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The change in the phase, from quiescence to surge, may perturb the glacier’s dynamical behavior. To understand these changes through geospatial technique, the fastest moving tidewater Monacobreen Glacier, Svalbard, was studied for the year 2016–2019. It was elucidated that the surge had initiated during 2017–2018 before it the glacier was in quiescence since 2001. Initially, the glacier radar zones, namely percolation refreeze, lower percolation, and clean ice, were identified using the multi-temporal SAR data. These zones were utilized to delineate the equilibrium line altitude, and it was found that it is continuously moving down during surging, unlike the quiescence phase. The accumulation area ratio was always more than 0.67 throughout the analysis. The seasonal change in glacier surface velocity for both the phases was estimated, adapting the most appropriate Offset Tracking approach using the SAR data. The mean velocity over the main trunk was found to vary from 0.5 to 4 m/day. Thereafter, Glen's flow law equation was used to estimate the ice-thickness and found that the glacier has an average thickness range of 216–326 m in quiescence and 136–244 m during the surging. The analysis depicted that the basal shear stresses are increasing with surging, and the viscosity is decreasing. It can be said that the transition of the glacier from quiescent to surging phase has entirely changed its dynamic behavior and characteristics. Further, in the absence of field observations, the geospatial technique may provide reasonable estimates of the glacier’s physical and dynamical parameters.

The study of hyperbolic waves involves various notions which help characterise how these structures evolve. One important facet is the notion of genuine nonlinearity, namely the ability for shocks and rarefactions to form instead of contact discontinuities. In the context of the Whitham modulation equations, this paper demonstrate that a loss of genuine nonlinearity leads to the appearance of a dispersive set of dynamics in the form of the modified Korteweg de-Vries equation governing the evolution of the waves instead. Its form is universal in the sense that its coefficients can be written entirely using linear properties of the underlying waves such as the conservation laws and linear dispersion relation. This insight is applied to two systems of physical interest, one an optical model and the other a stratified hydrodynamics experiment, to demonstrate how it can be used to provide insight into how waves in these systems evolve when genuine nonlinearity is lost.

Using in situ methods to characterize the state of a system during reactions is critical to understanding and improving solvothermal syntheses. This work demonstrates the use of in situ energy-dispersive X-ray diffraction (EDXRD) to investigate the local dynamics during solvothermal formation of Cu4O3 using a general-purpose full-sized laboratory oven. This allows for direct comparison of in situ data with laboratory-based reactions. Using in situ EDXRD, changes in the local amounts of Cu4O3, Cu2O and CuO within approximately 100 × 100 × 700 µm gauge volumes during solvothermal Cu4O3 formation were recorded. Fast conversion between Cu2O and CuO was observed in the solvothermal environment, whereas Cu4O3 was found to be chemically stable against disturbances once formed. The observed differences in local dynamics give further support to the differences in formation mechanisms between Cu4O3 and Cu2O/CuO proposed here.

In the present work, we present a new method for assessing causality in uni-directionally coupled chaotic oscillators with small frequency mismatch. This method is based on the correlation between changes in the phase dynamics of the slave oscillator and the dynamics of the phase difference between the oscillators. Application of the proposed approach to master-slave R¨ossler systems showed that the new algorithm is well-suited for assessing the presence and direction of coupling, especially in the case of weak coupling.

In this paper, a feedback control law based on two time‐scale assignment and state extension is implemented for the nonlinear non‐minimum phase dynamics of three degree‐of‐freedom autonomo...

The phase of low-frequency, rhythmic cortical activity is essential for organizing brain processes because it provides a recurrent temporal frame for information coding. However, the low-frequency cortical phase exhibits great flexibility in response to external influences. Given that brain rhythms have been found to track respiratory inputs, we hypothesized that slow breathing, commonly associated with mental regulation, could reorganize the relationship between these two rhythmic systems through the adjustment of the cortical phase to such a slow train of inputs. Based on simultaneous magnetoencephalography and respiratory measurements, we report that while participants performed paced breathing, slow relative to normal breathing modulated cortical phase activity in the alpha range across widespread brain areas. Such modulation effects were specifically locked to the middle of the inspiration stage and exhibited a well-structured pattern. At the single-subject level, the phase angles underlying the effects became more likely to be diametrically opposed across breaths, indicating unique and consistent phase adjustment to slow inspiratory inputs. Neither cardiac fluctuations nor breathing-unrelated task effects could account for the findings. We suggest that slow-paced inspiration could organize the cortical phase in a regularized phase pattern, revealing a rhythmic but dynamic neural network integrated with different neurophysiological systems through volitional control. NEW & NOTEWORTHY Breathing is more complicated than a simple gas exchange, as it is integrated with numerous cognitive and emotional functions. Controlled slow breathing has often been used to regulate mental processes. This magnetoencephalography study demonstrates that slow-paced relative to normal-paced inspiration could organize the timing of alpha rhythmic activities across breathing cycles in a structured manner over widespread brain areas. Our results reveal how a volitionally controlled change in respiratory behavior could systematically modulate cortical activity.

The synchronization of self-oscillating systems is vital to various biological functions, from the coordinated contraction of heart muscle to the self-organization of slime molds. Through modeling, we design bioinspired materials systems that spontaneously form shape-changing self-oscillators, which communicate to synchronize both their temporal and spatial behavior. Here, catalytic reactions at the bottom of a fluid-filled chamber and on mobile, flexible sheets generate the energy to “pump” the surrounding fluid, which also transports the immersed sheets. The sheets exert a force on the fluid that modifies the flow, which in turn affects the shape and movement of the flexible sheets. This feedback enables a single coated (active) and even an uncoated (passive) sheet to undergo self-oscillation, displaying different oscillatory modes with increases in the catalytic reaction rate. Two sheets (active or passive) introduce excluded volume, steric interactions. This distinctive combination of the hydrodynamic, fluid–structure, and steric interactions causes the sheets to form coupled oscillators, whose motion is synchronized in time and space. We develop a heuristic model that rationalizes this behavior. These coupled self-oscillators exhibit rich and tunable phase dynamics, which depends on the sheets’ initial placement, coverage by catalyst and relative size. Moreover, through variations in the reactant concentration, the system can switch between the different oscillatory modes. This breadth of dynamic behavior expands the functionality of the coupled oscillators, enabling soft robots to display a variety of self-sustained, self-regulating moves.

Intrinsic optical signal (IOS) imaging promises a noninvasive method for objective assessment of retinal function. This study demonstrates concurrent optical coherence tomography (OCT) of amplitude-IOS and phase-IOS changes in human photoreceptors. A new procedure for differential-phase-mapping (DPM) is validated to enable depth-resolved phase-IOS imaging. Dynamic OCT revealed rapid amplitude-IOS and phase-IOS changes, which occur almost right away after the stimulus onset. These IOS changes were predominantly observed within the photoreceptor outer segment (OS), particularly two boundaries connecting to the inner segment and retinal pigment epithelium. The comparative analysis supports that both amplitude-IOS and phase-IOS attribute to transient OS morphological change associated with phototransduction activation in retinal photoreceptors. A simulation modeling is proposed to discuss the relationship between the photoreceptor OS length and phase-IOS changes.

Traditional analysis of dynamics concerns coordinate-invariant features of the long-time-asymptotic behaviour of a system. Using the non-autonomous Adler equation with slowly varying forcing, we illustrate three of the limitations of this traditional approach. We discuss an alternative, “slow-fast finite-time dynamical systems” approach, that is more suitable for slowly time-dependent one-dimensional phase dynamics, and is likely to be suitable for more general dynamics of open systems involving two or more timescales.

Spontaneous contractions of cardiomyocytes are driven by calcium oscillations due to the activity of ionic calcium channels and pumps. The beating phase is related to the time-dependent deviation of the oscillations from their average frequency, due to noise and the resulting cellular response. Here, we demonstrate experimentally that, in addition to the short-time (1--2 Hz), beat-to-beat variability, there are long-time correlations (tens of minutes) in the beating phase dynamics of isolated cardiomyocytes. Our theoretical model relates these long-time correlations to cellular regulation that restores the frequency to its average, homeostatic value in response to stochastic perturbations.

The authors show that neural networks exhibiting noise-induced rhythms, also known as quasi-cycle oscillations, can synchronize through out-of-phase locking depending on the noise intensity and the size of the coupling delay

The problem of directional coupling detection in ensembles of temporally evolving systems from observed time series is important in diverse fields. A small ensemble of three oscillators is a model of large ensembles which carry important features such as the distinction between direct and indirect influences and, therefore, allows the study of the methods for coupling detection in respect of their ability to distinguish between such influences and, hence, correctly identify directional couplings and provide their appropriate quantitative description. This work considers an ensemble of three exemplary oscillators as a test system to improve the coupling estimation method based on phase dynamics modeling and compare it to the spectral approach called Granger – Geweke spectrum. Possibilities of false conclusions about the structure of couplings are analyzed, sensitivity of both methods and interpretations of coupling characteristics are discussed.

When a solute molecule is placed in solution, is it acceptable to presume that its electronic structure is essentially the same as that in the gas phase? In this paper, we address this question from a simulation perspective for the case of the sodium dimer cation (Na

The population and number dynamics of small mammals in the vicinity of the Torey lakes (Southeast Transbaikalia, Russia) were characterized between 2008 and 2017. The monitoring was carried out against the backdrop of the complete drying out of these largest lakes in the region. The stability of the fauna of small mammals was revealed over an 80-year period, although the structure of communities has changed significantly as a result of changes in moistening of the territory. Xerophilous species of mammals had an advantage in the period of studies in the dry climate phase. Correlation analysis showed a possible link between the population dynamics of some common species of small mammals with the precipitation amount of the current and previous years or with the precipitation amount of some spring (in one case, winter) months.

Time-lenses in general proved to be useful for many applications and specifically when utilizing them for temporal imaging schemes where they can image ultrafast signals that cannot be detected by any electronic based device. Over the last few years, we demonstrated that when joining together several time-lenses into a single time-lens array, it is possible to gain more information on the input signal. Such as measuring temporal depth imaging, the state of polarization of the input signal as a function of time, and retrieving the phase dynamics. However, when designing an array of time-lenses, there is a trade-off between joining large number of small time-lenses, so each signal will interact with many time-lenses but each one has low resolution, and joining small number of large time-lenses, so each has better temporal resolution but on the expanse of interacting with smaller number of time-lenses in the array. We showed that one way to overcome this drawback is to overlap adjacent timelenses. Thus it is possible to both have large number of time-lenses without compromising on the size of each time-lens and obtaining high temporal resolution. In this proceeding, we overlap two time-lenses and measure the spectrum of the idler. We compare the numerical simulations of the frequency domain of the idler to the measured spectrum of the idler.

Giant fractional Shapiro steps have been observed in Josephson junction arrays as resulting from magnetic flux quantization in the two-dimensional array. We demonstrate experimentally the appearance of giant fractional Shapiro steps in anisotropic Josephson junction arrays as unambiguous evidence of a skewed current phase relationship. Introducing anisotropy in the array results in a giant collective high frequency response that reflects the properties of a single junction, as evidenced by the observation of a Fraunhofer like magnetic field dependence of the total critical current of the system. The observed phase dynamics can be perfectly captured within an extended resistively shunted Josephson junction model. These results directly indicate the potential of Josephson junction arrays to explore the current phase relation in a very broad frequency range (down to 50 MHz) and in a wide variety of novel link materials exhibiting non-conventional current phase relationships. Giant fractional Shapiro steps have been observed in Josephson junction arrays as resulting from magnetic flux quantization in the two-dimensional array. Here, the authors demonstrate the observation of giant fractional Shapiro steps in an anisotropic Josephson junction array, as unambiguous evidence of a skewed current phase relationship.

While the literature has characterized balance control during quasi-static and/or dynamic tasks, comparatively few studies have examined relationships across paradigms. This study investigated whether quiet-stance postural steadiness metrics were associated with reactive control parameters (during both stepping and restabilization phases) following a lean-and-release perturbation. A total of 40 older adults participated. Postural steadiness (center of the pressure range, root mean square, velocity, and frequency) was evaluated in “feet together” and “tandem stance” positions. During the reactive control trials, the step length, step width, movement time, and reaction time were measured, in addition to the postural steadiness variables measured during the restabilization phase following the stepping response. Out of 64 comparisons, only 10 moderate correlations were observed between postural steadiness and reactive spatio-temporal stepping parameters (P ≤ .05, r = −.312 to −.534). However, postural steadiness metrics were associated with the center of pressure velocity and frequency during the restabilization phase of the reactive control trials (P ≤ .02, r = .383 to .775 for velocity and P ≤ .01, r = .386 to .550 for frequency). Although some elements of quasi-static center of pressure control demonstrated moderate associations with dynamic stepping responses, relationships were stronger for restabilization phase dynamics after foot-contact. Future work should examine the potential association between restabilization phase control and older adult fall-risk.

Although Mn-Ga alloys are discussed as novel permanent magnetic materials, there are several drawbacks, including the high price of Ga and the yet too small saturation magnetization to name a few. We used several guidelines to motivate the replacement of Ga by Sn to tackle these issues and explored the phase formation, structural and magnetic properties of a Mn 2.6 Ga 1− x Sn x alloy series. The structure of Sn-rich samples is dominated by the corresponding hexagonal and orthorhombic phases, while the structure of Ga-rich samples shows additional contributions from the tetragonal DO 22 structure. However, formation of the tetragonal phase happens only upon an appropriate annealing procedure. In contrast to the discussion in the literature, our experiments do not hint on the tetragonal DO 22 structure being metastable, but rather indicate the DO 22 structure being an equilibrium phase. In general, the experimentally found phase fractions impact on the magnetic properties and hard magnetic properties are downgraded very rapidly even if only a small amount of Ga is substituted by Sn. This indicates that the DO 22 phase found only in the Ga-rich samples is indeed the one which is most relevant for realization of high magnetic anisotropy.

The fragmentation of metals during megaplastic (severe) deformation is described in terms of a two-defect model. The process kinetics has been studied qualitatively. A general expression for the Lyapunov indices, which determine the stability of limiting (stationary) structures, is derived. Critical conditions for controlling parameters are found, and the diagrams that determine the stability of stationary states are constructed.

In a reserved forest parcel in a virgin eastern Hyrcanian mixed beech forest, 80 ha was surveyed to determine the pit and mound topography, canopy gaps and dead trees. The aim was to investigate the spatial patterns and correlation of pit and mound features with canopy gaps. Seventy-five canopy gaps and 61 pit and mound features were identified. The univariate first order nearest neighbor (RCE) and bivariate second order test (Ripley’s K) statistic were applied. RCE statistics highlighted a general aggregation pattern for canopy gaps and pits and mounds, while pits and mounds alone were more clumped. Distances between canopy gaps were 130 m average, whereas distances between pit and mound features and dead trees were 60 and 78 m, respectively. Spatial positive correlation of canopy gaps with pits and mounds were observed with all distances. The result of spatial correlations between canopy gaps with pits and mounds confirmed that windthrows cause micro successions in fallen tree ecosystem-scale correlated with gap phase dynamics in the forest community-scale.

Mycobacterium tuberculosis can exist within a host for lengthy periods, tolerating even antibiotic challenge. This non-heritable, antibiotic tolerant "persister" state, is thought to underlie latent Tuberculosis (TB) infection and a deeper understanding thereof could inform treatment strategies. In addition to experimental studies, mathematical and computational modelling approaches are widely employed to study persistence from both an in vivo and in vitro perspective. However, specialized models (partial differential equations, agent-based, multiscale, etc.) rely on several difficult to determine parameters. In this study, a dynamic mathematical model was developed to predict the response of Mycobacterium smegmatis (a model organism for M. tuberculosis) grown in batch culture and subjected to a range of in vitro environmental stresses. Lag phase dynamics, pH variations and internal nitrogen storage were mechanistically modelled. Experimental results were used to train model parameters using global optimization, with extensive subsequent model validation to ensure extensibility to more complex modelling frameworks. This included an identifiability analysis which indicated that seven of the thirteen model parameters were uniquely identifiable. Non-identifiable parameters were critically evaluated. Model predictions compared to validation data (based on experimental results not used during training) were accurate with less than 16% maximum absolute percentage error, indicating that the model is accurate even when extrapolating to new experimental conditions. The bulk growth model can be extended to spatially heterogeneous simulations such as an agent-based model to simulate in vitro granuloma models or, eventually, in vivo conditions, where distributed environmental conditions are difficult to measure.

Objective:We assessed the delay times in the interaction between the autonomic regulatory loop of Heart Rate Variability (HRV) and autonomic regulatory loop of photoplethysmographic waveform variability (PPGV), showing low-frequency oscillations.Material and Methods:In eight healthy subjects aged 25–30 years (3 male, 5 female), we studied at rest (in a supine position) the simultaneously recorded two-hour signals of RR intervals (RRIs) chain and finger photoplethysmogram (PPG). To extract the low-frequency components of RRIs and PPG signal, associated with the low-frequency oscillations in HRV and PPGV with a frequency of about 0.1 Hz, we filtered RRIs and PPG with a bandpass 0.05-0.15 Hz filter. We used a method for the detection of coupling between oscillatory systems, based on the construction of predictive models of instantaneous phase dynamics, for the estimation of delay times in the interaction between the studied regulatory loops.Results:Averaged value of delay time in coupling from the regulatory loop of HRV to the loop of PPGV was 0.9±0.4 seconds (mean ± standard error of the means) and averaged value of delay time in coupling from PPGV to HRV was 4.1±1.1 seconds.Conclusion:Analysis of two-hour experimental time series of healthy subjects revealed the presence of delay times in the interaction between regulatory loops of HRV and PPGV. Estimated delay time in coupling regulatory loops from HRV to PPGV was about one second or even less, while the delay time in coupling from PPGV to HRV was about several seconds. The difference in delay times is explained by the fact that PPGV to HRV response is mediated through the autonomic nervous system (baroreflex), while the HRV to PPGV response is mediated mechanically via cardiac output.

Under consideration is the system of partial differential equations describing the dynamics of a two-phase medium. Exact partially invariant solutions of rank 1 and defect 1 of this system are obtained with respect to some four-dimensional subalgebras. The phenomenon of collapse (an instantaneous source) in a two-phase medium is described.

The problem of interval estimation of the time delay of the coupling between oscillatory systems from observed time series is considered. It is shown that the known asymptotic estimates based on the empirical model in the form of a system of first-order phase oscillators and the maximum likelihood formalism can lead to false inferences of the value of the time delay in two typical situations: 1) nonlinear low-dimensional systems whose phases are well-determined but, as a result of significant amplitude fluctuations, the phase approximation is insufficient for describing the dynamics and 2) systems whose phases are defined not quite well because of too large amplitude fluctuations. A method for empirical diagnostics of problematic situations and its modification (coarse estimation) providing a low probability of false inferences in these situation are proposed. The efficiency of the diagnostic criterion and coarse estimation suggested is demonstrated on reference systems with different dynamic properties (linear stochastic oscillators, van der Pol oscillators, and Ressler and Lorenz chaotic systems).

We explore the phase reduction in networks of coupled oscillators in the higher orders of the coupling parameter. For coupled Stuart–Landau oscillators, where the phase can be introduced explicitly, we develop an analytic perturbation procedure to explicitly obtain the higher-order approximation. We demonstrate this by deriving the second-order phase equations for a network of three Stuart–Landau oscillators. For systems where explicit expressions of the phase are not available, we present a numerical procedure that constructs the phase dynamics equations for a small network of coupled units. We apply this approach to a network of three van der Pol oscillators and reveal components in the coupling with different scaling in the interaction strength.

Asymptotic AFM amplitude-phase dynamics is a powerful modeling tool for the development of AFM control systems and applications. We explain the model and demonstrate simple practical examples that can be easily programmed and analyzed using simulation tools such as Control and Simulation Loop (National Instruments) or Simulink (Mathworks). We first introduce asymptotic AFM models, then formulate their mathematical properties relevant to AFM, and provide examples of simulation for Hertz and Lennard-Jones tip-sample interaction forces. Material of this paper can be used to study and practice AFM dynamics in simulation, as well as for design of AFM model-based control modes and applications.

This paper analyzes the injection-locking and injection-pulling behavior of two mutually coupled single-loop free-running OEOs with nearby frequencies. We derive the phase dynamics equations of two mutually-coupled OEOs considering weak RF signal injection and the phase stability of the mutually injection-locked system is studied. Also, we present compact closed form expressions for the oscillation frequencies of two mutually injection-pulled OEOs in terms of the injection-ratios and the phase shifts of the mutually injected RF signals. The beat frequency of the mutually injection-pulled system is calculated in terms of the injection ratios. The influence of mutual injection-pulling on the RF output power spectrums of two OEOs are studied. It is shown that the RF output power spectrums of two mutually injection-pulled OEOs are highly asymmetric and strongly depends on the injection ratios. The experimental results corroborating the theoretical findings are given.

We investigate nonlinear phase dynamics of an ideal kink mode, induced by E × B flow. Here the phase is the cross phase (θ c) between perturbed stream function of velocity ( ϕ ˜ ) and magnetic field ( ψ ˜ ), i.e. θ c = θ ϕ − θ ψ . A dimensionless parameter, analogous to the Richardson number, R i = 16 γ kink 2 / ω ˆ E 2 (γ kink: the normalized growth rate of the pure kink mode; ω ˆ E : normalized E × B shearing rate) is defined to measure the competition between phase pinning by the current density and phase detuning by the flow shear. When R i > 1, θ c is locked to a fixed value, corresponding to the conventional eigenmode solution. When R i ≤ 1, θ c enters a phase slipping or oscillating state, corresponding to a nonmodal solution. The nonlinear phase dynamics method provides a more intuitive explanation of the complex dynamical behavior of the kink mode in the presence of E × B shear flow.

First order phase reduced model is a good approximation of the dynamics of forced nonlinear oscillators near its limit cycle. The phase evolution is determined by the unforced frequency, the forcing term, and the phase response curve (PRC). Such models arise in biological oscillations such as in circadian rhythm, neural signaling, heart beat, etc. This technical note focuses on the phase regulation of the circadian rhythm using light intensity as the input. Though the model is simple, the circle topology of the state space needs to be carefully addressed. The most common entrainment method is to use a periodic input, such as in our daily light-dark cycle. We obtain the complete stable entrainment condition based on the entraiment input and the PRC. Motivated by the jet-lag problem, we also consider the minimum time entrainment control to achieve a specified phase shift. Application of the Pontryagin Minimum Principle leads to an efficient solution strategy for the optimal control, without solving the two-point boundary value problem. The optimal control may be further represented as a feedback control law based on the current and desired phases. Our analysis allows the answer to questions such as: When traveling from New York to Paris, is it faster to use light to shift the phase forward by 6 hours or delay the phase by 18 hours? The answer is somewhat counter-intuitive—delaying by 18 hours requires less time. The general answer depends on the light intensity level and the shape of the PRC. PRCs for human and Drosophila from the literature are used to illustrate the results.

We review chimera patterns, which consist of coexisting spatial domains of coherent (synchronized) and incoherent (desynchronized) dynamics in networks of identical oscillators. We focus on chimera states involving amplitude as well as phase dynamics, complex topologies like small-world or hierarchical (fractal), noise, and delay. We show that a plethora of novel chimera patterns arise if one goes beyond the Kuramoto phase oscillator model. For the FitzHugh-Nagumo system, the Van der Pol oscillator, and the Stuart-Landau oscillator with symmetry-breaking coupling various multi-chimera patterns including amplitude chimeras and chimera death occur. To test the robustness of chimera patterns with respect to changes in the structure of the network, regular rings with coupling range R, small-world, and fractal topologies are studied. We also address the robustness of amplitude chimera states in the presence of noise. If delay is added, the lifetime of transient chimeras can be drastically increased.

The phase dynamics of a SQUID consisting of Josephson junctions with topologically nontrivial barriers has been studied. Its comparative analysis with the dynamics of a conventional SQUID has been performed. The current–voltage characteristics have been calculated. The dependence of the return current on the magnetic field has been found. It has been shown that the branch of the current–voltage characteristic corresponding to the resonance frequency in the case of the SQUID with nontrivial barriers is displaced by \(\sqrt 2 \) over voltage. This effect can be used for the detection of Majorana fermions.

This paper presents an analytical model to compute the phase noise spectrum of a self-injection-locked single-loop optoelectronic oscillator (OEO) from the phase dynamics equation of the oscillator. The stability of the self-injection-locked oscillator is also studied. The influence of the feedback coupling network on the phase noise performance of the self-injection-locked OEO is also investigated. The experimental results corroborating the theoretical findings are given. It is shown that the phase noise of the self-injection-locked OEO at the noise offset frequency near the carrier frequency can be reduced by increasing the strength of the self injection signal under stable condition of the loop.

In this work, a Lattice-Boltzmann-Method (LBM) model for simulating hysteresis in a proton exchange membrane (PEMFC) electrode is presented. One of the main challenges hindering study of the cathode catalyst layer (CCL) in PEMFCs is the lack of understanding of two-phase transport and how it affects electrochemical performance. Researchers have typically used high level approximations that oversimplify the microstructure of the CCL—these are known as macrohomogenous models. However, as the field has progressed, the flaws in these idealizations are being revealed, especially in areas of improving our understanding of flooding as well as catalyst layer hysteresis. Previously, the microstructure details needed to build an accurate mesoscale model have eluded researchers; however, with advances in tomography and focused-ion-beam scanning-electron-microscopy (FIB-SEM), creating these representations has become possible. Using LBM with these representations, the difficult problem of catalyst layer capillary hysteresis can be examined. In two-phase capillary hysteresis, both the equilibrium saturation position as well as absolute value depends on the wetting history. Based on the models, it is ascertained that at lower capillary numbers, the liquid begins to undergo capillary fingering – only above a capillary pressure of 5 MPa, a regime change into stable displacement is observed. As capillary fingering does not lead to uniform removal of liquid, the prediction is that because high capillary pressures are needed to change to the regime of stable displacement, wicking will not be an effective means of water removal. Figure 1

We develop a technique for the multivariate data analysis of perturbed self-sustained oscillators. The approach is based on the reconstruction of the phase dynamics model from observations and on a subsequent exploration of this model. For the system, driven by several inputs, we suggest a dynamical disentanglement procedure, allowing us to reconstruct the variability of the system's output that is due to a particular observed input, or, alternatively, to reconstruct the variability which is caused by all the inputs except for the observed one. We focus on the application of the method to the vagal component of the heart rate variability caused by a respiratory influence. We develop an algorithm that extracts purely respiratory-related variability, using a respiratory trace and times of R-peaks in the electrocardiogram. The algorithm can be applied to other systems where the observed bivariate data can be represented as a point process and a slow continuous signal, e.g. for the analysis of neuronal spiking., Comment: 10 pages, 8 figures

In this work, the scaling behavior of the sleep process is evaluated by using detrended fluctuation analysis based on wavelets. The analysis is carried out from arrivals of short and recurrent cortical events called A-phases, which in turn build up the Cyclic Alternating Pattern phenomenon, and are classified in three types: A1, A2 and A3. In this study, 61 sleep recordings corresponding to healthy, nocturnal frontal lobe epilepsy patients and sleep-state misperception subjects, were analyzed. From the A-phase annotations, the onsets were extracted and a binary sequence with one second resolution was generated. An item in the sequence has a value of one if an A-phase onset occurs in the corresponding window, and a value of zero otherwise. In addition, we consider other different temporal resolutions from 2[Formula: see text]s to 256[Formula: see text]s. Furthermore, the same analysis was carried out for sequences obtained from the different types of A-phases and their combinations. The results of the numerical analysis showed a relationship between the time resolutions and the scaling exponents; specifically, for higher time resolutions a white noise behavior is observed, whereas for lower time resolutions a behavior towards to [Formula: see text]-noise is exhibited. Statistical differences among groups were observed by applying various wavelet functions from the Daubechies family and choosing the appropriate sequence of A-phase onsets. This scaling analysis allows the characterization of the free-scale dynamic of the sleep process that is specific for each sleep condition. The scaling exponent could be useful as a diagnosis parameter in clinics when sleep macrostructure does not offer enough information.

This chapter discusses chemical reactions in solution; first, how solvents modify the potential energy surface of the reacting molecules and second, the role of diffusion. As a first approximation, solvent effects are described by models where the solvent is represented by a dielectric continuum, focusing on the Onsager reaction-field model for solvation of polar molecules. The reactants of bimolecular reactions are brought into contact by diffusion, and the interplay between diffusion and chemical reaction that determines the overall reaction rate is described. The solution to Fick’s second law of diffusion, including a term describing bimolecular reaction, is discussed. The limits of diffusion control and activation control, respectively, are identified. It concludes with a stochastic description of diffusion and chemical reaction based on the Fokker–Planck equation, which includes the diffusion of particles interacting via a potential U(r).

We used a method based on the modelling of phase dynamics to quantify the coupling strength between EEG channels. A distinction between the characteristics of coupling strength for the cases of limb movement and background activity is observed.

Sensorimotor predictions are essential for adaptive behavior. In natural environments, events that demand sensorimotor predictions unfold across many timescales, and corresponding temporal predictions (either explicit or implicit) should therefore emerge in brain dynamics. Neuronal oscillations are scale-specific processes found in several frequency bands. They underlie periodicity in sensorimotor processing and can represent temporal predictions via their phase dynamics. These processes build upon endogenous neural rhythmicity and adapt in response to exogenous timing demands. While much of the research on periodicity in neural processing has focused on subsecond oscillations, these fast-scale rhythms are in fact paralleled by critical-like, scale-free dynamics and fluctuations of brain activity at various timescales, ranging from seconds to hundreds of seconds. In this review, we put forth a framework positing that critical brain dynamics are essential for the role of neuronal oscillations in timing and that cross-frequency coupling flexibly organizes neuronal processing across multiple frequencies.

In multi-modular process architectures with independent but interacting subsystems, identification may not be the first choice at hand for closed loop control. A robust relay-based PID autotuning strategy is presented and validated on a quadruple tank system with non-minimum phase dynamics. The controller ensures a specified closed loop robustness, which is of great benefit to the overall performance. The experimental results suggest that the proposed method fulfils the robustness requirement and performs well in various operating conditions of the testbench.

A method for estimating the characteristics of the delayed coupling between the oscillatory systems, which is based on the empirical simulation of the phase dynamics with allowance for the phase-noise correlatedness, is proposed. The method efficiency is illustrated FOR the standard stochastic and chaotic oscillators in numerical experiments. Using this method for analyzing climatic time series, we confirm the presence of the delayed influence of the El Nino Southern oscillation on the North Atlantic Oscillation.

Tomography is a non-invasive, non-intrusive imaging technique allowing the visualization of phase dynamics in industrial and biological processes. This article reviews progress in Electrical Capacitance Volume Tomography (ECVT). ECVT is a direct 3D visualizing technique, unlike three-dimensional imaging, which is based on stacking 2D images to obtain an interpolated 3D image. ECVT has recently matured for real time, non-invasive 3-D monitoring of processes involving materials with strong contrast in dielectric permittivity. In this article, ECVT sensor design, optimization and performance of various sensors seen in literature are summarized. Qualitative Analysis of ECVT image reconstruction techniques has also been presented.

We examine the impact of small parity-time () symmetric perturbations on nonlinear optical honeycomb lattices in the tight-binding limit. We show for strained lattices that complex dispersion relationships do not form under perturbation, and we find a variety of nonlinear wave equations which describe the effective dynamics in this regime. The existence of semilocalized gap solitons in this case is also shown, though we numerically demonstrate these solitons are likely unstable. We show for unstrained lattices under the effect of a restricted class of perturbations, which prevent complex dispersion relationships from appearing, that nontrivial phase dynamics emerge as a result of the perturbation. This phase can be understood as momentum imparted to optical beams by the lattice, thus showing perturbations offer potentially novel means for the control of light in honeycomb lattices.

Phase identification is an arduous task during high-throughput processing experiments, which can be exacerbated by the need to reconcile results from multiple measurement techniques to form a holistic understanding of phase dynamics. Here, we demonstrate AutoPhase, a machine learning algorithm, which can identify the presence of the different phases in spectral and diffraction data. The algorithm uses training data to determine the characteristic features of each phase present and then uses these features to evaluate new spectral and diffraction data. AutoPhase was used to identify oxide phase growth during a high-throughput oxidation study of NiAl bond coats that used x-ray diffraction, Raman, and fluorescence spectroscopic techniques. The algorithm had a minimum overall accuracy of 88.9% for unprocessed data and 98.4% for postprocessed data. Although the features selected by AutoPhase for phase attribution were distinct from those of topical experts, these results show that AutoPhase can substantially increase the throughput high-throughput data analysis.

Although the phase shifts in ongoing oscillations seen in electroencephalograms (EEGs) and magnetoencephalograms are an important factor in discussions of phase dynamics, such as synchrony and reset, few studies have focused specifically on the phase shift. Here we investigate the relationship between phase shifts in alpha-frequency rhythms and reaction times during a visual simple reaction task by applying our previously described method (Naruse et al., 2013), which enables detection of phase shifts from a single EEG trial. In the left, parietal, and occipital areas, the reaction times in the trials in which phase shifts were detected before the button press were significantly longer than in those in which phase shifts were not so detected. These results indicate that phase shifts in the alpha and mu rhythms relate to variability in reaction times.

Rider models are employed to gain insight into bicycle rider steering behaviour and to improve characteristic properties of bicycles. In this paper, stability properties as well as basic dynamic characteristics of the passive (uncontrolled) bicycle-rider system and consequences on the rider control modelling are addressed. In particular, the unstable motion of the system at low velocities and bandwidth limitations caused by non-minimum phase dynamics are emphasized. To analyse the effectiveness of the steering torque and the lean torque as possible rider's inputs to control the dynamics of the bicycle, a controllability analysis of the bicycle-rider system has been performed. It turns out that lean torque input, in contrast to steering torque input, has marginal impact on the dynamics of the system. Finally, a bicycle rider control model considering human rider properties is presented, and its capabilities are demonstrated by performing a curve entering manoeuvre.