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Background and Objectives: the coupling of EEG signals during an epileptic seizure in patients during coma is being studied. Materials and Methods: the analysis of the applicability of the method of detecting the interaction between oscillatory systems based on the phase dynamics modeling to EEG signals during an epilepsy seizure in comatose patients is carried out. Results: a method of preliminary filtering of EEG signals has been proposed and the values of the method parameters have been selected, which allow obtaining reliable estimates of directional coupling at a significance level of 0.05. As an example, the analysis of the couplings between EEG signals of two patients with the mentioned pathologies was carried out using the method of the coupling estimation developed in this work.

We review an approach for reconstructing oscillatory networks’ undirected and directed connectivity from data. The technique relies on inferring the phase dynamics model. The central assumption is that we observe the outputs of all network nodes. We distinguish between two cases. In the first one, the observed signals represent smooth oscillations, while in the second one, the data are pulse-like and can be viewed as point processes. For the first case, we discuss estimating the true phase from a scalar signal, exploiting the protophase-to-phase transformation. With the phases at hand, pairwise and triplet synchronization indices can characterize the undirected connectivity. Next, we demonstrate how to infer the general form of the coupling functions for two or three oscillators and how to use these functions to quantify the directional links. We proceed with a different treatment of networks with more than three nodes. We discuss the difference between the structural and effective phase connectivity that emerges due to high-order terms in the coupling functions. For the second case of point-process data, we use the instants of spikes to infer the phase dynamics model in the Winfree form directly. This way, we obtain the network’s coupling matrix in the first approximation in the coupling strength.

Characterizing the brain’s dynamic pattern of response to an input in electroencephalography (EEG) is not a trivial task due to the entanglement of the complex spontaneous brain activity. In this context, the brain’s response can be defined as (1) the additional neural activity components generated after the input or (2) the changes in the ongoing spontaneous activities induced by the input. Moreover, the response can be manifested in multiple features. Three commonly studied examples of features are (1) transient temporal waveform, (2) time–frequency representation, and (3) phase dynamics. The most extensively used method of average event-related potentials (ERPs) captures the first one, while the latter two and other more complex features are attracting increasing attention. However, there has not been much work providing a systematic illustration and guidance for how to effectively exploit multifaceted features in neural cognitive research. Based on a visual oddball ERPs dataset with 200 participants, this work demonstrates how the information from the above-mentioned features are complementary to each other and how they can be integrated based on stereotypical neural-network-based machine learning approaches to better exploit neural dynamic information in basic and applied cognitive research.

The phase reduction approach has manifested its efficacy in investigating synchronization behaviors in limit-cycle oscillators. However, spatial distributions of the phase value on the limit cycle may lead to illusions of synchronizations for oscillators close to bifurcations. In this paper, we compared the phase sensitivity function in the spatial domain and time domain for oscillators close to saddle-node homoclinic (SNH) bifurcation, also known as saddle-node bifurcation on an invariant circle. It was found that the phase sensitivity function in the spatial domain can show the phase accumulation feature on the limit cycle, which can be ignored in the phase sensitivity function in the time domain. As an example, the synchronization distributions of uncoupled SNH oscillators driven by common and independent noises were investigated, where the space-dependent coupling function was considered on common noise. These results shed some light on the phase dynamics of oscillators close to bifurcations.

Noise can induce coherent oscillations in excitable systems without periodic orbits. Here, we establish a method to derive a hybrid system approximating the noise-induced coherent oscillations in excitable systems and further perform phase reduction of the hybrid system to derive an effective, dimensionality-reduced phase equation. We apply the reduced phase model to a periodically forced excitable system and two-coupled excitable systems, both undergoing noise-induced oscillations. The reduced phase model can quantitatively predict the entrainment of a single system to the periodic force and the mutual synchronization of two coupled systems, including the phase slipping behavior due to noise, as verified by Monte Carlo simulations. The derived phase model gives a simple and efficient description of noise-induced oscillations and can be applied to the analysis of more general cases.

Abstract The Earth's inner core (IC) is known to exhibit heterogeneous structures with their origins still unknown. From the onset of nucleation, the IC can grow via sedimentation and compaction of iron crystals freezing out from the fluid outer core. Previous studies of IC growth have shown entrapment of fluid within the solid matrix, and unstable density profiles in 1D can appear depending on the efficiency of fluid percolation. In this study, we perform simulations of IC growth in spherical geometries (assuming axisymmetry). We find that it is possible for the IC to develop large‐scale convective flows under certain conditions and, in some instances, produce small‐scale heterogeneites close to the IC boundary. Assuming representative values for the physical properties of the Earth's IC, we show that it is possible for the IC to exhibit compaction‐driven convection today.

A deep understanding of the processes in soft matter systems with liquid-liquid phase boundaries is of particular importance for materials science, chemistry and life sciences. A vast variety of physicochemical techniques have been proposed for the study of interfacial properties of liquid systems. Among them, electron microscopy methods occupy an important place due to the possibility of direct observation of the sample areas of interest with high spatial resolution; however, the harsh conditions of the electron microscope chamber impose significant restrictions on the possibilities of observing and manipulating unprotected liquids and related soft systems. To overcome these difficulties, in this work, we developed a methodology for direct probing of liquid-liquid interfaces with simultaneous control of the process using electron microscopy. Practically relevant liquid mixtures based on vacuum-compatible ionic liquid (IL) with water additives were probed with micrometer accuracy in real time inside an electron microscope chamber, which made it possible to reveal the role of specific ions aggregation and electrostatic phenomena in the stabilization of liquid microdomains. To test the versatility of the proposed approach, the morphology of a typical IL/water mixture was examined using a series of electron microscopes of various configurations, and it was shown that the best level of contrast between two chemically related liquid phases can be obtained in the case of a cold field emission electron source in combination with an in-lens secondary electron detector, regardless of the specific instrument manufacturer.

Objective To study the effects and mechanisms of molecular targeted drug combination on multi-driven proliferation hepatocellular carcinoma SK-Hep-1 cells. Methods Four molecular targeted drugs (HG6-64-1, Dasatinib, Crizotinib, and Sunitinib) were used to treat SK-Hep-1 cells, and the monophasic kinetic analysis curve and two-phase analysis curve were drawn. Western blot analysis was used to detect the effects of the above drugs on key signaling pathways in SK-Hep-1 cells. MTT assay was used to detect the effects of the above drugs and their combination on the proliferation of SK-Hep-1 cells. Results Compared with the monophasic kinetic analysis curve, the biphase analysis curve could better fit the effects of molecular targeted drugs on SK-Hep-1 cells, which predicted that the combination of HG6-64-1, Dasatinib, and MK-2206 could effectively inhibit the proliferation of SK-Hep-1 cells. Conclusion Two-phase kinetic analysis can quantitatively describe the response of multi-driven proliferation hepatocellular carcinoma SK-Hep-1 cells to molecular targeted therapy. The combination of HG6-64-1, Dasatinib, and MK-2206 is a potential drug combination for the treatment of hepatocellular carcinoma.

We study a reduced electrostatic fluid model for the tokamak scrape-off layer, which incorporates temperature gradient and vorticity gradient as two free energy fields. Two scenarios of field coupling are addressed: (1) sheath condition; (2) vortex wave coupling. For the sheath condition induced field coupling, the poloidal $\mathbf{E} \times \mathbf{B}$ flow shear is coupled with the temperature gradient. Combining an eigenmode analysis and the nonlinear phase dynamics approach, our findings indicate that in the absence of a vorticity gradient, the overall effect of the sheath condition induced flow shear can either stabilize or destabilize the interchange mode, depending on the competition between the flow shear suppression and the temperature gradient driving. This is different from the case where the gradient drive and shear damping are decoupled. When the field coupling is mediated by wave interactions, by setting an idealized step-like temperature and vorticity profiles, a joint mode forms through resonant interaction between the interfacial waves driven by the temperature and vorticity gradients, respectively. Near the phase locking condition, the joint mode can be more unstable than pure temperature gradient driven mode.

Summary: Control of cell proliferation is critical for the lymphocyte life cycle. However, little is known about how stage-specific alterations in cell cycle behavior drive proliferation dynamics during T cell development. Here, we employed in vivo dual-nucleoside pulse labeling combined with the determination of DNA replication over time as well as fluorescent ubiquitination-based cell cycle indicator mice to establish a quantitative high-resolution map of cell cycle kinetics of thymocytes. We developed an agent-based mathematical model of T cell developmental dynamics. To generate the capacity for proliferative bursts, cell cycle acceleration followed a “stretch model” characterized by the simultaneous and proportional contraction of both G1 and S phases. Analysis of cell cycle phase dynamics during regeneration showed tailored adjustments of cell cycle phase dynamics. Taken together, our results highlight intrathymic cell cycle regulation as an adjustable system to maintain physiologic tissue homeostasis and foster our understanding of dysregulation of the T cell developmental program.

Coupled oscillator networks often display transitions between qualitatively different phase-locked solutions—such as synchrony and rotating wave solutions—following perturbation or parameter variation. In the limit of weak coupling, these transitions can be understood in terms of commonly studied phase approximations. As the coupling strength increases, however, predicting the location and criticality of transition, whether continuous or discontinuous, from the phase dynamics may depend on the order of the phase approximation—or a phase description of the network dynamics that neglects amplitudes may become impossible altogether. Here we analyze synchronization transitions and their criticality systematically for varying coupling strength in theory and experiments with coupled electrochemical oscillators. First, we analyze bifurcations analysis of synchrony and splay states in an abstract phase model and discuss conditions under which synchronization transitions with different criticalities are possible. In particular, we show that such conditions can be understood by considering the relative contributions of higher harmonics to the phase dynamics. Second, we illustrate that transitions with different criticality indeed occur in experimental systems. Third, we highlight that the amplitude dynamics observed in the experiments can be captured in a numerical bifurcation analysis of delay-coupled oscillators. Our results showcase that reduced order phase models may miss important features that one would expect in the dynamics of the full system.

The human brain is a highly dynamic organ that operates across a variety of timescales, the intrinsic neural timescales (INT). In addition to the INT, the neural waves featured by its phase-related processes including their cycles with peak/trough and rise/fall play a key role in shaping the brain's neural activity. However, the relationship between the brain's ongoing wave dynamics and INT remains yet unclear. In this study, we utilized functional magnetic resonance imaging (fMRI) rest and task data from the Human Connectome Project (HCP) to investigate the relationship of infraslow wave dynamics [as measured in terms of speed by changes in its peak frequency (PF)] with INT. Our findings reveal that: (i) the speed of phase dynamics (PF) is associated with distinct parts of the ongoing phase cycles, namely higher PF in peak/trough and lower PF in rise/fall; (ii) there exists a negative correlation between phase dynamics (PF) and INT such that slower PF relates to longer INT; (iii) exposure to a movie alters both PF and INT across the different phase cycles, yet their negative correlation remains intact. Collectively, our results demonstrate that INT relates to infraslow phase dynamics during both rest and task states.

Abstract Analysis of ex vivo Per2 bioluminescent rhythm previously recorded in the mouse dorsal vagal complex reveals a characteristic phase relationship between three distinct circadian oscillators. These signals represent core clock gene expression in the area postrema (AP), the nucleus of the solitary tract (NTS) and the ependymal cells surrounding the 4th ventricle (4Vep). Initially, the data suggests a consistent phasing in which the AP peaks first, followed shortly by the NTS, with the 4Vep peaking 8–9 h later. Wavelet analysis reveals that this pattern is not consistently maintained throughout a recording, however, the phase dynamics strongly imply that oscillator interactions are present. A simple phase model of the three oscillators is developed and it suggests that realistic phase dynamics occur between three model oscillators with coupling close to a synchronisation transition. The coupling topology suggests that the AP bidirectionally communicates phase information to the NTS and the 4Vep to synchronise the three structures. A comparison of the model with previous experimental manipulations demonstrates its feasibility to explain DVC circadian phasing. Finally, we show that simulating steadily decaying coupling improves the model’s ability to capture experimental phase dynamics.

Abstract Due to its unique features, the flyback converter is widely used to convert DC to DC power. This converter has a non‐minimum phase (NMP) dynamic behavior, which is one of its most significant disadvantages. By changing the topology of the flyback converter to a forward‐flyback (FF) converter, the dynamic behavior of the converter can be minimum phase (MP) while the main advantages of the flyback converter such as isolation of the output from the input and simple topology are maintained. This article proposes an innovative FF converter topology by doubling the forward path and adding only one diode and one capacitor. In addition to better dynamic behavior, the proposed double FF (DFF) converter has the same advantages as the conventional FF converter. A new method is used to obtain the transfer function of the output voltage to the duty cycle of the proposed converter. The converter is designed in continuous conduction mode (CCM) with minimum phase dynamics. Finally, simulation and experimental results of both conventional FF and the proposed DFF converters are obtained and compared to validate the superiority of the proposed converter.

Abstract Compact laser interferometers with large dynamic range are one of the core emerging tools to improve low frequency performance in gravitational wave detectors by providing local displacement sensing with sub 1 $$\text {pm Hz}^{-0.5}$$ pm Hz - 0.5 precision. Strong sinusoidal frequency modulations are used in such laser interferometers to create heterodyne-like photodetector signals from which the phase and other parameters, such as the absolute distance, can be extracted. The nested sinusoidal function in such signals is a challenge for the real-time parameter estimation in low-noise applications. In this article, we present an algorithm to calculate exact signal parameters in a non-iterative way from such interferometric signals. The algorithm makes use of a recurrence relation between Bessel functions to enable a direct extraction of modulation parameters from the signal. Additionally, the algorithm is capable of dealing with high phase dynamics where the Doppler-shift of the signal becomes relevant and can limit the range and precision of the parameter estimation, if not accounted for. Simulations show that the algorithm is computationally efficient, can be well parallelised and the phase estimation is close to optimal precision given by the Cramer–Rao lower bound of the signal parameters.

Abstract Objective The objective of this study is to evaluate three‐dimensional vertical motion of the superior surface of the vocal folds in vivo in (a) typically developing children as a function of vocal frequency variations and (b) a child with vocal nodules. Methods A custom developed laser endoscope coupled with high‐speed videoendoscopy was used to obtain 3D parameters from 2 healthy children, one child with vocal nodules, and 23 vocally healthy adults (females = 11, males = 12). Parameters of amplitude (mm), maximum opening/closing velocity (mm/s), and mean opening/closing velocity (mm/s) were computed for the lateral and vertical vibratory motion along the anterior, middle, and posterior sections of the vocal folds were computed. Results We provide for the first time, absolute measurements of vertical amplitude and maximum/ mean velocity during the opening and closing phases, in vivo in children. Overall, the vertical motion was larger in vocally normal children compared with the lateral motion, especially along the visible posterior section of the vocal folds and during low pitch phonation. The opening phase dynamics were consistently large along the posterior section in the child with vocal nodules. Conclusions The study findings establish the feasibility of capturing 3D motion in a clinical setting and provide proof of concept for the application of the proposed 3D laser in the pediatric population. Future large sample size studies are needed to establish the diagnostic potential of examining the closing phase vertical motion to evaluate vibratory development in children with normal voice and investigating the opening phase vertical motion in children with nodules. Level of Evidence N/A.

Abstract Charge density waves (CDWs) involved with electronic and phononic subsystems simultaneously are a common quantum state in solid-state physics, especially in low-dimensional materials. However, CDW phase dynamics in various dimensions are yet to be studied, and their phase transition mechanism is currently moot. Here we show that using the distinct temperature evolution of orientation-dependent ultrafast electron and phonon dynamics, different dimensional CDW phases are verified in CuTe. When the temperature decreases, the shrinking of c-axis length accompanied with the appearance of interchain and interlayer interactions causes the quantum fluctuations (QF) of the CDW phase until 220 K. At T

The Aharonov–Bohm effect can be described as a phase difference in interfering charged particles that travel through two distinct pathways oppositely surrounding a perpendicularly-positioned solenoid. The magnetic field emanates from the solenoid but does not intersect the pathways. On the other hand, the Kuramoto model can be used to identify the synchronization conditions that lead to a particular phase difference by treating the phases as coupled oscillators. Starting with the overall wave function expression for the electron in an Aharonov–Bohm potential, we derive a version of the Kuramoto model describing the phase dynamics of the bound state of the quantum mechanical system. We show that the resulting synchronization condition of the model coincides with the allowable values of the flux parameter for our case to achieve an Aharonov–Bohm effect.

We experimentally investigate the electronic transport properties of a three-terminal graphene Josephson junction. We find that self-heating effects strongly influence the behavior of this multiterminal Josephson junction (MTJJ) system. We show that existing simulation methods based on resistively and capacitively shunted Josephson junction networks can be significantly improved by taking into account these heating effects. We also investigate the phase dynamics in our MTJJ by measuring its switching current distribution and find correlated switching events in different junctions. We show that the switching dynamics is governed by phase diffusion at low temperatures. Furthermore, we find that self-heating introduces additional damping that results in overdamped I−V characteristics when normal and supercurrents coexist in the device.

IntroductionAccording to various censuses, Europe has less than 1.5 million ha of old-growth forests (OGF). Most of them are in the boreal zone, while their presence in the temperate zone is residual and fragmented.In the framework of the EU biodiversity strategy, it has been adopted a broad definition of OGF which includes late-seral forests and forests with some management legacies. However, research purposes need to identify strictly defined OGFs characterized by structure, disturbance history, and processes typical and exclusive of the last stage of the forest dynamic.MethodsThe present paper wants to contribute to this debate by presenting a research network of four mixed (Fagus-Abies-Picea) montane OGFs in the Dinaric Alps (Lom, BiH; Janj, BiH; Perućica, BiH; Biogradska Gora, MNE), summarizing 20 years of multidisciplinary research by focusing on the structural characteristics and the disturbance history of the whole network and their coherency with strict OGF indicators. These sites were selected in relatively structurally uniform study areas, where 142 permanent plots have been established since 2002.Results and discussionThe study sites have a high living (747–1,201 m3 ha−1) and coarse woody debris (CWD) biomass (304–410 m3 ha−1), resulting in the highest forest carbon sink at the continental level (398–484 Mg C ha−1). The presence of large and old trees is one of the critical characteristics of the old-growth stage: in Lom and Perućica, there are 19 trees and 14 ha−1 larger than 1 m at breast height, respectively, and 14 trees and 15 trees ha−1 older than 400 years. In the last three centuries, continuous small-scale disturbances have driven forest dynamics, developing stands characterized by gap-phase dynamics and quasi-equilibrium structure. The Dinaric OGF network presents robust indicators of old-growthness, similar structural characteristics, and dynamic processes across all four sites. Identifying this sub-set of OGF using strict criteria is critical for recognizing conservation priorities and for quantifying, along an old-growthness chronosequence, the current structural differences of managed or recently abandoned forests. Besides, only OGF selected with rigorous criteria can act as a reliable reference for ecological restoration and sustainable forest management as a benchmark for carbon sink and for quantifying the impact of climate change on forests.

Dissipative soliton molecules (DSMs) are of great interest for studying the complexity of nonlinear optical problems as they can map with the matter molecules for making interdisciplinary analogies. In contrast to strongly bound DSMs that have a short time separation between the bound solitons, the complex dynamics and underlying binding mechanism of loosely bound soliton molecules (LBSMs) with orders of magnitude longer time separation remain open questions. To this end, here, we explore real-time spectroscopy using a dispersive temporal interferometer (DTI) to visualize the dynamics of LBSMs in a mode-locked fiber laser and unveil their underlying phase-evolving mechanism. The DTI enables fringe-resolved spectroscopy in real time of the LBSM’s evolution by creating duplicates of the LBSM that results in a much closer time separation between the individual solitons of the LBSM. The real-time evolution of the LBSM’s phase exhibits a diverging sliding landscape, which is theoretically and experimentally proved to be closely associated with gain dynamics. Based on the understanding of its phase dynamics, we finally demonstrate programmable phase-encoding modulation of the LBSM through gain control. These efforts not only shed light on understanding the mechanism of long-range interactions in LBSMs but also provide an alternative approach for all-optical information processing.

Abstract Here, we study the upper critical solution temperature triggered phase transition of thermally responsive poly(ethylene glycol)-block-poly(ethylene glycol) methyl ether acrylate-co-poly(ethylene glycol) phenyl ether acrylate-block-polystyrene nanoassemblies in isopropanol. To gain mechanistic insight into the organic solution-phase dynamics of the upper critical solution temperature polymer, we leverage variable temperature liquid-cell transmission electron microscopy correlated with variable temperature liquid resonant soft X-ray scattering. Heating above the upper critical solution temperature triggers a reduction in particle size and a morphological transition from a spherical core shell particle with a complex, multiphase core to a micelle with a uniform core and Gaussian polymer chains attached to the surface. These correlated solution phase methods, coupled with mass spectral validation and modeling, provide unique insight into these thermoresponsive materials. Moreover, we detail a generalizable workflow for studying complex, solution-phase nanomaterials via correlative methods.

In this paper, an optoelectronic oscillator (OEO) with ultra-low phase noise and high stability based on the injection-locked and phase-locked loop is proposed. In theory, the injection-locked frequency range of the injection signal is studied based on the phase dynamics equation, and the phase noise performance of the injection-locked OEO is analyzed. The role of the phase-locked loop on the frequency stability of the OEO is analyzed based on the phase-locked loop transfer function. In addition, this paper builds an injection-locked OEO based on a phase-locked loop. The injection-locked signal is the high-frequency output of the multiplication crystal oscillator (MCO). At the same time, this MCO synchronously outputs a low-frequency signal, which is used as the reference signal of the phase-locked loop. The experimental results show that the proposed OEO output frequency is 10 GHz, and the phase noise is −89.25 dBc/Hz@100 Hz, −121.71 dBc/Hz@1 kHz, and −145.39 dBc/Hz@10 kHz; the side-mode suppression ratio is 80 dB; the frequency stability is 2.06 × 10−11@1 s, 9.03 × 10−11@10 s, 1.03 × 10−10@100 s, and 3.03 × 10−10@1000 s. Consistent with the theoretical analysis results, the solution takes into account the frequency stability, side-mode suppression ratio, and phase noise performance. The simple structure is more advantageous in practical applications.

The route to chaos and the phase dynamics of the large scales in a rotating shallow-water model have been rigorously examined through the construction of an autonomous five-mode Galerkin truncated system employing complex variables, useful in investigating how large/meso-scales are destabilized and how their dynamics evolves and transits to chaos. This investigation revealed two distinct transitions into chaotic behaviour as the level of energy introduced into the system was incrementally increased. The initial transition manifests through a succession of bifurcations that adhere to the established Feigenbaum sequence. Conversely, the subsequent transition, which emerges at elevated levels of injected energy, is marked by a pronounced shift from quasi-periodic states to chaotic regimes. The genesis of the first chaotic state is predominantly attributed to the preeminence of inertial forces in governing nonlinear interactions. The second chaotic state, however, arises from the augmented significance of free surface elevation in the dynamical process. A novel reformulation of the system, employing phase and amplitude representations for each truncated variable, elucidated that the phase components present a temporal piece-wise locking behaviour by maintaining a constant value for a protracted interval, preceding an abrupt transition characterised by a simple rotation of ±π, even as the amplitudes display chaotic behaviour. It was observed that the duration of phase stability diminishes with an increase in injected energy, culminating in the onset of chaos within the phase components at high energy levels. This phenomenon is attributed to the nonlinear term of the equations, wherein the phase components are introduced through linear combinations of triads encompassing disparate modes. When the locking durations vary across modes, the resultant dynamics is a stochastic interplay of multiple π phase shifts, generating a stochastic dynamic within the coupled phase triads, observable even at minimal energy injections.

Understanding how past disturbances have influenced the development of forests is critical for deciphering their current structure and composition and forecasting future changes. In this study, dendrochronological methods were applied to uncover the disturbance history of old-growth hemlock-dominated forests in central Bhutan. Analysis of tree-ring samples from two old-growth hemlock stands, located in two different topographic settings, identified the importance of gap-phase dynamics in facilitating recruitment and growth releases and producing complex, multi-aged structures over time. One site showed evidence of a near stand-replacing disturbance in the late 1700s, while the other showed no evidence of high-severity disturbance at any time over the last 400 years. At both sites low-to medium-severity disturbances, some of which appear to be associated with cyclones originating in the Bay of Bengal, dominated the disturbance regime. The hemlock stands exhibited a significant positive association between cyclone occurrence and growth release events and between recruitment pulses and growth release events. From 1800 to 1970 there was an increase in recruitment of angiosperm tree species at most sites and a corresponding decline in conifer recruitment. Over the past 50 years there has been little new recruitment; this may be due to light limitation in the understory from shade-tolerant angiosperms and bamboo in the lower strata of these stands. Significant variations in disturbance dynamics and recruitment were observed across the study sites, suggesting that other factors, such as topography and climate, may be influencing long-term stand development patterns. This study highlights the complex interplay between historical disturbance regimes and tree recruitment in shaping the age and size structures of old-growth hemlock forests in central Bhutan. It also provides new insights into the dynamics of these forests that can be used to support effective forest conservation and management in the future.

In this work we explore a recently proposed biphasic cell-fluid chemotaxis-Stokes model which is able to represent two competing cancer cell migration mechanisms reported from experimental studies. Both mechanisms depend on the fluid flow but in a completely different way. One mechanism depends on chemical signaling and leads to migration in the downstream direction. The other depends on mechnical signaling and triggers cancer cells to go upstream. The primary objective of this paper is to explore an alternative numerical discretization of this model by borrowing ideas from [Qiao et al. (2020), M3AS 30]. Numerical investigations give insight into which parameters that are critical for the ability to generate aggressive cancer cell behavior in terms of detachment of cancer cells from the primary tumor and creation of isolated groups of cancer cells close to the lymphatic vessels. The secondary objective is to propose a reduced model by exploiting the fact that the fluid velocity field is largely dictated by the draining fluid from the leaky tumor vasculature and collecting peritumoral lymphatics and is more weakly coupled to the cell phase. This suggests that the fluid flow equations to a certain extent might be decoupled from the cell phase equations. The resulting model, which represents a counterpart of the much studied chemotaxis-Stokes model model proposed by [Tuval, et al. (2005), PNAS 102], is explored by numerical experiments in a one-dimensional tumor setting. We find that the model largely coincides with the original as assessed through numerical solutions computed by discrete schemes. This model might be more amenable for further explorations and analysis. We also investigate how to exploit the weaker coupling between cell phase dynamics and fluid dynamics to do more efficient calculations with fewer updates of the fluid pressure and velocity field.

Switched dynamical systems have been extensively studied in engineering literature in the context of system control. In these systems, the dynamical laws change between different subsystems depending on the environment, a process that is known to produce emergent behaviors---notably chaos. These dynamics are analogous to those of temporal networks, in which the network topology changes over time, thereby altering the dynamics on the network. It stands to reason that temporal networks may therefore produce emergent chaos and other exotic behaviors unanticipated in static networks, yet concrete examples remain elusive. Here, we present a minimal example of a networked system in which temporality produces chaotic dynamics not possible in any static subnetwork alone. Specifically, we consider a variant of the famous Kuramoto model, in which the network topology alternates between different configurations in response to the phase dynamics. We show under certain conditions this can produce a strange attractor, and we verify the presence of chaos by analyzing its geometrical properties. Our results provide new insights on the consequences of temporality for network dynamics, and acts as a proof of concept for a novel mechanism behind generating chaotic dynamics in networks.

The muscular-fibrous frame of the heart (MFFH) synchronizes and compensates for the pressor effect of the myocardium between the high/low-energy regions (left/right) of the heart. The anatomical structures of MFFH (plastic muscular-fibrous formation with a phase change in contours, valves, valve rings) form the "cardiac mean integral pressure" (CMIP). MFFH, a variable spatial structure, forms the starting pressor levels of “opening/closing” of valves and hemodynamic vectors of the heart chambers, systemic and pulmonary circulation, and also compensates for excess pressor pressure (having a variable gradient) at the boundaries of the heart chambers. Throughout the cardiac cycle (CC), on the path "venous block-lung-arterial block-aorta," variable pressure values, compensated by the structures of MFFH, are formed between the blood flows of the right and left parts of the heart. A mutual adaptation of SVs of the ventricles is formed by phase-by-phase compensatory plastics of MFFH. CMIP of MFFH is an integral indicator, where each point reflects: 1) CC phase (time and place); 2) the average value of the range of values in which the equilibrium point of pressor compensation between the high/low-energy processes of this CC phase is located. CMIP is a vector of the MFFH phase dynamics, which compensates for the excess pressor effect with a changing gradient through CC.

We analyze the circumgalactic medium (CGM) for eight commonly-used cosmological codes in the AGORA collaboration. The codes are calibrated to use identical initial conditions, cosmology, heating and cooling, and star formation thresholds, but each evolves with its own unique code architecture and stellar feedback implementation. Here, we analyze the results of these simulations in terms of the structure, composition, and phase dynamics of the CGM. We show properties such as metal distribution, ionization levels, and kinematics are effective tracers of the effects of the different code feedback and implementation methods, and as such they can be highly divergent between simulations. This is merely a fiducial set of models, against which we will in the future compare multiple feedback recipes for each code. Nevertheless, we find that the large parameter space these simulations establish can help disentangle the different variables that affect observable quantities in the CGM, e.g., showing that abundances for ions with higher ionization energy are more strongly determined by the simulation’s metallicity, while abundances for ions with lower ionization energy are more strongly determined by the gas density and temperature.

There are still essential aspects regarding cathodes requiring a comprehensive understanding. These include identifying the underlying phenomena that prevent reaching the theoretical capacity, explaining irreversible losses, and determining the cut-off potentials at which batteries should be cycled. We address these inquiries by investigating the cell’s capacity and phase dynamics by looking into the transport properties of electrons. This approach underlines the crucial role of electrons in influencing battery performance, similar to their significance in other materials and devices such as transistors, thermoelectrics, or superconductors. We use lithium iron phosphate LFP as a case study to demonstrate that understanding the electrochemical cycling behavior of a battery cell, particularly a Li//LFP configuration, hinges on factors like the total local potentials used to calculate chemical potentials, electronic density of states (DOS), and charge carrier densities. Our findings reveal that the stable plateau potential difference is 3.42 V, with maximum charge and minimum discharge potentials at 4.12 V and 2.80 V, respectively. The study illustrates the dynamic formation of metastable phases at a plateau voltage exceeding 3.52 V. Moreover, we establish that determining the working chemical potentials of elements like Li and Al can be achieved by combining their workfunction and DOS analysis. Additionally, we shed light on the role of carbon black beyond conductivity enhancement. Through Density functional theory (DFT) calculations and experimental methods involving scanning Kelvin probe (SKP) and electrochemical analysis, we comprehensively examine various materials, including Li, C, Al, Cu, LFP, FePO _4 , Li _0.25 FePO _4 , polyvinylidene fluoride, and Li _6 PS _5 Cl. The insights derived from this study, which solely rely on electrical properties, have broad applicability to all cathodes and batteries. They provide valuable information for efficiently selecting optimal formulations and conditions for cycling batteries.

Abstract Random lasers (RLs) are remarkable experimental platforms to advance the understanding of complex systems phenomena, such as the replica-symmetry-breaking (RSB) spin glass phase, dynamics modes correlations, and turbulence. Here we study these three phenomena jointly in a Nd:YAG based RL synthesized for the first time using a spray pyrolysis method. We propose a couple of modified Pearson correlation coefficients that are simultaneously sensitive to the emergence and fading out of photonic intermittency turbulent-like effects, dynamics evolution of modes correlations, and onset of RSB behavior. Our results show how intertwined these phenomena are in RLs, and suggest that they might share some common underlying mechanisms, possibly approached in future theoretical models under a unified treatment.

Piezoelectric actuators (PEAs) are widely used in many nano-resolution manipulations. A PEA’s hysteresis becomes the main factor limiting its motion accuracy. The distinctive feature of a PEA’s hysteresis is the interdependence between the width of the hysteresis loop and the frequency or rate of the control voltage. Generally, the control voltage is first amplified using a voltage amplifier (VA) and then exerted on the PEA. In this VA-PEA module, the linear dynamics of the VA and the nonlinearities of the PEA are coupled. In this paper, it is found that the phase lag of the VA also contributes to the rate dependence of the VA-PEA module. If only the PEA’s hysteresis is considered, it will be difficult to achieve high-frequency modeling and control. Consequently, great difficulties arise in high-frequency hysteresis compensation and trajectory tracking, e.g., in the fast scanning of atomic force microscopes. In this paper, the VA-PEA module is modeled to be the series connection of a linear subsystem and a nonlinear subsystem. Subsequently, a feedforward phase–dynamics compensator is proposed to compensate for both the PEA’s hysteresis and the phase lag of the VA. Further, an unscented Kalman-filter-based proportional–integral–derivative controller is adopted as the feedback controller. Under this feedforward–feedback combined control scheme, high-bandwidth hysteresis compensation and trajectory tracking are achieved. The trajectory tracking results show that the closed-loop trajectory tracking bandwidth has been increased to the range of 0–1500 Hz, exhibiting excellent performance for fast scanning applications.

We consider a one-dimensional driven-dissipative exciton-polariton condensate under incoherent pump, described by the stochastic generalized Gross-Pitaevskii equation. It was shown that the condensate phase dynamics maps under some assumptions to the Kardar-Parisi-Zhang (KPZ) equation, and the temporal coherence of the condensate follows a stretched exponential decay characterized by KPZ universal exponents. In this paper, we determine the main mechanisms, which lead to the departure from the KPZ phase, and identify three possible other regimes: (i) a soliton-patterned regime at large interactions and weak noise, populated by localized structures analog to dark solitons; (ii) a vortex-disordered regime at high noise and weak interactions, dominated by point-like phase defects in space-time; and (iii) a defect-free reservoir-textured regime where the adiabatic approximation breaks down. We characterize each regime by the space-time maps, the first-order correlations, the momentum distribution and the density of topological defects. We thus obtain the phase diagram at varying noise, pump intensity and interaction strength. Our predictions are amenable to observation in state-of-art experiments with exciton-polaritons.

Here, we report on a new approach based on laser driven molecular beams that provides simultaneously nanoscale liquid droplets and gas-phase sample delivery for femtosecond electron diffraction studies. The method relies on Picosecond InfraRed Laser (PIRL) excitation of vibrational modes to strongly drive phase transitions under energy confinement by a mechanism referred to as Desorption by Impulsive Vibrational Excitation (DIVE). This approach is demonstrated using glycerol as the medium with selective excitation of the OH stretch region for energy deposition. The resulting plume was imaged with both an ultrafast electron gun and a pulsed bright-field optical microscope to characterize the sample source simultaneously under the same conditions with time synchronization equivalent to sub-micrometer spatial resolution in imaging the plume dynamics. The ablation front gives the expected isolated gas phase, whereas the trailing edge of the plume is found to consist of nanoscale liquid droplets to thin films depending on the excitation conditions. Thus, it is possible by adjusting the timing to go continuously from probing gas phase to solution phase dynamics in a single experiment with 100% hit rates and very low sample consumption (

Td-WTe2 is a topologically nontrivial material and exhibits a variety of physical properties, such as giant unsaturated magnetoresistance and the unconventional thermoelectric effect, due to its topological nature. It is also known to exhibit ultrafast topological phase transitions that restore its inversion symmetry by intense terahertz and mid-infrared pulses, and these properties demonstrate the possibility of ultrafast control of devices based on topological properties. Recently, a novel photo-induced topological phase transition by using polarization-controlled infrared excitation has been proposed, which is expected to control the material topology by rearranging the atomic orbitals near the Weyl point. To examine this topological phase transition, we experimentally studied the excitation-polarization dependence of the infrared-induced phase dynamics in a thin-layer of Td-WTe2. Time-resolved second harmonic generation (SHG) measurements showed that SHG intensity decreases after the infrared pump regardless of the polarization. Polarization-resolved infrared pump–probe measurements indicated that the polarization-selected excited state relaxes quite rapidly (i.e., within 10–40 fs). Considering these experimental results, we conclude that it is difficult to control the photo-induced phase transition through orbital-selective excitation owing to the rapid loss of carrier distribution created by polarization-selective excitation in thin-layer Td-WTe2 under our experimental condition. These results indicate that the suppression of the electron scattering process is crucial for experimentally realizing the photo-induced phase transition based on the polarization selection rule of the materials.

The amplitude envelope of speech carries crucial low-frequency acoustic information that assists linguistic decoding at multiple time scales. Neurophysiological signals are known to track the amplitude envelope of adult-directed speech (ADS), particularly in the theta-band. Acoustic analysis of infant-directed speech (IDS) has revealed significantly greater modulation energy than ADS in an amplitude-modulation (AM) band centred on ∼2 Hz. Accordingly, cortical tracking of IDS by delta-band neural signals may be key to language acquisition. Speech also contains acoustic information within its higher-frequency bands (beta, gamma). Adult EEG and MEG studies reveal an oscillatory hierarchy, whereby low-frequency (delta, theta) neural phase dynamics temporally organize the amplitude of high-frequency signals (phase amplitude coupling, PAC). Whilst consensus is growing around the role of PAC in the matured adult brain, its role in the development of speech processing is unexplored.Here, we examined the presence and maturation of low-frequency (

The statistics of the fluctuations of quantum many-body systems are highly revealing of their nature. In driven-dissipative systems displaying macroscopic quantum coherence, as exciton polariton condensates under incoherent pumping, the phase dynamics can be mapped to the stochastic Kardar-Parisi-Zhang (KPZ) equation. However, it was argued theoretically that in two dimensions the KPZ regime may be hindered by the presence of vortices, and a nonequilibrium Berezinskii-Kosterlitz-Thouless behavior was reported close to the condensation threshold. We demonstrate here that, when a discretized two-dimensional (2D) polariton system is considered, universal KPZ properties can emerge. We support our analysis by extensive numerical simulations of the discrete stochastic generalized Gross-Pitaevskii equation. We show that the first-order correlation function of the condensate exhibits stretched exponential behaviors in space and time with critical exponents characteristic of the 2D KPZ universality class and find that the related scaling function accurately matches the KPZ theoretical one, stemming from functional renormalization group. We also obtain the distribution of the phase fluctuations and find that it is non-Gaussian, as expected for a KPZ stochastic process.

Source activity was extracted from resting-state magnetoencephalography data of 103 subjects aged 18–60 years. The directionality of information flow was computed from the regional time courses using delay symbolic transfer entropy and phase entropy. The analysis yielded a dynamic source connectivity profile, disentangling the direction, strength, and time delay of the underlying causal interactions, producing independent time delays for cross-frequency amplitude-to-amplitude and phase-to-phase coupling. The computation of the dominant intrinsic coupling mode (DoCM) allowed me to estimate the probability distribution of the DoCM independently of phase and amplitude. The results support earlier observations of a posterior-to-anterior information flow for phase dynamics in {α1, α2, β, γ} and an opposite flow (anterior to posterior) in θ. Amplitude dynamics reveal posterior-to-anterior information flow in {α1, α2, γ}, a sensory-motor β-oriented pattern, and an anterior-to-posterior pattern in {δ, θ}. The DoCM between intra- and cross-frequency couplings (CFC) are reported here for the first time and independently for amplitude and phase; in both domains {δ, θ, α1}, frequencies are the main contributors to DoCM. Finally, a novel brain age index (BAI) is introduced, defined as the ratio of the probability distribution of inter- over intra-frequency couplings. This ratio shows a universal age trajectory: a rapid rise from the end of adolescence, reaching a peak in adulthood, and declining slowly thereafter. The universal pattern is seen in the BAI of each frequency studied and for both amplitude and phase domains. No such universal age dependence was previously reported.

Biological processes are governed by the expression of proteins, and for some proteins, their level of expression can fluctuate periodically over time (i.e., they oscillate). Many oscillatory proteins (e.g., cell cycle proteins and those from the HES family of transcription factors) are connected in complex ways, often within large networks. This complexity can be elucidated by developing intuitive mathematical models that describe the underlying critical aspects of the relationships between these processes. Here, we provide a mathematical explanation of a recently discovered biological phenomenon: the phasic position of the gene Hes1’s oscillatory expression at the beginning of the cell cycle of an individual human breast cancer stem cell can have a predictive value on how long that cell will take to complete a cell cycle. We use a two-component model of coupled oscillators to represent Hes1 and the cell cycle in the same cell with minimal assumptions. Inputting only the initial phase angles, we show that this model is capable of predicting the dynamic mitosis to mitosis behaviour of Hes1 and predicting cell cycle length patterns as found in real-world experimental data. Moreover, we discover that bidirectional coupling between Hes1 and the cell cycle is critical within the system for the data to be reproduced and that nonfixed asymmetry in the interactions between the oscillators is required. The phase dynamics we present here capture the complex interplay between Hes1 and the cell cycle, helping to explain nongenetic cell cycle variability, which has critical implications in cancer treatment contexts.

This paper presents a control structure integrating guidance and control loops for tail-controlled missile systems. Motivated by the fact that common tail-controlled missiles involve non-minimum phase dynamics, the proposed controller is designed to prevent the internal dynamics from diverging, as well as achieving homing against the intended target. To minimize the miss distance at the end of homing, we derive a formulation of a zero-effort-miss using engagement kinematics that contain the rotating dynamics of the missile, which is different from existing approaches. Subsequently, to nullify the zero-effort-miss, a nonlinear controller is designed based on the Lyapunov stability theory. Since the derived controller has a similar structure to the conventional three-loop topology that has been utilized for various tail-controlled flight systems, it is expected that the proposed method can be applied to the actual system from a practical point of view. Numerical simulation results also show that the proposed method achieves target interception while possessing stable internal dynamics.

This manuscript provides a comprehensive tutorial on the operating principles of a bio-inspired Cellular Nonlinear Network, leveraging the local activity of NbOx memristors to apply a spike-based computing paradigm, which is expected to deliver such a separation between the steady-state phases of its capacitively-coupled oscillators, relative to a reference cell, as to unveal the classification of the nodes of the associated graphs into the least number of groups, according to the rules of a non-deterministic polynomial-hard combinatorial optimization problem, known as vertex coloring. Besides providing the theoretical foundations of the bio-inspired signal-processing paradigm, implemented by the proposed Memristor Oscillatory Network, and presenting pedagogical examples, illustrating how the phase dynamics of the memristive computing engine enables to solve the graph coloring problem, the paper further presents strategies to compensate for an imbalance in the number of couplings per oscillator, to counteract the intrinsic variability observed in the electrical behaviours of memristor samples from the same batch, and to prevent the impasse appearing when the array attains a steady-state corresponding to a local minimum of the optimization goal. The proposed Memristor Cellular Nonlinear Network, endowed with ad hoc circuitry for the implementation of these control strategies, is found to classify the vertices of a wide set of graphs in a number of color groups lower than the cardinality of the set of colors identified by traditional either software or hardware competitor systems. Given that, under nominal operating conditions, a biological system, such as the brain, is naturally capable to optimise energy consumption in problem-solving activities, the capability of locally-active memristor nanotechnologies to enable the circuit implementation of bio-inspired signal processing paradigms is expected to pave the way toward electronics with higher time and energy efficiency than state-of-the-art purely-CMOS hardware.

The phase sensitivity curve or phase response curve (PRC) quantifies the oscillator’s reaction to stimulation at a specific phase and is a primary characteristic of a self-sustained oscillatory unit. Knowledge of this curve yields a phase dynamics description of the oscillator for arbitrary weak forcing. Similar, though much less studied characteristic, is the amplitude response that can be defined either using an ad hoc approach to amplitude estimation or via the isostable variables. Here, we discuss the problem of the phase and amplitude response inference from observations using test stimulation. Although PRC determination for noise-free neuronal-like oscillators perturbed by narrow pulses is a well-known task, the general case remains a challenging problem. Even more challenging is the inference of the amplitude response. This characteristic is crucial, e.g. for controlling the amplitude of the collective mode in a network of interacting units—a task relevant to neuroscience. Here, we compare the performance of different techniques suitable for inferring the phase and amplitude response, particularly with application to macroscopic oscillators. We suggest improvements to these techniques, e.g. demonstrating how to obtain the PRC in case of stimuli of arbitrary shape. Our main result is a novel technique denoted by IPID-1, based on the direct reconstruction of the Winfree equation and the analogous first-order equation for isostable dynamics. The technique works for signals with or without well-pronounced marker events and pulses of arbitrary shape; in particular, we consider charge-balanced pulses typical in neuroscience applications. Moreover, this technique is superior for noisy and high-dimensional systems. Additionally, we describe an error measure that can be computed solely from data and complements any inference technique.

Swarmalators are entities with the simultaneous presence of swarming and synchronization that reveal emergent collective behavior due to the fascinating bidirectional interplay between phase and spatial dynamics. Although different coupling topologies have already been considered, here we introduce time-varying competitive phase interaction among swarmalators where the underlying connectivity for attractive and repulsive coupling varies depending on the vision (sensing) radius. Apart from investigating some fundamental properties like conservation of center of position and collision avoidance, we also scrutinize the cases of extreme limits of vision radius. The concurrence of attractive–repulsive competitive phase coupling allows the exploration of diverse asymptotic states, like static π , and mixed phase wave states, and we explore the feasible routes of those states through a detailed numerical analysis. In sole presence of attractive local coupling, we reveal the occurrence of static cluster synchronization where the number of clusters depends crucially on the initial distribution of positions and phases of each swarmalator. In addition, we analytically calculate the sufficient condition for the emergence of the static synchronization state. We further report the appearance of the static ring phase wave state and evaluate its radius theoretically. Finally, we validate our findings using Stuart–Landau oscillators to describe the phase dynamics of swarmalators subject to attractive local coupling.