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Journal articles on the topic 'Data dynamics'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Ma, Tieju. "Exploring Dynamics of PRODYs with International Trade Data." International Journal of Social Science and Humanity 6, no. 8 (August 2016): 600–603. http://dx.doi.org/10.7763/ijssh.2016.v6.717.

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2

Mei, Zhuanglin, and Toshiki Oguchi. "Network Structure Identification Based on Measured Output Data Using Koopman Operators." Mathematics 11, no. 1 (December 26, 2022): 89. http://dx.doi.org/10.3390/math11010089.

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This paper considers the identification problem of network structures of interconnected dynamical systems using measured output data. In particular, we propose an identification method based on the measured output data of each node in the network whose dynamic is unknown. The proposed identification method consists of three steps: we first consider the outputs of the nodes to be all the states of the dynamics of the nodes, and the unmeasurable hidden states to be dynamical inputs with unknown dynamics. In the second step, we define the dynamical inputs as new variables and identify the dynamics of the network system with measured output data using Koopman operators. Finally, we extract the network structure from the identified dynamics as the information transmitted via the network. We show that the identified coupling functions, which represent the network structures, are actually projections of the dynamical inputs onto the space spanned by some observable functions. Numerical examples illustrate the validity of the obtained results.

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Zyphur, Michael J., Manuel C. Voelkle, Louis Tay, Paul D. Allison, Kristopher J. Preacher, Zhen Zhang, Ellen L. Hamaker, et al. "From Data to Causes II: Comparing Approaches to Panel Data Analysis." Organizational Research Methods 23, no. 4 (May 24, 2019): 688–716. http://dx.doi.org/10.1177/1094428119847280.

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This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.

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4

SCHMID, PETER J. "Dynamic mode decomposition of numerical and experimental data." Journal of Fluid Mechanics 656 (July 1, 2010): 5–28. http://dx.doi.org/10.1017/s0022112010001217.

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The description of coherent features of fluid flow is essential to our understanding of fluid-dynamical and transport processes. A method is introduced that is able to extract dynamic information from flow fields that are either generated by a (direct) numerical simulation or visualized/measured in a physical experiment. The extracted dynamic modes, which can be interpreted as a generalization of global stability modes, can be used to describe the underlying physical mechanisms captured in the data sequence or to project large-scale problems onto a dynamical system of significantly fewer degrees of freedom. The concentration on subdomains of the flow field where relevant dynamics is expected allows the dissection of a complex flow into regions of localized instability phenomena and further illustrates the flexibility of the method, as does the description of the dynamics within a spatial framework. Demonstrations of the method are presented consisting of a plane channel flow, flow over a two-dimensional cavity, wake flow behind a flexible membrane and a jet passing between two cylinders.

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Wang, Haiyan, Yi Lin, and Fu Xiao. "A Lightweight Data Integrity Verification with Data Dynamics for Mobile Edge Computing." Security and Communication Networks 2022 (March 4, 2022): 1–15. http://dx.doi.org/10.1155/2022/1870779.

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As a special scenario of mobile cloud computing, mobile edge computing can meet the requirements of low latency of data integrity verification and support of mobility in mobile scenarios. However, most existing data integrity verification methods have relatively large computational overhead and few considerations of data dynamic update. To address the above problems, we propose a lightweight data integrity verification method that can support data dynamics in mobile edge computing scenarios. The proposed method is based on an algebraic signature and data integrity verification framework, which ensures security and reduces the computational overhead to achieve the requirement of lightweight. On this basis, analysis and proof of the feasibility, security, and privacy are given. At the same time, in order to support the dynamic update of the data, an optimized strategy based on matrix index is designed with low overhead. In comparison with other baseline methods, simulation experiments show that our method is superior in terms of computational overhead and has good performance in supporting data dynamics.

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Sivak, A. B., D. N. Demidov, and P. A. Sivak. "DIFFUSION CHARACTERISTICS OF RADIATION DEFECTS IN IRON: MOLECULAR DYNAMICS DATA." Problems of Atomic Science and Technology, Ser. Thermonuclear Fusion 44, no. 2 (2021): 148–57. http://dx.doi.org/10.21517/0202-3822-2021-44-2-148-157.

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7

Abraham, Ralph H. "Dynamics from Communications Data." American Journal of Psychotherapy 46, no. 4 (October 1992): 581–82. http://dx.doi.org/10.1176/appi.psychotherapy.1992.46.4.581.

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8

Thacker, William Carlisle, and Robert Bryan Long. "Fitting dynamics to data." Journal of Geophysical Research 93, no. C2 (1988): 1227. http://dx.doi.org/10.1029/jc093ic02p01227.

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9

Wang, Yan, Ying Liu, and Chao Ling Li. "Sensitive Cloud Data Deduplication with Data Dynamics." Applied Mechanics and Materials 556-562 (May 2014): 6236–40. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6236.

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To solve the confliction of data encryption and deduplication, a hMAC-Dedup scheme based on homomorphic MAC is proposed. In the scheme, every file is encrypted by the block level encryption and a tag is generated from each encrypted block. In the PoW (Proofs of oWnership) protocol, homomorphic MAC is used to check whether the file to store is real, by operating on the file’s encrypted blocks and pre-computed tags. The hMAC-Dedup can avoid the security shortcomings brought by hash-as-a-proof and provide encryption protection. It is also extended to support data dynamics, which includes block modification, insertion and deletion.

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10

Ahn, T. Y., K. F. Eman, and S. M. Wu. "Cutting Dynamics Identification by Dynamic Data System (DDS) Modeling Approach." Journal of Engineering for Industry 107, no. 2 (May 1, 1985): 91–94. http://dx.doi.org/10.1115/1.3185988.

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The dynamics of the cutting process have been conventionally characterized in terms of the Dynamic Cutting Force Coefficients (DCFC) which represent its transfer characteristics at discrete frequencies. However, this approach fails to obtain the transfer function of the process in closed analytical form. Anticipating the stochastic nature of the cutting process and the double modulation principle, a two-input one-output multivariate system was postulated for the dynamic cutting process identification model. The Dynamic Data System (DDS) methodology was used to formulate and characterize the dynamic cutting process using Modified Autoregressive Moving Average Vector (MARMAV) models. Subsequently, transfer functions of the inner and outer modulation dynamics of the cutting processes were obtained from the identified models.

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11

Martin, Benjamin T., Stephan B. Munch, and Andrew M. Hein. "Reverse-engineering ecological theory from data." Proceedings of the Royal Society B: Biological Sciences 285, no. 1878 (May 16, 2018): 20180422. http://dx.doi.org/10.1098/rspb.2018.0422.

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Ecologists have long sought to understand the dynamics of populations and communities by deriving mathematical theory from first principles. Theoretical models often take the form of dynamical equations that comprise the ecological processes (e.g. competition, predation) believed to govern system dynamics. The inverse of this approach—inferring which processes and ecological interactions drive observed dynamics—remains an open problem in ecology. Here, we propose a way to attack this problem using a machine learning method known as symbolic regression, which seeks to discover relationships in time-series data and to express those relationships using dynamical equations. We found that this method could rapidly discover models that explained most of the variance in three classic demographic time series. More importantly, it reverse-engineered the models previously proposed by theoretical ecologists to describe these time series, capturing the core ecological processes these models describe and their functional forms. Our findings suggest a potentially powerful new way to merge theory development and data analysis.

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12

Isea, Raúl. "Characterizing the Dynamics of Covid-19 Based on Data." Journal of Current Viruses and Treatment Methodologies 1, no. 3 (November 20, 2021): 25–30. http://dx.doi.org/10.14302/issn.2691-8862.jvat-21-3991.

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The objective of this paper is to apply datadriven discovery of dynamics modeling to obtain a system of differential equations that allows us to describe the transmission dynamics of Covid-19, based on the number of confirmed cases and deaths reported daily. This methodology was applied in four different countries: Brazil, Colombia, Venezuela, and the United States. The main advantage is that only one differential equation is needed to characterize the dynamic of Covid-19 without any mathematical assumption.

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Isea, Raúl. "Characterizing the Dynamics of Covid-19 Based on Data." Journal of Current Viruses and Treatment Methodologies 1, no. 3 (November 20, 2021): 25–30. http://dx.doi.org/10.14302/issn.2691-8862.jvat-21-3991.

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The objective of this paper is to apply datadriven discovery of dynamics modeling to obtain a system of differential equations that allows us to describe the transmission dynamics of Covid-19, based on the number of confirmed cases and deaths reported daily. This methodology was applied in four different countries: Brazil, Colombia, Venezuela, and the United States. The main advantage is that only one differential equation is needed to characterize the dynamic of Covid-19 without any mathematical assumption.

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14

Bergstra, J. A., and C. A. Middelburg. "Data Linkage Dynamics with Shedding." Fundamenta Informaticae 103, no. 1-4 (2010): 31–52. http://dx.doi.org/10.3233/fi-2010-317.

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15

Giacometti, Achille, and Maurice Rossi. "Interface dynamics from experimental data." Physical Review E 62, no. 2 (August 1, 2000): 1716–24. http://dx.doi.org/10.1103/physreve.62.1716.

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16

BONOMI, ERNESTO, and MARCO TOMASSINI. "MASSIVELY DATA-PARALLEL MOLECULAR DYNAMICS." International Journal of Modern Physics C 03, no. 04 (August 1992): 709–31. http://dx.doi.org/10.1142/s0129183192000440.

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In light of present day data-parallel computers, an appraisal of molecular dynamics simulations of large N-particle systems, isolated or in contact with a heat-bath, is given. Special attention is focused 011 the Connection Machine CM-2. Particularly the cases of long-range potentials and impulsive hard-core interactions are discussed in detail. Data-parallel strategies including data distribution, communications and computation are presented and compared with well-known sequential approaches. The conclusion offered is that the methods described here are easy to design and offer the possibility of reasonably fast implementations for the reliable simulation of macroscopic samples of matter.

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17

Chan, Grace, Kung-Sik Chan, Nils Chr Stenseth, and Ole Chr Lingjaerde. "Analyzing nonlinear population dynamics data." Journal of Agricultural, Biological, and Environmental Statistics 9, no. 2 (June 2004): 200–215. http://dx.doi.org/10.1198/1085711043587.

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18

Chen, Yaqing, Matthew Dawson, and Hans-Georg Müller. "Rank dynamics for functional data." Computational Statistics & Data Analysis 149 (September 2020): 106963. http://dx.doi.org/10.1016/j.csda.2020.106963.

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19

Müller, Hans-Georg, and Fang Yao. "Empirical dynamics for longitudinal data." Annals of Statistics 38, no. 6 (December 2010): 3458–86. http://dx.doi.org/10.1214/09-aos786.

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20

Dauber-Osguthorpe, Pnina, Colette M. Maunder, and David J. Osguthorpe. "Molecular dynamics: Deciphering the data." Journal of Computer-Aided Molecular Design 10, no. 3 (June 1996): 177–85. http://dx.doi.org/10.1007/bf00355041.

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21

EROĞLU, Deniz. "Network dynamics reconstruction from data." TURKISH JOURNAL OF PHYSICS 44, no. 4 (August 31, 2020): 394–403. http://dx.doi.org/10.3906/fiz-2004-7.

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22

Visaya, Maria Vivien, and David Sherwell. "Dynamics from Multivariable Longitudinal Data." Journal of Nonlinear Dynamics 2014 (March 19, 2014): 1–16. http://dx.doi.org/10.1155/2014/901838.

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We introduce a method of analysing longitudinal data in n≥1 variables and a population of K≥1 observations. Longitudinal data of each observation is exactly coded to an orbit in a two-dimensional state space Sn. At each time, information of each observation is coded to a point (x,y)∈Sn, where x is the physical condition of the observation and y is an ordering of variables. Orbit of each observation in Sn is described by a map that dynamically rearranges order of variables at each time step, eventually placing the most stable, least frequently changing variable to the left and the most frequently changing variable to the right. By this operation, we are able to extract dynamics from data and visualise the orbit of each observation. In addition, clustering of data in the stable variables is revealed. All possible paths that any observation can take in Sn are given by a subshift of finite type (SFT). We discuss mathematical properties of the transition matrix associated to this SFT. Dynamics of the population is a nonautonomous multivalued map equivalent to a nonstationary SFT. We illustrate the method using a longitudinal data of a population of households from Agincourt, South Africa.

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23

Farboodi, Maryam, Roxana Mihet, Thomas Philippon, and Laura Veldkamp. "Big Data and Firm Dynamics." AEA Papers and Proceedings 109 (May 1, 2019): 38–42. http://dx.doi.org/10.1257/pandp.20191001.

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We study a model where firms accumulate data as a valuable intangible asset. Data accumulation affects firms' dynamics. It increases the skewness of the firm size distribution as large firms generate more data and invest more in active experimentation. On the other hand, small data-savvy firms can overtake more traditional incumbents, provided they can finance their initial money-losing growth. Our model can be used to estimate the market and social value of data.

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24

Suzuki, Takao. "Data assimilation in fluid dynamics." Fluid Dynamics Research 47, no. 5 (September 23, 2015): 050001. http://dx.doi.org/10.1088/0169-5983/47/5/050001.

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25

Kirchdoerfer, T., and M. Ortiz. "Data-driven computing in dynamics." International Journal for Numerical Methods in Engineering 113, no. 11 (December 15, 2017): 1697–710. http://dx.doi.org/10.1002/nme.5716.

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26

Le Clainche, Soledad, and José M. Vega. "Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods." Complexity 2018 (December 12, 2018): 1–21. http://dx.doi.org/10.1155/2018/6920783.

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This article presents a review on two methods based on dynamic mode decomposition and its multiple applications, focusing on higher order dynamic mode decomposition (which provides a purely temporal Fourier-like decomposition) and spatiotemporal Koopman decomposition (which gives a spatiotemporal Fourier-like decomposition). These methods are purely data-driven, using either numerical or experimental data, and permit reconstructing the given data and identifying the temporal growth rates and frequencies involved in the dynamics and the spatial growth rates and wavenumbers in the case of the spatiotemporal Koopman decomposition. Thus, they may be used to either identify and extrapolate the dynamics from transient behavior to permanent dynamics or construct efficient, purely data-driven reduced order models.

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Pavlos, G. P., D. Kugiumtzis, M. A. Athanasiu, N. Hatzigeorgiu, D. Diamantidis, and E. T. Sarris. "Nonlinear analysis of magnetospheric data Part II. Dynamical characteristics of the AE index time series and comparison with nonlinear surrogate data." Nonlinear Processes in Geophysics 6, no. 2 (June 30, 1999): 79–98. http://dx.doi.org/10.5194/npg-6-79-1999.

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Abstract. In this study we have used dynamical characteristies such as Lyapunov exponents, nonlinear dynamic models and mutual information for the nonlinear analysis of the magnetospheric AE index time series. Similarly with the geometrical characteristic studied in Pavlos et al. (1999b), we have found significant differences between the original time series and its surrogate data. These results also suggest the rejection of the null hypothesis that the AE index belongs to the family of stochastic linear signals undergoing a static nonlinear distortion. Finally, we believe that these results support the hypothesis of nonlinearity and chaos for the magnetospheric dynamics.

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Lguensat, Redouane, Pierre Tandeo, Pierre Ailliot, Manuel Pulido, and Ronan Fablet. "The Analog Data Assimilation." Monthly Weather Review 145, no. 10 (October 2017): 4093–107. http://dx.doi.org/10.1175/mwr-d-16-0441.1.

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In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings.

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Wawro, Gregory. "Estimating Dynamic Panel Data Models in Political Science." Political Analysis 10, no. 1 (2002): 25–48. http://dx.doi.org/10.1093/pan/10.1.25.

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Panel data are a very valuable resource for finding empirical solutions to political science puzzles. Yet numerous published studies in political science that use panel data to estimate models with dynamics have failed to take into account important estimation issues, which calls into question the inferences we can make from these analyses. The failure to account explicitly for unobserved individual effects in dynamic panel data induces bias and inconsistency in cross-sectional estimators. The purpose of this paper is to review dynamic panel data estimators that eliminate these problems. I first show how the problems with cross-sectional estimators arise in dynamic models for panel data. I then show how to correct for these problems using generalized method of moments estimators. Finally, I demonstrate the usefulness of these methods with replications of analyses in the debate over the dynamics of party identification.

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Nafi’ah, Binti Azizatun. "Dynamics of Stakeholder Collaboration in Bojonegoro’s Open Data Program." Policy & Governance Review 4, no. 1 (February 11, 2020): 28. http://dx.doi.org/10.30589/pgr.v4i1.142.

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This paper discusses the complexity of collaboration dynamics and the open data of collaboration cycle in Bojonegoro Regency. Bojonegoro’s open data is a data development program that is collected from the PKK Dasawisma data updated once a month through the publication of Dasawisma data online. This paper has proven a very dynamic level of collaboration in open data initiation through the use of qualitative techniques by collecting data on interviews, observations, and documentation. The level of collaboration dynamics is promoted by drivers in the form of leadership, a culture of openness that has been formed, resource dependence on one another and strong local CSO roles. These drivers are determinants dynamics of open data collaboration to reach a mature collaboration cycle. Some findings indicating weaknesses are the “political will” of leaders determining the sustainability of open data; and collaborative programs that have not been aligned with the current RKPD.

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31

Torrens-Fontanals, Mariona, Tomasz Maciej Stepniewski, David Aranda-García, Adrián Morales-Pastor, Brian Medel-Lacruz, and Jana Selent. "How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs." International Journal of Molecular Sciences 21, no. 16 (August 18, 2020): 5933. http://dx.doi.org/10.3390/ijms21165933.

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G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.

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32

Yeung, Enoch, Jongmin Kim, Ye Yuan, Jorge Gonçalves, and Richard M. Murray. "Data-driven network models for genetic circuits from time-series data with incomplete measurements." Journal of The Royal Society Interface 18, no. 182 (September 2021): 20210413. http://dx.doi.org/10.1098/rsif.2021.0413.

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Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro , due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli -based transcriptional event detector.

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Klie, Hector, and Horacio Florez. "Data-Driven Prediction of Unconventional Shale-Reservoir Dynamics." SPE Journal 25, no. 05 (August 21, 2020): 2564–81. http://dx.doi.org/10.2118/193904-pa.

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Summary The present work introduces extended dynamic mode decomposition (EDMD) as a suitable data-driven framework for learning the reservoir dynamics entailed by flow/fracture interactions in unconventional shales. The proposed EDMD approach builds on the approximation of infinite-dimensional linear operators combined with the power of deep learning autoencoder networks to extract salient transient features from pressure/stress fields and bulks of production data. The data-driven model is demonstrated on three illustrative examples involving single- and two-phase coupled flow/geomechanics simulations and a real production data set from the Vaca Muerta unconventional shale formation in Argentina. We demonstrated that we could attain a high level of predictability from unseen field-state variables and well-production data given relatively moderate input requirements. As the main conclusion of this work, EDMD stands as a promising data-driven choice for efficiently reconstructing flow/fracture dynamics that are either partially or entirely unknown, or that are too complex to formulate using known simulation tools on unconventional plays.

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Bergstra, J. A., and C. A. Middelburg. "Data Linkage Algebra, Data Linkage Dynamics, and Priority Rewriting." Fundamenta Informaticae 128, no. 4 (2013): 367–412. http://dx.doi.org/10.3233/fi-2013-950.

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Dubey, Ms Vaishali, and Dr A. Gururani. "Materialize Secluded Data Integrity Checking Protocol For Secured Storage Services With Data Dynamics And Public Verifiability In Hybrid Cloud." American Journal of Engineering And Techonology 01, no. 03 (October 1, 2019): 1–6. http://dx.doi.org/10.37547/tajet/volume01issue03-01.

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Qiangqiang, Sun, Qiu Huijun, and Cai Meng. "Transmitting Systems Dynamics in SCADA using Uneven Sampling/ Cubic Spline Interpolation based Data Compression." Open Electrical & Electronic Engineering Journal 8, no. 1 (December 31, 2014): 544–51. http://dx.doi.org/10.2174/1874129001408010544.

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Grasping power system dynamic information is helpful for dispatcher in the control center to take correct control action in time under emergency condition. Traditionally, Supervisory Control and Data Acquisition (SCADA) cannot transmit power system dynamics information since it updates system information once every several seconds. Based on the development of substation automation system, a data compression based approach is proposed in this paper to transmit power system dynamics information in existing SCADA. An uneven sampling is utilized to extract the feature points that determine the profile of system dynamics. Thereafter, these feature points contain dynamic information is transmitted from Remote Terminal Units to the control center. The system dynamics can thus be reconstructed with cubic spline interpolation. Numerical simulation on a 36 nodes system suggests that the system dynamics could be transmitted with high fidelity in existing SCADA using the proposed approach. Moreover, the proposed approach can be implemented in SCADA with limited software update.

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Andrews, Ross N., Suresh Narayanan, Fan Zhang, Ivan Kuzmenko, and Jan Ilavsky. "Inverse transformation: unleashing spatially heterogeneous dynamics with an alternative approach to XPCS data analysis." Journal of Applied Crystallography 51, no. 1 (February 1, 2018): 35–46. http://dx.doi.org/10.1107/s1600576717015795.

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X-ray photon correlation spectroscopy (XPCS), an extension of dynamic light scattering (DLS) in the X-ray regime, detects temporal intensity fluctuations of coherent speckles and provides scattering-vector-dependent sample dynamics at length scales smaller than DLS. The penetrating power of X-rays enables XPCS to probe the dynamics in a broad array of materials, including polymers, glasses and metal alloys, where attempts to describe the dynamics with a simple exponential fit usually fail. In these cases, the prevailing XPCS data analysis approach employs stretched or compressed exponential decay functions (Kohlrausch functions), which implicitly assume homogeneous dynamics. This paper proposes an alternative analysis scheme based upon inverse Laplace or Gaussian transformation for elucidating heterogeneous distributions of dynamic time scales in XPCS, an approach analogous to theCONTINalgorithm widely accepted in the analysis of DLS from polydisperse and multimodal systems. Using XPCS data measured from colloidal gels, it is demonstrated that the inverse transform approach reveals hidden multimodal dynamics in materials, unleashing the full potential of XPCS.

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Villalon-Turrubiates, I. E. "DYNAMICAL PREDICTION TECHNIQUE FOR GEOSIMULATION USING MULTISPECTRAL REMOTE SENSING DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 213–19. http://dx.doi.org/10.5194/isprs-annals-iv-4-213-2018.

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<p><strong>Abstract.</strong> The analysis of dynamical models for prediction and geosimulation using the information extracted from a geographical region processed from the data provided by multispectral remote sensing systems provides useful information for urban planning and resource management. However, several topics of interest on this particular matter are still to be properly studied. Using the remote sensing data that has been extracted from multispectral images from a particular geographic region in discrete time, its dynamic study is performed in both, spatial resolution and time evolution, in order to obtain the dynamical model of the physical variables and the evolutionary information about the data. This provides a background for understanding the future trends in development of the dynamics inherent in the multispectral and high-resolution images. This proposition is performed via an intelligent computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of the data extracted from multispectral remote sensing images and using high-performance computational techniques to unify the available data scheme with its dynamic analysis and, therefore, provide a behavioral model of the sensed data.</p>

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39

ATSUMI, TOMOHIDE. "Group Dynamics and Conversation as Data." JAPANESE JOURNAL OF EXPERIMENTAL SOCIAL PSYCHOLOGY 36, no. 1 (1996): 142–47. http://dx.doi.org/10.2130/jjesp.36.142.

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Pessoa, Aline Neves, Beatriz Cavalcanti de Albuquerque Caiuby Novaes, Lilian Kuhn Pereira, and Zuleica Antonia Camargo. "Voice quality and voice dynamics data." Journal of Speech Sciences 1, no. 2 (February 3, 2021): 17–33. http://dx.doi.org/10.20396/joss.v1i2.15024.

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Acoustic and perceptual auditory analysis procedures present themselves as clinical tools which give support to the understanding of the speech features of hearing impaired children (HIC). Voice quality stems from the overlapped action of the larynx, the supralaryngeal vocal tract and the level of muscular tension throughout the speech flow. Nonetheless, voice dynamics is characterized by frequency, duration and intensity variations. This research aimed at investigating acoustic and perceptive correlates of a HIC child’s voice and dynamic quality. The child, who has a cochlear implanted, had his speech samples collected during speech therapy sessions. The male subject (R), who uses a unilateral cochlear implant (UCI), had his speech production samples recorded when he was 5 (05 samples) and 6 years old (05 samples), and which were later tagged Cut A and Cut B respectively. The recorded corpus was acoustically analyzed through the use of the SGEXpressionEvaluator script (Barbosa, 2009) running on the free software Praat v10. The measures which were automatically extracted by the script correspond to the fundamental frequency –f0, first f0 derivative, intensity, spectral fall and long term spectrum. The perceptual auditory analysis of the voice quality was based on the VPAS-PB script (Camargo e Madureira, 2008). The perceptual auditory judgments and the acoustic measures were subjected to statistical analysis procedures. At first the, the data (perceptual and acoustic) were separately analyzed through a hierarchical and agglomerative cluster analysis. Subsequently, they were examined together through the principal component analysis. Results revealed the existence of correspondence between the acoustic and perceptual auditory data. In the audio recorded data samples from Cut B (one year after the first one) greater variability tendencies in acoustic measures of f0 could be observed associated with laryngeal hyper function at the perceptual level plus silent pauses and the reduction of speech rate. From the integrated acoustic and perceptual analysis it was possible to keep a record of the child’s oral language development process. The data analysis in this study allowed the observation of several interaction levels between the vocal tract (lip movement extension adjusts, tongue and jaw, associated with velopharyngeal adjusts and muscular tension from the larynx), plus the inspection of speech dynamics elements (habitual pitch and speech rate) of a child’s speech who has a UCI implanted during a one-year-speech-therapy-process period. This source of information made the characterization of the child’s evolution possible, especially in terms of perceptual auditory analysis descriptions being phonetically motivated by the speech dynamics quality.

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Callies, Jörn. "Submesoscale Dynamics Inferred from Oleander Data." Oceanography 32, no. 3 (September 1, 2019): 138–39. http://dx.doi.org/10.5670/oceanog.2019.320.

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Small, Michael, David M. Walker, and Antoinette Tordesillas. "Verifying chaotic dynamics from experimental data." IEICE Proceeding Series 1 (March 17, 2014): 373–76. http://dx.doi.org/10.15248/proc.1.373.

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Junlin, Li, and Fu Hongguang. "Molecular dynamics-like data clustering approach." Pattern Recognition 44, no. 8 (August 2011): 1721–37. http://dx.doi.org/10.1016/j.patcog.2011.01.008.

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Matthews, Keith J. "Archaeological data, subcultures and social dynamics." Antiquity 69, no. 264 (September 1995): 586–94. http://dx.doi.org/10.1017/s0003598x00081989.

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The archaeological record is dominated by the repeated object and the repeated event, so we search for patterns that explain the regular in general terms. But human societies are not like that; the mass is actually made up of individuals, and the engine of change more often at the margin than at the centre.

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Chambers, Robert G., and Vangelis Tzouvelekas. "Estimating population dynamics without population data." Journal of Environmental Economics and Management 66, no. 3 (November 2013): 510–22. http://dx.doi.org/10.1016/j.jeem.2013.09.003.

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Broomhead, D. S., and Gregory P. King. "Extracting qualitative dynamics from experimental data." Physica D: Nonlinear Phenomena 20, no. 2-3 (June 1986): 217–36. http://dx.doi.org/10.1016/0167-2789(86)90031-x.

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Kostelich, Eric J. "Problems in estimating dynamics from data." Physica D: Nonlinear Phenomena 58, no. 1-4 (September 1992): 138–52. http://dx.doi.org/10.1016/0167-2789(92)90105-v.

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Bala, Venkatesh. "Reconstructing dynamics from intertemporal economic data." Economic Theory 9, no. 2 (June 1997): 325–39. http://dx.doi.org/10.1007/bf01213804.

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Verzelen, N., W. Tao, and H. G. Muller. "Inferring stochastic dynamics from functional data." Biometrika 99, no. 3 (July 9, 2012): 533–50. http://dx.doi.org/10.1093/biomet/ass015.

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Poyer, Salomé, Clothilde Comby-Zerbino, Chang Min Choi, Luke MacAleese, Claire Deo, Nicolas Bogliotti, Juan Xie, Jean-Yves Salpin, Philippe Dugourd, and Fabien Chirot. "Conformational Dynamics in Ion Mobility Data." Analytical Chemistry 89, no. 7 (March 16, 2017): 4230–37. http://dx.doi.org/10.1021/acs.analchem.7b00281.

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