Granular Network Research Articles

Collaborative flexibility scheduler using zonal intelligence

Swanbarton's experience in real-world local energy trading in the Innovate UK-funded EMBLEM and LEMDEx projects shows that high-resolution scheduling can deliver flexibility by reducing peak flows across market boundaries. Building on these findings, this study presents a new concept for automatically meeting sourcing flexibility requirements from multiple, commercially separate providers with the lowest overall cost. This novel approach enables prequalified providers to opportunistically join and leave flexibility markets, maximising individual revenues and minimising disruption of primary commercial activities through collaboration. Distribution system operators also save costs by being freed from complex scheduling, coordinating and dispatching of flexibility. With sufficiently granular and distributed network metering and improvements in meteorology, power flows may be measured and forecast with much higher fidelity, so enabling accurate quantification of near-term flexibility requirements. Rather than being locked into lengthy delivery windows against discrete settlement periods, flexibility needs are best expressed by shapes plotted on a continuous rolling power-time signal since these can exactly follow arbitrary fluctuations of local renewables or high penetrations of electric vehicles. To meet such flexibility requirements, these shapes may then be in-filled by the available local demand-side response processes, also expressed as shapes, on an automated, ongoing basis by the zonal flexibility scheduler.

Joint Forecasting and Interpolation of Time-Varying Graph Signals Using Deep Learning

We tackle the problem of forecasting network-signal snapshots using past signal measurements acquired by a subset of network nodes. This task can be seen as a combination of multivariate time-series forecasting (temporal prediction) and graph-signal interpolation (spatial prediction). This is a fundamental problem for many applications wherein deploying a high granularity network is impractical. Our solution combines recurrent neural networks with frequency-analysis tools from graph signal processing, and assumes that data is sufficiently smooth with respect to the underlying graph. The proposed learning model outperforms state-of-the-art deep learning techniques, especially when predictions are made using a small subset of network nodes, considering two distinct real world datasets: temperatures in the US and speed flow in Seattle. The results also indicate that our method can handle noisy signals and missing data, making it suitable to many practical applications.

Pulse transmission and acoustic non-reciprocity in a granular channel with symmetry-breaking clearances

Granular media composed of ordered arrays of discrete spherical granules have attracted considerable attention due to their highly nonlinear and discontinuous dynamics and acoustics that enable passively adaptive and tailorable properties. In this work we numerically study nonlinear pulse propagation in a two-dimensional (2D) granular channel composed of a main homogeneous lattice with several pairs of side granules. Depending on the direction of pulse transmission in the main lattice, a periodic series of symmetry-breaking clearances alter the topology of this 2D granular network. The rotational dynamics of the individual granules, as well as dissipative effects due to friction between granules (and their boundaries) and inherent material damping, are proven to play a significant role in the acoustics of the granular channel. This is demonstrated by comparing the theoretical predictions of this work to experimental measurements of the same system reported earlier. Moreover, the strong nonlinearity of this system in synergy with the topological asymmetry introduced by the clearances passively breaks acoustic reciprocity. To this end, a detailed study of pulse transmission in the 2D granular channel is performed, and it is shown that by simply changing the direction of pulse transmission it is possible to switch the nonlinear acoustics from Nesterenko solitary pulses to strongly decaying propagating pulses. In the latter case there is continuous and irreversible transfer of energy from the main propagating pulse to the side granules, which act, in essence, as nonlinear energy absorbers. This work highlights the important role that (even small) geometric imperfections (asymmetries) may play on the acoustics of 2D granular media.

Impact of particle size ratio on the percolation thresholds of 2D bidisperse granular systems composed of overlapping superellipses

Critical percolation threshold is a crucial parameter that describes the connectivity of heterogeneous structures. In recent decades, numerical researches in the field of 2D continuum percolation have been carried out based on the simple models of identical disks, rectangles and ellipses. However, not all of physically granular media in nature can be represented by the assembly of identical disks or ellipses, especially when it involves the existence of the diversity of object sizes. For the study of the impact of polydispersity of particle sizes on the percolation of granular networks with more complex particle geometries, a versatile class of superellipses with both deformation coefficient m in [0.5, + ∞) and aspect ratio a/b in [0.1, 1.0] is introduced. The 2D bidisperse models composed of overlapping superellipses of two different sizes are constructed then. By using the generally continuum percolation algorithm, the critical percolation thresholds ϕc for different binary-sized superellipse systems with the ranges of the equivalent radii ratio λ in [0.1, 1.0] and the number fraction of smaller superellipses f in [0.0, 1.0] are studied. Furthermore, the numerically generalized fitting functions of ϕc are further proposed for these polydisperse media with the broad ranges of m, a/b, λ and f. From the research, we can find that for 2D binary-sized superellipse systems, the intrinsic symmetry of ϕc occurs at the area proportion of smaller superellipses υ≈ 0.5. The maximum threshold is generally achieved in the case of (1−f) ≈λ2 and the minimum one is obtained for the corresponding monodispersion.

Cadmium removal from water by enhanced adsorption on iron-embedded granular acicular mullite ceramic network

Abstract Iron-embedded granular acicular mullite was synthesized and used to remove Cd(II) from water. Characterization of iron-embedded acicular mullite and its adsorption behavior for Cd(II) were studied with the commercial ceramsite as a comparison. Iron oxyhydroxide is successfully embedded into acicular mullite, but mainly hematite loaded on its counterpart. After iron embedment, the specific surface area, iron content and pore volume of acicular mullite vastly increase. The adsorption isothermal fits well by the Langmuir-Freundlich model. Based on the model, the maximum adsorption capacity (Qg) for Cd(II) on iron-embedded mullite is 2.808 mg/g, which is 8 times higher than the virgin acicular mullite and 3 times higher than the commercial ceramsite. Moreover, site energy distribution for Cd(II) adsorbed on iron-embedded mullite and commercial ceramsite were calculated to explore the adsorption mechanisms. The average site energy (µ(E*)) of Cd(II) adsorption on all samples is similar but the energy distribution frequency function F( E m * ) for Cd(II) adsorption on iron-embedded acicular mullite was 8 times higher than its counterpart. Consequently, this iron-embedded acicular mullite is efficient for the removal of Cd(II) in water and could be applied as advanced filter media on large scale.

Random-graph models and characterization of granular networks

Abstract Various approaches and measures from network analysis have been applied to granular and particulate networks to gain insights into their structural, transport, failure-propagation and other systems-level properties. In this article, we examine a variety of common network measures and study their ability to characterize various two-dimensional and three-dimensional spatial random-graph models and empirical two-dimensional granular networks. We identify network measures that are able to distinguish between physically plausible and unphysical spatial network models. Our results also suggest that there are significant differences in the distributions of certain network measures in two and three dimensions, hinting at important differences that we also expect to arise in experimental granular networks.

A granular functional network classifier for brain diseases analysis

ABSTRACT The development of interpretable and readable diagnosis support models in the medical field is becoming an active research area. Neuroimaging technology has been widely used in the study of various brain diseases, supported by several kind of machine learning algorithms. Such algorithms, in spite of their accuracy, have often a lack of interpretability. In order to address these issues, in this paper, a new classifier based on functional networks, revised from a granular perspective is proposed. Granular architectures allow high accuracy, without losing interpretability. Two classes of neurodevelopmental disorders, that is Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder, will be considered to perform numerical experiments on publicly available data. The numerical results against state-of-the-art methods show the good performance of the proposed scheme, by confirming some theoretical achievements.

Characterization of gelatin/chitosan ploymer films integrated with docosahexaenoic acids fabricated by different methods

In this study, docosahexaenoic acid powder-enhanced gelatin-chitosan edible films were prepared by casting, electrospinning and coaxial electrospinning, respectively. The color (CR), transparency (UV), light transmission (UV), mechanical strength (TA-XT), thermal stability (DSC), crystalline structures (XRD), molecular interactions (FTIR), and microstructure (SEM) were assessed in the analytical research. The results of the research showed that the electrospinning process and the coaxial electrospinning process produced a smooth surface visible to by the naked eye and a uniform granular network structure in a unique film-forming manner, thereby exhibiting good water solubility and mechanical properties. In contrast, the casted film was smooth, transparent, and mechanically strong but poorly water soluble. It was also found that the addition of docosahexaenoic acid powder affected the optical, physical and mechanical properties of the film to varying degrees.

Granular computing-based multi-level interactive attention networks for targeted sentiment analysis

Targeted sentiment analysis has shown great promise in the field of e-commerce review processing. In recent years, research is ongoing to optimize the current targeted sentiment analysis approaches. This paper, from the innovation perspective, proposes a method referred to as granular computing-based multi-level interactive attention networks (GC-MIA). The method is constructed on the basis of granular computing. Each word is treated as an indivisible granule which constitutes the contexts and the target, respectively. Aiming at exploring the information within each word in the sentence, the multi-level interactive attention networks are established to make full use of every single granule. The representations of the target and the contexts are revised via multi-level interactive learning. Experimental results on the SemEval 2014 dataset are presented to verify the effectiveness of the GC-MIA model. The high accuracy indicates the significance of the proposed method in the targeted sentiment analysis.

Second and higher moments of fundamentals: A literature review

AbstractThis literature review outlines the recent progress in fundamental second and higher moments of research. We survey the moments’ existence, formation, and financial market and macroeconomic implications. Research shows that time‐varying volatility and non‐Gaussian shocks exist throughout all measures of fundamentals at both the micro‐ and macro levels. In addition, the granular network among firms helps explain the origin of fundamental second and higher moments. Empirical evidence shows that the moments have strong predictive power on asset prices and macroeconomic variables. We also highlight several areas where more research is needed to better understand the moments.

Numerical simulations of the controlled motion of a hopping asteroid lander on the regolith surface

Previous missions have revealed that small Solar system bodies are topographically diverse, which raises an immense challenge to a lander that aims to perform scientific measurements at different locations on the surface of the target. In recent years, hopping mechanism has attracted considerable attention due to its adaptability to the granular regolith and the low-gravity environment. However, the hopping dynamics related to granular materials remains to be explored, which will contribute not only to future space missions but also to the understanding of the dynamical behaviour of granular systems under low gravity. In this paper, we studied the hopping locomotion of a cuboid lander on the regolith surface of an asteroid. Numerical simulations are performed based on the soft-sphere discrete element method. We systematically explored the effects of the controlled parameter and physical properties of the regolith particles. The results show that the hopping outcomes (velocity, angle, and morphology of the cavity left in the regolith) are strongly dependent on these parameters. The high resistance improves the robustness of granular force networks, therefore the lander hops farther in gravel-like media than less frictional media. When the cohesion between regolith particles is included, the cavity left after the hop becomes a mild indentation, differing from the non-cohesive cases, that give distinct crater-like cavities.

The effect of lithium carbonate on the ultrastructural organization of cells of hepatocarcinoma-29 in the dynamics of cultivation

        The theme of the study is an actual direction of modern experimental Oncology – the problem of studying the influence of inducers of cell death on ultrastructural changes in tumor cells that contribute to the development of apoptosis. The aim of this work was to study the ultrastructural organization of hepatocarcinoma-29 cells under the influence of lithium carbonate in the dynamics of their cultivation.         The scientific significance and novelty of the work is to obtain new data on the ultrastructural organization of hepatocarcinoma cells under the influence of lithium carbonate. The novelty of this study is to identify consistent changes in the content and structure of cytoplasmic organelles of hepatocarcinoma-29 cells in the dynamics of cultivation with the introduction of lithium carbonate.          The practical significance of the work lies in the possibility of using the obtained data for the development of targeted therapy of hepatocarcinoma by the combined use of lithium carbonate and cytostatics, which will simultaneously involve various cellular signaling pathways for the stimulation of apoptosis and autophagic cell death. The work was performed on the culture of hepatocarcinoma cells -29 using light and electron microscopy.          Changes in the volume and numerical density of cytoplasmic organelles of G-29 cells in the dynamics of cultivation under the influence of lithium carbonate were revealed. It is shown that carbonate has a damaging effect on the cells of GA-29, which increases in the dynamics of cultivation. There is a decrease in the volume density of mitochondria, tanks of the granular endoplasmic network, free polysomal complexes, while increasing the volume density of autophagosomes, autolysosomes and lysosomes. The data obtained indicate that lithium carbonate contributes to the reduction of energy and synthetic functions of GA-29 cells, the development of catabolic processes in them and the launch of cell death processes.          The data obtained make a significant contribution to cell biology, Cytology and histology, as well as Oncology. The practical significance of the results is that on the basis of the identified ultrastructural changes in hepatocarcinoma cells under the influence of lithium carbonate, it is possible to develop approaches to targeted therapy of this type of cancer.           Key words: hepatocarcinoma-29, ultrastructure, lithium carbonate

Marc: Multi-Granular Representation Learning for Networks Based on the 3-Clique

Network embedding is of paramount importance in many real applications, such as node classification, network visualization, and link prediction. Existing methods can effectively encode different structural properties into representations. Most of them are single-granular representation learning methods that do not enable the network to be easily analyzed at various scales. There are many kinds of hierarchical structures in reality. In this paper, we propose a novel algorithm multi-granular representation learning for networks based on the 3-clique named Marc. It makes the representations of the current network be formed by both considering the structure of this granular network and inheriting the coarser network representation results. Firstly, we propose the 3-clique coarsening strategy. A 3-clique is coarsened to be a supernode in the coarser network. These coarsened networks with different granularities, preserve the original network's main structure. Secondly, we use the single-granular optimization objective function to obtain the nodes' representations of each granular network. Finally, the refinement model learns the nodes' representations, starting from the coarsest network. The learned representations of supernodes serve as good initializations for embedding the corresponding coarsened nodes from the finer network. This process is repeated until we obtain an embedding for each node in the original network which preserves the relationship among multi-granular networks. The experimental results on five public datasets, Wiki, BlogCatalog, Cora, CiteSeer, and DBLP, demonstrate that Marc has a better Macro F1 value for classification tasks than the baseline methods.

Larger Stabilizing Particles Make Stronger Liquid Marble.

Understanding the mechanical stability of granular-armored liquid marbles is prerequisite for their applications including encapsulation, sensors, microreactions, and miniaturized liquid storage. Most liquid marbles are armored with agglomerated granular structure which complicates the wetting and interacting states of particles, hence, impeding one from understanding the effect of granular size on the mechanical stability of marbles. In this work, using a custom-built platform to examine the liquid marbles armored by a single layer of uniform grains, it is revealed that larger microsized grains produce stronger liquid marble. This finding is attributed to the gravity-induced capillary attraction which dominates the interaction of particles and provides additional tension to the granular network of the marble surface, which enhances the mechanical stability of marbles. In addition, different granular network structures are formed at the marble surface by using a binary mixture of monodisperse grains, and their effect on the mechanical stability of marbles is explored. The understandings offer important insights for application involving liquid marbles and provides guideline to formulate robust marble-based products.

Study of the shear-rate dependence of granular friction based on community detection

In this study, computer simulations are performed on three-dimensional granular systems under shear conditions. The system comprises granular particles that are confined between two rigid plates. The top plate is subjected to a normal force and driven by a shearing velocity. A positive shear-rate dependence of granular friction, known as velocity-strengthening, exists between the granular and shearing plate. To understand the origin of the dependence of frictional sliding, we treat the granular system as a complex network, where granular particles are nodes and normal contact forces are weighted edges used to obtain insight into the interiors of granular matter. Community structures within granular property networks are detected under different shearing velocities in the steady state. Community parameters, such as the size of the largest cluster and average size of clusters, show significant monotonous trends in shearing velocity associated with the shear-rate dependence of granular friction. Then, we apply an instantaneous change in shearing velocity. A dramatic increase in friction is observed with a change in shearing velocity in the non-steady state. The community structures in the non-steady state are different from those in the steady state. Results indicate that the largest cluster is a key factor affecting the friction between the granular and shearing plate.

Improved convolutional neural network combined with rough set theory for data aggregation algorithm

Data aggregation is a crucial method to relieve the energy consumption in wireless sensor networks (WSNs). However, how to perform data aggregation while preserving data fidelity and confidentiality is a challenging research task. Since many existing aggregation algorithms have large communication and computation overheads, this paper integrates rough set theory with an improved convolutional neural network, and proposes a novel information aggregation algorithm for wireless sensor network. Firstly, a feature extraction model is designed in our proposed algorithm and then trained in Sink node, where the rough set theory is adopted to effectively simplify information and cut down the tagged dimension. Once these data features from granular deep network are extracted by the cluster nodes, they will be sent to the Sink node by cluster heads, so as to reduce the quantity of data transmission and extend the network lifetime. Qualitative and quantitative simulation results show that compared with existing data aggregation algorithms, the energy consumption of our proposed granular CNN model can decrease obviously and the accuracy of the data aggregation can be effectively improved.

A Granular Functional Network with delay: Some dynamical properties and application to the sign prediction in social networks

Abstract In this paper, we propose a general scheme of Functional Network, by considering granularity of information and time delay. Functional Networks (FNs) are a relatively recent alternative to standard Neural Networks (NNs). They have shown better performance in comparison to performance of NNs. Data granulation used in the development of NNs allows for the formation of more efficient and transparent architectures. Time delay models have been recognized to be more realistic constructs of real-world systems. By keeping these observations in mind, we revise the usual design scheme of FN by casting it in the settings of information granules, defining a different learning algorithm, and by introducing time delay. Under some assumptions, we discuss some dynamical properties of the proposed model, in particular those concerning asymptotic stability and Neimark–Sacker bifurcation. Finally, we present an application of the proposed method to the problem of sign prediction in social networks. The results reported against those obtained by the state-of-the-art method show good performance of the proposed approach.

Numerical model for particle size effects on flash sintering temperature of ionic nanoparticles

A numerical model is presented to predict the particle size effect on the flash onset temperature, using a granular network consisting of ceramic nanoparticles and their contacts as resistors. The theoretical calculations are compared to the experimental data of 3 mol% yttria-stabilized zirconia as ionic ceramic from the literature. The electric resistances at the particle contacts and the granular compact volume primarily depend on the contact to particle radius ratio. This ratio increases with the increase in the cold compaction pressure and during the heating to the flash temperature. This ratio also dictates the electric resistance via conventional or quantum mode, depending on the bulk resistivity character. Smaller particles lead to higher neck to particle radius ratio during the heating, hence to lower flash sintering temperatures. Apparently, the need for a critical current through the necks is crucial in determining the flash temperature, in agreement with Joule heating.

Shear strength of wet granular materials: Macroscopic cohesion and effective stress : Discrete numerical simulations, confronted to experimental measurements.

Rheometric measurements on assemblies of wet polystyrene beads, in steady uniform quasistatic shear flow, for varying liquid content within the small saturation (pendular) range of isolated liquid bridges, are supplemented with a systematic study by discrete numerical simulations. The numerical results agree quantitatively with the experimental ones provided that the intergranular friction coefficient is set to the value [Formula: see text], identified from the behaviour of the dry material. Shear resistance and solid fraction [Formula: see text] are recorded as functions of the reduced pressure [Formula: see text], which, defined as [Formula: see text], compares stress [Formula: see text], applied in the velocity gradient direction, to the tensile strength [Formula: see text] of the capillary bridges between grains of diameter a, and characterizes cohesion effects. The simplest Mohr-Coulomb relation with [Formula: see text]-independent cohesion c applies as a good approximation for large enough [Formula: see text] (typically [Formula: see text]. Numerical simulations extend to different values of μ and, compared to experiments, to a wider range of [Formula: see text]. The assumption that capillary stresses act similarly to externally applied ones onto the dry granular contact network (effective stresses) leads to very good (although not exact) predictions of the shear strength, throughout the numerically investigated range [Formula: see text] and [Formula: see text]. Thus, the internal friction coefficient [Formula: see text] of the dry material still relates the contact force contribution to stresses, [Formula: see text], while the capillary force contribution to stresses, [Formula: see text], defines a generalized Mohr-Coulomb cohesion c, depending on [Formula: see text] in general. c relates to [Formula: see text] , coordination numbers and capillary force network anisotropy. c increases with liquid content through the pendular regime interval, to a larger extent, the smaller the friction coefficient. The simple approximation ignoring capillary shear stress [Formula: see text] (referred to as the Rumpf formula) leads to correct approximations for the larger saturation range within the pendular regime, but fails to capture the decrease of cohesion for smaller liquid contents.

Rupture of granular rafts: effects of particle mobility and polydispersity.

Reversible encapsulation of liquid materials is a technical challenge in many applications such as for the transport and controlled delivery of active ingredients. In contrast to most state-of-the-art processes, capillary adsorbed solid particles can achieve chemical-reaction-free encapsulation by forming dense rafts which isolate the liquid from its surroundings. While the production conditions of such capsules have been characterized, the control of the armor robustness remains poorly described and understood. In this paper, we probe the armor robustness via impacts of droplets on encapsulated materials. Thereby, we establish the mechanisms and conditions of armor rupture and derive models that predict the rupturing thresholds or probabilities. Using monodisperse sized particles and gradually increasing the impacting drop velocity, a sharp transition from sustained to coalescing drops is observed. On mobile rafts made of particles at the water/air interface, the velocity threshold increases with increasing particle diameter while an opposite trend is observed on immobile rafts made of particles trapped at a gelified interface. Two models based on particle pair and triplet interactions, respectively, quantitatively match the experiments. Assembling rafts with particles of two different sizes significantly smoothens the coalescence transition, regardless of particle mobility. Beyond apparent similarities, rationalizing the rupturing probability of mobile and immobile armor evidences very different sensitivity to heterogeneities. On immobile armor, drop coalescence remains random and thus well described by the statistical particle distribution while on mobile armor the ruptures are preferably localized at the non-percolated parts of the granular network.