Granular Network Research Articles

Visualize the Spatial Organization of Charge and Spin Stripes by Resonant Micro X-ray Diffraction (RμXRD)

The interplay between the charge-density wave (CDW), the spin-density wave (SDW), and the defect ordering in complex materials is under an intense discussion. There is a strong interest in how the competition between these striped orders plays a role in the promotion of the material’s functionality at the macroscopic scale. Up to now, only few works point the attention to the direct visualization of how SDW, CDW, and defects are organized in the materials. Here, we propose the use of an innovative technique that combines the sensitivity of resonant X-ray scattering (SXR) with the spatial resolution of scanning micro X-ray diffraction (μXRD), allowing the investigation of the spatial organization of the resonant magnetic and charge order. Recent results on nickelates, cobaltites, and cuprates demonstrate the presence of a nanoscale phase separation scenario where these multiple striped orders get self-organized and form competing granular networks of nanodomains.

Targeted energy transfers and passive acoustic wave redirection in a two-dimensional granular network under periodic excitation

We study passive pulse redirection and nonlinear targeted energy transfer in a granular network composed of two semi-infinite, ordered homogeneous granular chains mounted on linear elastic foundations and coupled by weak linear stiffnesses. Periodic excitation in the form of repetitive half-sine pulses is applied to one of the chains, designated as the “excited chain,” whereas the other chain is initially at rest and is regarded as the “absorbing chain.” We show that passive pulse redirection and targeted energy transfer from the excited to the absorbing chain can be achieved by macro-scale realization of the spatial analog of the Landau-Zener quantum tunneling effect. This is realized by finite stratification of the elastic foundation of the excited chain and depends on the system parameters (e.g., the percentage of stratification) and on the parameters of the periodic excitation. Utilizing empirical mode decomposition and numerical Hilbert transforms, we detect the existence of two distinct nonlinear phenomena in the periodically forced network; namely, (i) energy localization in the absorbing chain due to sustained 1:1 resonance capture leading to irreversible pulse redirection from the excited chain, and (ii) continuous energy exchanges in the form of nonlinear beats between the two chains in the absence of resonance capture. Our results extend previous findings of transient passive energy redirection in impulsively excited granular networks and demonstrate that steady state passive pulse redirection in these networks can be robustly achieved under periodic excitation.

Nonlinear localization, passive wave arrest and traveling breathers in two-dimensional granular networks with discontinuous lateral boundary conditions

We study nonlinear pulse propagation, breather formation and nonlinear localization leading to passive wave arrest in an impulsively forced two-dimensional granular network composed of two geometrically coupled ordered granular chains of spherical (heavy) beads with interstitial spherical (light) intruders, possessing discontinuous lateral boundary conditions. A striking feature of this network is that it completely lacks any linear acoustics, and, hence, it sustains zero speed of sound (as defined in classical acoustics). We show that this system possesses strongly nonlinear dynamics and acoustics that depend on the stiffness and mass ratios between the heavy beads and the light intruders. Moreover, polydispersity and nonlinearity within this granular network lead to complex phenomena, such as pulse propagation, motion localization, and formation of propagation and attenuation zones. In particular, depending on the design of this network the applied impulsive energy is either axially transmitted to its far field, or it is heavily scattered and localized close to the point of its generation by means of low-to-high energy transfers in its intrinsic dynamics. Such passive motion localization phenomena are due to nonlinear on-site potentials caused by the discontinuous lateral boundary conditions of the network, as well as to strongly nonlinear interactions between the heavy beads and the interstitial intruders, and, to the authors’ knowledge, are reported for the first time in the field of granular networks. These results demonstrate the high tunability of the nonlinear response of this system to energy and material properties.

Rough Cognitive Networks

Decision-making could informally be defined as the process of selecting the most appropriate actions among a set of possible alternatives in a given activity. In recent years several decision models based on Rough Set Theory (e.g. three-way decision rules) and Fuzzy Cognitive Maps have been introduced for addressing such problems. However, most of them are focused on decision-making problems with discrete attributes or they are oriented to specific domains. In this paper we present a decision model called Rough Cognitive Networks that combines the abstract semantic of the three-way decision model with the neural reasoning mechanism of Fuzzy Cognitive Maps for addressing numerical decision-making problems. The contribution of this study is two-fold. On one hand, it allows to explicitly handle decision-making problems with numerical features, where the target object could activate multiple regions at the same time. On the other hand, in such granular networks the three-way decision rules are used to design the topology of the map, addressing in some sense the inherent limitations in the expression and architecture of Fuzzy Cognitive Maps. Moreover, we propose a learning methodology using Harmony Search for adjusting the model parameters, leading to a parameter-free decision model where the human intervention is not required. A comparative analysis with standard classifiers and recently proposed rough recognition models is conducted in order to show the effectiveness of the proposal.

At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters.

Models of the cerebellar microcircuit often assume that input signals from the mossy-fibers are expanded and recoded to provide a foundation from which the Purkinje cells can synthesize output filters to implement specific input-signal transformations. Details of this process are however unclear. While previous work has shown that recurrent granule cell inhibition could in principle generate a wide variety of random outputs suitable for coding signal onsets, the more general application for temporally varying signals has yet to be demonstrated. Here we show for the first time that using a mechanism very similar to reservoir computing enables random neuronal networks in the granule cell layer to provide the necessary signal separation and extension from which Purkinje cells could construct basis filters of various time-constants. The main requirement for this is that the network operates in a state of criticality close to the edge of random chaotic behavior. We further show that the lack of recurrent excitation in the granular layer as commonly required in traditional reservoir networks can be circumvented by considering other inherent granular layer features such as inverted input signals or mGluR2 inhibition of Golgi cells. Other properties that facilitate filter construction are direct mossy fiber excitation of Golgi cells, variability of synaptic weights or input signals and output-feedback via the nucleocortical pathway. Our findings are well supported by previous experimental and theoretical work and will help to bridge the gap between system-level models and detailed models of the granular layer network.

Achilles, a New Family of Transcriptionally Active Retrotransposons from the Olive Fruit Fly, with Y Chromosome Preferential Distribution.

Sex chromosomes have many unusual features relative to autosomes. The in depth exploration of their structure will improve our understanding of their origin and divergence (degeneration) as well as the evolution of genetic sex determination pathways which, most often are attributed to them. In Tephritids, the structure of Y chromosome, where the male-determining factor M is localized, is largely unexplored and limited data concerning its sequence content and evolution are available. In order to get insight into the structure and organization of the Y chromosome of the major olive insect pest, the olive fly Bactrocera oleae, we characterized sequences from a Pulse Field Gel Electrophoresis (PFGE)-isolated Y chromosome. Here, we report the discovery of the first olive fly LTR retrotransposon with increased presence on the Y chromosome. The element belongs to the BEL-Pao superfamily, however, its sequence comparison with the other members of the superfamily suggests that it constitutes a new family that we termed Achilles. Its ~7.5 kb sequence consists of the 5’LTR, the 5’non-coding sequence and the open reading frame (ORF), which encodes the polyprotein Gag-Pol. In situ hybridization to the B. oleae polytene chromosomes showed that Achilles is distributed in discrete bands dispersed on all five autosomes, in all centromeric regions and in the granular heterochromatic network corresponding to the mitotic sex chromosomes. The between sexes comparison revealed a variation in Achilles copy number, with male flies possessing 5–10 copies more than female (CI range: 18–38 and 12–33 copies respectively per genome). The examination of its transcriptional activity demonstrated the presence of at least one intact active copy in the genome, showing a differential level of expression between sexes as well as during embryonic development. The higher expression was detected in male germline tissues (testes). Moreover, the presence of Achilles-like elements in different species of the Tephritidae family suggests an ancient origin of this element.

Gas migration regimes and outgassing in particle-rich suspensions

Understanding how gases escape from particle-rich suspensions has important applications in nature and industry. Motivated by applications such as outgassing of crystal-rich magmas, we map gas migration patterns in experiments where we vary (1) particle fractions and liquid viscosity (10 Pa s – 500 Pa s), (2) container shape (horizontal parallel plates and upright cylinders), and (3) methods of bubble generation (single bubble injections, and multiple bubble generation with chemical reactions). We identify two successive changes in gas migration behavior that are determined by the normalized particle fraction (relative to random close packing), and are insensitive to liquid viscosity, bubble growth rate or container shape within the explored ranges. The first occurs at the random loose packing, when gas bubbles begin to deform; the second occurs near the random close packing, and is characterized by gas migration in a fracture-like manner. We suggest that changes in gas migration behavior are caused by dilation of the granular network, which locally resists bubble growth. The resulting bubble deformation increases the likelihood of bubble coalescence, and promotes the development of permeable pathways at low porosities. This behavior may explain the efficient loss of volatiles from viscous slurries such as crystal-rich magmas.

Water-repellent flexible fabric strain sensor based on polyaniline/titanium dioxide-coated knit polyester fabric

A flexible fabric strain sensor was prepared by in situ chemical polymerization of aniline on knit polyester fabric surface in aqueous acid solutions using ammonium persulfate as an oxidant. Furthermore, the optimum addition ratio of titanium dioxide (TiO2) in polyaniline (PANI) in the granular conductive network membrane was investigated. The morphological, structural, thermal, electrical, strain-sensing, and water-repellent properties of the polyester knit fabrics modified with PANI and PANI/TiO2 hybrid were analyzed. The surface electrical resistance of PANI/TiO2 conductive films on the knit fabric was higher than that of pristine PANI, which was due to the particle blocking the conduction path effect caused by TiO2 embedded in the PANI matrix. However, it was further found that the addition of TiO2 could improve the durability properties of flexible fabric strain sensor against cycles of elongation, though with a little loss in electrical conductivity and sensitivity. Moreover, the water-repellent property was evaluated by measuring water contact angles. The results showed that for PANI/TiO2 nanocomposites-coated flexible fabric strain sensor, a desirable level of contact angle (127.5° ± 1.7°) was even preserved at 121.8° ± 2.6° after 100 cycles of elongation. This indicated that the flexible fabric strain sensor had rather high water-repellent efficiency and excellent durability during the elongation cycles.

솔벤트 도핑과 후처리 공정에 따른 전도성 고분자 PEDOT : PSS의 특성 변화

Poly(3,4-ethylenedioxythiophene) : poly(styrenesulfonate) (PEDOT : PSS) has attracted a great deal of attention as a transparent conductive material for organic solar cells or organic light-emitting diodes due to its high electrical conductivity, optical trans- parency, and excellent mechanical flexibility. It is well known that a solvent doping for PEDOT : PSS thin-films significantly increases the conductivity of films. In this paper, the effect of various kinds of solvent doping and post-treatment on the electrical and structural properties of PEDOT : PSS thin-films is investigated. The solvent doping greatly increases the con- ductivity of PEDOT : PSS thin-films up to 884 S/cm. A further enhancement of the conductivity of PEDOT : PSS thin-films is achieved by the solvent post-treatment which raises the conductivity up to 1131 S/cm. The enhancement is mainly caused by the depletion of insulating PSS and forming conducting PEDOT-rich granular networks. Strong optical absorption peaks at the wavelength of 225 nm of PEDOT : PSS thin-films indicate the depletion of insulating PSS by post-treatment. We be- lieve that the solvent post-treatment is a promising method to achieve highly conductive transparent PEDOT : PSS thin-films for applications in efficient, low-cost and flexible organic devices.

Combining granular computing and RBF neural network for process planning of part features

As an essential element in the overall process planning of a part, process planning of part features plays an important role in machining quality and productivity. Through the analysis for disadvantages of the previous methods based on rule (knowledge) base or back-propagation neural network (BPNN), a novel process planning methodology of part features is proposed on the basis of the integration between granular computing (GrC) and radial basis function neural network (RBFNN). Firstly, the similarity between training samples under the input vectors is calculated according to the weighted Euclidean distances. Secondly, in accordance with the theory of fuzzy tolerance quotient space, which is one of the theoretical models of GrC, granulation of the training samples is fulfilled, and a series of process information granular layers with different granularity composed of the different number of process information granules are constructed. Afterwards, depending on the divisions of training samples under the desired output vectors, Shannon information entropy algorithm is used to measure the granularity of a succession of granular layers, by which an optimal process information granular layer is determined. Finally, in terms of the number and distribution of the process information granules in the optimal granular layer, two crucial parameters of the hidden layer in RBFNN including the number of Gaussian functions and the corresponding centers are reasonably determined. An application example of hole feature demonstrates that RBFNN is superior to BPNN in the convergence speed, training accuracy as well as generalization ability, and meanwhile the proposed GrC-RBFNN is capable of planning more exact process routes of part features than RBFNN without increasing its scale and complexity.

FGSN: Fuzzy Granular Social Networks – Model and applications

Social network data has been modeled with several approaches, including Sociogram and Sociomatrices, which are popular and comprehensive. Similar to these we have developed here a novel modeling technique based on granular computing theory and fuzzy neighborhood systems, which provides a uniform framework to represent social networks. In this model, a social network is represented with a collection of granules. Fuzzy sets are used for defining the granules. The model is named Fuzzy Granular Social Network (FGSN). Familiar measures of networks viz. degree, betweenness, embeddedness and clustering coefficient are redefined in the context of this new framework. Two measures, namely, entropy of FGSN and energy of granules are defined to quantify the uncertainty involved in FGSN arising from fuzziness in the relationships of actors. Experimental results demonstrate the applicability of the model in two well known problems of social networks, namely, target set selection and community detection with comparative studies.

Optical Nyquist Filtering for Elastic OTDM Signals: Fundamentals and Demonstrations

Optical Nyquist filtering offers an efficient method to generate ultra-high speed optical time division multiplexing (OTDM) signals with bandwidth equal to the symbol rate without intersymbol interference. Since the Nyquist filtering can be simply implemented in a passive optical device such as a liquid crystal on silicon-based switching element, optical Nyquist filtering is potentially advantageous over digital filtering method in terms of energy efficiency and capacity. Especially in extremely coarse granular optical networks based on high baud rate Nyquist-OTDM signals, it would offer better spectral efficiency and elasticity through efficient add/drop multiplexing with small guard band and flexibility in bandwidth allocation. In this paper, we will discuss important roles of the optical Nyquist filtering as a platform to realize ultra-coarse granular elastic networks based on wavelength division multiplexing of Nyquist OTDM signals. While concepts of basic building blocks, such as elastic multi-Tbit/s transceiver and add/drop multiplexing, are presented, we will introduce proof-of-concept experimental demonstrations placing emphasis on the elasticity and the network aspect operations. We will also argue that spectral defragmentation is a critical network functionality at elastic add/drop nodes to improve the spectral utilization, and the realization of the spectral defragmentation by means of an all-optical wavelength converter is superior to the optical-electrical/electrical-optical approach thanks to the modulation format independent property of the employed optical parametric processes.

Controllability and observability of Boolean networks arising from biology.

Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

A Novel Pattern Classification using Granular Reflex Fuzzy Min-Max Neural Network

Pattern classification is a system for classifying patterns into dissimilar potential categories. The classifier that is used for classification is granular neural network. A granular neural network called granular reflex fuzzy min-max neural network (GrRFMN). GrRFMN uses hyperbox fuzzy set to signify grainy information. Using known data the neural network will be trained, and using this trained neural network data can be classified. Its structural design consists of a spontaneous effect system motivated from human brain to handle group overlies. The GFMN cannot hold data granules of dissimilar sizes professionally. It can be practically done that a convinced quantity of such preprocessing can assist to recover the presentation of a classifier. The GrRFMN is skilled of managing grainy information capably by the training algorithm. The experimental outcomes on valid datasets confirm a good presentation of GRFMN. Experimental results on valid data sets confirm that the GrRFMN can categorize granules of dissimilar granularity further acceptably.

Optical tracing of multiple charges in single-electron devices

Single molecules that exhibit narrow optical transitions at cryogenic temperatures can be used as local electric-field sensors. We derive the single-charge sensitivity of aromatic organic dye molecules, based on quantum mechanical considerations. Through numerical modeling, we demonstrate that by using currently available technologies it is possible to optically detect charging events in a granular network with a sensitivity better than ${10}^{\ensuremath{-}5}e/\sqrt{\mathrm{Hz}}$ and track positions of multiple electrons, simultaneously, with nanometer spatial resolution. Our results pave the way for minimally invasive optical inspection of electronic and spintronic nanodevices and building hybrid optoelectronic interfaces that function at both single-photon and single-electron levels.

Nonlinear mixed solitary—Shear waves and pulse equi-partition in a granular network

We study primary pulse transmission in a two-dimensional granular network composed of two ordered chains that are nonlinearly coupled through Hertzian interactions. Impulsive excitation is applied to one of the chains (designated as ‘excited chain’), and the resulting transmitted primary pulses in both chains are considered, especially in the non-directly excited chain (the ‘absorbing chain’). A new type of mixed nonlinear solitary pulses–shear waves is predicted for this system, leading to primary pulse equi-partition between chains. An analytical reduced model for primary pulse transmission is derived to study the strongly nonlinear acoustics in the small-amplitude approximation. The model is re-scalable with energy and parameter-free, and is asymptotically solved by extending the one-dimensional nonlinear mapping technique of Starosvetsky (2012). The resulting nonlinear maps governing the amplitudes of the mixed-type waves accurately capture the primary pulse propagation in this system and predict the first occurrence of energy equipartition in the network. To confirm, in part, the theoretical results we experimentally test a series of two-dimensional granular networks, and prove the occurrence of strong energy exchanges leading to eventual pulse equi-partition between the excited and absorbing chains, provided that the number of beads is sufficiently large.

Armoring a droplet: soft jamming of a dense granular interface.

Droplets and bubbles protected by armors of particles have found vast applications in encapsulation, stabilization of emulsions and foams, or flotation processes. The liquid phase stores capillary energy, while concurrently the solid contacts of the granular network induce friction and energy dissipation, leading to hybrid interfaces of combined properties. By means of nonintrusive tensiometric methods and structural measurements, we distinguish three surface phases of increasing rigidity during the evaporation of armored droplets. The emergence of surface rigidity is reminiscent of jamming of granular matter, but it occurs differently since it is marked by a step by step hardening under surface compression. These results show that the concept of the effective surface tension remains useful only below the first jamming transition. Beyond this point, the surface stresses become anisotropic.

Patterns in network activity and information processing in a detailed computer model of the cerebellar granular layer

In the cerebellar cortex, the granular layer is at the first stage of processing information from other brain regions delivered via mossy fibers. The major components of this neural network are numerous and tiny granule cells (GrC), which are the only excitatory neurons, and Golgi cells (GoC) that provide the only inhibitory inputs to GrCs. Despite such structural simplicity, many questions about their functions remain unanswered. Here we investigate the signal transformation property of the granular layer neural network with our three-dimensional large-scale and detailed computer model, composed of the biophysically detailed ~8×105 GrC and ~2000 GoC models with their physiological synaptic and electrical connectivity. With background and constant mossy fiber inputs, the model shows network-wide oscillations driven by the synchronized GoC firing, as in previous simulation and experimental studies [1-3]. Oscillation frequency was usually higher than the Golgi cell-firing rate, as some GoCs exhibited cycle skipping. With more physiological and diverse paradigms of mossy fiber stimulation, we could observe interesting patterns which hint that this oscillation and rate coding synergistically contribute to the network outputs. For example, when the mossy fiber stimulation is spatially limited, there is anti-correlation in the GrC spike count between the stimulated and unstimulated region, suggesting center-surround “receptive fields” [4], while the oscillation persists and opens time windows for well-timed spikes [5] (Figure ​(Figure1).1). Our results suggest how the complex dynamics of the granular layer network due to cellular and synaptic properties can contribute to its rich information processing. Figure 1 Cross-correlogram between the average GrC activity within the stimulated and unstimulated region (diameter of each region = 200 μm, distance = 500 μm). The stimulated region was given high frequency mossy fiber input of 20 Hz and 50 Hz, ...

Mechanisms for acoustic emissions generation during granular shearing

Shear deformation of granular media leads to continual restructuring of particle contact network and mechanical interactions. These changes to the mechanical state include jamming of grains, collisions, and frictional slip of particles—all of which present abrupt perturbations of internal forces and release of strain energy. Such energy release events typically result in the generation of elastic waves in the kHz frequency range, termed acoustic emissions (AE). The close association between grain-scale mechanics and AE generation motivated the use of AE as surrogate observations to assess the mechanical state of complex materials and granular flows. The study characterizes AE generation mechanisms stemming from grain-scale mechanical interactions. Basic mechanisms are considered, including frictional slip between particles, and mechanical excitation of particle configurations during force network restructuring events. The intrinsic frequencies and energy content of generated AEs bear the signature of source mechanisms and of structural features of the grain network. Acoustic measurements in simple shear experiments of glass beads reveal distinct characteristics of AE associated with different source mechanisms. These findings offer new capabilities for non-invasive interrogation of micromechancial interactions and linkage to a stochastic model of shear zone mechanics. Certain statistical features of restructuring events and associated energy release during shearing were predicted with a conceptual fiber-bundle model (FBM). In the FBM the collective behavior of a large number of basic mechanical elements (representing e.g. grain contacts), termed fibers, reproduces the reaction of disordered materials to progressive loading. The failure of fibers at an individual threshold force corresponds to slipping of a particle contact or a single rearrangement event of the granular network. The energy release from model fiber breakage is the equivalent to elastic energy from abrupt grain rearrangement events and provides an estimate of the energy available for elastic wave generation. The coupled FBM–AE model was in reasonable agreement with direct shear experiments that were performed on large granular assemblies. The results underline the potential of using AE as a diagnostic tool to study micro-mechanical interactions, shear failure and mobilization in granular material.

Networks of superconducting nano-puddles in 1/8 doped YBa2Cu3O6.5 + y controlled by thermal manipulation

While it is known that the nature and the arrangement of defects in complex oxides have an impact on the material functionalities, little is known about control of superconductivity by oxygen interstitial organization in cuprates. Here we report direct compelling evidence for the control of Tc by manipulation of the superconducting granular networks of nanoscale puddles, made of ordered oxygen stripes, in a single crystal of YBa2Cu3O6.5 + y with average formal hole doping p close to 1/8. Upon thermal treatments we were able to switch from a first network of oxygen defect striped puddles with OVIII modulation (qOVIII(a*) = (h + 3/8, k, 0) and qOVIII(a*) = (h + 5/8, k, 0)) to a second network characterized by OXVI modulation (qOXVI(a*) = (h + 7/16, k, 0) and qox-VI(a*) = (h + 9/16, k, 0)) and finally to a third network with puddles of OV periodicity (qOV(a*) = (4/10, 1, 0) and qOV(a*) = (6/10, 1, 0)). We map the microscopic spatial evolution of the out of plane OVIII, OXVI and OV puddle nano-size distribution via scanning micro-diffraction measurements. In particular, we calculated the number of oxygen chains (n) and the charge density (hole concentration p) inside each puddle, analyzing areas of 160 × 80 μm2, and recording 12 800 diffraction patterns to reconstruct each spatial map. The high spatial inhomogeneity shown by all the reconstructed spatial maps reflects the intrinsic granular structure that characterizes cuprates and iron chalcogenides, disclosing the presence of several complex networks of coexisting superconducting domains with different lattice modulations, charge densities and gaps as in the proposed multi-gap scenario called superstripes.