Journal articles on the topic '3D dynamical fibre networks'
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1
Callegari, Francesca, Martina Brofiga, and Paolo Massobrio. "Modeling the three-dimensional connectivity of in vitro cortical ensembles coupled to Micro-Electrode Arrays." PLOS Computational Biology 19, no. 2 (February 13, 2023): e1010825. http://dx.doi.org/10.1371/journal.pcbi.1010825.
Abstract:
Nowadays, in vitro three-dimensional (3D) neuronal networks are becoming a consolidated experimental model to overcome most of the intrinsic limitations of bi-dimensional (2D) assemblies. In the 3D environment, experimental evidence revealed a wider repertoire of activity patterns, characterized by a modulation of the bursting features, than the one observed in 2D cultures. However, it is not totally clear and understood what pushes the neuronal networks towards different dynamical regimes. One possible explanation could be the underlying connectivity, which could involve a larger number of neurons in a 3D rather than a 2D space and could organize following well-defined topological schemes. Driven by experimental findings, achieved by recording 3D cortical networks organized in multi-layered structures coupled to Micro-Electrode Arrays (MEAs), in the present work we developed a large-scale computational network model made up of leaky integrate-and-fire (LIF) neurons to investigate possible structural configurations able to sustain the emerging patterns of electrophysiological activity. In particular, we investigated the role of the number of layers defining a 3D assembly and the spatial distribution of the connections within and among the layers. These configurations give rise to different patterns of activity that could be compared to the ones emerging from real in vitro 3D neuronal populations. Our results suggest that the introduction of three-dimensionality induced a global reduction in both firing and bursting rates with respect to 2D models. In addition, we found that there is a minimum number of layers necessary to obtain a change in the dynamics of the network. However, the effects produced by a 3D organization of the cells is somewhat mitigated if a scale-free connectivity is implemented in either one or all the layers of the network. Finally, the best matching of the experimental data is achieved supposing a 3D connectivity organized in structured bundles of links located in different areas of the 2D network.
2
Hewavidana, Yasasween, Mehmet N. Balci, Andrew Gleadall, Behnam Pourdeyhimi, Vadim V. Silberschmidt, and Emrah Demirci. "Assessing Crimp of Fibres in Random Networks with 3D Imaging." Polymers 15, no. 4 (February 20, 2023): 1050. http://dx.doi.org/10.3390/polym15041050.
Abstract:
The analysis of fibrous structures using micro-computer tomography (µCT) is becoming more important as it provides an opportunity to characterise the mechanical properties and performance of materials. This study is the first attempt to provide computations of fibre crimp for various random fibrous networks (RFNs) based on µCT data. A parametric algorithm was developed to compute fibre crimp in fibres in a virtual domain. It was successfully tested for six different X-ray µCT models of nonwoven fabrics. Computations showed that nonwoven fabrics with crimped fibres exhibited higher crimp levels than those with non-crimped fibres, as expected. However, with the increased fabric density of the non-crimped nonwovens, fibres tended to be more crimped. Additionally, the projected fibre crimp was computed for all three major 2D planes, and the obtained results were statistically analysed. Initially, the algorithm was tested for a small-size, nonwoven model containing only four fibres. The fraction of nearly straight fibres was computed for both crimped and non-crimped fabrics. The mean value of the fibre crimp demonstrated that fibre segments between intersections were almost straight. However, it was observed that there were no perfectly straight fibres in the analysed RFNs. This study is applicable to approach employing a finite-element analysis (FEA) and computational fluid dynamics (CFD) to model/analyse RFNs.
3
Marulier, C., P. J. J. Dumont, L. Orgéas, D. Caillerie, and S. Rolland du Roscoat. "Towards 3D analysis of pulp fibre networks at the fibre and bond levels." Nordic Pulp & Paper Research Journal 27, no. 2 (May 1, 2012): 245–55. http://dx.doi.org/10.3183/npprj-2012-27-02-p245-255.
Abstract:
Abstract By using X-ray synchrotron microtomography imaging, this work aims at proposing a method to get 3D information on paper fibrous microstructures. Such technique is useful to better understand the links between the manufacturing conditions, the resulting microstructural and mechanical properties of the paper fibrous networks, together with the morphology of fibres and fibre-fibre bonds. Its usefulness is illustrated for the 3D analysis of model papers being produced by changing the wet pressing conditions. It is demonstrated that the image analysis allows the changes of parameters describing, for example, the fibre cross section shape and inclination, the bond area surfaces, the distance between bonds to be followed with respect to the processing conditions for a large set of fibres and bonds. The distributions of properties that can be drawn from this experimental analysis will allow mechanical or physical discrete modelling approaches for papers to be enriched.
4
Eekhoff, Jeremy D., and Spencer P. Lake. "Three-dimensional computation of fibre orientation, diameter and branching in segmented image stacks of fibrous networks." Journal of The Royal Society Interface 17, no. 169 (August 2020): 20200371. http://dx.doi.org/10.1098/rsif.2020.0371.
Abstract:
Fibre topography of the extracellular matrix governs local mechanical properties and cellular behaviour including migration and gene expression. While quantifying properties of the fibrous network provides valuable data that could be used across a breadth of biomedical disciplines, most available techniques are limited to two dimensions and, therefore, do not fully capture the architecture of three-dimensional (3D) tissue. The currently available 3D techniques have limited accuracy and applicability and many are restricted to a specific imaging modality. To address this need, we developed a novel fibre analysis algorithm capable of determining fibre orientation, fibre diameter and fibre branching on a voxel-wise basis in image stacks with distinct fibre populations. The accuracy of the technique is demonstrated on computer-generated phantom image stacks spanning a range of features and complexities, as well as on two-photon microscopy image stacks of elastic fibres in bovine tendon and dermis. Additionally, we propose a measure of axial spherical variance which can be used to define the degree of fibre alignment in a distribution of 3D orientations. This method provides a useful tool to quantify orientation distributions and variance on image stacks with distinguishable fibres or fibre-like structures.
5
Golubyatnikov, V. P. "ON NON-UNIQUENESS OF CYCLES IN 3D MODELS OF CIRCULAR GENE NETWORKS." Челябинский физико-математический журнал 9, no. 1 (March 27, 2024): 23–34. http://dx.doi.org/10.47475/2500-0101-2024-9-1-23-34.
Abstract:
We describe 3-dimensional dynamical systems with piecewise linear right hand sides which simulate functioning of simplest molecualr repressilators and contain infinite oneparametric families of cycles in their phase portraits. An analogous dynamical system with step functions in its right hand sides is constructed; its phase portrait contains two piecewise linear cycles. A surface separating these two cycles is described.
6
Wan, Wubo, Yu Li, Shiwei Bai, Xiaoyan Yang, Mingming Chi, Yaqin Shi, Changhua Liu, and Peng Zhang. "Three-Dimensional Porous ZnO-Supported Carbon Fiber Aerogel with Synergistic Effects of Adsorption and Photocatalysis for Organics Removal." Sustainability 15, no. 17 (August 30, 2023): 13088. http://dx.doi.org/10.3390/su151713088.
Abstract:
A three-dimensional (3D) ZnO-supported carbon fiber aerogel (ZnO/CFA) was successfully prepared by using natural cotton with hydrophilicity as the precursor. The facile synthetic strategy includes two steps: Zn2+ exchange on the surface of cotton and thermal treatment at high temperatures. Particularly, the calcination temperature was found to greatly affect the content, dispersity, and size of supported ZnO nanoparticles, and the product obtained at 600 °C (ZnO/CFA-600) exhibited both high ZnO loading and well-dispersed ZnO nanoparticles. Therefore, ZnO/CFA-600 has superior photocatalytic activity for tetracycline (TC) degradation under UV light irradiation compared with others. Additionally, the unique 3D crosslinking network inside the ZnO/CFA generates an open channel for the rapid migration and diffusion of reactants and products. In a dynamical water-treated system, the 3D porous ZnO/CFA-600 continuously works for TC removal without any separation operation and maintains high synergistic performance of adsorption and photocatalysis for at least 8 h. Consequently, the 3D porous ZnO/CFA product, with its large adsorbability and high photoactivity, shows a lot of industrial potential in wastewater treatments.
7
Ning, Guoqing, Yanming Cao, Chuanlei Qi, Xinlong Ma, and Xiao Zhu. "Elasticity-related periodical Li storage behavior delivered by porous graphene." Journal of Materials Chemistry A 5, no. 19 (2017): 9299–306. http://dx.doi.org/10.1039/c7ta01061c.
8
ARENA, PAOLO, MAIDE BUCOLO, STEFANO FAZZINO, LUIGI FORTUNA, and MATTIA FRASCA. "THE CNN PARADIGM: SHAPES AND COMPLEXITY." International Journal of Bifurcation and Chaos 15, no. 07 (July 2005): 2063–90. http://dx.doi.org/10.1142/s0218127405013307.
Abstract:
The paper stresses the universal role that Cellular Nonlinear Networks (CNNs) are assuming today. It is shown that the dynamical behavior of 3D CNN-based models allows us to approach new emerging problems, to open new research frontiers as the generation of new geometrical forms and to establish some links between art, neuroscience and dynamical systems.
9
Pollet, Andreas M. A. O., Erik F. G. A. Homburg, Ruth Cardinaels, and Jaap M. J. den Toonder. "3D Sugar Printing of Networks Mimicking the Vasculature." Micromachines 11, no. 1 (December 30, 2019): 43. http://dx.doi.org/10.3390/mi11010043.
Abstract:
The vasculature plays a central role as the highway of the body, through which nutrients and oxygen as well as biochemical factors and signals are distributed by blood flow. Therefore, understanding the flow and distribution of particles inside the vasculature is valuable both in healthy and disease-associated networks. By creating models that mimic the microvasculature fundamental knowledge can be obtained about these parameters. However, microfabrication of such models remains a challenging goal. In this paper we demonstrate a promising 3D sugar printing method that is capable of recapitulating the vascular network geometry with a vessel diameter range of 1 mm down to 150 µm. For this work a dedicated 3D printing setup was built that is capable of accurately printing the sugar glass material with control over fibre diameter and shape. By casting of printed sugar glass networks in PDMS and dissolving the sugar glass, perfusable networks with circular cross-sectional channels are obtained. Using particle image velocimetry, analysis of the flow behaviour was conducted showing a Poisseuille flow profile inside the network and validating the quality of the printing process.
10
Lich, Julian, Tom Glosemeyer, Jürgen Czarske, and Robert Kuschmierz. "Single-shot 3D endoscopic imaging exploiting a diffuser and neural networks." EPJ Web of Conferences 266 (2022): 04005. http://dx.doi.org/10.1051/epjconf/202226604005.
Abstract:
Lens-based endoscopes offer high lateral resolution, but suffer from rigid imaging properties, such as a fixed focal plane. We present a miniaturized 0.5 mm diameter endoscope in which the objective lens is replaced by an optical diffuser. The intensity information of the object space is scattered and passed to a camera via a coherent fibre bundle. The image is reconstructed by a neural network. The field of view and resolution depend on the object distance. 3D-single-shot imaging up to video rate can be enabled. The approach shows great potential for applications like robust 3D fluorescence imaging.
11
Gurung, Kshitij, Petr Šimek, Alexandr Jegorov, and Lukáš Palatinus. "Structure and absolute configuration of natural fungal product beauveriolide I, isolated from Cordyceps javanica, determined by 3D electron diffraction." Acta Crystallographica Section C Structural Chemistry 80, no. 3 (February 27, 2024): 56–61. http://dx.doi.org/10.1107/s2053229624001359.
Abstract:
Beauveriolides, including the main beauveriolide I {systematic name: (3R,6S,9S,13S)-9-benzyl-13-[(2S)-hexan-2-yl]-6-methyl-3-(2-methylpropyl)-1-oxa-4,7,10-triazacyclotridecane-2,5,8,11-tetrone, C27H41N3O5}, are a series of cyclodepsipeptides that have shown promising results in the treatment of Alzheimer's disease and in the prevention of foam cell formation in atherosclerosis. Their crystal structure studies have been difficult due to their tiny crystal size and fibre-like morphology, until now. Recent developments in 3D electron diffraction methodology have made it possible to accurately study the crystal structures of submicron crystals by overcoming the problems of beam sensitivity and dynamical scattering. In this study, the absolute structure of beauveriolide I was determined by 3D electron diffraction. The cyclodepsipeptide crystallizes in the space group I2 with lattice parameters a = 40.2744 (4), b = 5.0976 (5), c = 27.698 (4) Å and β = 105.729 (6)°. After dynamical refinement, its absolute structure was determined by comparing the R factors and calculating the z-scores of the two possible enantiomorphs of beauveriolide I.
12
Sivashangaran, Shathushan, and Azim Eskandarian. "Deep Reinforcement Learning for Autonomous Ground Vehicle Exploration Without A-Priori Maps." Advances in Artificial Intelligence and Machine Learning 03, no. 02 (2023): 1198–219. http://dx.doi.org/10.54364/aaiml.2023.1170.
Abstract:
Autonomous Ground Vehicles (AGVs) are essential tools for a wide range of applications stemming from their ability to operate in hazardous environments with minimal human operator input. Effective motion planning is paramount for successful operation of AGVs. Conventional motion planning algorithms are dependent on prior knowledge of environment characteristics and offer limited utility in information poor, dynamically altering environments such as areas where emergency hazards like fire and earthquake occur, and unexplored subterranean environments such as tunnels and lava tubes on Mars. We propose a Deep Reinforcement Learning (DRL) framework for intelligent AGV exploration without a-priori maps utilizing Actor-Critic DRL algorithms to learn policies in continuous and high-dimensional action spaces directly from raw sensor data. The DRL architecture comprises feedforward neural networks for the critic and actor representations in which the actor network strategizes linear and angular velocity control actions given current state inputs, that are evaluated by the critic network which learns and estimates Q-values to maximize an accumulated reward. Three off-policy DRL algorithms, DDPG, TD3 and SAC, are trained and compared in two environments of varying complexity, and further evaluated in a third with no prior training or knowledge of map characteristics. The agent is shown to learn optimal policies at the end of each training period to chart quick, collision-free exploration trajectories, and is extensible, capable of adapting to an unknown environment without changes to network architecture or hyperparameters. The best algorithm is further evaluated in a realistic 3D environment.
13
Chen, Xueli. "Dynamical Systems of Differential Equations Based on Information Technology: Effects of Integral Step Size on Bifurcation and Chaos Control of Discrete Hindmarsh–Rose Models." Mobile Information Systems 2022 (October 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/1425403.
Abstract:
Based on the differential equation dynamical system of information technology, this article analyzes how the integral step affects the bifurcation and chaos control of the discrete Hindmarsh–Rose model and its effect. By introducing the advantages of information technology in information management and information processing to the application of the differential equation dynamical system, the stability of the differential equation dynamical system model can be guaranteed. The integration step size is an important factor that affects the accuracy of the study results, and therefore, this paper understands how it affects the 3D discrete Hindmarsh–Rose model by choosing the appropriate step size.
14
Karamov, Radmir, Christian Breite, Stepan V. Lomov, Ivan Sergeichev, and Yentl Swolfs. "Super-Resolution Processing of Synchrotron CT Images for Automated Fibre Break Analysis of Unidirectional Composites." Polymers 15, no. 9 (May 6, 2023): 2206. http://dx.doi.org/10.3390/polym15092206.
Abstract:
Fibre breaks govern the strength of unidirectional composite materials under tension. The progressive development of fibre breaks is studied using in situ X-ray computed tomography, especially with synchrotron radiation. However, even with synchrotron radiation, the resolution of the time-resolved in situ images is not sufficient for a fully automated analysis of continuous mechanical deformations. We therefore investigate the possibility of increasing the quality of low-resolution in situ scans by means of super-resolution (SR) using 3D deep learning techniques, thus facilitating the subsequent fibre break identification. We trained generative neural networks (GAN) on datasets of high—(0.3 μm) and low-resolution (1.6 μm) statically acquired images. These networks were then applied to a low-resolution (1.1 μm) noisy image of a continuously loaded specimen. The statistical parameters of the fibre breaks used for the comparison are the number of individual breaks and the number of 2-plets and 3-plets per specimen volume. The fully automated process achieves an average accuracy of 82% of manually identified fibre breaks, while the semi-automated one reaches 92%. The developed approach allows the use of faster, low-resolution in situ tomography without losing the quality of the identified physical parameters.
15
Thoemen, Heiko, Thomas Walther, and Andreas Wiegmann. "3D simulation of macroscopic heat and mass transfer properties from the microstructure of wood fibre networks." Composites Science and Technology 68, no. 3-4 (March 2008): 608–16. http://dx.doi.org/10.1016/j.compscitech.2007.10.014.
16
Ketola, Annika E., Anders Strand, Anna Sundberg, Jarmo Kouko, Antti Oksanen, Kristian Salminen, Shiyu Fu, and Elias Retulainen. "Effect of micro- and nanofibrillated cellulose on the drying shrinkage, extensibility, and strength of fibre networks." BioResources 13, no. 3 (May 22, 2018): 5319–42. http://dx.doi.org/10.15376/biores.13.3.5319-5342.
Abstract:
Elongation is an important property of many packaging board and paper grades. Paper with high extensibility could provide an alternative for oil-based packaging materials. Micro- (CMF) and nanofibrillated (CNF) cellulose are known to increase the strength of a paper, but their effect on the drying shrinkage and elongation is not well-studied. In this work, paper was reinforced with fibrillated material. Added fibrillated material increased the drying shrinkage, which was generally proportional to the increase of paper elongation before breakage. Results differed depending on the fibrillated material and how it was added to paper (wet-end addition or spray application). The papers were dried unrestrained in order to achieve the highest elongation potential for the paper. Spray application of CMF increased elongation by 13%, while wet-end additions increased elongation by 20% and also strength by 10%, but only with high dosages. Spray application of oxidized-CNF improved elongation by 33%, while wet-end applications increased only strength by 20%. Thus, boosting the drying shrinkage with fibrillated cellulose is one potential way to increase elongation and 3D formability of paper.
17
Rajan, K. S. "MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 671–77. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-671-2023.
Abstract:
Abstract. The evacuation path from inside a building to safe point outside becomes highly unpredictable due to changes in the local geometry or presence of obstacles during a disaster like a fire. During emergencies, Evacuees need appropriate information and hence prediction of an unobstructed path, as it emerges, needs to be computed well. Understanding the exits with its allowable people flow rate; the type - door or alternative exit such as windows, balconies, etc.; and its role as a node in the graph network is important to ensure safe and timely evacuation from a building. The study here evaluates how obstacles present in the evacuation route affect the removal of the last person. These obstacles, such as furniture, decrease the flow rate at which evacuees can escape. A subspace model is proposed for geometric spaces or carpet areas containing obstacles and is used to compute the shortest obstacle-free paths. The occupancy is considered within the subspaces containing obstacles. The proposed method clearly shows that a graph-based path generation using a subspace model improves the computation time, can be dynamically adapted, and can be scalable across geometric spaces. The results clearly show the impact of the obstacles, with a 2× to 6× rise when compared to obstacle-free scenarios.
18
Jandl, Christian, Gunther Steinfeld, Keyao Li, Pokka Ka Chuen Pang, Chun Lung Choi, Cengan Wang, Petra Simoncic, and Ian D. Williams. "Absolute Structure Determination of Chiral Zinc Tartrate MOFs by 3D Electron Diffraction." Symmetry 15, no. 5 (April 26, 2023): 983. http://dx.doi.org/10.3390/sym15050983.
Abstract:
The absolute structure of the 3D MOF anhydrous zinc (II) tartrate with space group I222 has been determined for both [Zn(L-TAR)] and [Zn(D-TAR)] by electron diffraction using crystals of sub-micron dimensions. Dynamical refinement gives a strong difference in R factors for the correct and inverted structures. These anhydrous MOFs may be prepared phase pure from mild hydrothermal conditions. Powder X-ray diffraction indicates that isostructural or pseudo-isostructural phases can be similarly prepared for several other M2+ = Mg, Mn, Co, Ni and Cu. I222 is a relatively uncommon space group since it involves intersecting two-fold axes that place constraints on molecular crystals. However, in the case of MOFs the packing is dominated by satisfying the octahedral coordination centers. These MOFs are dense 3D networks with chiral octahedral metal centers that may be classed as Δ (for L-TAR) or Λ (for D-TAR).
19
Neilson, Peter, Megan Neilson, and Robin Bye. "A Riemannian Geometry Theory of Three-Dimensional Binocular Visual Perception." Vision 2, no. 4 (December 5, 2018): 43. http://dx.doi.org/10.3390/vision2040043.
Abstract:
We present a Riemannian geometry theory to examine the systematically warped geometry of perceived visual space attributable to the size–distance relationship of retinal images associated with the optics of the human eye. Starting with the notion of a vector field of retinal image features over cortical hypercolumns endowed with a metric compatible with that size–distance relationship, we use Riemannian geometry to construct a place-encoded theory of spatial representation within the human visual system. The theory draws on the concepts of geodesic spray fields, covariant derivatives, geodesics, Christoffel symbols, curvature tensors, vector bundles and fibre bundles to produce a neurally-feasible geometric theory of visuospatial memory. The characteristics of perceived 3D visual space are examined by means of a series of simulations around the egocentre. Perceptions of size and shape are elucidated by the geometry as are the removal of occlusions and the generation of 3D images of objects. Predictions of the theory are compared with experimental observations in the literature. We hold that the variety of reported geometries is accounted for by cognitive perturbations of the invariant physically-determined geometry derived here. When combined with previous description of the Riemannian geometry of human movement this work promises to account for the non-linear dynamical invertible visual-proprioceptive maps and selection of task-compatible movement synergies required for the planning and execution of visuomotor tasks.
20
Ziman, Martin, Martin Feiler, Tomas Mizera, Anton Kuzma, Dusan Pudis, and Frantisek Uherek. "Design of a Power Splitter Based on a 3D MMI Coupler at the Fibre-Tip." Electronics 11, no. 18 (September 6, 2022): 2815. http://dx.doi.org/10.3390/electronics11182815.
Abstract:
Planar MMI couplers based on inorganic material platforms have played an essential role in photonic integrated circuits development. Advances in organic polymer fabrication techniques enable the design of components beyond a single plane, thus facilitating vertical integration for a wide range of components, including the MMI coupler. This paper presents the design of two 3D IP-dip polymer-based MMI power splitters operating in the near-infrared part of the spectrum at a wavelength of 1550 nm. The resulting output power ratio, modal field distributions, spectral characteristics, and the effects of input fibre misalignment are investigated using the beam propagation method. The fabrication method used to realise the designed splitters was direct laser writing. The function of the splitters was then verified by a highly resolved near-field scanning optical microscope.
21
Velazquez, Alejandro, Fernando Martell, Irma Y. Sanchez, and Carlos A. Paredes. "Cyberphysical System Modeled with Complex Networks and Hybrid Automata to Diagnose Multiple and Concurrent Faults in Manufacturing Systems." Applied Sciences 13, no. 19 (September 22, 2023): 10603. http://dx.doi.org/10.3390/app131910603.
Abstract:
Cyber–physical systems use digital twins to provide advanced monitoring and control functions, including self-diagnosis. The digital twin is often conceptualized as a 3D model, but mathematical models implemented in numerical simulations are required to reproduce the dynamical and functional characteristics of physical systems. In this work, a cyber–physical system scheme is proposed to monitor and diagnose failures. The virtual system, embedded at the supervisory control level, combines concepts from complex networks and hybrid automata to detect failures in the hardware components and in the execution of the sequential logic control. An automated storage and retrieval system is presented as a case study to show the applicability of the proposed scheme. The functional test and the obtained results validate the implemented system that is shown to be capable of fault diagnosis and location in real time. The online execution of the digital twin present several advantages for diagnosing multiple concurrent failures in sensors, actuators, and the control unit. This approach can be incorporate into diverse manufacturing systems.
22
Allain, Ariane, Isaure Chauvot de Beauchêne, Florent Langenfeld, Yann Guarracino, Elodie Laine, and Luba Tchertanov. "Allosteric pathway identification through network analysis: from molecular dynamics simulations to interactive 2D and 3D graphs." Faraday Discuss. 169 (2014): 303–21. http://dx.doi.org/10.1039/c4fd00024b.
Abstract:
Allostery is a universal phenomenon that couples the information induced by a local perturbation (effector) in a protein to spatially distant regulated sites. Such an event can be described in terms of a large scale transmission of information (communication) through a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. To elaborate a rational description of allosteric coupling, we propose an original approach – MOdular NETwork Analysis (MONETA) – based on the analysis of inter-residue dynamical correlations to localize the propagation of both structural and dynamical effects of a perturbation throughout a protein structure. MONETA uses inter-residue cross-correlations and commute times computed from molecular dynamics simulations and a topological description of a protein to build a modular network representation composed of clusters of residues (dynamic segments) linked together by chains of residues (communication pathways). MONETA provides a brand new direct and simple visualization of protein allosteric communication. A GEPHI module implemented in the MONETA package allows the generation of 2D graphs of the communication network. An interactive PyMOL plugin permits drawing of the communication pathways between chosen protein fragments or residues on a 3D representation. MONETA is a powerful tool for on-the-fly display of communication networks in proteins. We applied MONETA for the analysis of communication pathways (i) between the main regulatory fragments of receptors tyrosine kinases (RTKs), KIT and CSF-1R, in the native and mutated states and (ii) in proteins STAT5 (STAT5a and STAT5b) in the phosphorylated and the unphosphorylated forms. The description of the physical support for allosteric coupling by MONETA allowed a comparison of the mechanisms of (a) constitutive activation induced by equivalent mutations in two RTKs and (b) allosteric regulation in the activated and non-activated STAT5 proteins. Our theoretical prediction based on results obtained with MONETA was validated for KIT by in vitro experiments. MONETA is a versatile analytical and visualization tool entirely devoted to the understanding of the functioning/malfunctioning of allosteric regulation in proteins – a crucial basis to guide the discovery of next-generation allosteric drugs.
23
Mofokeng, Thapelo P., Zikhona N. Tetana, and Kenneth I. Ozoemena. "Defective 3D nitrogen-doped carbon nanotube-carbon fibre networks for high-performance supercapacitor: Transformative role of nitrogen-doping from surface-confined to diffusive kinetics." Carbon 169 (November 2020): 312–26. http://dx.doi.org/10.1016/j.carbon.2020.07.049.
24
Kanda, Keiichi, and Takehisa Matsuda. "Mechanical Stress-Induced Orientation and Ultrastructural Change of Smooth Muscle Cells Cultured in Three-Dimensional Collagen Lattices." Cell Transplantation 3, no. 6 (November 1994): 481–92. http://dx.doi.org/10.1177/096368979400300605.
Abstract:
The effect of tensile stress on the orientation and phenotype of arterial smooth muscle cells (SMCs) cultured in three-dimensional (3D) type I collagen gels was morphologically investigated. Ring-shaped hybrid tissues were prepared by thermal gelation of a cold mixed solution of type I collagen and SMCs derived from bovine aorta. The tissues were subjected to three different modes of tensile stress. They were floated (isotonic control), stretched isometrically (static stress) and periodically stretched and recoiled by 5% above and below the resting tissue length at 60 RPM frequency (dynamic stress). After incubation for up to four wk, the tissues were investigated under a light microscope (LM) and a transmission electron microscope (TEM). Hematoxylin and eosinstained LM samples revealed that, irrespective of static or dynamic stress loading, SMCs in stress-loaded tissues exhibited elongated bipolar spindle shape and were regularly oriented parallel to the direction of the strain, whereas those in isotonic control tissues were polygonal or spherical and had no preferential orientation. In Azan-stained samples, collagen fiber bundles in isotonic control tissues were somewhat retracted around the polygonal SMCs to form a random network. On the other hand, those in statically and dynamically stressed tissues were accumulated and prominently oriented parallel to the stretch direction. Ultrastructural investigation using a TEM showed that SMCs in control and statically stressed tissues were almost totally filled with synthetic organelles such as rough endoplasmic reticulums, free ribosomes, Golgi complexes and mitochondria, indicating that the cells remained in the synthetic phenotype. On the other hand, SMCs in dynamically stressed tissues had increased fractions of contractile apparatus, such as myofilaments, dense bodies and extracellular filamentous materials equivalent to basement membranes, that progressed with incubation time. These results indicate that periodic stretch, in concert with 3-D extracellular collagen matrices, play a significant role in the phenotypic modulation of SMCs from the synthetic to the contractile state, as well as cellular and biomolecular orientation.
25
Lee, Juhyun, Jungho Im, Dong-Hyun Cha, Haemi Park, and Seongmun Sim. "Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data." Remote Sensing 12, no. 1 (December 28, 2019): 108. http://dx.doi.org/10.3390/rs12010108.
Abstract:
For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs.
26
Meng, Qiaoling, Haolun Kang, Xiaojin Liu, and Hongliu Yu. "Dynamic Path Planning Based on 3D Cloud Recognition for an Assistive Bathing Robot." Electronics 13, no. 7 (March 22, 2024): 1170. http://dx.doi.org/10.3390/electronics13071170.
Abstract:
Assistive bathing robots have become a popular point due to their metrics, such as a humanoid working approach in the solution of elder care. However, the abilities of dynamic recognition and path planning are the key to obtain the advantages. This paper proposes a novel approach to recognize and track the dynamical human back, and path planning on it via a 3D point cloud. Firstly, the human back geometric features are recognized through coarse-to-fine alignment. The Intrinsic Shape Signature (ISS) algorithm combined with the Fast Point Feature Histogram (FPFH) and the Sample Consensus Initial Alignment (SAC-IA) algorithm are adopted to complete the coarse alignment, and the Iterative Closest Point (ICP) algorithm is applied to the fine alignment to improve the accuracy of recognition. Then, the dynamic transformation matrix between the contiguous recognized back is deduced based on spatial motion between two adjacent recognized back point clouds. The path can be planned on the tracked human back. Finally, a set of testing experiments are conducted to verify the proposed algorithm. The results show that the running time is reduced by 66.18% and 96.29% compared with the other two common algorithms, respectively.
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Alraddadi, Ibrahim, M. Akher Chowdhury, M. S. Abbas, K. El-Rashidy, J. R. M. Borhan, M. Mamun Miah, and Mohammad Kanan. "Dynamical Behaviors and Abundant New Soliton Solutions of Two Nonlinear PDEs via an Efficient Expansion Method in Industrial Engineering." Mathematics 12, no. 13 (June 30, 2024): 2053. http://dx.doi.org/10.3390/math12132053.
Abstract:
In this study, we discuss the dynamical behaviors and extract new interesting wave soliton solutions of the two significant well-known nonlinear partial differential equations (NPDEs), namely, the Korteweg–de Vries equation (KdVE) and the Jaulent–Miodek hierarchy equation (JMHE). This investigation has applications in pattern recognition, fluid dynamics, neural networks, mechanical systems, ecological systems, control theory, economic systems, bifurcation analysis, and chaotic phenomena. In addition, bifurcation analysis and the chaotic behavior of the KdVE and JMHE are the main issues of the present research. As a result, in this study, we obtain very effective advanced exact traveling wave solutions with the aid of the proposed mathematical method, and the solutions involve rational functions, hyperbolic functions, and trigonometric functions that play a vital role in illustrating and developing the models involving the KdVE and the JMHE. These new exact wave solutions lead to utilizing real problems and give an advanced explanation of our mentioned mathematical models that we did not yet have. Some of the attained solutions of the two equations are graphically displayed with 3D, 2D, and contour panels of different shapes, like periodic, singular periodic, kink, anti-kink, bell, anti-bell, soliton, and singular soliton wave solutions. The solutions obtained in this study of our considered equations can lead to the acceptance of our proposed method, effectively utilized to investigate the solutions for the mathematical models of various important complex problems in natural science and engineering.
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Benkouider, Khaled, Issam A. R. Moghrabi, Aceng Sambas, Sezgin Kaçar, Süleyman Uzun, Basim A. Hassan, Ibrahim Mohammed Sulaiman, Sundarapandian Vaidyanathan, Mohamad Afendee Mohamed, and Jumadil Saputra. "A novel four-wing chaotic system with multiple equilibriums: Dynamical analysis, multistability, circuit simulation and pseudo random number generator (PRNG) based on the voice encryption." International Journal of Data and Network Science 8, no. 2 (2024): 989–1000. http://dx.doi.org/10.5267/j.ijdns.2023.12.008.
Abstract:
Recently, there has been tremendous interest worldwide in the possibility of using chaos in communication systems. Many different chaos-based secure communication schemes have been proposed up until now. However, systems with strong chaoticity are more suitable for chaos-based secure communication. From the viewpoint of Lyapunov exponents, a chaotic system with a larger positive Lyapunov exponent is said to be more complex. This paper constructing a multistable chaotic system that can produce coexisting attractors is an attractive field of research due to its theoretical and practical usefulness. An innovative 3D dynamical system is presented in this research. It can display various coexisting attractors for the same values of parameters. The new system is more suitable for chaos-based applications than recently reported systems since it exhibits strong multistable chaotic behavior, as proved by its large positive Lyapunov exponent. Furthermore, the accuracy of the numerical calculation and the system's physical implementations are confirmed by analog circuit simulation. Finally, implementing the proposed voice encryption is done using a four-wing chaotic system based on the PRNG.
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Pang, Xiaoying, Shih-Yun Tang, Yuqian Li, Zeqiu Yu, Long Wang, Jiayu Li, Yezhang Li, et al. "3D Morphology of Open Clusters in the Solar Neighborhood with Gaia EDR 3. II. Hierarchical Star Formation Revealed by Spatial and Kinematic Substructures." Astrophysical Journal 931, no. 2 (June 1, 2022): 156. http://dx.doi.org/10.3847/1538-4357/ac674e.
Abstract:
Abstract We identify members of 65 open clusters in the solar neighborhood using the machine-learning algorithm StarGO based on Gaia EDR3 data. After adding members of 20 clusters from previous studies we obtain 85 clusters, and study their morphology and kinematics. We classify the substructures outside the tidal radius into four categories: filamentary (f1) and fractal (f2) for clusters <100 Myr, and halo (h) and tidal tail (t) for clusters >100 Myr. The kinematical substructures of f1-type clusters are elongated; these resemble the disrupted cluster Group X. Kinematic tails are distinct in t-type clusters, especially Pleiades. We identify 29 hierarchical groups in four young regions (Alessi 20, IC 348, LP 2373, LP 2442); 10 among these are new. The hierarchical groups form filament networks. Two regions (Alessi 20, LP 2373) exhibit global orthogonal expansion (stellar motion perpendicular to the filament), which might cause complete dispersal. Infalling-like flows (stellar motion along the filament) are found in UBC 31 and related hierarchical groups in the IC 348 region. Stellar groups in the LP 2442 region (LP 2442 gp 1–5) are spatially well mixed but kinematically coherent. A merging process might be ongoing in the LP 2442 subgroups. For younger systems (≲30 Myr), the mean axis ratio, cluster mass, and half-mass–radius tend to increase with age values. These correlations between structural parameters may imply two dynamical processes occurring in the hierarchical formation scenario in young stellar groups: (1) filament dissolution and (2) subgroup mergers.
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Kim, Yuho, Hailey A. Parry, T. Bradley Willingham, Greg Alspaugh, Eric Lindberg, Christian A. Combs, Jay R. Knutson, Christopher K. E. Bleck, and Brian Glancy. "Reorganization of mitochondria–organelle interactions during postnatal development in skeletal muscle." Journal of Physiology 602, no. 5 (March 2024): 891–912. http://dx.doi.org/10.1113/jp285014.
Abstract:
AbstractSkeletal muscle cellular development requires the integrated assembly of mitochondria and other organelles adjacent to the sarcomere in support of muscle contractile performance. However, it remains unclear how interactions among organelles and with the sarcomere relates to the development of muscle cell function. Here, we combine 3D volume electron microscopy, proteomic analyses, and live cell functional imaging to investigate the postnatal reorganization of mitochondria–organelle interactions in skeletal muscle. We show that while mitochondrial networks are disorganized and loosely associated with the contractile apparatus at birth, contact sites among mitochondria, lipid droplets and the sarcoplasmic reticulum are highly abundant in neonatal muscles. The maturation process is characterized by a transition to highly organized mitochondrial networks wrapped tightly around the muscle sarcomere but also to less frequent interactions with both lipid droplets and the sarcoplasmic reticulum. Concomitantly, expression of proteins involved in mitochondria–organelle membrane contact sites decreases during postnatal development in tandem with a decrease in abundance of proteins associated with sarcomere assembly despite an overall increase in contractile protein abundance. Functionally, parallel measures of mitochondrial membrane potential, NADH redox status, and NADH flux within intact cells revealed that mitochondria in adult skeletal muscle fibres maintain a more activated electron transport chain compared with neonatal muscle mitochondria. These data demonstrate a developmental redesign reflecting a shift from muscle cell assembly and frequent inter‐organelle communication toward a muscle fibre with mitochondrial structure, interactions, composition and function specialized to support contractile function. imageKey points Mitochondrial network organization is remodelled during skeletal muscle postnatal development. The mitochondrial outer membrane is in frequent contact with other organelles at birth and transitions to more close associations with the contractile apparatus in mature muscles. Mitochondrial energy metabolism becomes more activated during postnatal development. Understanding the developmental redesign process within skeletal muscle cells may help pinpoint specific areas of deficit in muscles with developmental disorders.
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Вольцингер, Наум Евсеевич, and Алексей Анатольевич Андросов. "Extreme nonhydrostatic tidal dynamics." Вычислительные технологии, no. 2(24) (April 17, 2019): 37–51. http://dx.doi.org/10.25743/ict.2019.24.2.004.
Abstract:
Моделирование длинноволновых океанологических процессов традиционно выполняется в гидростатическом (Гс) приближении, обеспечивающем высокую точность расчета гидрофизических полей, когда вертикальным ускорением движения можно пренебречь. На горном рельефе это не так, и учет динамической компоненты давления становится необходимым. Негидростатическое (Нг) моделирование крупномасштабных океанологических явлений реализуется решением 3D краевой гидродинамической задачи. Структуру метода составляют этапы решения Гс-задачи, краевой задачи для уравнения Пуассона (Нг) и коррекции полей гидрофизических характеристик. Значимость Нг-фактора выявляется при рассмотрении безразмерного вида уравнений, когда безразмерные параметры характеризуют горный рельеф области. Случай резких изменений рельефа, требующий решения Нг-задачи, - пролив Ломбок. Приводятся оценки Нг-фактора в водообмене между океанами, результаты сравнения спектров вертикальной скорости в Гс- и Нг-постановках. Modelling of long-wave oceanological processes is traditionally performed in a hydrostatic (Hs) approximation, which ensures high accuracy of the calculation of hydrophysical fields, when the vertical acceleration of vertical motion can be neglected. In mountainous terrain, this is not the case, and consideration of the dynamic pressure component becomes necessary. Non-hydrostatic (Nh) modelling of large-scale oceanological phenomena is implemented by solving hydrodynamic boundary value problem in an arbitrary 3D domain. The structure of the method consists of the stages of solving the Hs problem, the boundary value problem for the Poisson equation (Nh), and the correction of the fields of hydrophysical characteristics. That is the pressure is presented as a sum of its hydrostatic and dynamical components. Significance of Nh is revealed when considering the dimensionless type of equations, when dimensionless parameters characterize the mountain relief of the region. The Lombok Strait having a complex morphometric structure is an important link in the water exchange between the Pacific and Indian Oceans, it has been chosen as the object for modelling. Estimates of the role of Nh in water exchange between the oceans are given using the comparison of the solution for problems in Hs and Nh sets. It indicates the need to take into account Nh in conditions of pronounced sea mountain relief.
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De Gerónimo, Francisco C., Marcelo M. Miller Bertolami, Francisco Plaza, and Márcio Catelan. "The composition of massive white dwarfs and their dependence on C-burning modeling." Astronomy & Astrophysics 659 (March 2022): A150. http://dx.doi.org/10.1051/0004-6361/202142341.
Abstract:
Context. Recent computations of the interior composition of ultra-massive white dwarfs (WDs) have suggested that some WDs could be composed of neon (Ne)-dominated cores. This result is at variance with our previous understanding of the chemical structure of massive WDs, where oxygen is the predominant element. In addition, it is not clear whether some hybrid carbon (C) oxygen (O)-Ne WDs might form when convective boundary mixing is accounted for during the propagation of the C-flame in the C-burning stage. Both the Ne-dominated and hybrid CO-Ne core would have measurable consequences for asteroseismological studies based on evolutionary models. Aims. In this work, we explore in detail to which extent differences in the adopted micro- and macro-physics can explain the different final WD compositions that have been found by different authors. Additionally, we explore the impact of such differences on the cooling times, crystallization, and pulsational properties of pulsating WDs. Methods. We performed numerical simulations of the evolution of intermediate massive stars from the zero age main sequence to the WD stage varying the adopted physics in the modeling. In particular, we explored the impact of the intensity of convective boundary mixing during the C-flash, extreme mass-loss rates, and the size of the adopted nuclear networks on the final composition, age, as well crystallization and pulsational properties of WDs. Results. In agreement with previous authors, we find that the inclusion of convective boundary mixing quenches the carbon flame leading to the formation of hybrid CO-Ne cores. Based on the insight coming from 3D hydro-dynamical simulations, we expect that the very slow propagation of the carbon flame will be altered by turbulent entrainment affecting the inward propagation of the flame. Also, we find that Ne-dominated chemical profiles of massive WDs recently reported appear in their modeling due to a key nuclear reaction being overlooked. We find that the inaccuracies in the chemical composition of the ultra-massive WDs recently reported lead to differences of 10% in the cooling times and degree of crystallization and about 8% in the period spacing of the models once they reach the ZZ Ceti instability strip.
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Jhangeer, Adil, Farheen Ibraheem, Tahira Jamal, Ariana Abdul Rahimzai, and Ilyas Khan. "Investigating pseudo parabolic dynamics through phase portraits, sensitivity, chaos and soliton behavior." Scientific Reports 14, no. 1 (July 2, 2024). http://dx.doi.org/10.1038/s41598-024-64985-7.
Abstract:
AbstractThis research examines pseudoparabolic nonlinear Oskolkov-Benjamin-Bona-Mahony-Burgers (OBBMB) equation, widely applicable in fields like optical fiber, soil consolidation, thermodynamics, nonlinear networks, wave propagation, and fluid flow in rock discontinuities. Wave transformation and the generalized Kudryashov method is utilized to derive ordinary differential equations (ODE) and obtain analytical solutions, including bright, anti-kink, dark, and kink solitons. The system of ODE, has been then examined by means of bifurcation analysis at the equilibrium points taking parameter variation into account. Furthermore, in order to get insight into the influence of some external force perturbation theory has been employed. For this purpose, a variety of chaos detecting techniques, for instance poincaré diagram, time series profile, 3D phase portraits, multistability investigation, lyapounov exponents and bifurcation diagram are implemented to identify the quasi periodic and chaotic motions of the perturbed dynamical model. These techniques enabled to analyze how perturbed dynamical system behaves chaotically and departs from regular patterns. Moreover, it is observed that the underlying model is quite sensitivity, as it changing dramatically even with slight changes to the initial condition. The findings are intriguing, novel and theoretically useful in mathematical and physical models. These provide a valuable mechanism to scientists and researchers to investigate how these perturbations influence the system’s behavior and the extent to which it deviates from the unperturbed case.
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Ramanah, Doogesh Kodi, Radosław Wojtak, and Nikki Arendse. "Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks." Monthly Notices of the Royal Astronomical Society, December 21, 2020. http://dx.doi.org/10.1093/mnras/staa3922.
Abstract:
Abstract We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions in the sky and their line-of-sight velocities. By formulating the mass estimation problem within this simulation-based inference framework, we are able to quantify the uncertainties on the inferred masses in a straightforward and robust way. We generate a realistic mock catalogue emulating the Sloan Digital Sky Survey (SDSS) Legacy spectroscopic observations (the main galaxy sample) for redshifts z ≲ 0.09 and explicitly illustrate the challenges posed by interloper (non-member) galaxies for cluster mass estimation from actual observations. Our approach constitutes the first optimal machine learning-based exploitation of the information content of the full 3D projected phase-space distribution, including both the virialized and infall cluster regions, for the inference of dynamical cluster masses. We also present, for the first time, the application of a simulation-based inference machinery to obtain dynamical masses of around 800 galaxy clusters found in the SDSS Legacy Survey, and show that the resulting mass estimates are consistent with mass measurements from the literature.
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Hadiji, Hajer, Wajdi Zouari, Mustapha Assarar, Bassem Zouari, Rezak Ayad, and Karim Behlouli. "Finite element modelling of the elastic and dynamic behaviour of nonwoven flax/polypropylene composite." Journal of Reinforced Plastics and Composites, March 23, 2023, 073168442311660. http://dx.doi.org/10.1177/07316844231166092.
Abstract:
2D and 3D finite element models of nonwoven flax/polypropylene composite are developed in this work to estimate its elastic and dynamic properties. These models are based on an in-plane random generation of the nonwoven flax fibres embedded in 2D and 3D networks of polypropylene (PP) matrix with porosities. First, the elastic properties of the flax/PP nonwoven composite are numerically predicted and a comparison with the experimental results from tensile tests is conducted. Second, the dynamic properties resulted from free vibration tests, at different fibre weight ratios and porosity contents, are numerically determined. Finally, the flax fibre damping is predicted using the strain energy method and the finite element results. This allows proposing a first estimation of the damping coefficient of this natural fibre.
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Reitbauer, Jürgen, Eduardo Machado Charry, Rene Eckhart, Cemile Sözeri, and Wolfgang Bauer. "Bulk characterization of highly structured tissue paper based on 2D and 3D evaluation methods." Cellulose, July 19, 2023. http://dx.doi.org/10.1007/s10570-023-05314-5.
Abstract:
AbstractThe structure of the fibre network in tissue paper can be complex and difficult to analyze, due to the presence of superimposed structures such as creping and patterns that occur, for example, in through-air-dried (TAD) tissue. Properties like high absorbency, a pleasant handfeel, and strength-related characteristics are closely related to the fibre network structure. Therefore, in addition to standard tissue testing methods, techniques that provide insights into the intrinsic properties of the tissue fibre network are essential for a deeper understanding and potential for further optimization. In this study, we utilized 2D cross-sectional images and 3D X-ray microtomography ($$\mu$$ μ -CT) to evaluate and quantify the intrinsic properties of highly structured TAD tissue. We compared the results obtained from these two methods, focusing on intrinsic thickness, porosity, and the fibre volume to fibre surface area (Fv/Fs) ratio. The open structure of the fibre network, fabric patterns, creping, and protruding fibres make it challenging to define bulk boundaries. Therefore, we examined the effect of different bulk expansion diameters on intrinsic properties. This procedure allows to quantify the effects of under- and overestimation of bulk boundaries, and to determine which regions within the fibre network are affected by bulk expansion. In terms of intrinsic thickness, both 2D and 3D evaluations show similar trends, which facilitates direct comparison of 2D and 3D data. Porosity, on the other hand, does not show any correlation between 2D and 3D-based data. Together with the Fv/Fs parameter, this leads to the conclusion that the depiction of 2D data does not represent the whole fibrous material but predominantly fibres perpendicular or close to perpendicular to the cut plane, whereas 3D data represents all fibres, fibre bonds and network connectivity. This work aims at presenting modern approaches and novel procedures to quantify intrinsic properties of open fibre structures such as tissue, but could also be applied to fibrous networks in general. The introduced methods could provide the basis for future research on the interrelations between intrinsic properties and key tissue properties such as absorbency and handfeel.
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Ismail, Yaser, Lei Wan, Jiayun Chen, Jianqiao Ye, and Dongmin Yang. "An ABAQUS® plug-in for generating virtual data required for inverse analysis of unidirectional composites using artificial neural networks." Engineering with Computers, October 31, 2021. http://dx.doi.org/10.1007/s00366-021-01525-1.
Abstract:
AbstractThis paper presents a robust ABAQUS® plug-in called Virtual Data Generator (VDGen) for generating virtual data for identifying the uncertain material properties in unidirectional lamina through artificial neural networks (ANNs). The plug-in supports the 3D finite element models of unit cells with square and hexagonal fibre arrays, uses Latin-Hypercube sampling methods and robustly imposes periodic boundary conditions. Using the data generated from the plug-in, ANN is demonstrated to explicitly and accurately parameterise the relationship between fibre mechanical properties and fibre/matrix interphase parameters at microscale and the mechanical properties of a UD lamina at macroscale. The plug-in tool is applicable to general unidirectional lamina and enables easy establishment of high-fidelity micromechanical finite element models with identified material properties.
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Nelson, Maria, Francesca Tallia, Samuel J. Page, John V. Hanna, Yuki Fujita, Akiko Obata, Toshihiro Kasuga, and Julian R. Jones. "Electrospun cotton–wool-like silica/gelatin hybrids with covalent coupling." Journal of Sol-Gel Science and Technology, October 21, 2020. http://dx.doi.org/10.1007/s10971-020-05420-x.
Abstract:
Abstract Inorganic/organic sol–gel hybrids consist of co-networks of inorganic and organic components that can lead to unique properties, compared to conventional composites, especially when there is covalent bonding between the networks. The aim here was to develop new electrospun silica/gelatin sol–gel hybrids, with covalent coupling and unique 3D cotton–wool-like morphology for application as regenerative medicine scaffolds. Covalent coupling is critical for obtaining sustained dissolution of the fibres and we identified the sol–gel synthesis conditions needed for coupling within the electrospun fibres. Under carefully controlled conditions, such as constant humidity, we investigated the effect of the electrospinning process variables of sol viscosity (and aging time) and amount of coupling agent on the 3D morphology of the fibres, their structure (bonding) and dissolution, identifying a detailed optimised protocol for fibre scaffold production.
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Baiocchi, Alessandro, Stefano Giagu, Christian Napoli, Marco SERRA, Pietro Nardelli, and Martina Valleriani. "Artificial neural networks exploiting point cloud data for fragmented solid objects classification." Machine Learning: Science and Technology, October 13, 2023. http://dx.doi.org/10.1088/2632-2153/ad035e.
Abstract:
Abstract This paper presents a novel approach for fragmented solid object classification exploiting Neural Networks based on point clouds. This work is the initial step of a project in collaboration with the Institution of ”Ente Parco Archeologico del Colosseo” in Rome, which aims to reconstruct ancient artefacts from their fragments. 
We built from scratch a synthetic dataset of fragments of different 3D objects including aging effects. We used this dataset to train Deep Learning models for the task of classifying internal and external fragments. As model architectures we adopted PointNet and Dynamical Graph Convolutional Neural Network, which take as input a point cloud representing the spatial geometry of a fragment, and we optimized models performance by adding additional features sensitive to local geometry characteristics. 
We tested the approach by performing several experiments to check the robustness and generalization capabilities of the models. Finally, we test the models on a real case using a 3D scan of artefacts preserved in different museums, artificially fragmented, obtaining good performance.
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Zhang, Ling, Jianfeng Chen, Hang Zhang, and Duan Huang. "The prediction of dynamical quantities in granular avalanches based on graph neural networks." Journal of Chemical Physics 159, no. 21 (December 1, 2023). http://dx.doi.org/10.1063/5.0172022.
Abstract:
The study of granular avalanches in rotating drums is not only essential to understanding various complex behaviors of interest in granular media from a scientific perspective; it also has valuable applications in regard to industrial processes and geological catastrophes. Despite decades of research studies on avalanches, a proper understanding of their dynamic properties still remains a great challenge to scientists due to a lack of state-of-the-art techniques. In this study, we accurately predict the avalanche dynamic features of three-dimensional granular materials in rotating drums, by using graph neural networks on the basis of their initial static microstructures alone. We find that our method is robust to changes in various model parameters, such as the interaction potential, size polydispersity, and noise in particle coordinates. In addition, with the grain-scale velocities obtained either from our network or from numerical simulations, we find an approximately equal and strong correlation between the global velocity and global velocity fluctuation in our 3D granular avalanche systems, which further demonstrates the predictive power of our trained graph neural networks to uncover the fundamental physics of granular avalanches. We expect our method to provide more insight into the avalanche dynamics of granular materials and other amorphous systems in the future.
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Yang, Jongin, Joseph Oh, Baik Jin Kim, and Alan Palazzolo. "Digital-Twin Implementation for 3D Rotordynamic Response via Physics-Informed LSTM Neural Networks." Journal of Vibration and Acoustics, June 11, 2024, 1–32. http://dx.doi.org/10.1115/1.4065714.
Abstract:
Abstract The rotating assemblies of critical machinery are complex dynamical systems and rotordynamic model response prediction inaccuracy risks machinery failure leading to high production losses. Jeffcott, Euler beam, and high-fidelity 3D solid finite element models are frequently utilized for rotordynamic analyses. Even though the 3D rotor has the higher accuracy, beam models are still widely used in industrial applications. To improve prediction accuracy of the lower fidelity Jeffcott and beam models a Rotordynamics Physics Informed Neural Network (R-PINN) is proposed. This models physics-informed Long Short-Term Memory LSTM neural networks which utilize partial or limited measured data, by incorporating physical laws. This approach enables the creation of a Digital Twin, which can produce additional data, and helps remove noise and outliers. In the current study, two R-PINNs are introduced to validate the superior capability of the model for both low and high-fidelity physics. Random noise of 10% is introduced into the measured data produced by the Digital Twin to replicate real-world noisy measurements. The result shows that both low and high-fidelity physics R-PINNs can achieve high accuracy even with high noise data, thereby increasing the robustness of the model. The results clearly demonstrate the ability of the proposed R-PINN algorithm to enhance an Euler beam model's predicted response to the level of accuracy of a 3D solid element model's predicted response, the latter acting as a surrogate for test measurements in an actual application.
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Billaud-Friess, Marie, Antonio Falcó, and Anthony Nouy. "A new splitting algorithm for dynamical low-rank approximation motivated by the fibre bundle structure of matrix manifolds." BIT Numerical Mathematics, July 21, 2021. http://dx.doi.org/10.1007/s10543-021-00884-x.
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Barbosa, Marconi, Ted Maddess, Samyoul Ahn, and Tailoi Chan-Ling. "Novel morphometric analysis of higher order structure of human radial peri-papillary capillaries: relevance to retinal perfusion efficiency and age." Scientific Reports 9, no. 1 (September 17, 2019). http://dx.doi.org/10.1038/s41598-019-49443-z.
Abstract:
Abstract We apply novel analyses to images of superficial capillaries that are located near and around the optic disc of the human retina: the radial peri-papillary capillaries (RPCs). Due to their unique perfusion of the nerve fibre layer the RPCs are particularly significant for optic-neuropathies. The inputs to the analysis were z-stacks from 3D confocal fluorescence microscopy from 62 human retinas aged 9 to 84 years. Our aim was to find morphometric correlates of age. The retinas had no ophthalmic history. The analysis was undertaken in two stages: (1) converting the z-stacks to 3D tubular networks of vessels, and (2) characterizing the tubular networks using features derived from the Minkowski functionals (MFs). The MFs measure: the capillary volume, surface area, mean breadth, and Euler number. The mean breadth is related to tortuosity, wall shear stress and resistance to flow, and the Euler number is related to the density of loops (collaterals). Features derived from the surface area, mean breadth and Euler number were most related to age (all p ≤ 0.006). The results indicate the importance of pressure-equalizing loops and tortuosity as quantitative measures related to perfusion efficiency. The novel morphometric analysis could quantify disease-related accelerated aging and vessel malformation.
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Zhou, Rongxin, Xiaorui Dong, Yanqing Li, Zhidong Yang, and Kejie Chen. "Cell migration-inspired stochastic steering strategy of magnetic particles in vascular networks." Physics of Fluids 35, no. 11 (November 1, 2023). http://dx.doi.org/10.1063/5.0173577.
Abstract:
One primary challenge of magnetic drug targeting is to achieve the efficient and accurate delivery of drug particles to the desired sites in complex physiological conditions. Though a majority of drugs are delivered through intravenous administration, until now, the kinematics and dynamics of drug particles influenced by the magnetic field, vascular topology, and blood flows are still less understood. In this work, a multi-physics dynamical model, which captures transient particle motions inside the vascular networks manipulated by the external magnetic field, is developed. Based on the model, we studied the transport efficiency of particles in the two-dimensional (2D), three-dimensional (3D) artificial, and in vivo-relevant vascular networks. It is found that particles that perform a random walk with correlated speed and persistence, recapitulating some characteristics of migratory motion of immune and metastasis cells, have the largest mean square displacements in various vascular network topologies. Next, we designed a stochastic magnetic steering strategy, using a time-varying gradient magnetic field, to manipulate particles to perform the cell migration-inspired random motions in the vasculature. The capability of the proposed steering strategy to improve the particle spreading speed and reduce the consumed magnetic energy has been demonstrated using our multi-physics numerical model. Furthermore, the influence of heterogeneous flows in the vascular networks on the particle steering efficiency was investigated. Overall, the numerical model and the proposed stochastic magnetic steering strategy can be used to assist the development of drug delivery systems for precise medicine research.
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M. N., Harshavardhana, and Suraiya Tarannum. "Performance analysis of optical spectral amplitude CDMA system unipolar codes with double weight construction design." Journal of Optical Communications, September 5, 2022. http://dx.doi.org/10.1515/joc-2022-0110.
Abstract:
Abstract Optical Codes Division Multiple Access is a multiplexing approach in which the transmitter a distinguishable optical code is assigned to each user in the communication channel. OCDMA is designed for radio frequency (RF) communication systems and it is a variation of the CDMA system. Subsequently, to develop all OCDMA communication systems, the success of CDMA in wireless networks, as well as the full benefits of fibre optics, such as good security, extremely high bandwidth, low attenuation, and long-haul transmission. In this research Spectral Amplitude Coding-Optical Code Division Multiple Access is proposed. Initially, Modified Double Weight and Zero Cross-Correlation (MDW-ZCC) is proposed in this research to construct the code design of SAC-OCDMA, it produces the best possible autocorrelation and cross-correlation values. Subsequently, a combined Single Photodiode (SPD) and Direct detection technique is introduced to decode the transmitter information and it reduces the noise. Accordingly, a Gaussian approximation technique is presented to estimate the Bit error rate from the eye diagram of BER and Q-factor. Accordingly, the proposed method is simulated using Python software. The performance metrics are Q-factor, and bit error rate and the proposed method is compared with the existing Three-Dimensional Single Weight Zero Cross-Correlation (3D-SWZCC) Code, Two Dimensional Spectral/Spatial Cyclic Shift (2D-SSCS), and Permutation Matrix Zero Cross-Correlation (PM-ZCC) Code. Subsequently, these results exhibit that the proposed method produces the best performance than the other methods and it estimates the BER. Consequently, future work is based on the study of 3D codes with m-sequence code in the spectral/time/spatial OCDMA domain. The approach has elegantly integrated the design of an encoder and a decoder, allowing the system to efficiently create code in 3D bipolar mode, respectively.
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Szarski, Martin, and Sunita Chauhan. "Instant flow distribution network optimization in liquid composite molding using deep reinforcement learning." Journal of Intelligent Manufacturing, August 8, 2022. http://dx.doi.org/10.1007/s10845-022-01990-5.
Abstract:
AbstractCarbon fibre reinforced plastic (CFRP) manufacturing cycle time is a major driver of production rate and cost for aerospace manufacturers. In vacuum assisted resin transfer molding (VARTM) where liquid thermoset resin is infused into dry carbon reinforcement under vacuum pressure, the design of a resin distribution network to minimize fill time while ensuring the preform is completely full of resin is critical to achieving acceptable quality and cycle time. Complex resin distribution networks in aerospace composites increase the need for quick, optimized virtual design feedback. Framing the problem flow media placement in terms of reinforcement learning, we train a deep neural network agent using a 3D Finite Element based process model of resin flow in dry carbon preforms. Our agent learns to place flow media on thin laminates in order to avoid resin starvation and reduce total infusion time. Due to the knowledge the agent has gained during training on a variety of thin laminate geometries, when presented with a new thin laminate geometry it is able to propose a good flow media layout in less than a second. On a realistic aerospace part with a complex 12-dimensional flow media network, we demonstrate our method reduces fill time by 32% when compared to an expert designed placement, while maintaining the same fill quality.
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Meschini, Silvia, Laura Pellegrini, Mirko Locatelli, Daniele Accardo, Lavinia Chiara Tagliabue, Giuseppe Martino Di Giuda, and Marco Avena. "Toward cognitive digital twins using a BIM-GIS asset management system for a diffused university." Frontiers in Built Environment 8 (December 1, 2022). http://dx.doi.org/10.3389/fbuil.2022.959475.
Abstract:
The integrated use of building information modeling (BIM) and geographic information system (GIS) is promising for the development of asset management systems (AMSs) for operation and maintenance (O&M) in smart university campuses. The combination of BIM-GIS with cognitive digital twins (CDTs) can further facilitate the management of complex systems such as university building stock. CDTs enable buildings to behave as autonomous entities, dynamically reacting to environmental changes. Timely decisions based on the actual conditions of buildings and surroundings can be provided, both in emergency scenarios or when optimized and adaptive performances are required. The research aims to develop a BIM-GIS-based AMS for improving user experience and enabling the optimal use of resources in the O&M phase of an Italian university. Campuses are complex assets, mainly diffused with buildings spread across the territory, managed with still document-based and fragmented databases handled by several subjects. This results in incomplete and asymmetrical information, often leading to ineffective and untimely decisions. The paper presents a methodology for the development of a BIM-GIS web-based platform (i.e., AMS-app) providing the real-time visualization of the asset in an interactive 3D map connected to analytical dashboards for management support. Two buildings of the University of Turin are adopted as demonstrators, illustrating the development of an easily accessible, centralized database by integrating spatial and functional data, useful also to develop future CDTs. As a first attempt to show the AMS app potential, crowd simulations have been conducted to understand the buildings’ actual level of safety in case of fire emergency and demonstrate how CDTs could improve it. The identification of data needed, also gathered through the future implementation of suitable sensors and Internet of Things networks, is the core issue together with the definition of effective asset visualization and monitoring methods. Future developments will explore the integration of artificial intelligence and immersive technologies to enable space use optimization and real-time wayfinding during evacuation, exploiting digital tools to alert and drive users or authorities for safety improvement. The ability to easily optimize the paths with respect to the actual occupancy and conditions of both the asset and surroundings will be enabled.
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Njitacke, Zeric, Sishu Shankar Muni, Soumyajit Seth, Jan Awrejcewicz, and Jacques Kengne. "Complex dynamics of a heterogeneous network of Hindmarsh-Rose neurons." Physica Scripta, February 21, 2023. http://dx.doi.org/10.1088/1402-4896/acbdd1.
Abstract:
Abstract This contribution is devoted to the study of the collective behavior of two HR neurons followed by a network of HR neurons. The collective behavior of the two coupled neuron was obtained from the connection between the traditional 3D HR and a memristive 2D HR neuron via a gap junction. The dynamical properties of this rst topology revealed that it is dissipative therefore can support complex phenomena. From numerical simulations, it is found that the coupled neurons display a variety of behaviors just by varying the control parameter. Amongst these behaviors found, we have periodic bursting or spiking, quasi-periodic bursting or spiking, and chaotic bursting or spiking. Non-synchronized motion is observed when the electrical coupling strength is weak. However, synchronized cluster states are observed when the coupling strength is increased. Also varied of cross ring networks made of combination of N = 100 these different HR neurons in the network are also investigated. It is discovered that the spatiotemporal patterns are affected by the network topology. The cluster states are represented in the non- homogenous network's ring and star structures. The ring and ring-star structures contain single and double-well chimera states. Finally, in the PSIM simulation environment, a comparable electronic circuit for the two coupled heterogeneous neurons is designed and investigated. The results obtained from the designed analog circuit and the mathematical model of the two coupled neurons match perfectly.
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Borodina, Olga, Thomas G. Williams, Mattia C. Sormani, Sharon Meidt, and Eva Schinnerer. "On the Tremaine-Weinberg method: how much can we trust gas tracers to measure pattern speeds?" Monthly Notices of the Royal Astronomical Society, July 11, 2023. http://dx.doi.org/10.1093/mnras/stad2068.
Abstract:
Abstract Pattern speeds are a fundamental parameter of the dynamical features (e.g. bars, spiral arms) of a galaxy, setting resonance locations. Pattern speeds are not directly observable, so the Tremaine-Weinberg (TW) method has become the most common method used to measure them in galaxies. However, it has not been tested properly whether this method can straightforwardly be applied to gas tracers, despite this being widely done in the literature. When applied to observations, the TW method may return invalid results, which are difficult to diagnose due to a lack of ground truth for comparison. Although some works applying the TW method to simulated galaxies exist, only stellar populations have been tested. Therefore, here we explore the applicability of the TW method for gas gracers, by applying it to hydrodynamical simulations of galaxies, where we know the true value of the bar pattern speed. We perform some simple tests to see if the TW method has a physically reasonable output. We add different kinds of uncertainties (e.g. in position angle or flux) to the data to mock observational errors based on the magnitude of uncertainty present in the observations. Second, we test the method on 3D simulations with chemical networks. We show that in general, applying TW to observations of gas will not recover the true pattern speed. These results have implications for many “pattern speeds” reported in the literature, and based on these tests we also give some best practices for measuring pattern speeds using gas tracers going forwards.