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Congenital high airway obstruction syndrome (CHAOS) is characterized by over-distended lungs leading to impaired cardiac return and fetal hydrops. Survivors have been reported following prenatal spontaneous fistulization, fetal procedures to decompress the airway, or ex-utero intrapartum treatment (EXIT). The long-term outcomes of survivors are unclear. We performed a retrospective chart review on patients diagnosed with CHAOS in our center between 2005-2025. Of the 28 patients with CHAOS, three (10.7%) underwent a fetal procedure to decompress the airway. Three patients (10.7%) had evidence of spontaneous fistulization. Four patients (14.3%) terminated the pregnancy and four (14.3%) had in-utero fetal demise. Twenty patients (71.4%) were live-born; of these, 14 (70%) died shortly after delivery and two (10%) died in the neonatal period. Seven patients (35%) underwent EXIT-to-tracheostomy at our center, of which four (57.1%) are long-term survivors ranging in age from 4 to 19 years old. Three patients have undergone airway reconstruction between 1.6 and 5.6 years of age; one remains tracheostomy-dependent due to recurrent airway stenosis, one patient has undergone reconstruction and is likely to be decannulated soon, and one patient had successful reconstruction and was decannulated. The fourth patient has not yet undergone airway reconstruction. CHAOS remains a highly morbid diagnosis, but long-term survivorship and liberation from tracheostomy is possible.

University students face various stresses, including academic and career anxieties and a lack of interpersonal relationships. These stresses can elevate psychological burdens, negatively affecting their studies and daily lives. This pilot study aims to quantitatively evaluate the effects of mindful breathing exercises using tablet devices on autonomic nervous system activity in university students by analysis of finger plethysmogram (pulse wave amplitude values) and chaos analysis (Lyapunov exponent and fractal dimension). In this parallel-group randomized controlled trial, 18 nursing students (Mindful Breathing Group [Mi group], n = 9; control group [nMi group], n = 9) were randomly assigned. On the first day, the Mi group performed mindful breathing, the nMi group performed cross fixation, and finger plethysmograms were measured. For the next 9 days, the Mi group performed mindful breathing at home before bedtime, while the nMi group performed cross gazing, and finger plethysmograms were measured on days 1 and 9. Data were analyzed using one-way analysis of variance and t-tests. The Mi group showed a significant increase in pulse wave amplitude values over time (P = .001), whereas the nMi group showed a decrease (P = .001). Chaos analysis revealed no statistically significant differences between groups in the fractal dimension or Lyapunov exponent. Although descriptive differences were observed, these did not reach statistical significance. Both groups demonstrated positive Lyapunov exponents, suggesting nonlinear characteristics of the pulse wave signals. Mindful breathing using tablet devices may be associated with changes in pulse wave amplitude in university students, which could reflect alterations in peripheral autonomic activity under the present experimental conditions. However, no statistically significant differences were observed in chaos analysis indices. Further research with larger samples and additional physiological measures is required to clarify the relationship between mindful breathing and nonlinear autonomic dynamics. UMIN Clinical Trials Registry (UMIN000056166; Registered November 15, 2024).

The Dicke model, renowned for its superradiant quantum phase transition, also exhibits a transition from regular to chaotic dynamics. In this work we provide a systematic, comparative study of static and dynamical indicators of chaos for the closed and open Dicke models. In the closed Dicke model, we find that indicators of chaos sensitive to long-range correlations in the energy spectrum, such as the spectral form factor, can deviate from the Poissonian predictions and show a dip-ramp-plateau feature even in the regular region of the Dicke model unless very large values of the spin size are chosen. Thus, care is needed in using such indicators of chaos in general. In the open Dicke model with cavity damping, we find that the dissipative spectral form factor emerges as a robust diagnostic displaying a quadratic dip-ramp-plateau behavior in agreement with the Ginibre Unitary Ensemble (GinUE) in the superradiant regime. Moreover, by examining the spectral properties of the Liouvillian, we provide indirect evidence for the concurrence of the dissipative superradiant quantum phase transition and the change in Liouvillian eigenvalue statistics from 2D Poissonian to GinUE behavior.

We investigate a class of models of high-dimensional dynamics with all-to-all, random, and nonreciprocal interactions. We determine the dynamical phase diagram and show the emergence of chaotic dynamics. Correspondingly, we show that the dynamical equations exhibit a number of equilibria that is exponentially large in the system's dimensionality, all linearly unstable in the chaotic phases. Solving the effective equations governing the dynamics in the infinite-dimensional limit, we determine the typical properties (magnetization, overlap) of the configurations belonging to the attractor manifold. We show that these properties cannot be inferred from those of the equilibria, challenging the expectation that chaos can be understood purely in terms of the numerous unstable equilibria of the dynamical equations. We discuss the dependence of this scenario on the strength of nonreciprocity. The results are obtained combining analytical methods such as dynamical mean-field theory and the Kac-Rice formalism.

We consider Rayleigh particles in a periodically modulated optical trap formed by two counterpropagating Gaussian beams. It is shown that for certain values of the parameters the system exhibits transient chaos which manifests itself in particle acceleration and subsequent directional ejection out of the trap. The escape flights are terminated at a distance of hundreds of wavelengths from the trap center and the particles return to the trap under the action of the Stokes force. The particle escape is shown to be a threshold effect that can be potentially employed for particle sorting.

Aiming at the problems of uneven population initialization distribution, easy trapping in local optima, unbalanced exploration and exploitation capabilities, insufficient optimization accuracy and convergence speed of the original Greater Cane Rat Algorithm (GCRA), this paper proposes a Chaos-Integrated Difference-Enhanced Greater Cane Rat Algorithm (CEGCRA). Firstly, the algorithm adopts the piecewise chaotic map to generate the initial population, which effectively improves the uniformity and diversity of the population and reduces the risk of premature convergence. Secondly, an accumulated difference foraging strategy is designed to integrate the position and fitness difference information between individuals and the optimal individual, dynamically adjust the search direction and step size, and realize the adaptive balance between global exploration and local exploitation capabilities. Finally, the dynamic switching mechanism between the exploration and exploitation stages of the algorithm is improved, and the boundary constraint handling strategy is optimized to further enhance the algorithm stability. To verify the performance of the CEGCRA, comparative experiments were carried out on the CEC2014 and CEC2020 benchmark test suites. The results show that compared with the original GCRA, the optimal fitness value of the CEGCRA is reduced by an average of 35.3%, the standard deviation is reduced by an average of 22.7%, and the convergence speed is increased by an average of 28.9%. In two typical engineering constrained optimization problems, namely, welded beam design and cantilever beam design, the cost of the welded beam solved by the CEGCRA is 12.5% lower than that of the original GCRA and 8.7% lower than that of the PSO algorithm; the weight of the cantilever beam is 0.012% lower than that of the original GCRA and 0.008% lower than that of the GA, with a constraint satisfaction rate of 100%. The experimental results fully prove that the CEGCRA is superior to the original GCRA and seven comparison algorithms such as PSO, DE and SSA in terms of optimization accuracy, convergence speed, robustness and constraint handling ability and can effectively solve complex engineering optimization problems with high dimensionality, nonlinearity and multiple constraints.

This paper investigates the dynamic behavior of a Kolmogorov-type permanent-magnet synchronous generator for wind power systems. Firstly, the chaotic model of the salient-pole permanent-magnet synchronous generator is derived and subsequently transformed into a Kolmogorov-type system. Secondly, by analyzing the derived Kolmogorov system, the system's stability is established, and the boundary ellipsoid of the chaotic attractor is determined via the Casimir energy function. Thirdly, the analysis focuses on the mechanisms leading to chaos, including period-doubling bifurcation and the onset of double Hopf bifurcation. Finally, the basins of attraction associated with the coexisting static attractors are determined to characterize their long-term dynamical behavior. The analytical results show good agreement with the numerical simulations.

Ecological populations fluctuate for reasons that are often ascribed-separately-to historical legacies, intrinsic nonlinear dynamics, or stochastic noise. Yet these forces rarely act in isolation. We assembled a global database of 302 abundance time series spanning birds, mammals, fishes, insects, and plankton to ask how memory, nonlinearity, and dynamical stability interact to shape predictability. We used empirical dynamic modeling to estimate the memory length and classified time series stability via effective Lyapunov exponents. We also examined the degree to which incorporating memory and nonlinearity reduce apparent noise and improve prediction. We found that memory length was greatest in neutrally stable series and proportional to the Lyapunov horizon in chaotic series. Both memory and nonlinearity improved forecasts, but gains were largest in series exhibiting positive Lyapunov exponents (oscillations and chaos). By contrast, populations with strongly stable dynamics are well captured by low dimensional linear models. Hence, nonlinearity and memory are both important components of ecological dynamics and the effective Lyapunov exponent is a useful prognosticator of predictability.

We study the aspects of quantum chaos in mushroom billiards introduced by Bunimovich. This family of billiards classically has the property of mixed phase space with precisely one entirely regular and one fully chaotic (ergodic) component, whose size depends on the width w of the stem, and has two limiting geometries, namely the circle (as the integrable system) and stadium (as the fully chaotic system). Therefore, this one-parameter system is ideal to study the semiclassical behavior of the quantum counterpart. Here, in paper I, we study the spectral statistics as a function of the geometry defined by w and as a function of the semiclassical parameter k, which in this case is just the wave number k. We show that at sufficiently large k the level spacing distribution is excellently described by the Berry-Robnik distribution (without fitting). At lower k the small deviations from it can be well described by the Berry-Robnik-Brody distribution, which captures the effects of weak dynamical localization of Poincaré-Husimi functions. We also employ the analytical theory of the level spacing ratios distribution P(r) for mixed-type systems, recently obtained by Yan [Phys. Rev. E 111, 054213 (2025)2470-004510.1103/PhysRevE.111.054213], which does not require a spectral unfolding procedure, and show excellent agreement with numerics in the semiclassical limit of large k. In paper II [M. Orel and M. Robnik, Phys. Rev. E 113, 044218 (2026)10.1103/rwq2-rydx] we shall analyze the eigenstates by means of Poincaré-Husimi functions.

This study presents a unified analytical-numerical framework for the El Borhamy-Rashad-Sobhy equation with Duffing-type nonlinearity, augmented by an external harmonic forcing term. The used application in the study is motivated by the electromechanical dynamics of salient-pole synchronous machines. Starting from an energy-based formulation, the harmonic balance method is used to obtain closed-form frequency-response relations. The local stability of the periodic orbit is further quantified via a Floquet-based test derived from the linear variational equation, yielding stability-classified frequency-response curves. While the method of multiple scales captures primary, superharmonic, and subharmonic resonance conditions and the associated stability boundaries are analyzed. Beyond the weakly nonlinear regime, numerical bifurcation analysis is performed to trace a period-doubling route to chaos, supported by Poincaré sections, the largest Lyapunov exponent, and time-series diagnostics. The system is further coupled with linear and nonlinear piezoelectric energy-harvesting branches, to demonstrate that, the large-amplitude responses enhance harvested power and the cubic stiffness can be tuned for bandwidth optimization. Overall, the results bridge perturbation theory with nonlinear time-domain diagnostics, providing design-relevant insights for broadband energy harvesters and electromechanical systems operating under parametric excitation and external forcing.

We study hyperbolic chaotic dynamics for maps of a two-dimensional torus. We introduce a two-parameter family of diffeomorphisms which, as we show, demonstrates all types of hyperbolic chaotic dynamics that can appear in the two-dimensional case. In addition, we describe all the bifurcations responsible for the transitions between these chaotic regimes.

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The fractional Kuramoto-Sivashinsky equation (F-KSE) describes a class of spatiotemporal chaotic systems characterized by nonlocal dissipative interactions. Controlling such systems presents significant challenges due to the interplay between nonlinear convection and fractional diffusion. This study investigates a model-free feedback control framework to stabilize the F-KSE. By formulating the control problem as a high-dimensional optimization task, a continuous control law is derived without assuming prior knowledge of the governing equations. Numerical experiments demonstrate that the proposed controller effectively suppresses chaotic wave shedding, forcing the system into a near-trivial stationary state with an energy reduction consistently exceeding 96% (reaching a peak of 98.55% across the diffusive regime). Spectral analysis reveals that the stabilization is achieved through broadband suppression of dominant instability modes. Most notably, a sensitivity analysis demonstrates that a single control policy, trained at a specific fractional order (α=1.85), exhibits universal robustness across the regime α∈[1.0,2.0] without retraining. This suggests the data-driven agent identifies a dominant stabilization mechanism that primarily counteracts energy-containing structures driven by the convective nonlinearity. The resulting policy is largely invariant to the underlying dissipative structure.

This review aims to describe the key mechanisms behind ocular immune privilege and how disruption of this immunological balance contributes to ocular disease. A narrative review of the literature was performed focusing on ocular immune privilege and diseases in which these mechanisms are altered. The review considered evidence related to corneal transplantation, peripheral ulcerative keratitis, age-related macular degeneration, autoimmune uveitis, diabetic retinopathy, ocular tumors, and allergic conjunctivitis. The reviewed literature demonstrates how ocular immune privilege is maintained mainly by a combination of anatomical barriers, local immunomodulatory mediators, regulatory immune-cell interactions, and ACAID-mediated systemic tolerance. Across the ocular conditions reviewed, disease was generally associated with partial disruption, remodeling, or exploitation of these mechanisms rather than complete loss of immuneprivilege. These alterations varied according to the affected ocular compartment and clinical context, but recurrently involved disruption of immune homeostasis, increased inflammatory signaling, altered leukocyte activity, impaired barrier integrity, and reduced local immune restraint. Overall, the evidence supports ocular immune privilege as a dynamic system that is not absolute, preserving ocular homeostasis under normal conditions but contributing to disease when its protective mechanisms are disrupted. Ocular immune privilege is best understood as a protective but conditional state. Its disruption does not entirely explain most ocular diseases by itself, but it helps illustrate why inflammation, transplantation, tumors, allergy, and other pathologies can behave differently in ocular tissues. Recognizing which elements are preserved or lost in each setting may improve understanding of ocular pathology and support more precise immune-directed approaches.

The goal of structuring electronic health data with multiple models has created an interoperability paradox. We analyzed over 140,000 aggregated clinical data elements to identify the root causes of this semantic failure. Key challenges include semantic drift from non-versioned dictionaries, missing traceability from attribute modification, and loss of context when form layout is absent in the data warehouse. This work demonstrates that the solution is not the creation of new models, but the implementation of robust data governance, mandated versioning, and semantic standardization using semantic-centered approaches.

It is well known that the spectral form factor (SFF) of a possibly degenerate many-body Hamiltonian can be identified with a planar random walk taking steps of unequal length. In this paper we push this identification further and propose to study the chaotic content of a Hamiltonian H via its associated random walk seen as a fractal, using the tools of fractal geometry. In particular, we conjecture that for chaotic Hamiltonians the Hausdorff dimension of the frontier of the corresponding random walk approaches the universal value d_{F}=4/3-the same value obtained when the random walk describes a Wiener process. Our numerical simulations for nonintegrable models confirm this expectation while for quasifree integrable models we obtain a value d_{F}=1. Additionally, we numerically show that "Bethe-Ansatz walkers" fall into a category similar to the nonintegrable walkers. To motivate this conjecture we consider many-body Hamiltonians with degenerate but rationally independent eigenvalues. We prove that if the degeneracies satisfy certain Lyapunov conditions, the random walk becomes a Wiener process, d_{F}=4/3, and the distribution of the SFF becomes Gaussian. This is the familiar Gaussian approximation for the SFF which we show to be violated at very low temperature. We also compute the moments of the SFF exactly under milder hypotheses thus solving the classical problem of determining the moments of a random walker taking steps of unequal lengths. Finally, we consider quasifree Fermionic models with possibly degenerate but rationally independent one-particle spectra. We show that in this case the distribution of the SFF becomes lognormal and also give the exact form of the moments under milder hypotheses.