Fast Time Scale Research Articles

Photoluminescence Study of Carrier Localization and Recombination in Nearly Strain‐Balanced Nonpolar InGaN/AlGaN Quantum Wells

Temperature‐dependent continuous‐excitation and time‐resolved photoluminescence are studied to probe carrier localization and recombination in nearly strain‐balanced m‐plane In0.09Ga0.91N/Al0.19Ga0.81N multi‐quantum wells grown by plasma‐assisted molecular‐beam epitaxy. An average localization depth of 21 meV is estimated for the undoped sample. This depth is much smaller than the reported values in polar structures and m‐plane InGaN quantum wells. As part of this study, temperature and magnetic field dependence of time‐resolved photoluminescence is performed. At 2 K, an initial fast decay time of ≈0.3 ns is measured for both undoped and doped structures. The undoped sample also exhibits a slow decay component with a time scale of 2.2 ns. The existence of two relaxation paths in the undoped structure can be attributed to different localization centers. The fast relaxation decays are relatively insensitive to external magnetic fields, while the slower relaxation time constant decreases significantly with increasing magnetic fields. The fast decay time scale in the undoped sample is likely due to indium fluctuations in the quantum well. The slow decay time may be related to carrier localization in the barriers. The addition of doping leads to a single fast decay time likely due to stronger exciton localization in the InGaN quantum wells.

Thinking takes time: Children use agents' response times to infer the source, quality, and complexity of their knowledge

Limits on mental speed entail speed-accuracy tradeoffs for problem-solving, but memory and perception are accurate on much faster timescales. While response times drive inference across the behavioral sciences, they may also help laypeople interpret each othejognrs' everyday behavior. We examined children's (ages 5 to 10) use of agents' response time to infer the source and quality of their knowledge. In each trial, children saw a pathfinding puzzle presented to an agent, who claimed to have solved it after either 3s or 20s. In Experiment 1 (n = 135), children used agents' response speed to distinguish between memory, perception, and novel inference. In Experiment 2 (n = 135), children predicted that fast responses would be inaccurate, but were less skeptical of slow agents. In Experiment 3 (n = 128), children inferred task complexity from agents' speed. Our findings suggest that the simple intuition that thinking takes time may scaffold everyday social cognition.

Critical Role of Structural Water for Enhanced Li+ Insertion Kinetics in Crystalline Tungsten Oxides

Electrochemical ion insertion into transition metal oxides forms the foundation of several energy technologies. Transition metal oxides can exhibit sluggish ion transport and/or phase-transformation kinetics during ion insertion that can limit their performance at high rates (<10 min). In this study, we investigate the role of structural water in transition metal oxides during Li+ insertion using staircase potentiostatic electrochemical impedance spectroscopy (SPEIS) and electrochemical quartz crystal microbalance (EQCM) analysis of WO3·H2O and WO3 thin-film electrodes. Overall, the presence of structural water in WO3·H2O improves Li+ insertion kinetics compared to WO3 and leads to a less potential-dependent insertion process. Operando electrogravimetry and 3D Bode impedance analyses of nanostructured films reveal that the presence of structural water promotes charge accommodation without significant co-insertion of solvent, leading to our hypothesis that the electrochemically induced structural transitions of WO3 hinder the electrode response at faster timescales (<10 min). Designing layered materials with confined fluids that exhibit less structural transitions may lead to more versatile ion-insertion hosts for next-generation electrochemical technologies.

Effect of Slow–Fast Time Scale on Transient Dynamics in a Realistic Prey-Predator System

Systems with multiple time scales, often referred to as `slow–fast systems’, have been a focus of research for about three decades. Such systems show a variety of interesting, sometimes counter-intuitive dynamical behaviors and are believed to, in many cases, provide a more realistic description of ecological dynamics. In particular, the presence of slow–fast time scales is known to be one of the main mechanisms resulting in long transients—dynamical behavior that mimics a system’s asymptotic regime but only lasts for a finite (albeit very long) time. A prey–predator system where the prey growth rate is much larger than that of the predator is a paradigmatic example of slow–fast systems. In this paper, we provide detailed investigation of a more advanced variant of prey–predator system that has been overlooked in previous studies, that is, where the predator response is ratio-dependent and the predator mortality is nonlinear. We perform a comprehensive analytical study of this system to reveal a sequence of bifurcations that are responsible for the change in the system dynamics from a simple steady state and/or a limit cycle to canards and relaxation oscillations. We then consider how those changes in the system dynamics affect the properties of long transient dynamics. We conclude with a discussion of the ecological implications of our findings, in particular to argue that the changes in the system dynamics in response to an increase of the time scale ratio are counter-intuitive or even paradoxical.

Magneto-stimulation limits in medical imaging applications with rapid field dynamics

Objective. The goal of this work is to extend previous peripheral nerve stimulation (PNS) studies to scenarios relevant to magnetic particle imaging (MPI) and low-field magnetic resonance imaging (MRI), where field dynamics can evolve at kilo-hertz frequencies. Approach. We have constructed an apparatus for PNS threshold determination on a subject’s limb, capable of narrow and broad-band magnetic stimulation with pulse characteristic times down to 40 μs. Main result. From a first set of measurements on 51 volunteers, we conclude that the PNS dependence on pulse frequency/rise-time is compatible with traditional stimulation models where nervous responses are characterized by a rheobase and a chronaxie. Additionally, we have extended pulse length studies to these fast timescales and confirm thresholds increase significantly as trains transition from tens to a few pulses. We also look at the influence of field spatial distribution on PNS effects, and find that thresholds are higher in an approximately linearly inhomogeneous field (relevant to MRI) than in a rather homogeneous distribution (as in MPI). Significance. PNS constrains the clinical performance of MRI and MPI systems. Extensive magneto-stimulation studies have been carried out recently in the field of MPI, where typical operation frequencies range from single to tens of kilo-hertz. However, PNS literature is scarce for MRI in this fast regime, relevant to small (low inductance) dedicated MRI setups, and where the resonant character of MPI coils prevents studies of broad-band stimulation pulses. This work advances in this direction.

Strong convergence rates in averaging principle for slow-fast McKean-Vlasov SPDEs

In this paper, we aim to study the asymptotic behavior for a class of McKean-Vlasov stochastic partial differential equations with slow and fast time-scales. Using the variational approach and classical Khasminskii time discretization, we show that the slow component strongly converges to the solution of the associated averaged equation. In particular, the corresponding convergence rates are also obtained. The main results can be applied to demonstrate the averaging principle for various McKean-Vlasov nonlinear SPDEs such as stochastic porous media type equation, stochastic p-Laplace type equation and also some McKean-Vlasov stochastic differential equations.

Strong and weak convergence rates for slow–fast stochastic differential equations driven by α-stable process

In this paper, we study the averaging principle for a class of stochastic differential equations driven by α-stable processes with slow and fast time-scales, where α∈(1,2). We prove that the strong and weak convergence order are 1−1/α and 1 respectively. We show, by a simple example, that 1−1/α is the optimal strong convergence rate.

Distinct Surface Warming Response Over the Western and Eastern Equatorial Pacific to Radiative Forcing

AbstractThis study examines regional characteristics of atmospheric and oceanic feedback processes in the western and eastern equatorial Pacific, by applying a localized surface heating in the respective region in a hierarchy of climate models. A western Pacific forcing is largely offset by a negative shortwave cloud radiative effect and damping via wind‐evaporation‐SST feedback. In contrast, an eastern Pacific forcing, while being partially compensated for by ocean dynamical adjustments, induces an amplified warming extending to the central Pacific due to weak local damping mechanisms. As for the inter‐model spread of the future tropical Pacific surface warming pattern, the ocean heat uptake response in the east can explain much of the spread both on fast (&lt;5 years) and slow (&gt;100 years) timescales. Our results suggest that an “El Niño‐like” warming pattern is probable in response to increasing greenhouse gases owing to the strong negative feedback intrinsic to the western Pacific.

Modeling Tumor Disease and Sepsis by Networks of Adaptively Coupled Phase Oscillators.

In this study, we provide a dynamical systems perspective to the modelling of pathological states induced by tumors or infection. A unified disease model is established using the innate immune system as the reference point. We propose a two-layer network model for carcinogenesis and sepsis based upon the interaction of parenchymal cells and immune cells via cytokines, and the co-evolutionary dynamics of parenchymal, immune cells, and cytokines. Our aim is to show that the complex cellular cooperation between parenchyma and stroma (immune layer) in the physiological and pathological case can be qualitatively and functionally described by a simple paradigmatic model of phase oscillators. By this, we explain carcinogenesis, tumor progression, and sepsis by destabilization of the healthy homeostatic state (frequency synchronized), and emergence of a pathological state (desynchronized or multifrequency cluster). The coupled dynamics of parenchymal cells (metabolism) and nonspecific immune cells (reaction of innate immune system) are represented by nodes of a duplex layer. The cytokine interaction is modeled by adaptive coupling weights between the nodes representing the immune cells (with fast adaptation time scale) and the parenchymal cells (slow adaptation time scale) and between the pairs of parenchymal and immune cells in the duplex network (fixed bidirectional coupling). Thereby, carcinogenesis, organ dysfunction in sepsis, and recurrence risk can be described in a correct functional context.

Analysis and characterization of ZnO QDs in hybrid device

Recent advances in nanomaterials have been led by new synthetic methods that provide control over size, morphology and crystal quality of material. In the present study, the effect of ultrasonic waves on characteristics and crystal quality of ZnO quantum dots (QDs) is evaluated by comparing two samples prepared by sonochemical and conventional methods. Ultrasonic waves cause to the production of smaller QDs in a fast time scale with improved morphology and crystal quality. Also, the growth of ZnO QDs is tried to discuss by study of crystal growth kinetic. Then, the performance of two hybrid devices constituted from poly (N-vinylcarbazol) and two types of ZnO is examined by current voltage curves. The device fabricated from ultrasonic-prepared ZnO shows larger photocurrent density indicating the enhancement of its applicability to be used as UV light switches or photodetectors.

Kinetic study on growth of organic lead halide perovskite nanocrystals through water-oil interfacial reaction

Formation of organometal lead halide perovskite nanocrystals (OPNCs) occurs in fast time scales from milliseconds to seconds, thus posing a hurdle to the growth kinetic study of OPNCs. Water-oil interfacial reaction provides an effective approach for retarding the growth of CH3NH3PbBr3 OPNCs. Herein, we investigate systematically the kinetics of CH3NH3PbBr3 NCs in water–oil reaction by in situ optical characterization, and identify the key factors governing the growth of OPNCs. Molecular polarity and viscosity of the solvent are found to be crucial to the diffusion step by affecting the diffusion coefficient of CH3NH3+. Covalent characteristic of the Pb source and molecular structure of the ligands have an impact on the rate-limiting step of PbBr64- formation. In addition, we establish the reaction condition that enables controlled growth of formamidinium lead bromide NCs, thereby demonstrating the application of the growth kinetic in the synthesis of other OPNCs. Kinetic investigation of OPNCs provides a better understanding of their crystallization process that helps guide the development of quantum confined OPNCs for optoelectronic applications.

Self-sustained vibrations in the system of a bi-harmonically driven pendulum

In this paper, we investigate self-sustained oscillations in a weakly damped pendulum system under the action of two harmonic forces with close frequencies. With no limitations on the amplitude of oscillations, we construct an asymptotic procedure; separating the dynamics of the system into fast, slow, and super-slow timescales, we show that the movements of the pendulum on the fast and slow scales are decoupled. The system of equations for the amplitude of the envelope and the phase of the state function in the leading approximation on a slow scale become autonomous when the super-slow time parameter depends rather weakly on slow time. Using this technique, it is possible to analyse relaxation oscillations in a wide frequency range along with their dependence on the friction coefficient. The results obtained are in good agreement with the numerical solution of the original system.

Buckling versus Crystal Expulsion Controlled by Deformation Rate of Particle-Coated Air Bubbles in Oil.

Oil foams stabilized by crystallizing agents exhibit outstanding stability and show promise for applications in consumer products. The stability and mechanics imparted by the interfacial layer of crystals underpin product shelf life, as well as optimal processing conditions and performance in applications. Shelf life is affected by the stability against bubble dissolution over a long time scale, which leads to slow compression of the interfacial layer. In processing flow conditions, the imposed deformation is characterized by much shorter time scales. In practical situations, the crystal layer is therefore subjected to deformation on extremely different time scales. Despite its importance, our understanding of the behavior of such interfacial layers at different time scales remains limited. To address this gap, here we investigate the dynamics of single, crystal-coated bubbles isolated from an oleofoam, at two extreme time scales: the diffusion-limited time scale characteristic of bubble dissolution, ∼104 s, and a fast time scale characteristic of processing flow conditions, ∼10–3 s. In our experiments, slow deformation is obtained by bubble dissolution, and fast deformation in controlled conditions with real-time imaging is obtained using ultrasound-induced bubble oscillations. The experiments reveal that the fate of the interfacial layer is dramatically affected by the dynamics of deformation: after complete bubble dissolution, a continuous solid layer remains; after fast, oscillatory deformation of the layer, small crystals are expelled from the layer. This observation shows promise toward developing stimuli-responsive systems, with sensitivity to deformation rate, in addition to the already known thermoresponsiveness and photoresponsiveness of oleofoams.

Predictive Neuronal Adaptation as a Basis for Consciousness.

Being able to correctly predict the future and to adjust own actions accordingly can offer a great survival advantage. In fact, this could be the main reason why brains evolved. Consciousness, the most mysterious feature of brain activity, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Similarly at a single neuron level, predicting future activity and adapting synaptic inputs accordingly was shown to be the best strategy to maximize the metabolic energy for a neuron. Following on these ideas, here we examined if surprise minimization by single neurons could be a basis for consciousness. First, we showed in simulations that as a neural network learns a new task, then the surprise within neurons (defined as the difference between actual and expected activity) changes similarly to the consciousness of skills in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of milliseconds) resulted in improved network performance. This improvement is likely because adapting activity based on the internal predictive model allows each neuron to make a more “educated” response to stimuli. Based on those results, we propose that the neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. Such adaptation allows neurons to exchange information about own predictions and thus to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this can allow testing directly our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational, and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.

Direct Determination of Reduced Models of a Class of Singularly Perturbed Nonlinear Systems on Three Time Scales in a Bond Graph Approach

One of the most important features in the analysis of the singular perturbation methods is the reduction of models. Likewise, the bond graph methodology in dynamic system modeling has been widely used. In this paper, the bond graph modeling of nonlinear systems with singular perturbations is presented. The class of nonlinear systems is the product of state variables on three time scales (fast, medium, and slow). Through this paper, the symmetry of mathematical modeling and graphical modeling can be established. A main characteristic of the bond graph is the application of causality to its elements. When an integral causality is assigned to the storage elements that determine the state variables, the dynamic model is obtained. If the storage elements of the fast dynamics have a derivative causality and the storage elements of the medium and slow dynamics an integral causality is assigned, a reduced model is obtained, which consists of a dynamic model for the medium and slow time scales and a stationary model of the fast time scale. By applying derivative causality to the storage elements of the fast and medium dynamics and an integral causality to the storage elements of the slow dynamics, the quasi-steady-state model for the slow dynamics is obtained and stationary models for the fast and medium dynamics are defined. The exact and reduced models of singularly perturbed systems can be interpreted as another symmetry in the development of this paper. Finally, the proposed methodology was applied to a system with three time scales in a bond graph approach, and simulation results are shown in order to indicate the effectiveness of the proposed methodology.

Federated Learning-Based Explainable Anomaly Detection for Industrial Control Systems

We are now witnessing the rapid growth of advanced technologies and their application, leading to Smart Manufacturing (SM). The Internet of Things (IoT) is one of the main technologies used to enable smart factories, which is connecting all industrial assets, including machines and control systems, with the information systems and the business processes. Industrial Control Systems of smart IoT-based factories are one of the top industries attacked by numerous threats, especially unknown and novel attacks. As a result, with the distributed structure of plenty of IoT front-end sensing devices in SM, an effectively distributed anomaly detection (AD) architecture for IoT-based ICSs should: achieve high detection performance, train and learn new data patterns in a fast time scale, and have lightweight to be deployed on resource-constrained edge devices. To date, most solutions for anomaly detection have not fulfilled all of these requirements. In addition, the interpretability of why an instance is predicted to be abnormal is hardly concerned. In this paper, we propose the so- called FedeX architecture to address those challenges. The experiments show that FedeX outperforms 14 other existing anomaly detection solutions on all detection metrics with the liquid storage data set. And with Recall of 1 and F1-score of 0.9857, it also outperforms those solutions on the SWAT data set. FedeX is also proven to be fast in terms of training time of about 7.5 minutes and lightweight in terms of hardware requirement with memory consumption of 14&#x0025;, allowing us to deploy anomaly detection tasks on top of edge computing infrastructure and in real-time. Besides, FedeX is considered as one of the frameworks at the forefront of interpreting the predicted anomalies by using XAI, which enables experts to make quick decisions and trust the model more.

Online Feedback-based Optimization with Multi-input Direct Constraint Control

The starting point of this paper is to solve a steady-state optimization problem with simple feedback controllers using the Primal-Dual approach. However, this approach controls the constraint indirectly and on a slow time scale by manipulating the Dual variables (Lagrange multipliers). This control strategy may lead to unacceptable dynamic violations. The main contribution of this paper is to reduce the violations by introducing direct constraint control on the fast time scale using a selector. To get consistency with the steady-state optimization, we need to change the constraints in the Primal-Dual approach to involve the input used for direct constraint control. As a further refinement of this idea, we propose a multi-input direct constraint control for cases where many inputs affect the constraint value, and where pairing with a single input may lead to input rate saturation. The proposed control structure is applied to a subsea oil production network, showing that the proposed approach can minimize the dynamic violation during transient, and can achieve optimal steady-state conditions using simple feedback controllers.

Computational Approaches and Tools as Applied to the Study of Rhythms and Chaos in Biology.

The temporal dynamics in biological systems displays a wide range of behaviors, from periodic oscillations, as in rhythms, bursts, long-range (fractal) correlations, chaotic dynamics up to brown and white noise. Herein, we propose a comprehensive analytical strategy for identifying, representing, and analyzing biological time series, focusing on two strongly linked dynamics: periodic (oscillatory) rhythms and chaos. Understanding the underlying temporal dynamics of a system is of fundamental importance; however, it presents methodological challenges due to intrinsic characteristics, among them the presence of noise or trends, and distinct dynamics at different time scales given by molecular, dcellular, organ, and organism levels of organization. For example, in locomotion circadian and ultradian rhythms coexist with fractal dynamics at faster time scales. We propose and describe the use of a combined approach employing different analytical methodologies to synergize their strengths and mitigate their weaknesses. Specifically, we describe advantages and caveats to consider for applying probability distribution, autocorrelation analysis, phase space reconstruction, Lyapunov exponent estimation as well as different analyses such as harmonic, namely, power spectrum; continuous wavelet transforms; synchrosqueezing transform; and wavelet coherence. Computational harmonic analysis is proposed as an analytical framework for using different types of wavelet analyses. We show that when the correct wavelet analysis is applied, the complexity in the statistical properties, including temporal scales, present in time series of signals, can be unveiled and modeled. Our chapter showcase two specific examples where an in-depth analysis of rhythms and chaos is performed: (1) locomotor and food intake rhythms over a 42-day period of mice subjected to different feeding regimes; and (2) chaotic calcium dynamics in a computational model of mitochondrial function.

Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach

Frequency control plays a pivotal role in reliable power system operations. It is conventionally performed in a hierarchical way that first rapidly stabilizes the frequency deviations and then slowly recovers the nominal frequency. However, as the generation mix shifts from synchronous generators to renewable resources, power systems experience larger and faster frequency fluctuations due to the loss of inertia, which adversely impacts the frequency stability. This has motivated active research in algorithms that jointly address frequency degradation and economic efficiency in a fast timescale, among which the distributed averaging-based integral (DAI) control is a notable one that sets controllable power injections directly proportional to the integrals of frequency deviation and economic inefficiency signals. Nevertheless, DAI does not typically consider the transient performance of the system following power disturbances and has been restricted to quadratic operational cost functions. This paper aims to leverage nonlinear optimal controllers to simultaneously achieve optimal transient frequency control and find the most economic power dispatch for frequency restoration. To this end, we integrate reinforcement learning (RL) to the classic DAI, which results in RL-DAI control. Specifically, we use RL to learn a neural network-based control policy mapping from the integral variables of DAI to the controllable power injections which provides optimal transient frequency control, while DAI inherently ensures the frequency restoration and optimal economic dispatch. Compared to existing methods, we provide provable guarantees on the stability of the learned controllers and extend the set of allowable cost functions to a much larger class. Simulations on the 39-bus New England system illustrate our results.

Comparison of Simple Feedback Control Structures for Constrained Optimal Operation

Optimal operation of systems subject to constraints can be challenging, especially when the set of active constraints changes during operation due to disturbances. The use of traditional model-based real-time optimization strategies has limitations related to model-plant mismatch and computational effort, and therefore the use of simple feedback strategies in lower layers is a good alternative to avoid these issues on fast timescales. This work aims to evaluate the viability of region-based control structures using selectors and the use of a primal-dual feedback optimizing control framework on this kind of problem, through the study of two process systems with changing active constraints. While region-based strategies focus on the effective control in specific active constraint regions, the primal-dual approach allows for control in all possible regions, with the introduction of intermediate variables that estimate the Lagrange multiplier values. In the first case study, the traditional region-based strategy could not handle all constraint regions, and an additional logical switching was necessary to account for the remaining region. The implementation of primal-dual feedback optimizing control was flexible enough to control the system in all regions without the need for additional logic. The second case study presents more constraints than the first and increased nonlinearities, which makes finding controlled variables for the unconstrained degrees of freedom challenging. The primal-dual control framework was able to drive the system to the optimum in all considered regions. Therefore, this framework is deemed as a promising control structure for optimal operation in the presence of changing active constraints.