# gst: extreme event precursor prediction in turbulence.

<< ️(AA) present a general framework to predict precursors to extreme events in turbulent dynamical systems. The approach combines phase-space reconstruction techniques with recurrence matrices and convolutional neural networks (CNN) to identify precursors to extreme events. >>

<< ️(They) evaluate the framework across three distinct testbed systems: a triad turbulent interaction model, a prototype stochastic anisotropic turbulent flow, and the Kolmogorov flow. This method offers three key advantages: (1) a threshold-free classification strategy that eliminates subjective parameter tuning, (2) efficient training using only O (100) recurrence matrices, and (3) ability to generalize to unseen systems. >>

<< ️The results demonstrate robust predictive performance across all test systems: 96% detection rate for the triad model with a mean lead time of 1.8 time units, 96% for the anisotropic turbulent flow with a mean lead time of 6.1 time units, and 93% for the Kolmogorov flow with a mean lead time of 22.7 units. >> 

Rahul Agarwal, Mustafa A. Mohamad. Extreme Event Precursor Prediction in Turbulent Dynamical Systems via CNN-Augmented Recurrence Analysis. arXiv: 2508.04301v1 [cs.CE]. Aug 6, 2025.

https://arxiv.org/abs/2508.04301

Also: turbulence, chaos, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, turbulence, chaos, extreme events, convolutional neural networks (CNN).

# gst: transition to chaos with conical billiards.

<< ️(AA) adapt ideas from geometrical optics and classical billiard dynamics to consider particle trajectories with constant velocity on a cone with specular reflections off an elliptical boundary formed by the intersection with a tilted plane, with tilt angle γ. >>

<< ️(They) explore the dynamics as a function of γ and the cone deficit angle χ that controls the sharpness of the apex, where a point source of positive Gaussian curvature is concentrated. >>

<< ️(AA) find regions of the (γ,χ) plane where, depending on the initial conditions, either (A) the trajectories sample the entire cone base and avoid the apex region; (B) sample only a portion of the base region while again avoiding the apex; or (C) sample the entire cone surface much more uniformly, suggestive of ergodicity. >>

<< ️The special case of an untilted cone displays only type A trajectories which form a ring caustic at the distance of closest approach to the apex. However, (They) observe an intricate transition to chaotic dynamics dominated by Type (C) trajectories for sufficiently large χ and γ. A Poincaré map that summarizes trajectories decomposed into the geodesic segments interrupted by specular reflections provides a powerful method for visualizing the transition to chaos. (AA) then analyze the similarities and differences of the path to chaos for conical billiards with other area-preserving conservative maps. >>

Lara Braverman, David R. Nelson. Transition to chaos with conical billiards. arXiv: 2508.02786v1 [nlin.CD]. Aug 4, 2025.

https://arxiv.org/abs/2508.02786

Also: billiard, particle, transition, chaos,  in https://www.inkgmr.net/kwrds.html 

Keywords: gst, billiard, particles, transitions, chaos. 

# drugs: Parrondo's paradox in tumor ecosystems; adaptive therapy strategies to delay the development of drug resistance.

<< ️Maximum tolerated dose (MTD) and low-dose metronomic (LDM) schedules are widely used clinical strategies in cancer chemotherapy despite the fact that both approaches have inherent limitations. MTD often leads to drug resistance in tumors, whereas LDM usually results in the predominance of drug-sensitive or drug-resistant cancer cells, depending on the specific dose applied. >>

<< ️To circumvent these unfavorable outcomes, (AA) propose in (Their) work a promising adaptive therapy strategy that alternates MTD and LDM schemes across the treatment cycles. By studying a three-component tumor system with replicator equations, (They) find that alternate administration of MTD and LDM at different lengths of the treatment cycle can significantly delay the development of drug-resistant phenotypes compared to when the two schemes are operated separately. >>

<< ️This outcome recapitulates the weak form of the Parrondo's paradox, where appropriate combinations of two individually suboptimal strategies can produce an even more superior outcome (a more favorable therapeutic effect in (this) case). (AA) also validate the feasibility of the proposed adaptive therapy strategy in spatially distributed tumor ecosystems by implementing agent-based simulations. (Their) findings offer potential clues to the challenges in state-of-the-art adaptive therapy methods, which often require precise regulation of drug dose and timing based on the proportions of drug-sensitive and drug-resistant cancer cells within a tumor. >>

De-Ming Liu, Yi-Yang Liu, et al. Parrondo's paradox in tumor ecosystems: Adaptive therapy strategies to delay the development of drug resistance. Phys. Rev. E 112, 024404. Aug 6, 2025.

https://journals.aps.org/pre/abstract/10.1103/xjh4-mfvx

Also: Parrondo, game, in https://www.inkgmr.net/kwrds.html 

Keywords: drugs, pharma, Parrondo's paradox, game, cancer, tumors, cancer chemotherapy, maximum tolerated dose (MTD), low-dose metronomic (LDM), drug resistance, drug sensitivity, adaptive therapy strategy.

# gst: stability of twisted states; the role of phase lag, pairwise and higher-order interactions.

<< ️Stability analysis remains a key focus in the nonlinear field. The stability of oscillator networks considering phase-lag coupling is still an unsolved puzzle. Here, (AA)  investigate the linear stability using the maximal Lyapunov exponent and evaluate the basin stability by numerical simulations. >>

<< ️The results show that the phase lag has no significant effect on the proportion of twisted states with linear stability and on their basin sizes, without considering higher-order interactions. In contrast to the stabilizing effect of higher-order interactions on twisted states, pairwise coupling suppresses their linear stability but enhances their basin stability. >>

<< ️When pairwise coupling dominates, phase lag acts to strengthen the linear stability of the twisted state. Furthermore, the phase lag reduces the degree of ordering of the system and shrinks the basin of the twisted state. >>

<< ️These results highlight the synergistic effect of phase lag with pairwise and higher-order interactions on twisted-state basins. >>

Xueqin Wang, Dong Yu, et al. Stability of twisted states: The role of phase lag, pairwise and higher-order interactions. Phys. Rev. E 112, 024202. Aug 1, 2025.

https://journals.aps.org/pre/abstract/10.1103/qmgz-h5qm

Also: network, pause, chimera, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, network, chimera, phase lags, pause, coupled oscillators, synchronization.

# gst: nonreciprocal coupling triggers pinning-depinning transitions of wavefronts in bistable systems chains.

<< ️Coupled discrete systems with reciprocal and nonreciprocal couplings exhibit unexpected and counterintuitive phenomena compared to continuous systems. (AA) investigate the pinning-depinning transition induced by nonreciprocal coupling in nonlinear wave propagation connecting different equilibria in bistable system chains. >>

Experimentally, their system << ️exhibits the propagation of fronts between homogeneous states. The fronts display a pinning-depinning transition by increasing the nonreciprocal coupling, achieved by increasing optical feedback offset. Close to the bifurcation, the front velocity is characterized by a square root law as a function of the bifurcation parameter. >>

L. B. Ramirez-Moya, M. Diaz-Zuniga, et al. Nonreciprocal coupling triggers pinning-depinning transitions of wavefronts in bistable systems chains. Phys. Rev. Research 7, L032023. Aug 1, 2025.

https://journals.aps.org/prresearch/abstract/10.1103/9ypt-4kvs

Also: waves, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, waves, transitions, pinning-depinning transitions

# behav: make silence speak for itself.

<< ️Silence is a common phenomenon in classrooms, yet its implicit nature limits a clear understanding of students' underlying learning statuses. >>

<< ️This (AA) study proposed a nuanced framework to classify classroom silence based on class events and student status, and examined neurophysiological markers to reveal similarities and differences in silent states across achievement groups. (..) The study involved 54 middle school students during 34 math lessons, with simultaneous recordings of electroencephalogram (EEG), electrodermal activity (EDA), and heart rate signals, alongside video coding of classroom behaviors. >>

AA << found that high-achieving students showed no significant difference in mean EDA features between strategic silence (i.e., students choose silence deliberately) and active speaking during open questioning but exhibited higher EEG high-frequency relative power spectral density (RPSD) during strategic silence. In structural silence (i.e., students maintain silence following an external command) during directed questioning, they demonstrated significantly higher heart rates while listening to lectures compared to group activities, indicating heightened engagement. Both high- and medium-achieving students displayed elevated heart rates and EDA tonic components in structural silence during questioning compared to teaching. Furthermore, high-achieving students exhibited lower high-frequency RPSD during structural silence than strategic silence, a pattern not observed in other groups, highlighting group heterogeneity. >>

<< ️The (AA) findings contribute to validating the complexity of silence, challenge its traditional association with passivity, and offer a novel classification framework along with preliminary empirical evidence to deepen the understanding of silent learning behaviors in classroom contexts. >>

Mingxuan Gao, Jingjing Chen, et al. Make Silence Speak for Itself: a multi-modal learning analytic approach with neurophysiological data. arXiv: 2507.21063v1 [q-bio.NC]. May 23, 2025.

https://arxiv.org/abs/2507.21063

Also: silence, pause, behav, brain, in https://www.inkgmr.net/kwrds.html 

Keywords: behavior, brain, pause, silence, strategic silence, structural silence

# gst: fluctuations around turbulence

<< ️Numerical simulations of turbulent flows at realistic Reynolds numbers generally rely on filtering out small scales from the Navier Stokes equations and modeling their impact through the Reynolds stress tensor τ_ij. Traditional models approximate τ_ij solely as a function of the filtered velocity gradient, leading to deterministic subgrid scale closures. However, small scale fluctuations can locally exhibit instantaneous values whose deviation from the mean can have a significant influence on flow dynamics. >>

<< ️In this work, (AA) investigate these effects by employing direct numerical simulations combined with Gaussian filtering to quantify subgrid scale effects and evaluating the local energy flux in both space and time. The mean performance of the canonical Clark model is assessed by conditioning the energy flux distributions on the invariants of the filtered velocity gradient tensor, Q and R. The Clark model captures to a good degree the mean energy flux. However, the fluctuations around these mean values for given (Q, R) are of the order of the mean displaying fat tailed distributions. ️To become more precise, (AA) examine the joint distributions of true energy flux and the predictions from both the Clark and the Smagorinsky models. >>

<< ️This approach mirrors the strategy adopted in early stochastic subgrid scale models. Clear non Gaussian characteristics emerge from the obtained distributions, particularly through the appearance of heavy tails. The mean, the variance, the skewness and flatness of these distributions are quantified. (Their) results emphasize that fluctuations are an integral component of the small scale feedback onto large scale dynamics and should be incorporated into subgrid scale modeling through an appropriate stochastic framework. >>

Flavio Tuteri, Alexandros Alexakis, Sergio Chibbaro. Fluctuations around Turbulence Models. arXiv: 2507.17575v1 [physics.flu-dyn]. Jul 23, 2025. 

https://arxiv.org/abs/2507.17575

Also: disorder & fluctuations, turbulence, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, disorder & fluctuations, turbulence.