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The maximum tensile stress and stress integral were produced by SAWs on the stone boundary under asymmetric loading conditions, which drove the initiation and extension of surface cracks into the bulk of the stone that is confirmed by micro-computed tomography analysis.In the context of building acoustics and the acoustic diagnosis of an existing room, it introduces and investigates a new approach to estimate the mean absorption coefficients solely from a room impulse response (RIR). This inverse problem is tackled via virtually supervised learning, namely, the RIR-to-absorption mapping is implicitly learned by regression on a simulated dataset using artificial neural networks. buy Vorinostat Simple models based on well-understood architectures are the focus of this work. The critical choices of geometric, acoustic, and simulation parameters, which are used to train the models, are extensively discussed and studied while keeping in mind the conditions that are representative of the field of building acoustics. Estimation errors from the learned neural models are compared to those obtained with classical formulas that require knowledge of the room's geometry and reverberation times. Extensive comparisons made on a variety of simulated test sets highlight different conditions under which the learned models can overcome the well-known limitations of the diffuse sound field hypothesis underlying these formulas. Results obtained on real RIRs measured in an acoustically configurable room show that at 1 kHz and above, the proposed approach performs comparably to classical models when reverberation times can be reliably estimated and continues to work even when they cannot.Surfactant-coated gas microbubbles are widely used as contrast agents in ultrasound imaging and increasingly in therapeutic applications. The response of microbubbles to ultrasound can be strongly influenced by their size and coating properties, and hence the production method. Ultrasonic emulsification (sonication) is the most commonly employed method and can generate high concentrations of microbubbles rapidly, but with a broad size distribution, and there is a risk of contamination and/or degradation of sensitive components. Microfluidic devices provide excellent control over microbubble size, but are often challenging or costly to manufacture, offer low production rates (108s-1 using a single device. Microbubbles were prepared using either the sonofluidic device or conventional sonication, and their size, concentration, and stability were comparable. The mean diameter, concentration, and stability were found to be comparable between techniques, but the microbubbles produced by the sonofluidic device were all less then 5 μm in diameter and thus did not require any post-production fractionation.A three-dimensional (3D) finite difference (FD) model with formal fourth-order accuracy has been developed for the ocean acoustic Helmholtz equation (HE), which can be used to address arbitrary bathymetry and provide more accurate benchmark solutions for other 3D underwater acoustic approximate models. The derivatives in the acoustic HE are numerically discretized based on regular grids, and the perfectly matched layer is introduced to absorb unphysical reflections from the boundaries where Sommerfeld radiation conditions are deployed. The system of linear equations is solved using a parallel matrix-free geometric multigrid preconditioned biconjugate gradient stabilized iteration method, and the code (named COACH) is run on the Tianhe-2 supercomputer in China. Four 3D topographic benchmark acoustic cases-a wedge waveguide, Gaussian canyon, conical seamount, and corrugated seabed-are simulated to test the present FD model, and the maximum number of grid points reaches 33.15 × 109 in the wedge waveguide case, running in parallel with 988 central processing unit cores. Furthermore, the accuracy and generality of the present model have been verified by solution comparisons with other available 3D acoustic propagation models, and the two-dimensional and 3D transmission loss contours are presented to facilitate the distinguishing among the acoustic field features of these cases.The celebrated Kuramoto model provides an analytically tractable framework to study spontaneous collective synchronization and comprises globally coupled limit-cycle oscillators interacting symmetrically with one another. The Sakaguchi-Kuramoto model is a generalization of the basic model that considers the presence of a phase lag parameter in the interaction, thereby making it asymmetric between oscillator pairs. Here, we consider a further generalization by adding an interaction that breaks the phase-shift symmetry of the model. The highlight of our study is the unveiling of a very rich bifurcation diagram comprising of both oscillatory and non-oscillatory synchronized states as well as an incoherent state There are regions of two-state as well as an interesting and hitherto unexplored three-state coexistence arising from asymmetric interactions in our model.In recent years, the artificial intelligence community has seen a continuous interest in research aimed at investigating dynamical aspects of both training procedures and machine learning models. Of particular interest among recurrent neural networks, we have the Reservoir Computing (RC) paradigm characterized by conceptual simplicity and a fast training scheme. Yet, the guiding principles under which RC operates are only partially understood. In this work, we analyze the role played by Generalized Synchronization (GS) when training a RC to solve a generic task. In particular, we show how GS allows the reservoir to correctly encode the system generating the input signal into its dynamics. We also discuss necessary and sufficient conditions for the learning to be feasible in this approach. Moreover, we explore the role that ergodicity plays in this process, showing how its presence allows the learning outcome to apply to multiple input trajectories. Finally, we show that satisfaction of the GS can be measured by means of the mutual false nearest neighbors index, which makes effective to practitioners theoretical derivations.Phytoplankton-zooplankton interaction is a topic of high interest among the interrelationships related to marine habitats. In the present manuscript, we attempt to study the dynamics of a three-dimensional system with three types of plankton non-toxic phytoplankton, toxic producing phytoplankton, and zooplankton. We assume that both non-toxic and toxic phytoplankton are consumed by zooplankton via Beddington-DeAngelis and general Holling type-IV responses, respectively. We also incorporate gestation delay and toxic liberation delay in zooplankton's interactions with non-toxic and toxic phytoplankton correspondingly. First, we have studied the well-posedness of the system. Then, we analyze all the possible equilibrium points and their local and global asymptotic behavior. Furthermore, we assessed the conditions for the occurrence of Hopf-bifurcation and transcritical bifurcation. Using the normal form method and center manifold theorem, the conditions for stability and direction of Hopf-bifurcation are also studied. Various time-series, phase portraits, and bifurcation diagrams are plotted to confirm our theoretical findings. From the numerical simulation, we observe that a limited increase in inhibitory effect of toxic phytoplankton against zooplankton can support zooplankton's growth, and rising predator's interference can also boost zooplankton expansion in contrast to the nature of Holling type IV and Beddington-DeAngelis responses. Next, we notice that on variation of toxic liberation delay, the delayed system switches its stability multiple times and becomes chaotic. Furthermore, we draw the Poincaré section and evaluate the maximum Lyapunov exponent in order to verify the delayed system's chaotic nature. Results presented in this article might be helpful to interpret biological insights into phytoplankton-zooplankton interactions.Quasiperiodic perturbations of two-dimensional nearly Hamiltonian systems with a limit cycle are considered. The behavior of solutions in a small neighborhood of a degenerate resonance is studied. Special attention is paid to the synchronization problem. Bifurcations of quasiperiodic solutions that arise when the limit cycle passes through the neighborhood of a resonance phase curve are investigated. The study is based on an analysis of an autonomous pendulum-type system, which is obtained by the method of averaging and determines the dynamics in the resonance zone. Two possible topological structures of the unperturbed averaged system are distinguished. For each case, the intervals of a control parameter that correspond to oscillatory synchronization are found. The results are applied to a Duffing-Van der Pol-type equation.In the present article, we demonstrate the emergence and existence of the spiral wave chimera-like transient pattern in coupled ecological systems, composed of prey-predator patches, where the patches are connected in a three-dimensional medium through local diffusion. We explore the transition scenarios among several collective dynamical behaviors together with transient spiral wave chimera-like states and investigate the long time behavior of these states. The transition from the transient spiral chimera-like pattern to the long time synchronized or desynchronized pattern appears through the deformation of the incoherent region of the spiral core. We discuss the transient dynamics under the influence of the species diffusion at different time instants. By calculating the instantaneous strength of incoherence of the populations, we estimate the duration of the transient dynamics characterized by the persistence of the chimera-like spatial coexistence of coherent and incoherent patterns over the spatial domain. We generalize our observations on the transient dynamics in a three-dimensional grid of diffusive ecological systems by considering two different prey-predator systems.Inferring nonlinear and asymmetric causal relationships between multivariate longitudinal data is a challenging task with wide-ranging application areas including clinical medicine, mathematical biology, economics, and environmental research. A number of methods for inferring causal relationships within complex dynamic and stochastic systems have been proposed, but there is not a unified consistent definition of causality in the context of time series data. We evaluate the performance of ten prominent causality indices for bivariate time series across four simulated model systems that have different coupling schemes and characteristics. Pairwise correlations between different methods, averaged across all simulations, show that there is generally strong agreement between methods, with minimum, median, and maximum Pearson correlations between any pair (excluding two similarity indices) of 0.298, 0.719, and 0.955, respectively. In further experiments, we show that these methods are not always invariant to real-world relevant transformations (data availability, standardization and scaling, rounding errors, missing data, and noisy data). We recommend transfer entropy and nonlinear Granger causality as particularly strong approaches for estimating bivariate causal relationships in real-world applications. Both successfully identify causal relationships and a lack thereof across multiple simulations, while remaining robust to rounding errors, at least 20% missing data and small variance Gaussian noise. Finally, we provide flexible open-access Python code for computation of these methods and for the model simulations.
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