Fast Time Scale Research Articles

Ground-Based Characterisation of a Compact Instrument for Gamma-ray Burst Detection on a CubeSat Platform

Gamma-ray bursts (GRBs) are intense and short-lived cosmic explosions. Miniaturised CubeSat-compatible instruments for the study of GRBs are being developed to help bridge the gap in large missions and assist in achieving full sky coverage. CubeSats are small, compact satellites conforming to a design standard and have transformed the space industry. They are relatively low-cost and are developed on fast timescales, which has provided unparalleled access to space. This paper focuses on GMOD, the gamma-ray module, onboard the 2U CubeSat EIRSAT-1, launched on December 1st 2023. GMOD is a scintillation-based instrument with a cerium bromide crystal coupled to an array of sixteen silicon photomultipliers, designed for the detection of GRBs. The characterisation of GMOD in the spacecraft, along with the validation of an updated spacecraft MEGAlib model is presented and this approach can be followed by other CubeSats with similar science goals. The energy resolution of the flight model is 7.07% at 662 keV and the effective area peaks in the tens to hundreds of keV, making it a suitable instrument for the detection of GRBs. An investigation into the instrument’s angular response is also detailed. The results from this characterisation campaign are a benchmark for the instrument’s performance pre-launch and will be used to compare with the detector’s performance in orbit.

Dynamic UAV Deployment Scheme Based on Edge Computing for Forest Fire Scenarios.

This study investigates the dynamic deployment of unmanned aerial vehicles (UAVs) using edge computing in a forest fire scenario. We consider the dynamically changing characteristics of forest fires and the corresponding varying resource requirements. Based on this, this paper models a two-timescale UAV dynamic deployment scheme by considering the dynamic changes in the number and position of UAVs. In the slow timescale, we use a gate recurrent unit (GRU) to predict the number of future users and determine the number of UAVs based on the resource requirements. UAVs with low energy are replaced accordingly. In the fast timescale, a deep-reinforcement-learning-based UAV position deployment algorithm is designed to enable the low-latency processing of computational tasks by adjusting the UAV positions in real time to meet the ground devices' computational demands. The simulation results demonstrate that the proposed scheme achieves better prediction accuracy. The number and position of UAVs can be adapted to resource demand changes and reduce task execution delays.

Constraints on the subsecond modulation of striatal dynamics by physiological dopamine signaling.

Dopaminergic neurons play a crucial role in associative learning, but their capacity to regulate behavior on subsecond timescales remains debated. It is thought that dopaminergic neurons drive certain behaviors by rapidly modulating striatal spiking activity; however, a view has emerged that only artificially high (that is, supra-physiological) dopamine signals alter behavior on fast timescales. This raises the possibility that moment-to-moment striatal spiking activity is not strongly shaped by dopamine signals in the physiological range. To test this, we transiently altered dopamine levels while monitoring spiking responses in the ventral striatum of behaving mice. These manipulations led to only weak changes in striatal activity, except when dopamine release exceeded reward-matched levels. These findings suggest that dopaminergic neurons normally play a minor role in the subsecond modulation of striatal dynamics in relation to other inputs and demonstrate the importance of discerning dopaminergic neuron contributions to brain function under physiological and potentially nonphysiological conditions.

Short-timescale Spatial Variability of Ganymede’s Optical Aurora

Ganymede’s auroras are the product of complex interactions between its intrinsic magnetosphere and the surrounding Jovian plasma environment and can be used to derive both atmospheric composition and density. In this study, we analyzed a time series of Ganymede’s optical auroras taken with Keck I/HIRES during eclipse by Jupiter on 2021 June 8 UTC, one day after the Juno flyby of Ganymede. The data had sufficient signal-to-noise in individual 5 minute observations to allow for the first high-cadence analysis of the spatial distribution of the optical aurora brightness and the ratio between the [O i] 630.0 and 557.7 nm disk-integrated auroral brightnesses—a quantity diagnostic of the relative abundances of O, O2, and H2O in Ganymede’s atmosphere. We found that the hemisphere closer to the centrifugal equator of Jupiter’s magnetosphere (where electron number density is highest) was up to twice as bright as the opposing hemisphere. The dusk (trailing) hemisphere, subjected to the highest flux of charged particles from Jupiter’s magnetosphere, was also consistently almost twice as bright as the dawn (leading) hemisphere. We modeled emission from simulated O2 and H2O atmospheres during eclipse and found that if Ganymede hosts an H2O sublimation atmosphere in sunlight, it must collapse on a faster timescale than expected to explain its absence in our data given our current understanding of Ganymede’s surface properties.

Real-Time Simulation of Ultrafast Electronic Dynamics of Nanoscale Systems Involving an Organic Molecule and a Nanoparticle Dimer.

Understanding and predicting the behavior of nanomaterials composed of plasmons interacting with quantum emitters at ultrafast timescales is crucial for the better manipulation of light at the nanoscale and advancing technologies like ultrafast communication and computing. Here we perform a simulation of the "real-time" electronic dynamics of a coupled molecule-metal nanoparticle dimer interacting with an ultrashort resonant laser pulse by combining the real-time time-dependent density functional theory (RT-TDDFT) approach with the time-domain frequency-dependent fluctuating charge (TD-ωFQ) model, an atomistic electromagnetic (AEM) model for the dynamic plasmonic response of nanoparticles. It is shown that the induced dipoles evolve from an exponential decay pattern to a beat pattern with an increase in coupling strength, which is altered by changing the molecular orientation relative to the dimer axis. It is further shown that in the strong coupling regime, both the excited molecule and the plasmon relax rapidly due to the molecule-plasmon interaction, and the efficient coherent energy exchange between the interacting molecule and plasmon modes occurs on a femtosecond (fs) timescale. This work provides guidance on manipulating light-matter interaction and studying molecular plasmonics at extremely fast timescales.

Tabular and Deep Learning for the Whittle Index

The Whittle index policy is a heuristic that has shown remarkably good performance (with guaranteed asymptotic optimality) when applied to the class of problems known as Restless Multi-Armed Bandit Problems (RMABPs). In this paper we present QWI and QWINN, two reinforcement learning algorithms, respectively tabular and deep, to learn the Whittle index for the total discounted criterion. The key feature is the use of two time-scales, a faster one to update the state-action Q -values, and a relatively slower one to update the Whittle indices. In our main theoretical result we show that QWI, which is a tabular implementation, converges to the real Whittle indices. We then present QWINN, an adaptation of QWI algorithm using neural networks to compute the Q -values on the faster time-scale, which is able to extrapolate information from one state to another and scales naturally to large state-space environments. For QWINN, we show that all local minima of the Bellman error are locally stable equilibria, which is the first result of its kind for DQN-based schemes. Numerical computations show that QWI and QWINN converge faster than the standard Q -learning algorithm, neural-network based approximate Q-learning and other state of the art algorithms.

Plasma in liquids induced modification of Cu surfaces

Copper oxide surfaces are commonly used as the catalyst for the CO2 reduction reaction towards hydrocarbons. However, the lifetime of these catalyst surfaces is limited. In this paper, a method of production of copper oxides through in-liquid plasma is explored, which may be a suitable reactivation method in such applications. The influence of the plasma, ignited in distilled water, with copper and its oxides is monitored in − situ using infrared spectroscopy and ex-situ using scanning electron spectroscop and x-ray photoelectron spectroscopy of the samples. It is shown that the interaction of the plasma with the samples causes a reduction of the copper oxide on a fast time scale and an oxidation on a longer time scale. The formation of preferentially oriented copper nanocubes is observed.

The impact of GLP-1 signalling on the energy metabolism of pancreatic islet β-cells and extrapancreatic tissues

Glucagon-like peptide-1 signalling impacts glucose homeostasis and appetite thereby indirectly affecting substrate availability at the whole-body level. The incretin canonically produces an insulinotropic effect, thereby lowering blood glucose levels by promoting the uptake and inhibiting the production of the sugar by peripheral tissues. Likewise, GLP-1 signalling within the central nervous system reduces the appetite and food intake, whereas its gastric effect delays the absorption of nutrients, thus improving glycaemic control and reducing the risk of postprandial hyperglycaemia. We review the molecular aspects of the GLP-1 signalling, focusing on its impact on intracellular energy metabolism. Whilst the incretin exerts its effects predominantly via a Gs receptor, which decodes the incretin signal into the elevation of intracellular cAMP levels, the downstream signalling cascades within the cell, acting on fast and slow timescales, resulting in an enhancement or an attenuation of glucose catabolism, respectively.

Analysis of long transients and detection of early warning signals of extinction in a class of predator-prey models exhibiting bistable behavior.

In this paper, we develop a method of analyzing long transient dynamics in a class of predator-prey models with two species of predators competing explicitly for their common prey, where the prey evolves on a faster timescale than the predators. In a parameter regime near a singular zero-Hopf bifurcation of the coexistence equilibrium state, we assume that the system under study exhibits bistability between a periodic attractor that bifurcates from the singular Hopf point and another attractor, which could be a periodic attractor or a point attractor, such that the invariant manifolds of the coexistence equilibrium point play central roles in organizing the dynamics. To find whether a solution that starts in a vicinity of the coexistence equilibrium approaches the periodic attractor or the other attractor, we reduce the equations to a suitable normal form, and examine the basin boundary near the singular Hopf point. A key component of our study includes an analysis of the long transient dynamics, characterized by their rapid oscillations with a slow variation in amplitude, by applying a moving average technique. We obtain a set of necessary and sufficient conditions on the initial values of a solution near the coexistence equilibrium to determine whether it lies in the basin of attraction of the periodic attractor. As a result of our analysis, we devise a method of identifying early warning signals, significantly in advance, of a future crisis that could lead to extinction of one of the predators. The analysis is applied to the predator-prey model considered in Sadhu (Discrete Contin Dyn Syst B 26:5251-5279, 2021) and we find that our theory is in good agreement with the numerical simulations carried out for this model.

Pandemic Equation and COVID-19 Evolution

The Pandemic Equation describes multiple pandemic waves and has been applied to describe the COVID-19 pandemic. Using the generalized approaches of solid-state physics, we derive the Pandemic Equation, which accounts for the effects of pandemic mitigation measures and multiple pandemic waves. The Pandemic Equation uses slow and fast time scales for “curve flattening” and describing vaccination and mitigation measures and the Scaled Fermi–Dirac distribution functions for describing transitions between pandemic waves. The Pandemic Equation parameters extracted from the pandemic curves can be used for comparing different scenarios of the pandemic evolution and for extrapolating the pandemic evolution curves for the periods of time on the order of the instantaneous Pandemic Equation characteristic time constant. The parameter extraction for multiple locations could also allow for uncertainty quantification for such pandemic evolution predictions.

Fast learning without synaptic plasticity in spiking neural networks

Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to reproduce fast learning capabilities of the brain in spiking neural networks. Biological data suggest that a synergy of synaptic plasticity on a slow time scale with network dynamics on a faster time scale is responsible for fast learning capabilities of the brain. We show here that a suitable orchestration of this synergy between synaptic plasticity and network dynamics does in fact reproduce fast learning capabilities of generic recurrent networks of spiking neurons. This points to the important role of recurrent connections in spiking networks, since these are necessary for enabling salient network dynamics. We show more specifically that the proposed synergy enables synaptic weights to encode more general information such as priors and task structures, since moment-to-moment processing of new information can be delegated to the network dynamics.

A tonically active master neuron modulates mutually exclusive motor states at two timescales.

Continuity of behaviors requires animals to make smooth transitions between mutually exclusive behavioral states. Neural principles that govern these transitions are not well understood. Caenorhabditis elegans spontaneously switch between two opposite motor states, forward and backward movement, a phenomenon thought to reflect the reciprocal inhibition between interneurons AVB and AVA. Here, we report that spontaneous locomotion and their corresponding motor circuits are not separately controlled. AVA and AVB are neither functionally equivalent nor strictly reciprocally inhibitory. AVA, but not AVB, maintains a depolarized membrane potential. While AVA phasically inhibits the forward promoting interneuron AVB at a fast timescale, it maintains a tonic, extrasynaptic excitation on AVB over the longer timescale. We propose that AVA, with tonic and phasic activity of opposite polarities on different timescales, acts as a master neuron to break the symmetry between the underlying forward and backward motor circuits. This master neuron model offers a parsimonious solution for sustained locomotion consisted of mutually exclusive motor states.

Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep

Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.

Photosensitizers Based on Bichromophoric Dyads Combining Ru(II)-Polypyridyl Complexes and Dissymmetric Perylene Monoimide Derivatives: The Nontrivial Role of Ligand Substitution.

The covalent modification of Ru(II) polypyridyl complexes (RPCs) with organic chromophores is a powerful strategy to obtain metal-based photosensitizer agents (PSs) with improved performance for application in photodynamic therapy (PDT). In this respect, perylene-imides are of particular interest due to their rich chemical-physical repertoire, and it is therefore quite surprising that their combination with RPCs has been poorly considered so far. Herein, we report on the photophysical behavior of two newly synthesized RPCs bearing a perylene monoimide appendant (PMI-Ad). Differently from the majority of RPCs-perylene-imides dyads, these chromophores are dissymmetric and are tethered to the metal centers through a single C-C bond in the 3- or 5-position of 1,10-phenanthroline (Ru-3PMI-Ad and Ru-5PMI-Ad). Both compounds show excellent singlet oxygen photosensitizing activity, with quantum yields reaching >90% in the case of Ru-3PMI-Ad. A combined spectroscopic and theoretical analysis, also involving transient absorption and luminescence lifetime measurements, demonstrates that both compounds undergo intersystem crossing on a very fast time scale (tens of picoseconds) and with high efficiency. Our results further demonstrate that the increased electron delocalization between the metal center and the PMI-Ad chromophore observed for Ru-3PMI-Ad additionally contributes to increase the singlet oxygen quantum yields by prolonging the lifetime of the triplet state.

Interaction of acetylcholine and oxytocin neuromodulation in the hippocampus

A postulated role of subcortical neuromodulators is to control brain states. Mechanisms by which different neuromodulators compete or cooperate at various temporal scales remain an open question. We investigated the interaction of acetylcholine (ACh) and oxytocin (OXT) at slow and fast timescales during various brain states. Although these neuromodulators fluctuated in parallel during NREM packets, transitions from NREM to REM were characterized by a surge of ACh but a continued decrease of OXT. OXT signaling lagged behind ACh. High ACh was correlated with population synchrony and gamma oscillations during active waking, whereas minimum ACh predicts sharp-wave ripples (SPW-Rs). Optogenetic control of ACh and OXT neurons confirmed the active role of these neuromodulators in the observed correlations. Synchronous hippocampal activity consistently reduced OXT activity, whereas inactivation of the lateral septum-hypothalamus path attenuated this effect. Our findings demonstrate how cooperative actions of these neuromodulators allow target circuits to perform specific functions.

Biexponential I = 3/2 Spin-Lattice Relaxation in the Solid State: Multiple-Quantum 7Li NMR as a Probe of Fast Ion Dynamics.

Spin-lattice relaxation measurements are used in 7Li NMR studies of materials of potential use in solid-state Li-ion batteries as a probe of ion mobility on a fast (nanosecond to picosecond) time scale. The relaxation behavior is often analyzed by assuming exponential behavior or, equivalently, a single T1 time constant. However, the spin-lattice relaxation of spin I = 3/2 nuclei, such as 7Li, is in general biexponential; this is a fundamental property of I = 3/2 nuclei and unrelated to any compartmentalization within the solid. Although the possibility of biexponential 7Li (and other I = 3/2 nuclei) spin-lattice relaxation in the solid state has been noted by a number of authors, it can be difficult to observe unambiguously using conventional experimental NMR techniques, such as inversion or saturation recovery. In this work, we show that triple-quantum-filtered NMR experiments, as previously exploited in I = 3/2 NMR of liquids, can be used in favorable circumstances to observe and readily quantify biexponential 7Li spin-lattice relaxation in solids with high ion mobility. We demonstrate a triple-quantum-filtered inversion-recovery experiment on the candidate solid electrolyte material Li2OHCl at 325 K, which has previously been shown to exhibit fast ion mobility, and we also introduce a novel triple-quantum-filtered saturation-recovery experiment. The results of these solid-state NMR experiments are less straightforward than those in liquids as a consequence of the unwanted direct excitation of triple-quantum coherences by the weak (compared with the unaveraged 7Li quadrupolar interaction) pulses used, but we show that this unwanted excitation can be accounted for and, in the example shown here, does not impede the extraction of the two 7Li spin-lattice relaxation times.

The Spiraling Cognitive-Emotional Brain: Combinatorial, Reciprocal, and Reentrant Macro-organization.

This article proposes a framework for understanding the macro-scale organization of anatomical pathways in the mammalian brain. The architecture supports flexible behavioral decisions across a spectrum of spatio-temporal scales. The proposal emphasizes the combinatorial, reciprocal, and reentrant connectivity-called CRR neuroarchitecture-between cortical, BG, thalamic, amygdala, hypothalamic, and brainstem circuits. Thalamic nuclei, especially midline/intralaminar nuclei, are proposed to act as hubs routing the flow of signals between noncortical areas and pFC. The hypothalamus also participates in multiregion circuits via its connections with cortex and thalamus. At slower timescales, long-range behaviors integrate signals across levels of the neuroaxis. At fast timescales, parallel engagement of pathways allows urgent behaviors while retaining flexibility. Overall, the proposed architecture enables context-dependent, adaptive behaviors spanning proximate to distant spatio-temporal scales. The framework promotes an integrative perspective and a distributed, heterarchical view of brain function.

Fast and Slow Time-Scale Effects of Photoinduced Surface Oxygen Vacancies on the Charge Carrier Dynamics of TiO2

Fast and Slow Time-Scale Effects of Photoinduced Surface Oxygen Vacancies on the Charge Carrier Dynamics of TiO₂

The collapse of a sonoluminescent cavitation bubble imaged with X-ray free-electron laser pulses

Single bubble sonoluminescence (SBSL) is the phenomenon of synchronous light emission due to the violent collapse of a single spherical bubble in a liquid, driven by an ultrasonic field. During the bubble collapse, matter inside the bubble reaches extreme conditions of several gigapascals and temperatures on the order of 10000 K, leading to picosecond flashes of visible light. To this day, details regarding the energy focusing mechanism rely on simulations due to the fast dynamics of the bubble collapse and spatial scales below the optical resolution limit. In this work we present phase-contrast holographic imaging with single x-ray free-electron laser (XFEL) pulses of a SBSL cavitation bubble in water. X-rays probe the electron density structure and by that provide a uniquely new view on the bubble interior and its collapse dynamics. The involved fast time-scales are accessed by sub-100 fs XFEL pulses and a custom synchronization scheme for the bubble oscillator. We find that during the whole oscillation cycle the bubble’s density profile can be well described by a simple step-like structure, with the radius R following the dynamics of the Gilmore model. The quantitatively measured internal density and width of the boundary layer exhibit a large variance. Smallest reconstructed bubble sizes reach down to R≃0.8μm , and are consistent with spherical symmetry. While we here achieved a spatial resolution of a few 100 nm, the visibility of the bubble and its internal structure is limited by the total x-ray phase shift which can be scaled with experimental parameters.

Electromechanical enhancement of live jellyfish for ocean exploration.

The vast majority of the ocean's volume remains unexplored, in part because of limitations on the vertical range and measurement duration of existing robotic platforms. In light of the accelerating rate of climate change impacts on the physics and biogeochemistry of the ocean, the need for new tools that can measure more of the ocean on faster timescales is becoming pressing. Robotic platforms inspired or enabled by aquatic organisms have the potential to augment conventional technologies for ocean exploration. Recent work demonstrated the feasibility of directly stimulating the muscle tissue of live jellyfish via implanted microelectronics. We present a biohybrid robotic jellyfish that leverages this external electrical swimming control, while also using a 3D printed passive mechanical attachment to streamline the jellyfish shape, increase swimming performance, and significantly enhance payload capacity. A six-meter-tall, 13 600 l saltwater facility was constructed to enable testing of the vertical swimming capabilities of the biohybrid robotic jellyfish over distances exceeding 35 body diameters. We found that the combination of external swimming control and the addition of the mechanical forebody resulted in an increase in swimming speeds to 4.5 times natural jellyfish locomotion. Moreover, the biohybrid jellyfish were capable of carrying a payload volume up to 105% of the jellyfish body volume. The added payload decreased the intracycle acceleration of the biohybrid robots relative to natural jellyfish, which could also facilitate more precise measurements by onboard sensors that depend on consistent platform motion. While many robotic exploration tools are limited by cost, energy expenditure, and varying oceanic environmental conditions, this platform is inexpensive, highly efficient, and benefits from the widespread natural habitats of jellyfish. The demonstrated performance of these biohybrid robots suggests an opportunity to expand the set of robotic tools for comprehensive monitoring of the changing ocean.