ferroelectric P(VDF‐TrFE). Through density‐functional theory calculations and in situ Raman scattering and photoluminescence spectroscopic analysis, the modulation is understood to arise from a charge redistribution in the solution‐processed MoS2. Beyond MoS2, the modulation may be shown effective for the other solution‐processed 2D materials and low‐dimensional materials. The modulation can open their electronic device applications, for instance, thin‐film nonlinear electronics and non‐volatile memories.

A general-purpose tool for modeling multifunctional thin porous media (POREnet): From pore network to effective property tensors

POREnet, a novel approach to model effective properties of thin porous media, TPM, is presented. The methodology allows the extraction of local effective property tensors by volume averaging from discrete pore networks, PNs, built on the tessellated continuum space of a TPM. The gradient theorem is used to describe 3D transport in bulk tessellated space, providing an appropriate metric to normalize network fluxes. Implemented effective transport properties include diffusivity, permeability, solid-phase conductivity, and entry capillary pressure and contact angle under two-phase conditions, considering multi-component materials with several solid phases and local contact resistances. Calculated property tensors can be saved on 3D image stacks, where interfacial and sub-CV scale features can be added before exporting data to CFD meshes for simulation. Overall, POREnet provides a general-purpose, versatile methodology for modeling TPM in an ample range of conditions within a single CFD framework. Among other advantages, coupling of PN and continuum models at TPM-channel interfaces is simplified, interfacial contact resistances can be included using robin boundary conditions, and transient multiphysics simulations can be implemented more easily using CFD. The code is tested against a miscellaneousness of examples extracted from electrochemical applications.

Passivity-based control and asymptotic synchronization for multi-variable discrete stochastic genetic regulatory networks with complex network dynamics

Passivity-based control and asymptotic synchronization for multi-variable discrete stochastic genetic regulatory networks with complex network dynamics