Статті в журналах: "Dynamic response variability"

1

KATAFYGIOTIS, L. S., and C. PAPADIMITRIOU. "DYNAMIC RESPONSE VARIABILITY OF STRUCTURES WITH UNCERTAIN PROPERTIES." Earthquake Engineering & Structural Dynamics 25, no. 8 (August 1996): 775–93. http://dx.doi.org/10.1002/(sici)1096-9845(199608)25:8<775::aid-eqe581>3.0.co;2-1.

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2

Papadopoulos, Vissarion, and Odysseas Kokkinos. "Variability response functions for stochastic systems under dynamic excitations." Probabilistic Engineering Mechanics 28 (April 2012): 176–84. http://dx.doi.org/10.1016/j.probengmech.2011.08.002.

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3

Papadopoulos, Vissarion, and Odysseas Kokkinos. "Transient response of stochastic finite element systems using Dynamic Variability Response Functions." Structural Safety 52 (January 2015): 100–112. http://dx.doi.org/10.1016/j.strusafe.2014.09.006.

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4

Schaette, Roland, Tim Gollisch, and Andreas V. M. Herz. "Spike-Train Variability of Auditory Neurons In Vivo: Dynamic Responses Follow Predictions From Constant Stimuli." Journal of Neurophysiology 93, no. 6 (June 2005): 3270–81. http://dx.doi.org/10.1152/jn.00758.2004.

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Reliable accounts of the variability observed in neural spike trains are a prerequisite for the proper interpretation of neural dynamics and coding principles. Models that accurately describe neural variability over a wide range of stimulation and response patterns are therefore highly desirable, especially if they can explain this variability in terms of basic neural observables and parameters such as firing rate and refractory period. In this work, we analyze the response variability recorded in vivo from locust auditory receptor neurons under acoustic stimulation. In agreement with results from other systems, our data suggest that neural refractoriness has a strong influence on spike-train variability. We therefore explore a stochastic model of spike generation that includes refractoriness through a recovery function. Because our experimental data are consistent with a renewal process, the recovery function can be derived from a single interspike-interval histogram obtained under constant stimulation. The resulting description yields quantitatively accurate predictions of the response variability over the whole range of firing rates for constant-intensity as well as amplitude-modulated sound stimuli. Model parameters obtained from constant stimulation can be used to predict the variability in response to dynamic stimuli. These results demonstrate that key ingredients of the stochastic response dynamics of a sensory neuron are faithfully captured by a simple stochastic model framework.

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5

Whiting, W. C., M. Peralta, A. Zink, and G. Settle. "FORCE PARAMETER VARIABILITY IN RESPONSE TO LOWER EXTREMITY DYNAMIC FATIGUE922." Medicine &amp Science in Sports &amp Exercise 28, Supplement (May 1996): 155. http://dx.doi.org/10.1097/00005768-199605001-00920.

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6

Manitaras, Theofilos-Ioannis, Vissarion Papadopoulos, and Manolis Papadrakakis. "Dynamic variability response functions for stochastic wave propagation in soils." Soil Dynamics and Earthquake Engineering 97 (June 2017): 60–73. http://dx.doi.org/10.1016/j.soildyn.2017.02.004.

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7

Žcaronivanović, Stana, and Aleksandar Pavić. "Quantification of Dynamic Excitation Potential of Pedestrian Population Crossing Footbridges." Shock and Vibration 18, no. 4 (2011): 563–77. http://dx.doi.org/10.1155/2011/403147.

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Due to their slenderness, many modern footbridges may vibrate significantly under pedestrian traffic. Consequently, the vibration serviceability of these structures under human-induced dynamic loading is becoming their governing design criterion. Many current vibration serviceability design guidelines, concerned with prediction of the vibration in the vertical direction, estimate a single response level that corresponds to an "average" person crossing the bridge with the step frequency that matches a footbridge natural frequency. However, different pedestrians have different dynamic excitation potential, and therefore could generate significantly different vibration response of the bridge structure. This paper aims to quantify this potential by estimating the range of structural vibrations (in the vertical direction) that could be induced by different individuals and the probability of occurrence of any particular vibration level. This is done by introducing the inter- and intra-subject variability in the walking force modelling. The former term refers to inability of a pedestrian to induce an exactly the same force with each step while the latter refers to different forces (in terms of their magnitude, frequency and crossing speed) induced by different people. Both types of variability are modelled using the appropriate probability density functions. The probability distributions were then implemented into a framework procedure for vibration response prediction under a single person excitation. Instead of a single response value obtained using currently available design guidelines, this new framework yields a range of possible acceleration responses induced by different people and a distribution function for these responses. The acceleration ranges estimated are then compared with experimental data from two real-life footbridges. The substantial differences in the dynamic response induced by different people are obtained in both the numerical and the experimental results presented. These results therefore confirm huge variability in different people's dynamic potential to excite the structure. The proposed approach for quantifying this variability could be used as a sound basis for development of new probability-based vibration serviceability assessment procedures for pedestrian bridges.

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8

de Ruiter, Naomi M. P., Tom Hollenstein, Paul L. C. van Geert, and E. Saskia Kunnen. "Self-Esteem as a Complex Dynamic System: Intrinsic and Extrinsic Microlevel Dynamics." Complexity 2018 (September 12, 2018): 1–19. http://dx.doi.org/10.1155/2018/4781563.

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The variability of self-esteem is an important characteristic of self-esteem. However, little is known about the mechanisms that underlie it. The goal of the current study was to empirically explore these underlying mechanisms. It is commonly assumed that state self-esteem (the fleeting experience of the self) is a response to the immediate social context. Drawing from a complex dynamic systems perspective, the self-organizing self-esteem model asserts that this responsivity is not passive or stimulus-response like, but that the impact of the social context on state self-esteem is intimately connected to the intrinsic dynamics of self-esteem. The model suggests that intrinsic dynamics are the result of higher-order self-esteem attractors that can constrain state self-esteem variability. The current study tests this model, and more specifically, the prediction that state self-esteem variability is less influenced by changes in the immediate context if relatively strong, as opposed to weak, self-esteem attractors underlie intrinsic dynamics of self-esteem. To test this, parent-adolescent dyads (N=13, Mage=13.6) were filmed during seminaturalistic discussions. Observable components of adolescent state self-esteem were coded in real time, as well as real-time parental autonomy-support and relatedness. Kohonen’s self-organizing maps were used to derive attractor-like patterns: repeated higher-order patterns of adolescents’ self-esteem components. State space grids were used to assess how much adolescents’ self-esteem attractors constrained their state self-esteem variability. We found varying levels of attractor strength in our sample. In accordance with our prediction, we found that state self-esteem was less sensitive to changes in parental support and relatedness for adolescents with stronger self-esteem attractors. Discussion revolves around the implications of our findings for the ontology of self-esteem.

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9

Bell, Iris R., John A. Ives, and B. Jonas Wayne. "Nonlinear Effects of Nanoparticles: Biological Variability from Hormetic Doses, Small Particle Sizes, and Dynamic Adaptive Interactions." Dose-Response 12, no. 2 (November 7, 2013): dose—response.1. http://dx.doi.org/10.2203/dose-response.13-025.bell.

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10

Chen, Zhuo Shi, Xiao Ming Yuan, and Shang Jiu Meng. "Analysis on the Bias of Seismic Ground Motion Prediction in a Shallow Stiff-Soil Site by LSSRL-1 Program." Advanced Materials Research 915-916 (April 2014): 18–21. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.18.

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The main causes of the ground motion blind prediction bias are the variability of the adopted program, the shear-wave velocity of the site, and the soil nonlinear dynamic parameters. By considering the variability of shear-wave velocity and the dynamic parameters, this essay used LSSRLI-1 Codes and Mw6.0 seismic record of Parkfield earthquake to calculate ground responses of 9 different conditions at Turkey Flat site. The authors believe that the variability of shear-wave velocity caused the dominant impact to the blind prediction of this shallow stiff-soil site. That impact is much greater than that of the dynamic parameters. LSSRLI-1 program may either underestimate the ground response of the shallow stiff-soil site or may overestimate it, so we should combine the specific site conditions and a large amounts of data to do the further analysis.

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11

Zheng, Wanping, and Marek R. Kujath. "Extraction of Transient Signals From Nonperiodic Dynamic Responses." Journal of Dynamic Systems, Measurement, and Control 117, no. 3 (September 1, 1995): 270–76. http://dx.doi.org/10.1115/1.2799116.

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In practice, vibration signals generated by machines that are considered both linear and excited periodically are often slightly nonperiodic. A small variability in the periodic response may result from small variations of speed in the driving motors, in small variation in the system parameters, or nonlinearities causing mild chaotic motion. This paper deals with the problem of extracting transient components from almost periodic responses. One of the difficulties involved in the problem results from the presence of a broad range of frequencies in a transient response. The main difficulty, however, comes from the fact that the steady-state response is not purely periodic and the transient is totally submerged in it. Two major approaches of extracting the transient component have been investigated: algebraic subtraction and adaptive notch filtering. Both methods have been tested numerically before they were applied to the signals obtained from experiments. The results show that it is possible to extract transients from a dynamic response even if the period of the response varies.

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12

Wang, Zhaojing, and Hao Hu. "Dynamic response to demand variability for precast production rescheduling with multiple lines." International Journal of Production Research 56, no. 16 (January 3, 2018): 5386–401. http://dx.doi.org/10.1080/00207543.2017.1414970.

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13

Aguayo, Oscar, and Samuel Sepúlveda. "Variability Management in Dynamic Software Product Lines for Self-Adaptive Systems—A Systematic Mapping." Applied Sciences 12, no. 20 (October 12, 2022): 10240. http://dx.doi.org/10.3390/app122010240.

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Context: Dynamic software product lines (DSPLs) have considerably increased their adoption for variability management for self-adaptive systems. The most widely used models for managing the variability of DSPLs are the MAPE-K control loop and context-aware feature models (CFMs). Aim: In this paper, we review and synthesize evidence of using variability constraint approaches, methodologies, and challenges for DSPL. Method: We conducted a systematic mapping, including three research questions. This study included 84 papers published from 2010 to 2021. Results: The main results show that open-dynamic variability shows a presence in 57.1% of the selected papers, and on the other hand, closed-dynamic variability appears in 38.1%. The most commonly used methodology for managing a DSPL environment is based on proprietary architectures (60.7%), where the use of CFMs predominates. For open-dynamic variability approaches, the MAPE-K control loop is mainly used. The main challenges in DSPL management are based on techniques (28.6%) and open variation (21.4%). Conclusions: Open-dynamic variability has prevailed over the years as the primary approach to managing variability in DSPL, where its primary methodology is the MAPE-K control loop. Response RQ3 requires further review.

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14

Assländer, Lorenz, and Robert J. Peterka. "Sensory reweighting dynamics following removal and addition of visual and proprioceptive cues." Journal of Neurophysiology 116, no. 2 (August 1, 2016): 272–85. http://dx.doi.org/10.1152/jn.01145.2015.

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Removing or adding sensory cues from one sensory system during standing balance causes a change in the contribution of the remaining sensory systems, a process referred to as sensory reweighting. While reweighting changes have been described in many studies under steady-state conditions, less is known about the temporal dynamics of reweighting following sudden transitions to different sensory conditions. The present study changed sensory conditions by periodically adding or removing visual (lights On/Off) or proprioceptive cues (surface sway referencing On/Off) in 12 young, healthy subjects. Evidence for changes in sensory contributions to balance was obtained by measuring the time course of medial-lateral sway responses to a constant-amplitude 0.56-Hz sinusoidal stimulus, applied as support surface tilt (proprioceptive contribution), as visual scene tilt (visual contribution), or as binaural galvanic vestibular stimulation (vestibular contribution), and by analyzing the time course of sway variability. Sine responses and variability of body sway velocity showed significant changes following transitions and were highly correlated under steady-state conditions. A dependence of steady-state responses on upcoming transitions was observed, suggesting that knowledge of impending changes can influence sensory weighting. Dynamic changes in sway in the period immediately following sensory transitions were very inhomogeneous across sway measures and in different experimental tests. In contrast to steady-state results, sway response and variability measures were not correlated with one another in the dynamic transition period. Several factors influence sway responses following addition or removal of sensory cues, partly instigated by but also obscuring the effects of reweighting dynamics.

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15

Hooper, Ryan M., Ruben A. Tikidji-Hamburyan, Carmen C. Canavier, and Astrid A. Prinz. "Feedback control of variability in the cycle period of a central pattern generator." Journal of Neurophysiology 114, no. 5 (November 2015): 2741–52. http://dx.doi.org/10.1152/jn.00365.2015.

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We address how feedback to a bursting biological pacemaker with intrinsic variability in cycle length can affect that variability. Specifically, we examine a hybrid circuit constructed of an isolated crab anterior burster (AB)/pyloric dilator (PD) pyloric pacemaker receiving virtual feedback via dynamic clamp. This virtual feedback generates artificial synaptic input to PD with timing determined by adjustable phase response dynamics that mimic average burst intervals generated by the lateral pyloric neuron (LP) in the intact pyloric network. Using this system, we measure network period variability dependence on the feedback element's phase response dynamics and find that a constant response interval confers minimum variability. We further find that these optimal dynamics are characteristic of the biological pyloric network. Building upon our previous theoretical work mapping the firing intervals in one cycle onto the firing intervals in the next cycle, we create a theoretical map of the distribution of all firing intervals in one cycle to the distribution of firing intervals in the next cycle. We then obtain an integral equation for a stationary self-consistent distribution of the network periods of the hybrid circuit, which can be solved numerically given the uncoupled pacemaker's distribution of intrinsic periods, the nature of the network's feedback, and the phase resetting characteristics of the pacemaker. The stationary distributions obtained in this manner are strongly predictive of the experimentally observed distributions of hybrid network period. This theoretical framework can provide insight into optimal feedback schemes for minimizing variability to increase reliability or maximizing variability to increase flexibility in central pattern generators driven by pacemakers with feedback.

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16

James, J. R., M. Gonzales, A. R. C. Gerlt, E. J. Payton, R. John, R. Arroyave, and D. L. Allaire. "Parameter range estimation and uncertainty analysis for high strain rate material response via model fusion." Journal of Applied Physics 132, no. 23 (December 21, 2022): 235101. http://dx.doi.org/10.1063/5.0089809.

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The dynamic stress–strain response of a material can be described by a number of different models of varying fidelity. However, an individual model’s ability to replicate the dynamic stress–strain response of a material can be hindered by experimental variability. Reification, an approach to fusing models and experimental data with inherent scatter, is presented. It is then used to determine the optimum parameters of the Johnson–Cook (JC) and Zerilli–Armstrong (ZA) models using a fusion of Split-Hopkinson Pressure Bar (SHPB) data and the JC and ZA models fit to the SHPB data using a traditional approach. The output of the fused model is a dataset that represents a “best-guess” sampling of the possible stress–strain response of a high strength steel. In the present work, the dynamic response of a newly developed steel, AF9628, is evaluated. Under the reification framework, the experimental variability and limitations of the mathematical model expressions are addressed by the optimized sampling of data and combined fitting process. The JC and ZA models are then re-fit to partitions of the fused dataset, which bound the responses of the traditionally fit JC and ZA models. The behavior of the re-fit models and the traditionally fit models are compared via a simulated Taylor anvil test.

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17

Haggerty, Raymond A., and Jeremy E. Purvis. "Inferring the structures of signaling motifs from paired dynamic traces of single cells." PLOS Computational Biology 17, no. 2 (February 4, 2021): e1008657. http://dx.doi.org/10.1371/journal.pcbi.1008657.

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Individual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained in part by a single signaling motif that maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in a population of cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cell’s upstream signal was randomly paired with another cell’s downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be systematically extracted from the dynamical patterns of single cells.

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18

Hardegree, Stuart P., Thomas A. Jones, Frederick B. Pierson, Patrick E. Clark, and Gerald N. Flerchinger. "Dynamic variability in thermal-germination response of squirreltail (Elymus elymoides and Elymus multisetus)." Environmental and Experimental Botany 62, no. 2 (March 2008): 120–28. http://dx.doi.org/10.1016/j.envexpbot.2007.07.010.

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19

GUPTA, S., and C. S. MANOHAR. "DYNAMIC STIFFNESS METHOD FOR CIRCULAR STOCHASTIC TIMOSHENKO BEAMS: RESPONSE VARIABILITY AND RELIABILITY ANALYSES." Journal of Sound and Vibration 253, no. 5 (June 2002): 1051–85. http://dx.doi.org/10.1006/jsvi.2001.4082.

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20

Slangen, Aimée B. A., John A. Church, Xuebin Zhang, and Didier P. Monselesan. "The Sea Level Response to External Forcings in Historical Simulations of CMIP5 Climate Models*." Journal of Climate 28, no. 21 (October 30, 2015): 8521–39. http://dx.doi.org/10.1175/jcli-d-15-0376.1.

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Abstract Changes in Earth’s climate are influenced by internal climate variability and external forcings, such as changes in solar radiation, volcanic eruptions, anthropogenic greenhouse gases (GHG), and aerosols. Although the response of surface temperature to external forcings has been studied extensively, this has not been done for sea level. Here, a range of climate model experiments for the twentieth century is used to study the response of global and regional sea level change to external climate forcings. Both the global mean thermosteric sea level and the regional dynamic sea level patterns show clear responses to anthropogenic forcings that are significantly different from internal climate variability and larger than the difference between models driven by the same external forcing. The regional sea level patterns are directly related to changes in surface winds in response to the external forcings. The spread between different realizations of the same model experiment is consistent with internal climate variability derived from preindustrial control simulations. The spread between the different models is larger than the internal variability, mainly in regions with large sea level responses. Although the sea level responses to GHG and anthropogenic aerosol forcing oppose each other in the global mean, there are differences on a regional scale, offering opportunities for distinguishing between these two forcings in observed sea level change.

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21

Guidetti, Giulia, Yu Wang, and Fiorenzo G. Omenetto. "Active optics with silk." Nanophotonics 10, no. 1 (August 1, 2020): 137–48. http://dx.doi.org/10.1515/nanoph-2020-0358.

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AbstractOptical devices have been traditionally fabricated using materials whose chemical and physical properties are finely tuned to perform a specific, single, and often static function, whereby devices’ variability is achieved by design changes. Due to the integration of optical systems in multifunctional platforms, there is an increasing need for intrinsic dynamic behavior, such as devices built with materials whose optical response can be programmed to change by leveraging the material’s variability. Here, regenerated silk fibroin is presented as an enabler of devices with active optical response due to the protein’s intrinsic properties. Silk’s abilities to controllably change conformation, reversibly swell and shrink, and degrade in a programmable way affect the form and the response of the optical structure in which it is molded. Representative silk-based devices whose behavior depends on the silk variability are presented and discussed with a particular focus on structures that display reconfigurable, reversibly tunable and physically transient optical responses. Finally, new research directions are envisioned for silk-based optical materials and devices.

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22

Serova, Olga, Larisa Timofeeva, Nikolai Reshin, and Dmitry Abramov. "DYNAMIC NATURE OF HYDROLOGICAL SIMILARITY." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 1 (June 20, 2019): 258. http://dx.doi.org/10.17770/etr2019vol1.4083.

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Nowadays, there is a growing interest in understanding how water bodies and their catchments react to environment, landscape and climate change. Runoff change is an integral indicator of climate and landscape changes. Similar landscapes form a similar hydrological catchment response to precipitation. The algorithm for identification of homogeneous groups of catchments (in terms of hydrometeorology) has been developed and tested. The 26 catchments studied are situated in the south-eastern part of the Baltic Sea Basin. Observational data from 1986 to 2016 were used for cluster analysis. Catchments clustering over three consecutive ten-year periods has shown some variability in the clusters content due to changes in the hydrological response of the study catchments. The results obtained were analyzed based on both hydrogrometeorological and landscape characteristics.

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23

Winterstein, S. R., and R. Torhaug. "Extreme Jack-Up Response: Simulation and Nonlinear Analysis Methods." Journal of Offshore Mechanics and Arctic Engineering 118, no. 2 (May 1, 1996): 103–8. http://dx.doi.org/10.1115/1.2828817.

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The nonlinear dynamic response of a jack-up structure under random wave loads is considered. For a simplified jack-up model, average behavior and variability in extreme forces and responses are found from simulation over many 6-h seastates. Weibull and Hermite analytical models of response extremes are also presented and evaluated. These models use shorter, less expensive simulations to estimate a limited number of response statistics, such as moments or parameters of the response peak distribution, and fit analytical models to estimate global extremes. Necessary simulation lengths are established both for direct simulation of extremes, and for analytical extreme models.

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24

Douthe, Cyril, Chloé Girardon, and Romain Boulaud. "Sensitivity Analysis of the Global Response of Flexible Rockfall Barriers." Geosciences 12, no. 2 (February 6, 2022): 75. http://dx.doi.org/10.3390/geosciences12020075.

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Protection barriers against the fall of boulders and rocks are structures with non-linear mechanical behaviour that make the study particularly complex. In this study, the understanding of an experimentally observed variability was investigated numerically using a non-linear spring-mass equivalence. First, key figures of the experiments on which this study is based are detailed. Then, the numerical model for the dynamic simulation of the barrier deformation under impact is presented. Finally, the variability due to block-related parameters and then net-related parameters are explored and evidence the role of the cables’ geometric stiffness in the global response of the fence.

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25

Son, Young Kap, and Gordon J. Savage. "Efficient Stability and Robustness Analysis of Uncertain Nonlinear Systems using a New Response-based Method." International Journal of Reliability, Quality and Safety Engineering 24, no. 02 (March 30, 2017): 1750010. http://dx.doi.org/10.1142/s0218539317500103.

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A stable dynamic system implies safety, reliability, and satisfactory performance. However, the determination of stability is very difficult when the system is nonlinear and when the ever present uncertainties in the components must be considered. Herein a response-based approach that uses both system and time information obtained through singular value decomposition is presented to determine the stability space of nonlinear, uncertain dynamic systems: any approximating linearization of the nonlinearities has been obviated. The approach extends previous work for linear systems that invoked only the variability of the left singular vectors to predict stability. In the new approach, the variability of the right singular vectors is augmented to that of the left singular vectors and it is shown that a simulation time span, as short as two or three periods, is sufficient to predict stability over the entire life-time dynamics rendering the method very efficient. The stability space is a subset of the design space and its robustness is proportional to the tolerances assigned to the random design variables. Errors due to sampling size, time increments, and number of singular vectors used are controllable. The method can be implemented with readily available software. A study of a practical engineering system with different tolerances and different time spans shows the efficacy of the proposed approach.

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26

Zettergren, M., J. Semeter, B. Burnett, W. Oliver, C. Heinselman, P. L. Blelly, and M. Diaz. "Dynamic variability in F-region ionospheric composition at auroral arc boundaries." Annales Geophysicae 28, no. 2 (February 25, 2010): 651–64. http://dx.doi.org/10.5194/angeo-28-651-2010.

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Abstract. The work presents a data-model synthesis examining the response of the auroral F-region ion temperature, composition, and density to short time scale (<1 min) electric field disturbances associated with auroral arcs. Ion temperature profiles recorded by the Sondrestrom incoherent scatter radar (ISR) are critically analyzed with the aid of theoretical calculations to infer ion composition variability. The analyses presented include a partial accounting for the effects of neutral winds on frictional heating and show promise as the groundwork for future attempts to address ion temperature-mass ambiguities in short-integration ISR data sets. Results indicate that large NO+ enchancements in the F-region can occur in as little as 20 s in response to impulsive changes in ion frictional heating. Enhancements in molecular ion density result in recombination and a depletion in plasma, which is shown to occur on time scales of several minutes. This depletion process, thus, appears to be of comparable importance to electrodynamic evacuation processes in producing auroral arc-related plasma depletions. Furthermore, the potential of ionospheric composition in regulating the amounts and types of ions supplied to the magnetosphere is outlined.

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27

Vamborg, F. S. E., V. Brovkin, and M. Claussen. "Background albedo dynamics improve simulated precipitation variability in the Sahel region." Earth System Dynamics 5, no. 1 (February 5, 2014): 89–101. http://dx.doi.org/10.5194/esd-5-89-2014.

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Abstract. Using the general circulation model ECHAM5–JSBACH forced by observed sea surface temperatures (SSTs) for the 20th century, we investigate the role of vegetation and land surface albedo dynamics in shaping rainfall variability in the Sahel. We use two different land surface albedo schemes, one in which the albedo of the canopy is varying and one in which the albedo changes of the surface below the canopy are also taken into account. The SST forcing provides the background for simulating the observed decadal signal in Sahelian rainfall, though the response to SST forcing only is not strong enough to fully capture the observed signal. The introduction of dynamic vegetation leads to an increase in interannual variability of the rainfall, and gives rise to an increased number of high-amplitude rainfall anomaly events. The dynamic background albedo leads to an increased persistence of the rainfall anomalies. The increase in persistence means that the difference between the dry and the wet decades is increased compared to the other simulations, and thus more closely matching the observed absolute change between these two periods. These results highlight the need for a consistent representation of land surface albedo dynamics for capturing the full extent of rainfall anomalies in the Sahel.

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Peterka, R. J., F. O. Black, and M. B. Schoenhoff. "Age-Related Changes in Human Vestibulo-Ocular and Optokinetic Reflexes: Pseudorandom Rotation Tests." Journal of Vestibular Research 1, no. 1 (July 1, 1990): 61–71. http://dx.doi.org/10.3233/ves-1990-1107.

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The dynamic response properties of horizontal vestibulo-ocular reflex (VOR) and optokinetic reflex (OKR) were characterized in 216 human subjects ranging in age from 7 to 81 y. The object of this cross-sectional study was to determine the effects of aging on VOR and OKR reflex dynamics, and to identify the distributions of parameters that describe VOR and OKR responses to pseudorandom stimuli in a putatively normal population. In general, VOR and OKR response parameters changed in a manner consistent with declining function with increasing age. For the VOR this was reflected in declining response amplitudes, although the magnitude of the decline was small relative to the variability of the data. For the OKR the lag time of the response, probably associated with the time required for visual information processing, increased linearly with age at a rate of about 1 ms per year.

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Jensen, H., and A. O. Cifuentes. "A Global Sensitivity Approach for the Dynamic Response of Printed Wiring Boards." Journal of Electronic Packaging 117, no. 1 (March 1, 1995): 7–13. http://dx.doi.org/10.1115/1.2792071.

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This paper is concerned with the sensitivity of the dynamic response of printed wiring boards (PWB). A general method to study the sensitivity of the response of the board as a function of the variability of the design variables is presented. The method, which is based on a probabilistic approach, assumes that the design variables belong to a given interval and follow a known probabilistic distribution.

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30

Bank, L. C., and C. H. Kao. "Dynamic Response of Thin-Walled Composite Material Timoshenko Beams." Journal of Energy Resources Technology 112, no. 2 (June 1, 1990): 149–54. http://dx.doi.org/10.1115/1.2905723.

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Thin-walled structural members are used extensively in the offshore industry in applications ranging from marine risers to platforms and frames. Advanced fiber composite structural members may offer advantages over their conventional steel counterparts in certain situations. Use of composite members will require modifications to existing structural analysis codes. This paper presents a beam theory for thin-walled composite beams that can be incorporated into existing codes. Timoshenko beam theory is utilized to account for shear deformation effects, which cannot be neglected in composite beams, and for the variability in material properties in different walls of the beam cross section. The theory is applied to the analysis of the free vibration problem and shows the dependence of the natural frequencies and mode shapes on the in-plane properties of the laminates that form the walls of the beam. Forced periodic and forced arbitrary problems are also discussed and the deflected shapes and maximum deflections are shown as functions of wall layups.

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31

Deck, J. F., and S. Dubowsky. "On the Limitations of Predictions of the Dynamic Response of Machines with Clearance Connections." Journal of Mechanical Design 116, no. 3 (September 1, 1994): 833–41. http://dx.doi.org/10.1115/1.2919457.

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The results of experimental and analytical studies of the dynamic response of machines with flexible links and connection clearances are presented. These results suggest that both a large amount of variability and high sensitivity to small parameter changes and operating conditions are inherent properties of the dynamic response of such systems. This work indicates that the accuracy of dynamic force predictions given by computer simulations for such systems may be fundamentally limited, and therefore such simulations should be used with care in system design.

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32

Mikhail, Magdy Y., and Michael S. Mamlouk. "Effect of Vehicle-Pavement Interaction on Pavement Response." Transportation Research Record: Journal of the Transportation Research Board 1570, no. 1 (January 1997): 78–88. http://dx.doi.org/10.3141/1570-10.

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The structural response of flexible pavements is studied under different dynamic loads and pavement roughness conditions. The factors affecting dynamic load variability are investigated with regard to pavement-vehicle interaction. Furthermore, the study considers the viscoelastic nature of asphalt concrete and the nonlinearity and plasticity of granular and subgrade materials. The Florida COMPAS computer program was used to estimate the dynamic wheel force, and the ABAQUS three-dimensional finite-element program was used to determine the pavement response. The effects of vehicle and pavement characteristics such as vehicle type, vehicle speed, suspension type, level of roughness, pavement stiffness, and layer thickness were studied and statistically analyzed. The walking-beam suspension causes more dynamic load variation than the air-bag and leaf-spring suspension. The dynamic load coefficient for the walking-beam suspension is approximately twice the other suspensions. Vehicle speed is an important factor; the 20 km/hr speed resulted in permanent displacement approximately 10 times the permanent displacement produced by the 130 km/hr speed. The pavement response varies with distance due to roughness. Pavement stiffness and thickness had some effect on pavement response, but truck type and truck suspension type did not have a large effect.

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33

Papesh, Megan H., Jason L. Hicks, and Juan D. Guevara Pinto. "Retrieval dynamics of recognition and rejection." Quarterly Journal of Experimental Psychology 72, no. 9 (March 28, 2019): 2328–41. http://dx.doi.org/10.1177/1747021819836753.

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Recognition memory is often viewed as the end-product of discrete cognitive events, involving the combination of latent operations such as the assessment of memory strength, the decision time, and the memory judgement. Recently, researchers have begun using the physical dynamics of memory retrieval to provide insight into the dynamic, possibly non-discrete, processes that underlie memory decisions. In this study, the underlying distributional properties of targets and lures were manipulated by populating lists with items drawn from either homogeneous or heterogeneous word frequency and context variability ranges. In all conditions, participants’ x-, y-mouse coordinates were recorded as they processed test items, allowing estimates of response dynamics (e.g., initial deviation and area under the curve [AUC]), and eventual old/new responses. The stimulus manipulations affected the distribution shapes and, to a greater degree, the placements of subjective confidence thresholds. We observed tight correspondences between confidence and AUC for both hits and correct rejections. We interpret these results within dynamic models of recognition memory.

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34

Son, Young Kap, and Gordon J. Savage. "Stability Analysis of Uncertain Systems Using a Singular Value Decomposition-Based Metric." International Journal of Reliability, Quality and Safety Engineering 22, no. 05 (October 2015): 1550021. http://dx.doi.org/10.1142/s0218539315500217.

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The stability of dynamic systems is important for satisfactory performance, safety and reliability. The study becomes more difficult when the system is nonlinear and when the ever present uncertainties in the components are considered. Herein a new approach is presented that uses time-domain information: It invokes design of experiments based on the uncertainty within the system, computer simulation of the dynamics to generate a matrix of discrete time responses that presents the variability of the response, and finally, singular value decomposition to separate out parameter information from time information. The variability in the elements in the first few left singular vectors predicts any instability that might occur over the complete life-time of the system. The key to the approach is the introduction of random variables and subsequent co-variance operations. A real-world example and comparison to established methods show the efficacy of the approach.

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35

Lucà, Francescantonio, Marta Berardengo, Stefano Manzoni, Diego Scaccabarozzi, Marcello Vanali, and Loris Drago. "Experimental Evaluation of the Driving Parameters in Human–Structure Interaction." Vibration 5, no. 1 (February 18, 2022): 121–40. http://dx.doi.org/10.3390/vibration5010008.

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Many studies in the literature have already evidenced that pedestrians are able to change the dynamic properties of slender structures (e.g., footbridges and staircases). The aim of this paper is to analyse which pedestrians’ features mostly affect the structure behaviour, in order to properly account for them in a human–structure interaction problem, while disregarding the less relevant ones. This is accomplished by measuring the apparent mass (i.e., the frequency response function between the vibration of the structure at the contact point and the consequent force exerted by the pedestrian to the structure itself) curves of human bodies and coupling them to the dynamics of a slender structure. In more detail, this paper aims at analysing which factors must be accounted for among intra-subject variability (i.e., the dynamic behaviour of the same subject can change because it is characterised by a natural dispersion), inter-subject variability (i.e., different subjects have different dynamic behaviours) and the posture (i.e., the same subject changes posture during motion and this causes a change of his/her dynamic features). The influence of the apparent mass properties on the modal parameters of the hosting structure is addressed by means of a modal approach.

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36

Roberts, C. D., D. Calvert, N. Dunstone, L. Hermanson, M. D. Palmer, and D. Smith. "On the Drivers and Predictability of Seasonal-to-Interannual Variations in Regional Sea Level." Journal of Climate 29, no. 21 (October 5, 2016): 7565–85. http://dx.doi.org/10.1175/jcli-d-15-0886.1.

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Abstract Observations and eddy-permitting ocean model simulations are used to evaluate the drivers of sea level variability associated with 15 modes of climate variability covering the Atlantic, Pacific, Indian, and Southern Oceans. Sea level signals are decomposed into barotropic, steric, and inverted barometer contributions. Forcings are decomposed into surface winds, buoyancy fluxes, and Ekman pumping. Seasonal-to-interannual sea level variability in the low latitudes is governed almost entirely by the thermosteric response to wind forcing associated with tropical modes of climate variability. In the extratropics, changes to dynamic sea level associated with atmospheric modes of variability include a substantial barotropic response to wind forcing, particularly over the continental shelf seas. However, wind-driven steric changes are also important in some locations. On interannual time scales, wind-forced steric changes dominate, although heat and freshwater fluxes are important in the northwest Atlantic, where low-frequency sea level variations are associated with changes in the Atlantic meridional overturning circulation. Using the version 3 of the Met Office Decadal Prediction System (DePreSys3), the predictability of large-scale dynamic sea level anomalies on seasonal-to-interannual time scales is evaluated. For the first year of the hindcast simulations, DePreSys3 exhibits skill exceeding persistence over large regions of the Pacific, Atlantic, and Indian Oceans. Skill is particularly high in the tropical Indo-Pacific because of the accurate initialization and propagation of thermocline depth anomalies associated with baroclinic adjustments to remote wind forcing. Skill in the extratropics is hindered by the limited predictability of wind anomalies associated with modes of atmospheric variability that dominate local and/or barotropic responses.

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37

Zhang, Xuezhen, and Qiuhong Tang. "Response of Simulated Surface Air Temperature to the Interannual Variability of Leaf Area Index in Eastern China." Advances in Meteorology 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/817870.

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Using the coupled WRF-Noah model, we conducted two experiments to investigate impacts of the interannual variability of leaf area index (LAI) on the surface air temperature (SAT) in eastern China. The Moderate Resolution Imaging Spectroradiometer (MODIS) observed dynamic LAI data from 2002 to 2009 were used in one modeling experiment, and the climatological seasonal cycle of the MODIS LAI was used in the other experiment. The results show that the use of dynamic LAI improves model performance. Compared with the use of climatological LAI, the use of dynamic LAI may reduce the warm (cool) bias in the years with large positive (negative) LAI anomalies. The reduction of the warm bias results from the modeled cooling effect of LAI increase through reducing canopy resistance, promoting transpiration, and decreasing sensible heat flux. Conversely, the reduction of cool bias is a result of the warming effect of negative anomaly of LAI. The use of dynamic LAI can improve model performance in summer and to a lesser extent, spring and autumn. Moreover, the dynamic LAI exerts a detectable influence on SAT in the WRF model when the LAI anomaly is at least 20% of the climatological LAI.

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38

KOHNO, Tetsuya, Shunsuke TANIMOTO, Shigeyoshi ANDO, Junichi SAKAI, and Jun-ichi HOSHIKUMA. "Effect of variability of evaluation of geotechnical parameters on dynamic response of pile foundations." Japanese Geotechnical Journal 9, no. 2 (2014): 119–39. http://dx.doi.org/10.3208/jgs.9.119.

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39

Carvalho, Alda, Tiago Silva, and Maria Loja. "Assessing Static and Dynamic Response Variability due to Parametric Uncertainty on Fibre-Reinforced Composites." Journal of Composites Science 2, no. 1 (February 1, 2018): 6. http://dx.doi.org/10.3390/jcs2010006.

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40

Coen, Muireann, Françoise Goldfain-Blanc, Gaëlle Rolland-Valognes, Bernard Walther, Donald G. Robertson, Elaine Holmes, John C. Lindon, and Jeremy K. Nicholson. "Pharmacometabonomic Investigation of Dynamic Metabolic Phenotypes Associated with Variability in Response to Galactosamine Hepatotoxicity." Journal of Proteome Research 11, no. 4 (March 2, 2012): 2427–40. http://dx.doi.org/10.1021/pr201161f.

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41

Bayat, Elyas, and Federica Tubino. "Dynamic response of floors induced by a single walking pedestrian including walking path variability." Structures 46 (December 2022): 1280–92. http://dx.doi.org/10.1016/j.istruc.2022.10.112.

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42

Gussin, Daniel, Jan Benda, and Leonard Maler. "Limits of Linear Rate Coding of Dynamic Stimuli by Electroreceptor Afferents." Journal of Neurophysiology 97, no. 4 (April 2007): 2917–29. http://dx.doi.org/10.1152/jn.01243.2006.

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We estimated the frequency-intensity (f-I) curves of P-unit electroreceptors using 4-Hz random amplitude modulations (RAMs) and using the covariance method (50-Hz RAMs). Both methods showed that P units are linear encoders of stimulus amplitude with additive noise; the gain of the f-I curve was, on average, 0.32 and 2.38 spikes·s−1·μV−1 for the low- and high-frequency cutoffs, respectively. There were two sources of apparent noise in the encoding process: the first was the variability of baseline P-unit discharge and the second was the variation of receptor discharge due to variability of the stimulus slope independent of its intensity. The covariance method showed that a linear combination of eigenvectors representing the time-weighted stimulus intensity (E1) and its derivative (E2) could account for, on average, 92% of the total response variability; E1 by itself accounted for 76% of the variability. The low gain of the low-frequency f-I curve implies that detection of small (1 μV) signals would require integration over many receptors (∼1,200) and time (200 ms); even then, signals that elicit behavioral responses could not be detected using rate coding with the estimated gain and noise levels. Weak signals at the limit of behavioral thresholds could be detected if the animal were able to extract E1 from the population of responding P units; we propose a tentative mechanism for this operation although there is no evidence as to whether it is actually implemented in the nervous system of these fish.

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43

Mancuso, M., E. Carol, E. Kruse, and F. Mendes Rodrigues. "Coastal aquifer hydrodynamics and salinity in response to the tide: case study in Lisbon, Portugal." Hydrology Research 48, no. 1 (February 1, 2016): 240–52. http://dx.doi.org/10.2166/nh.2016.203.

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The variability in dynamics and salinity of the coastal alluvial aquifer on which the city of Lisbon is located was evaluated. The evaluation was based on the analysis of level and groundwater electrical conductivity fluctuations depending on the tide in the Tagus River. The results obtained made it possible to recognize three sectors. First, a littoral sector where the variations in level and salinity are larger on the coast and decrease towards the innermost sections of the alluvial fan. Second, a sector close to the docks where there is greater dynamic and salinity variability than in the coastal sectors, as the excavations of the docks favour the tidal propagation towards the aquifer. And third, a sector located towards the apex of the alluvial fan associated with the dynamics of the stormwater channel. In this sector, the largest periodical water table fluctuations in the aquifer occur, since the freshwater that cannot drain towards the river enters the aquifer at high tide, causing a slight decrease in salinity content. On the basis of these results, conceptual models of hydrogeological behaviour were used to describe the spatial and temporal variations in the hydrodynamic and salinity characteristics of groundwater.

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44

Basalyga, Gleb, and Emilio Salinas. "When Response Variability Increases Neural Network Robustness to Synaptic Noise." Neural Computation 18, no. 6 (June 2006): 1349–79. http://dx.doi.org/10.1162/neco.2006.18.6.1349.

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Cortical sensory neurons are known to be highly variable, in the sense that responses evoked by identical stimuli often change dramatically from trial to trial. The origin of this variability is uncertain, but it is usually interpreted as detrimental noise that reduces the computational accuracy of neural circuits. Here we investigate the possibility that such response variability might in fact be beneficial, because it may partially compensate for a decrease in accuracy due to stochastic changes in the synaptic strengths of a network. We study the interplay between two kinds of noise, response (or neuronal) noise and synaptic noise, by analyzing their joint influence on the accuracy of neural networks trained to perform various tasks. We find an interesting, generic interaction: when fluctuations in the synaptic connections are proportional to their strengths (multiplicative noise), a certain amount of response noise in the input neurons can significantly improve network performance, compared to the same network without response noise. Performance is enhanced because response noise and multiplicative synaptic noise are in some ways equivalent. So if the algorithm used to find the optimal synaptic weights can take into account the variability of the model neurons, it can also take into account the variability of the synapses. Thus, the connection patterns generated with response noise are typically more resistant to synaptic degradation than those obtained without response noise. As a consequence of this interplay, if multiplicative synaptic noise is present, it is better to have response noise in the network than not to have it. These results are demonstrated analytically for the most basic network consisting of two input neurons and one output neuron performing a simple classification task, but computer simulations show that the phenomenon persists in a wide range of architectures, including recurrent (attractor) networks and sensorimotor networks that perform coordinate transformations. The results suggest that response variability could play an important dynamic role in networks that continuously learn.

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45

Iwan, W. D., and H. Jensen. "On the Dynamic Response of Continuous Systems Including Model Uncertainty." Journal of Applied Mechanics 60, no. 2 (June 1, 1993): 484–90. http://dx.doi.org/10.1115/1.2900819.

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This paper presents a technique for obtaining the response of linear continuous systems with parameter uncertainties subjected to deterministic excitation. The parameter uncertainties are modeled as random fields and are assumed to be time independent. The general formulation of the method is developed for a particular class of partial differential equations with random coefficients. Random shape functions are introduced to approximate the solution in the spatial domain and in the random space. A system of linear ordinary differential equations for the unknowns of the problem is derived using the weighted residual method. The system of equations is integrated in time and the response variability is computed. Application of the new method is made to a continuum described by the one-dimensional wave equation in which the stiffness properties exhibit a spatial random variation. Validation calculations show that the results from the method agree well with those obtained by direct numerical integration.

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46

Cannella, Salvatore, Roberto Dominguez, Jose M. Framinan, and Manfredi Bruccoleri. "Demand Sharing Inaccuracies in Supply Chains: A Simulation Study." Complexity 2018 (July 16, 2018): 1–13. http://dx.doi.org/10.1155/2018/1092716.

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We investigate two main sources of information inaccuracies (i.e., errors and delays) in demand information sharing along the supply chain (SC). Firstly, we perform a systematic literature review on inaccuracy in demand information sharing and its impact on supply chain dynamics. Secondly, we model several SC settings using system dynamics and assess the impact of such information inaccuracies on SC performance. More specifically, we study the impact of four factors (i.e., demand error, demand delay, demand variability, and average lead times) using three SC dynamic performance indicators (i.e., bullwhip effect, inventory variability, and average inventory). The results suggest that demand error has a negative impact on SC performance, which is exacerbated by the magnitude of the error and by low demand variability scenarios. In contrast, demand delay produces a nonlinear behavior in the supply chain response (i.e., a short delay may have a negative impact and a long delay may have a positive impact), being influenced by the supply chain configuration.

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47

Stolarik, Martin, Miroslav Pinka, Jan Nedoma, and Michael Fridrich. "Variability of Seismic Loading over the Surface of a Concrete Slab in Interaction with the Subsoil." Sustainability 12, no. 22 (November 16, 2020): 9530. http://dx.doi.org/10.3390/su12229530.

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This article is aimed at the analysis of the behavior of a fiber-reinforced concrete slab in contact with subsoil during dynamic loading in close proximity. The properties of such slabs are important for evaluating their dynamic response, though the properties of the subsoil environment through which the vibrations propagate must also be taken into account. The analysis itself was performed on the basis of the results obtained from experimental measurements during seismic excitation with a calibrated impact. There were three concrete slabs tested, with varying amounts of fiber. The standard Vistec seismic instrumentation was used for measuring the dynamic response. The results of the experiment were processed in both the amplitude and frequency domains, and a graphic comparison in the waveform and frequency fields was made. The results acquired from this experimental research may support a more objective approach during the evaluation of dynamic impacts ranging from anthropogenic impacts to building structures.

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48

Bouasla, Seif El Islam, Youcef Zennir, and MECHHOUD EL-Arkam. "The use of Functional Resonance Analysis Method (FRAM) to model an emergency response plan." Algerian Journal of Signals and Systems 7, no. 2 (June 30, 2022): 61–70. http://dx.doi.org/10.51485/ajss.v7i2.154.

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Accident models, analysis methods and emergency response plans affect what accident investigators and emergency response leaders look for, which contributing factors are found, and which recommendations are issued. This paper contrasts the Functional Resonance Analysis Method (FRAM) for an emergency response plan. FRAM illustrates the dynamic interactions within socio-technical systems and lets the analyst understand the how and why by describing non-linear dependencies, performance conditions, variability, and their resonance across functions. So, it defines a systemic framework to model complex systems from the perspective of function and views accidents and emergency response strategies as emergent phenomenon of function’s variability, It is playing an increasingly significant role in the development of systemic emergency response plan.

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49

Huston, Alan, Nicholas Siler, Gerard H. Roe, Erin Pettit, and Nathan J. Steiger. "Understanding drivers of glacier-length variability over the last millennium." Cryosphere 15, no. 3 (April 1, 2021): 1645–62. http://dx.doi.org/10.5194/tc-15-1645-2021.

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Abstract. Changes in glacier length reflect the integrated response to local fluctuations in temperature and precipitation resulting from both external forcing (e.g., volcanic eruptions or anthropogenic CO2) and internal climate variability. In order to interpret the climate history reflected in the glacier moraine record, the influence of both sources of climate variability must therefore be considered. Here we study the last millennium of glacier-length variability across the globe using a simple dynamic glacier model, which we force with temperature and precipitation time series from a 13-member ensemble of simulations from a global climate model. The ensemble allows us to quantify the contributions to glacier-length variability from external forcing (given by the ensemble mean) and internal variability (given by the ensemble spread). Within this framework, we find that internal variability is the predominant source of length fluctuations for glaciers with a shorter response time (less than a few decades). However, for glaciers with longer response timescales (more than a few decades) external forcing has a greater influence than internal variability. We further find that external forcing also dominates when the response of glaciers from widely separated regions is averaged. Single-forcing simulations indicate that, for this climate model, most of the forced response over the last millennium, pre-anthropogenic warming, has been driven by global-scale temperature change associated with volcanic aerosols.

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50

Kawasaki, Hiroto, Naotsugu Tsuchiya, Christopher K. Kovach, Kirill V. Nourski, Hiroyuki Oya, Matthew A. Howard, and Ralph Adolphs. "Processing of Facial Emotion in the Human Fusiform Gyrus." Journal of Cognitive Neuroscience 24, no. 6 (June 2012): 1358–70. http://dx.doi.org/10.1162/jocn_a_00175.

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Electrophysiological and fMRI-based investigations of the ventral temporal cortex of primates provide strong support for regional specialization for the processing of faces. These responses are most frequently found in or near the fusiform gyrus, but there is substantial variability in their anatomical location and response properties. An outstanding question is the extent to which ventral temporal cortex participates in processing dynamic, expressive aspects of faces, a function usually attributed to regions near the superior temporal cortex. Here, we investigated these issues through intracranial recordings from eight human surgical patients. We compared several different aspects of face processing (static and dynamic faces; happy, neutral, and fearful expressions) with power in the high-gamma band (70–150 Hz) from a spectral analysis. Detailed mapping of the response characteristics as a function of anatomical location was conducted in relation to the gyral and sulcal pattern on each patient's brain. The results document responses with high responsiveness for static or dynamic faces, often showing abrupt changes in response properties between spatially close recording sites and idiosyncratic across different subjects. Notably, strong responses to dynamic facial expressions can be found in the fusiform gyrus, just as can responses to static faces. The findings suggest a more complex, fragmented architecture of ventral temporal cortex around the fusiform gyrus, one that includes focal regions of cortex that appear relatively specialized for either static or dynamic aspects of faces.

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