Slow Fluctuations Research Articles

Relaxed hydrodynamic theory of electrically driven nonequilibrium steady states

The capability of hydrodynamics to accurately describe slow and long-wavelength fluctuations around nonequilibrium steady states (NESS), characterized by a stationary flow of energy or matter in the presence of a driving force, remains an open question. In this study, we explicitly construct a hydrodynamic description of electrically driven nonequilibrium charged steady states in the limit in which the relaxation of the first nonhydrodynamic excitation is parametrically slow. Our approach involves introducing gapped modes and extending the effective description into a relaxed hydrodynamic theory (RHT). Leveraging the gauge-gravity duality as a tool for controlled computations within nonequilibrium systems, we establish an ultraviolet complete model for these NESS that confirms the validity of our RHT. In summary, our findings provide a concrete realization of the validity of hydrodynamics beyond thermal equilibrium, offering valuable insights into the dynamics of nonequilibrium systems. Published by the American Physical Society 2024

Seasonal and Weekly Patterns of Korean Adolescents' Web Search Activity on Insomnia: Retrospective Study.

Sleep deprivation in adolescents is a common but serious public health issue. Adolescents often have a progressive circadian delay and suffer from insufficient sleep during weekdays due to the school schedule. Temporal patterns in internet search activity data can provide relevant information for understanding the characteristic sleep problems of the adolescent population. We aimed to reveal whether adolescents exhibit distinct temporal seasonal and weekly patterns in internet search activity on insomnia compared to adults. We hypothesized that adolescents exhibit larger variations in the internet search volume for insomnia, particularly in association with the school schedule (e.g., academic vacations and weekends). We extracted the daily search volume for insomnia in South Korean adolescents (13-18 years old), adults (19-59 years old), and young adults (19-24 years old) during the years 2016-2019 using NAVER DataLab, the most popular search engine in South Korea. The daily search volume data for each group were normalized with the annual median of each group. The time series of the search volume was decomposed into slow fluctuation (over a year) and fast fluctuation (within a week) using fast Fourier transform. Next, we compared the normalized search volume across months in a year (slow fluctuation) and days in a week (fast fluctuation). In the annual trend, 2-way ANOVA revealed a significant (group) × (month) interaction (P<.001). Adolescents exhibited much greater seasonal variations across a year than the adult population (coefficient of variation=0.483 for adolescents vs 0.131 for adults). The search volume for insomnia in adolescents was notably higher in January, February, and August, which are academic vacation periods in South Korea (P<.001). In the weekly pattern, 2-way ANOVA revealed a significant (group) × (day) interaction (P<.001). Adolescents showed a considerably increased search volume on Sunday and Monday (P<.001) compared to adults. In contrast, young adults demonstrated seasonal and weekly patterns similar to adults. Adolescents demonstrate distinctive seasonal and weekly patterns in internet searches on insomnia (ie, increased search in vacation months and weekend-weekday transitions), which are closely associated with the school schedule. Adolescents' sleep concerns might be potentially affected by the disrupted daily routine and the delayed sleep phase during vacations and weekends. As we demonstrated, comparing various age groups in infodemiology and infoveillance data might be helpful in identifying distinctive features in vulnerable age groups.

A Bayesian Hierarchical Model of Trial-To-Trial Fluctuations in Decision Criterion.

Classical decision models assume that the parameters giving rise to choice behavior are stable, yet emerging research suggests these parameters may fluctuate over time. Such fluctuations, observed in neural activity and behavioral strategies, have significant implications for understanding decision-making processes. However, empirical studies on fluctuating human decision-making strategies have been limited due to the extensive data requirements for estimating these fluctuations. Here, we introduce hMFC (Hierarchical Model for Fluctuations in Criterion), a Bayesian framework designed to estimate slow fluctuations in the decision criterion from limited data. We first showcase the importance of considering fluctuations in decision criterion: incorrectly assuming a stable criterion gives rise to apparent history effects and underestimates perceptual sensitivity. We then present a hierarchical estimation procedure capable of reliably recovering the underlying state of the fluctuating decision criterion with as few as 500 trials per participant, offering a robust tool for researchers with typical human datasets. Critically, hMFC does not only accurately recover the state of the underlying decision criterion, it also effectively deals with the confounds caused by criterion fluctuations. Lastly, we provide code and a comprehensive demo at www.github.com/robinvloeberghs/hMFC to enable widespread application of hMFC in decision-making research.

Inertial range of magnetorotational turbulence.

Accretion disks around compact stars are formed due to turbulence driven by magnetorotational instability. Despite over 30 years of numerous computational studies on magnetorotational turbulence, the properties of fluctuations in the inertial range-where cross-scale energy transfer dominates over energy injection-have remained elusive, primarily due to insufficient numerical resolution. Here, we report the highest-resolution simulation of magnetorotational turbulence ever conducted. Our simulations reveal a constant cross-scale energy flux, a hallmark of the inertial range. We found that as the cascade proceeds to smaller scales in the inertial range, the kinetic and magnetic energies tend toward equipartitioning with the same spectral slope, and slow magnetosonic fluctuations dominate over Alfvénic fluctuations, having twice the energy. These findings align remarkably with the theoretical expectations from the reduced magnetohydrodynamic model, which assumes a near-azimuthal mean magnetic field. Our results provide important implications for interpreting the radio observations by the Event Horizon Telescope.

Efficient adaptive Bayesian estimation of a slowly fluctuating Overhauser field gradient

Slow fluctuations of Overhauser fields are an important source for decoherence in spin qubits hosted in III-V semiconductor quantum dots. Focusing on the effect of the field gradient on double-dot singlet-triplet qubits, we present two adaptive Bayesian schemes to estimate the magnitude of the gradient by a series of free induction decay experiments. We concentrate on reducing the computational overhead, with a real-time implementation of the schemes in mind. We show how it is possible to achieve a significant improvement of estimation accuracy compared to more traditional estimation methods. We include an analysis of the effects of dephasing and the drift of the gradient itself.

Exceptionally Slow, Long-Range, and Non-Gaussian Critical Fluctuations Dominate the Charge Density Wave Transition.

(TaSe_{4})_{2}I is a well-studied quasi-one-dimensional compound long-known to have a charge-density wave (CDW) transition around 263K. We argue that the critical fluctuations of the pinned CDW order parameter near the transition can be inferred from the resistance noise on account of their coupling to the dissipative normal carriers. Remarkably, the critical fluctuations of the CDW order parameter are slow enough to survive the thermodynamic limit and dominate the low-frequency resistance noise. The noise variance and relaxation time show rapid growth (critical opalescence and critical slowing down) within a temperature window of ϵ≈±0.1, where ϵ is the reduced temperature. This is very wide but consistent with the Ginzburg criterion. We further show that this resistance noise can be quantitatively used to extract the associated critical exponents. Below |ϵ|≲0.02, we observe a crossover from mean-field to a fluctuation-dominated regime with the critical exponents taking anomalously low values. The distribution of fluctuations in the critical transition region is skewed and strongly non-Gaussian. This non-Gaussianity is interpreted as the breakdown of the validity of the central limit theorem as the diverging coherence volume becomes comparable to the macroscopic sample size. The large magnitude critical fluctuations observed over an extended temperature range, as well as the crossover from the mean-field to the fluctuation-dominated regime highlight the role of the quasi-one-dimensional character in controlling the phase transition.

Cell-specific models of hiPSC-CMs developed by the gradient-based parameter optimization method fitting two different action potential waveforms

Parameter optimization (PO) methods to determine the ionic current composition of experimental cardiac action potential (AP) waveform have been developed using a computer model of cardiac membrane excitation. However, it was suggested that fitting a single AP record in the PO method was not always successful in providing a unique answer because of a shortage of information. We found that the PO method worked perfectly if the PO method was applied to a pair of a control AP and a model output AP in which a single ionic current out of six current species, such as IKr, ICaL, INa, IKs, IKur or IbNSC was partially blocked in silico. When the target was replaced by a pair of experimental control and IKr-blocked records of APs generated spontaneously in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), the simultaneous fitting of the two waveforms by the PO method was hampered to some extent by the irregular slow fluctuations in the Vm recording and/or sporadic alteration in AP configurations in the hiPSC-CMs. This technical problem was largely removed by selecting stable segments of the records for the PO method. Moreover, the PO method was made fail-proof by running iteratively in identifying the optimized parameter set to reconstruct both the control and the IKr-blocked AP waveforms. In the lead potential analysis, the quantitative ionic mechanisms deduced from the optimized parameter set were totally consistent with the qualitative view of ionic mechanisms of AP so far described in physiological literature.

Phenotypic plasticity and the effects of thermal fluctuations on specialists and generalists.

Classical theories predict that relatively constant environments should generally favour specialists, while fluctuating environments should be selected for generalists. However, theoretical and empirical results have pointed out that generalist organisms might, on the contrary, perform poorly under fluctuations. In particular, if generalism is underlaid by phenotypic plasticity, performance of generalists should be modulated by the temporal characteristics of environmental fluctuations. Here, we used experiments in microcosms of Tetrahymena thermophila ciliates and a mathematical model to test whether the period or autocorrelation of thermal fluctuations mediate links between the level of generalism and the performance of organisms under fluctuations. In the experiment, thermal fluctuations consistently impeded performance compared with constant conditions. However, the intensity of this effect depended on the level of generalism: while the more specialist strains performed better under fast or negatively autocorrelated fluctuations, plastic generalists performed better under slow or positively autocorrelated fluctuations. Our model suggests that these effects of fluctuations on organisms' performance may result from a time delay in the expression of plasticity, restricting its benefits to slow enough fluctuations. This study points out the need to further investigate the temporal dynamics of phenotypic plasticity to better predict its fitness consequences under environmental fluctuations.

Drift of neural ensembles driven by slow fluctuations of intrinsic excitability.

Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.

Drift of neural ensembles driven by slow fluctuations of intrinsic excitability

Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.

What drives the variability in AGN? Explaining the UV-Xray disconnect through propagating fluctuations

ABSTRACT Intensive broad-band reverberation mapping campaigns have shown that AGN variability is significantly more complex than expected from disc reverberation of the variable X-ray illumination. The UV/optical variability is highly correlated and lagged, with longer lags at longer wavelengths as predicted, but the observed time-scales are longer than expected. Worse, the UV/optical light curves are not well correlated with the X-rays, which should drive them. Instead, we consider an intrinsically variable accretion disc, where slow mass accretion rate fluctuations are generated in the optical-UV disc, propagating down to modulate intrinsically faster X-ray variability from the central regions. We match our model to Fairall 9, a well-studied AGN with L ∼ 0.1LEdd, where the spectrum is dominated by the UV/EUV. Our model produces light curves where the X-rays and UV have very different fast variability, yet are well correlated on longer time-scales, as observed. It predicts that the intrinsic variability has optical/UV leading the X-rays, but including reverberation of the variable EUV from an inner wind produces a lagged bound-free continuum that matches the observed UV-optical lags. We conclude that optical/UV AGN variability is likely driven by intrinsic fluctuations within the disc, not X-ray reprocessing: the observed longer than expected lags are produced by reverberation of the EUV illuminating a wind, not by X-ray illumination of the disc: the increasing lag with increasing wavelength is produced by the increased contribution of the (constant lag) bound-free continuum to the spectrum, rather than indicating intrinsically larger reverberation distances for longer wavelengths.

Reconstructing the Spectrum of a Polyharmonic Signal under Slow Fluctuations in the Sampling Period

Reconstructing the Spectrum of a Polyharmonic Signal under Slow Fluctuations in the Sampling Period

Power-modulated integrated path differential absorption lidar for probing benzene concentration.

Aimed at the regional open-path detection of benzene (C 6 H 6) in the atmosphere, a power-modulated integrated path differential absorption (PM-IPDA) lidar is introduced and demonstrated. Two tunable interband cascade lasers (ICLs) with about 3.2µm wavelength are utilized to generate the required PM optical signal. These two operation central wavelengths (CWs) of the PM-IPDA lidar are, respectively, 3236.6 and 3187.1nm, which can mitigate the influence of significant gases such as H 2 O, C H 4, and HCl on the detection performance. In this work, the fast Fourier transform algorithm is used to retrieve the measured values with the time resolution of 0.1s corresponding to 104 sampling bins at the sampling rate of 100kSps/s. The modulated frequency of the PM-IPDA lidar is selected as 10kHz by laboratory experiments. The slow fluctuation characteristic of the benzene absorption spectrum within the vicinity region of 3.2µm reduces the impact of small wavelength fluctuations on the performance of PM-IPDA lidar, although a scheme modulated only the driving current causes wavelength fluctuations of ∼±0.2n m. These laboratory experiments also indicate the PM-IPDA lidar can reduce the error resulting from 1/f noise. Open-path observation experiments show that the detection limit is about 0.60m g⋅m -3 and that the PM-IPDA lidar can be used for the regional open-path real-time detection of benzene.

Two-time-scale stochastic functional differential equations with wideband noises and jumps

This work examines a class of path-dependent stochastic systems which are hybrid with wideband noise, Poisson jumps and a singularly perturbed Markov chain. The addition of multi-scale Markov chain allows for modeling of discrete events with both fast and slow fluctuation. While this more realistic approach presents analytical challenges due to the non-Markovian formulation resulting from the wideband noise and the singularly perturbed Markov chain. By virtue of the weak convergence method and Itô functional formula, we prove that as ɛ→0, we obtain a Markovian switching jump diffusion. Finally, we offer several examples to illustrate our findings.

Biomimetic dual sensing polymer nanocomposite for biomedical applications.

There is a growing need for sensing materials that can provide multiple sensing capabilities for wearable devices, implantable sensors, and diagnostics tools. As complex human physiology requires materials that can simultaneously detect and respond to slow and fast pressure fluctuations. Mimicking the slow adaptive (SA) and fast adaptive (FA) mechanoreceptors in skin can lead to the development of dual sensing electrospun polymer nanocomposites for biomedical applications. These dual sensing nanocomposites can provide simultaneous sensing of both slow and fast pressure fluctuations, making them ideal for applications such as monitoring vital signs, detecting a wider range of movements and pressures. Here we develop a novel dual sensing PVDF-HFP-based nanocomposite that combines the advantages of capacitive and piezoelectric properties through controling electrospinning environment and processing parameters, polymer solution composition, and addition of nucleating agents such as Carbon Black (CB) to enhance the crystalline development of β-phase, fibre thickness, and morphology. The developed PVDF-HFP/CB nanocomposite presents and response to both slow and fast pressure fluctuations with high capacitance (5.37nF) and output voltage (1.51V) allowing for accurate and reliable measurements.

Causal phase-dependent control of non-spatial attention in human prefrontal cortex

Non-spatial attention is a fundamental cognitive mechanism that allows organisms to orient the focus of conscious awareness towards sensory information that is relevant to a behavioural goal while shifting it away from irrelevant stimuli. It has been suggested that attention is regulated by the ongoing phase of slow excitability fluctuations of neural activity in the prefrontal cortex, a hypothesis that has been challenged with no consensus. Here we developed a behavioural and non-invasive stimulation paradigm aiming at modulating slow excitability fluctuations of the inferior frontal junction. Using this approach, we show that non-spatial attention can be selectively modulated as a function of the ongoing phase of exogenously modulated excitability states of this brain structure. These results demonstrate that non-spatial attention relies on ongoing prefrontal excitability states, which are probably regulated by slow oscillatory dynamics, that orchestrate goal-oriented behaviour.

Experiment on long-term forecasting of geomagnetic activity based on nonlocal correlations

The experiment was performed on the use of advanced macroscopic nonlocal correlations to forecast slow random fluctuations of the Dst index of geomagnetic activity. The global maximum correlation of Dst with the signal of the electrode detector reaches 0.97, which is sufficient for the forecast, and its time shift corresponds to the advance of the detector signal relative to Dst by 329 days. The large magnitude of the time shift is due to the slow diffusion mechanism of entanglement swapping between the detector and the source. At the same time, the position of the global maximum of the correlation function coincides with the position of the global minimum of the entropy independence function, which confirms its undistorted by possible nonlinearity of the relationship and determines the optimal lead time of the forecast. Long series of test forecasts Dst have been calculated using data from a nonlocal correlation detector with a fixed lead time using three methods: current regression, current impulse transient response and current neural network. The accuracy of the forecasts is sufficient for all practical purposes.

An Improved PSO-Based MPPT Technique Using Stability and Steady State Analyses Under Partial Shading Conditions

Partial shading (PS) conditions create challenging issues for the maximum power point tracking (MPPT) algorithms in photovoltaic (PV) systems such as getting stuck in the local maximum power points (LMPP), slow tracking time and fluctuations in the generated power during tracking the global maximum power point (GMPP). To address these issues, this paper brings contributions by proposing an improved PSO-based MPPT technique tailored for PS conditions. First, to highlight the weakness of the traditional PSO-MPPT technique, its stability and steady state behavior in the PS conditions is analyzed in depth. Second, the required criteria to achieve a stable response are obtained. Finally, a novel technique to estimate the convex area of the power versus voltage (P-V) curve is presented where the GMPP is located using two voltage boundaries. The performance of the proposed MPPT technique is experimentally validated. The results highlight the capabilities of the proposed technique in finding the GMPP with a rapid convergence and small fluctuations. The proposed MPPT is applicable to most PV inverters under any PS conditions.

Harvesting a population model with Allee effect in a periodically varying environment

&lt;abstract&gt;&lt;p&gt;A nonautonomous logistic population model with a feature of an Allee threshold has been investigated in a periodically fluctuating environment. A slow periodicity of the harvesting effort was considered and may arise in response to relatively slow fluctuations of the environment. This assumption permits obtaining the analytical approximate solutions of such model using the perturbation approach based on the slow variation. Thus, the analytical expressions of the population evolution in the situation of subcritical and the supercritical harvesting were obtained and discussed in the framework of the Allee effect. Since the exact solution was not available due to the nonlinearity of the system, the numerical computation was considered to validate our analytical approximation. The comparison between the two methods showed a remarkable agreement as the time progressed, while such agreement fell off when the time was close to the initial density. Moreover, in the absence of the periodicity of the harvesting term, the expressions of the population evolution reduced to the exact solutions but in implicit forms. The finding results were appropriate for a wide range of parameter values, which lead to avoiding extensive recalculations while displaying the population behavior.&lt;/p&gt;&lt;/abstract&gt;

Conformational Dynamics in Proteins: Entangled Slow Fluctuations and Nonequilibrium Reaction Events.

Proteins exhibit conformational fluctuations and changes over various time scales, ranging from rapid picosecond-scale local atomic motions to slower microsecond-scale global conformational transformations. In the presence of these intricate fluctuations, chemical reactions occur and functions emerge. These conformational fluctuations of proteins are not merely stochastic random motions but possess distinct spatiotemporal characteristics. Moreover, chemical reactions do not always proceed along a single reaction coordinate in a quasi-equilibrium manner. Therefore, it is essential to understand spatiotemporal conformational fluctuations of proteins and the conformational change processes associated with reactions. In this Perspective, we shed light on the complex dynamics of proteins and their role in enzyme catalysis by presenting recent results regarding dynamic couplings and disorder in the conformational dynamics of proteins and rare but rapid enzymatic reaction events obtained from molecular dynamics simulations.