Dynamic Fingerprint Research Articles

Preliminary Research on the Nonlinear Ultrasonic Detection of the Porosity of Porous Material Based on Dynamic Wavelet Fingerprint Technology.

Porosity is an important characteristic of porous material, which affects mechanical and material properties. In order to solve the problem that the large distribution range of pore size of porous materials leads to the large detection errors of porosity, the non-linear ultrasonic testing technique is applied. A graphite composite was used as the experimental object in the study. As the accuracy of porosity is directly related with feature extraction, the dynamic wavelet fingerprint (DWFP) technology was utilized to extract the feature parameter of the ultrasonic signals. The effects of the wavelet function, scale factor, and white slice ratio on the extraction of the nonlinear feature are discussed. The SEM photos were conducted using gray value to identify the aperture. The relationship between pore diameter and detection accuracy was studied. Its results show that the DWFP technology could identify the second harmonic component well, and the extracted nonlinear feature could be used for the quantitative trait of porosity. The larger the proportion of the small diameter holes and the smaller the aperture distribution range was, the smaller the error was. This preliminary research aimed to improve the nondestructive testing accuracy of porosity and it is beneficial to the application of porous material in the manufacturing field.

Continuum nonlinear dynamics of unstable shock waves induced by structural phase transformations in iron

Iron is the predominant metal in the interior of the Earth and other rocky planets and has also received substantial consideration in materials science and engineering due to its technological importance. Here, a finite deformation continuum framework for combining nonlinear elasto-viscoplasticity with multivariant phase-field theory is used to investigate shock-induced polymorphic phase transitions in single-crystal iron. A large number of low- and high-pressure variants with explicit anisotropic pressure-dependent stiffnesses are included with respect to the point-symmetry groups of cubic and hexagonal crystal lattices. Using the element-free Galerkin method with high-performance computing resources, the three-dimensional nonlinear calculations accurately describe some important features reported by the experimental literature, and strongly complement our understanding of the phase-change dynamics in iron at larger time and length scales than hitherto explored by molecular dynamics simulations in the last two decades. The numerical model is able to reproduce unstable shock waves (which break up into elastic, plastic and phase-transition waves), providing new stress-informed insights into the coupling between the high strain-rate plasticity and microstructure evolution during the displacive phase transitions. The analyses of time-position diagrams indicate that the prompt plastic relaxation to a nearly hydrostatic state from uniaxial shock-compression is responsible for the peculiar multiphase microstructure with an unexpected gradient selection of high-pressure variants behind the phase-transition wave front. The existence of two-zone sequential sets of “release” and “reload” variants that results from a heterogeneous nucleation instability leads to a specific microstructural fingerprint of the nonlinear dynamics of phase transitions, thus offering novel guidelines for future experimental diagnostics of shock wave propagation in iron.

Structure and dynamics of short-chain polymerized ionic liquids.

Combining experimental results obtained with X-ray scattering and field-gradient nuclear magnetic resonance (NMR) and an assessment of new and previous dielectric and rheology data, our study focuses on the molecular weight (Mw) evolution of local structure and dynamics in a homologous series of covalently bonded ionic liquids. Performed on a family of electrolytes with a tailored degree of ionic decoupling, this study reveals the differences between monomeric and oligomeric melts with respect to their structural organization, mass and charge transport, and molecular diffusion. Our study demonstrates that for the monomeric compound, the broadband conductivity and mechanical spectra reflect the same underlying distribution of activation barriers and that the Random Barrier Model describes fairly well both the ionic and structural relaxation processes in these materials. Moreover, the oligomers with chains comprising ten segments only exhibit both structural and dynamical fingerprints of a genuine polymer. A comparison of conductivity levels estimated using the self-diffusion coefficients probed via NMR and those probed directly with dielectric spectroscopy reveals the emerging of ion correlations which are affecting the macroscopic charge transport in these materials in a chain-length dependent manner.

Noncrossing run-and-tumble particles on a line.

We study active particles performing independent run-and-tumble motion on an infinite line with velocities v_{0}σ(t), where σ(t)=±1 is a dichotomous telegraphic noise with constant flipping rate γ. We first consider a single particle in the presence of an absorbing wall at x=0 and calculate the probability that it has survived up to time t and is at position x at time t. We then consider two particles with independent telegraphic noises and compute exactly the probability that they do not cross up to time t. In contrast with the case of passive (Brownian) particles this problem of two run-and-tumble particles (RTPs) cannot be reduced to a single RTP with an absorbing wall. Nevertheless, we are able to compute exactly the probability of no crossing of two independent RTPs up to time t and find that it decays at large time as t^{-1/2} with an amplitude that depends on the initial condition. We show that this amplitude vanishes if one extrapolates the starting distance between the two particles to a negative value-similarly to the Milne extrapolation length in neutron scattering.

Continuous in vivo Metabolism by NMR.

Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties of metabolism. Here we extend high-resolution-magic angle spinning (HR-MAS) to enable continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) and provide analysis tools for these data. First, we reproduced a result in human chronic lymphoid leukemia cells by using isotope-edited CIVM-NMR to rapidly and unambiguously demonstrate unidirectional flux in branched-chain amino acid metabolism. We then collected untargeted CIVM-NMR datasets for Neurospora crassa, a classic multicellular model organism, and uncovered dynamics between central carbon metabolism, amino acid metabolism, energy storage molecules, and lipid and cell wall precursors. Virtually no sample preparation was required to yield a dynamic metabolic fingerprint over hours to days at ~4-min temporal resolution with little noise. CIVM-NMR is simple and readily adapted to different types of cells and microorganisms, offering an experimental complement to kinetic models of metabolism for diverse biological systems.

CuCoTrack: Cuckoo filter based connection tracking

This paper introduces CuCoTrack, a cuckoo hash based data structure designed to efficiently implement connection tracking. The proposed scheme exploits the fact that queries always match one existing connection to compress the 5-tuple that identifies the connection. This significantly reduces the amount of memory needed to store the connections and also the memory bandwidth needed for lookups. CuCoTrack uses a dynamic fingerprint to avoid collisions thus ensuring that queries are completed in at most two memory accesses and facilitating a hardware implementation. The proposed scheme has been analyzed theoretically and validated by simulation. The results show that using 16 bits for the fingerprint is enough to avoid collisions in practical configurations.

Guest Editorial Special Issue on Neural Systems Engineering and Mathematical Modeling of Brain Dynamics Using ECoG/EEG/MEG Oscillations and Machine Learning Methods

The coherent spatiotemporal dynamical interplay among neurons, neuronal populations, cortical circuits, and networks cross the micro-, meso- and macro-scales of the human brain, providing the functional organization of the brain and supporting our daily life. The details of how these multiple scales of organization in the brain influence one another and produce a multitude of human behaviors remains a clouded mystery. Mathematical, computational, and experimental neuroscientists employ a variety of cutting-edge methods, techniques, and algorithms in both humans and animals to identify the core mechanisms that govern the perception and action characteristics of the human brain. Integration of functional MRI (fMRI)–as an indirect measure of brain activity-with electroencephalogram (EEG) and magnetoencephalogram (MEG) as direct measures of spatiotemporal and spectral fingerprints of brain dynamics has provided novel insights into how the human brain operates in the resting state and in goal-oriented task performance. The ultimate goal is to identify the fundamental mechanisms that explain brain function across multiple spatiotemporal dynamics. Although our knowledge has grown exponentially in the past few decades, the integration of this knowledge to provide a conceptual framework of emergent behavior and pattern formation occurring on different levels of spatial organization remains challenging.

Dynamical signatures of topological order in the driven-dissipative Kitaev chain

We investigate the effects of dissipation and driving on topological order in superconducting nanowires. Rather than studying the non-equilibrium steady state, we propose a method to classify and detect dynamical signatures of topological order in open quantum systems. Bulk winding numbers for the Lindblad generator \hat{\mathcal{L}}ℒ̂ of the dissipative Kitaev chain are found to be linked to the presence of Majorana edge master modes – localized eigenmodes of \hat{\mathcal{L}}ℒ̂. Despite decaying in time, these modes provide dynamical fingerprints of the topological phases of the closed system, which are now separated by intermediate regions where winding numbers are ill-defined and the bulk-boundary correspondence breaks down. Combining these techniques with the Floquet formalism reveals higher winding numbers and different types of edge modes under periodic driving. Finally, we link the presence of edge modes to a steady state current.

Two-channel model for optical conductivity of high-mobility organic crystals

We show that the temperature dependence of conductivity of high-mobility organic crystals pentacene and rubrene can be quantitatively described in the framework of the model where carriers are scattered by quenched local impurities and interact with phonons by Su-Schrieffer-Hegger (SSH) coupling. Within this model, we present approximation-free results for mobility and optical conductivity obtained by world line Monte Carlo, which we generalize to the case of coupling both to phonons and impurities. We find fingerprints of carrier dynamics in these compounds which differ from conventional metals and show that the dynamics of carriers can be described as a superposition of a Drude term representing diffusive mobile particles and a Lorentz term associated with dynamics of localized charges.

Airyscan Cca Provides Structural and Dynamics Fingerprinting of Subcellular Compartments in Living Cells

In this work we will present our current advances in the development of the CCA (Comprehensive Correlation Analysis) technique using the Zeiss Airyscan detector. This detector consists of 32 GaAsP PMT arranged in a hexagonal pattern, featuring a fast temporal sampling (down to 1.28 μs per frame). It can also be used in a super-resolution configuration as per the ISM [1] principle, while the arrangement of the detectors allows for the implementation of advanced state-of-the-art spatiotemporal correlation techniques. We will present a novel application of the spot-variation FCS technique [2] in super-resolution, as well as the implementation of the 2D-pCF [3], iMSD [4] and number and brightness [5] analysis using this fast detector array. This simultaneous analysis of the same dataset provides biophysical information regarding the fluorescent probe and the surrounding environment, such as diffusion coefficient, concentration, diffusion modality, environment organization, direction and anisotropy of molecular flows, diffusion connectivity and oligomerization state in a single analysis in a few seconds. Using CCA we obtained structural and dynamic fingerprints of several subcellular compartments of living NIH-3T3 cells, namely structured and unstructured cytoplasm, the nucleoli and heterochromatin- and euchromatin-rich regions in the nucleus. CCA provides a new methodology for the investigation of sub-resolution organelle structure and dynamics as well as insights on chromatin compaction states in living cells. The parallel acquisition of numerous biophysical parameters opens the doors to new multidimensional characterization and classification methods that can be applied to virtually any fluorescent or fluorescently-labeled probe diffusing in complex environments. [Work supported by NIH P41 GM103540 grant] [1] Sheppard et al Opt.Lett. (2013) [2] Wawrezinieck et al Biophys.J. (2005) [3] Hinde et al PNAS. (2010) [4] Di Rienzo et al Nat.Comm. (2014) [5] Digman et al Biophys.J. (2008)

Photoelectron Spectroscopy of the Hexafluorobenzene Cluster Anions: (C6F6) n- ( n = 1-5) and I-(C6F6).

Frequency-resolved (2D) photoelectron (PE) spectra of the anionic clusters (C6F6) n-, for n = 1-5, and time-resolved PE spectra of I-C6F6 are presented using a newly built instrument and supported by electronic structure calculations. From the 2D PE spectra, the vertical detachment energy (VDE) of C6F6- was measured to be 1.60 ± 0.01 eV, and the adiabatic detachment energy (ADE) was ≤0.70 eV. The PE spectra also contain fingerprints of resonance dynamics over certain photon energy ranges, in agreement with the calculations. An action spectrum over the lowest resonance is also presented. The 2D spectra of (C6F6) n- show that the cluster can be described as C6F6-(C6F6) n-1. The VDE increases linearly (200 ± 20 meV n-1) due to the stabilizing influence on the anion of the solvating C6F6 molecules. For I-C6F6, action spectra of the absorption just below both detachment channels are presented. Time-resolved PE spectra of I-C6F6 excited at 3.10 eV and probed at 1.55 eV reveal a short-lived nonvalence state of C6F6- that coherently evolves into the valence ground state of the anion and induces vibrational motion along a specific buckling coordinate. Electronic structure calculations along the displacement of this mode show that at the extreme buckling angle the probe can access an excited state of the anion that is bound at that geometry but adiabatically unbound. Hence, slow electrons are emitted and show dynamics that predominantly probe the outer-turning point of the motion. A PE spectrum taken at t = 0 contains a vibrational structure assigned to a specific Raman- or IR-active mode of C6F6.

Insights into the substrate binding specificity of quorum-quenching acylase PvdQ

Quorum sensing is a cell to cell signaling mechanism that enables them to coordinate their behaviors in a density-dependent manner mediated by small diffusible signaling molecules, which can control the virulence and biofilm gene expression in many Gram-negative and positive bacteria. N-acyl homoserine lactone acylase PvdQ from human opportunistic pathogen Pseudomonas aeruginosa is a quorum-quenching enzyme that can hydrolyze the amide bond of the quorum signaling N-acyl homoserine lactones (AHLs) thereby degrading the signaling molecules, turning off the biofilm phenotype and resulting in a reduction of bacterial virulence. Previous studies demonstrated that PvdQ has different preferences for N-acyl substrates with different acyl chain lengths and substituents. However, the substrate binding specificity determinants of the quorum-quenching enzyme PvdQ with the different bacterial ligands are unknown and unintuitive. Further, elucidation of these determinants can lead to mutants with efficiency and broader substrate promiscuity. To investigate this question, a computational study was carried out combining multiple molecular docking methods, molecular dynamics simulations, residue interaction network analysis, and binding free energy calculations. The main findings are: firstly, the results from pKa predictions support that the pKa of the N-terminus of Serβ1 was depressed due to the surrounding residues. Multiple molecular docking studies provide useful information about the detailed binding modes and binding affinities. Secondly, 300 ns molecular dynamics simulations were carried out to analyze the overall molecular motions of substrate-bound and substrate-free PvdQ. The specific interactions between the active site of PvdQ and different ligands revealed the determinants for the preference among the ligands. A systematic comparison and analysis of the protein dynamic fingerprint of each complex demonstrated that binding of the most favorable ligand, C12-homoserine lactone (C12-HSL), reduced the global motions of the complex and maintained the correct arrangement of the catalytic site. Further, the residue interaction network analysis of each system illustrated that there are more communication contacts and pathways between the residues in the C12-HSL complex as compared to complexes with the other ligands. The binding of the C12-HSL ligand facilitates structural communication between the two knobs and the active site. While the binding of the other ligands tend to impair specific communication pathways between the two knobs and the active site, and lead to a catalytically inefficient state. Finally, simulation results from free energy landscape and binding free energy analysis revealed that the C12-HSL ligand has the lowest binding free energy and greater stability than the less favored ligands. Each of the following residues: Serβ1, Hisβ23, Pheβ24, Metβ30, Pheβ32, Leuβ50, Asnβ57, Thrβ69, Valβ70, Trpβ162, Trpβ186, Asnβ269, Argβ297 and Leuα146, play different roles in substrate binding specificity. This is the first computational study that provides molecular information for structure-dynamic-function relationships of PvdQ with different ligands and demonstrates determinants of bacterial substrate binding specificity.

Bridge Performance Evaluation via Dynamic Fingerprints and Data Fusion

Abstract Rapidly aging bridges are a serious problem plaguing the transportation industry. Bridges are subjected to accumulative damage due to environmental effects, traffic, and inappropriate mana...

Comprehensive correlation analysis for super-resolution dynamic fingerprinting of cellular compartments using the Zeiss Airyscan detector

The availability of the Airyscan detector in the Zeiss LSM 880 has made possible the development of a new concept in fluctuation correlation spectroscopy using super-resolution. The Airyscan unit acquires data simultaneously on 32 detectors arranged in a hexagonal array. This detector opens up the possibility to use fluctuation methods based on time correlation at single points or at a number of points simultaneously, as well as methods based on spatial correlation in the area covered by the detector. Given the frame rate of this detector, millions of frames can be acquired in seconds, providing a robust statistical basis for fluctuation data. We apply the comprehensive analysis to the molecular fluctuations of free GFP diffusing in live cells at different subcellular compartments to show that at the nanoscale different cell environments can be distinguished by the comprehensive fluctuation analysis.

Vibration-based Damage Detection in Bridges via Machine Learning

Environmental corrosion and external loads degrade the performance of a bridge over the course of its service life. Although dynamic fingerprints are damage-sensitive, they are rarely applied to bridges in-situ due to environmental noise. Machine learning techniques can facilitate effective structural damage detection. This paper proposes a detection method based on dynamic fingerprints and machine learning techniques for multi-damage problems in bridges. Vibration analysis is conducted to acquire the dynamic fingerprints, then the Bayesian fusion is used to integrate these features and preliminarily locate the damage. The RSNB method, which combines Rough Set theory and the Naive-Bayes classifier, is introduced as a robust classification tool for damage qualification. A continuous bridge is numerically simulated to validate the effectiveness of the proposed method. The RSNB method is compared with back propagation neural network, support vector machine, and decision tree techniques, it is found that the RSNB outperforms other three methods in terms of transparency, accuracy, efficiency, noise robustness, and stability.

Dynamics fingerprints of active conformers of epidermal growth factor receptor kinase.

Epidermal growth factor receptor (EGFR) is a prototypical cell-surface receptor that plays a key role in the regulation of cellular signaling, proliferation and differentiation. Mutations of its kinase domain have been associated with the development of a variety of cancers and, therefore, it has been the target of drug design. Single amino acid substitutions (SASs) in this domain have been proven to alter the equilibrium of pre-existing conformer populations. Despite the advances in structural descriptions of its so-called active and inactive conformations, the associated dynamics aspects that characterize them have not been thoroughly studied yet. As the dynamic behaviors and molecular motions of proteins are important for a complete understanding of their structure-function relationships we present a novel procedure, using (or based on) normal mode analysis, to identify the collective dynamics shared among different conformers in EGFR kinase. The method allows the comparison of patterns of low-frequency vibrational modes defining representative directions of motions. Our procedure is able to emphasize the main similarities and differences between the collective dynamics of different conformers. In the case of EGFR kinase, two representative directions of motions have been found as dynamics fingerprints of the active conformers. Protein motion along both directions reveals to have a significant impact on the cavity volume of the main pocket of the active site. Otherwise, the inactive conformers exhibit a more heterogeneous distribution of collective motions. © 2018 Wiley Periodicals, Inc.

Intrinsic dynamics of the electric-field-induced phase switching in antiferroelectric PbZrO3 ultrathin films

Antiferroelectric ultrathin ${\mathrm{PbZrO}}_{3}$ films can exhibit both ferroelectric and antiferroelectric behavior depending on the thickness. We use first-principles-based nanoscopic simulations to investigate the intrinsic high-frequency dynamics of the electric-field-induced phase switching in such films which so far remains unknown. In this comprehensive study we report (i) the size and frequency evolution of the polarization response to the electric field; (ii) the intrinsic time for the phase switching; (iii) detailed comparison between the polarization reversal in the films with ferroelectric and antiferroelectric behavior; (iv) dynamics of the antiferroelectric and antiferrodistortive order parameters; (v) nanoscopic mechanism responsible for the phase switching. The nanoscopic insight leads to the prediction of the existence of two possible scenarios for the antipolar-polar phase switching depending on the mutual orientation of the antiferroelectric order parameter and the electric field. The two scenarios have different dynamical fingerprints. The polar-antipolar phase switching is found to be assisted by the formation of a nonpolar phase. Computational data indicate that the phase switching time is only fractions of nanoseconds for the polar-polar phase switching in ferroelectric films and polar-antipolar phase switching in antiferroelectric films. The antipolar-polar phase switching in antiferroelectric films is just a bit slower and takes the order of nanosecond. Under nonequilibrium conditions we find formation of antiferroelectric and antiferrodistortive nanodomains and coexistence of polar and antipolar order parameters.

Research on Damage Identification of a Beam Based on Curvature Mode

To study the bridge damage location and degree identification ability based on curvature mode, with a three-span continuous steel beam as the research object, compared with the damage location identification of curvature modal difference and displacement modal difference, analyzing the influence of damage location identification on the curvature modal difference index considering the two factors of the measuring point number reduce and the noise, identifying the damage degree by the method of relative variation of the curvature modal shapes. The results show that the location damage identification effect of curvature modal difference is more obvious than that of displacement modal difference, the identification effect has higher requirements for the density of the measuring points and has low anti-noise ability; when the damage degree is recognized, the larger the damage degree is, the smaller the identification result error is. The method of curvature calculation can be incorporated into other dynamic fingerprint methods, and then the ability of damage identification will be further improved.

Multidamage Detection of Bridges Using Rough Set Theory and Naive-Bayes Classifier

This paper is intended to introduce a two-stage detection method to solve the multidamage problem in bridges. Vibration analysis is conducted to acquire the dynamic fingerprints which are regarded as information sources. Bayesian fusion is used to integrate these sources and preliminarily locate the damage. Then, the RSNB method which combines rough set theory and Naive-Bayes classifier is proposed to simplify the sample dimensions and fuse the remaining attributes for damage extent detection. A numerical simulation of a real structure, the Sishui Bridge in Shenyang, China, is conducted to validate the effectiveness of the proposed detection method. Data fusion based method is compared with single-valued index method at the damage localization stage. The proposed RSNB method is compared with the Back Propagation Neural Network (BPNN) method at the damage qualification stage. The results show that the proposed two-stage damage detection method has better performances in regard to transparency, accuracy, efficiency, noise robustness, and stability. Furthermore, an ambient excitation modal test was carried out on the bridge to obtain the vibration responses and assess the damage condition with the proposed method. This novel approach is applicable for early damage detection and provides a basis for bridge management and maintenance.

Ultrafast photodissociation dynamics of 1,4-diiodobenzene.

The photodissociation dynamics of 1,4-diiodobenzene is investigated using ultrafast time-resolved photoelectron spectroscopy. Following excitation by laser pulses at 271 nm, the excited-state dynamics is probed by resonance-enhanced multiphoton ionization with 405 nm probe pulses. A progression of Rydberg states, which come into resonance sequentially, provide a fingerprint of the dissociation dynamics of the molecule. The initial excitation decays with a lifetime of 33 ± 4 fs, in good agreement with a previous study. The spectrum is interpreted by reference to ab initio calculations at the CASPT2(18,14) level, including spin-orbit coupling. We propose that both the 5B1 and 6B1 states are excited initially, and based on the calculations, we identify diabatic spin-orbit coupled states corresponding to the main dissociation pathways.