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he supporting boundaries.Nonlinear waves of electrical excitation initiate cardiac contraction. Abnormal wave propagation in the heart, e.g., spiral waves, can lead to sudden cardiac arrest. This study analyzed the dynamics of spiral waves under the influence of an instability called negative filament tension, and examined how the spiral waves can be eliminated through high-frequency pacing. A generic anatomical model of the left ventricle of the human heart and the Aliev-Panfilov model for cardiac tissue were used. The study showed that the source of such arrhythmia is elongated filaments with lengths that can be 10-20 times greater than the characteristic thickness of the heart wall. In anisotropic tissue, the filament elongated before it was annihilated at the base of the heart. The spiral waves were eliminated through overdrive pacing with stimulation periods from 0.8 to 0.95 relative to the spiral wave period. The minimum time for the expulsion was about 10 s.Understanding the low-temperature pure state structure of spin glasses remains an open problem in the field of statistical mechanics of disordered systems. Here we study Monte Carlo dynamics, performing simulations of the growth of correlations following a quench from infinite temperature to a temperature well below the spin-glass transition temperature T_c for a one-dimensional Ising spin-glass model with diluted long-range interactions. In this model, the probability P_ij that an edge i,j has nonvanishing interaction falls as a power law with chord distance, P_ij∝1/R_ij^2σ, and we study a range of values of σ with 1/22/3 which corresponds to short-range systems below six dimensions. For σ less then 2/3, the decay exponent α_d follows the RSB prediction for the decay exponent α_s=3-4σ of the static metastate, consistent with a conjectured statics-dynamics relation, while it approaches α_d=1-σ in the regime 2/3 less then σ less then 1; however, it deviates from both lines in the vicinity of σ=2/3.Nanoparticles in intercellular gaps, junctions, or seals could have close contact with neighboring cells simultaneously. Understanding the interaction between intercellular nanoparticles and confining cell membranes is of fundamental importance, not only to the unravelling of endocytic mechanisms but also to implications such as controlled drug delivery in tumor tissues. Here we theoretically examine the mechanical behaviors of adhesive cylindrical nanoparticles confined between two lipid membrane patches of finite size. As the size of the particle-membrane contact region or wrapping degree increases, neighboring cylindrical nanoparticles become separated and the nanoparticle distance increases first and then decreases until the particles are fully trapped by adjacent membrane patches. Depending on the nanoparticle size, adhesion energy, membrane bending rigidity and tension, and intermembrane distance, three characteristic particle-membrane interaction phases are determined as no wrapping, partial trapping, and full trapping, and the corresponding interaction phase diagram is established. Further energy comparison indicates that multiple nanoparticles undergoing the two-membrane trapping process do not exhibit cooperative effects. Analytical estimations on the system energy and configurations at equilibrium are performed based on the force balance of the membranes at small deformation and match well with numerical solutions. The results shed light on the mechanical behaviors of multiple nanoparticles in cell junctions or gaps and may have implications for drug delivery in tumor tissues.The dynamic process of mitotic spindle assembly depends on multitudes of inter-dependent interactions involving kinetochores (KTs), microtubules (MTs), spindle pole bodies (SPBs), and molecular motors. Before forming the mitotic spindle, multiple visible microtubule organizing centers (MTOCs) coalesce into a single focus to serve as an SPB in the pathogenic budding yeast, Cryptococcus neoformans. To explain this unusual phenomenon in the fungal kingdom, we propose a "search and capture" model, in which cytoplasmic MTs (cMTs) nucleated by MTOCs grow and capture each other to promote MTOC clustering. Our quantitative modeling identifies multiple redundant mechanisms mediated by a combination of cMT-cell cortex interactions and inter-cMT coupling to facilitate MTOC clustering within the physiological time limit as determined by time-lapse live-cell microscopy. Besides, we screen various possible mechanisms by computational modeling and propose optimal conditions that favor proper spindle positioning-a critical determinant for timely chromosome segregation. These analyses also reveal that a combined effect of MT buckling, dynein pull, and cortical push maintains spatiotemporal spindle localization.Surface growth properties during irreversible multilayer deposition of straight semirigid rods on linear and square lattices have been studied by Monte Carlo simulations and analytical considerations. The filling of the lattice is carried out following a generalized random sequential adsorption mechanism where the depositing objects can be adsorbed on the surface forming multilayers. The results of our simulations show that the roughness evolves in time following two different behaviors an "homogeneous growth regime" at initial times, where the heights of the columns homogeneously increase, and a "segmented growth regime" at long times, where the adsorbed phase is segmented in actively growing columns and inactive nongrowing sites. Under these conditions, the surface growth generated by the deposition of particles of different sizes is studied. At long times, the roughness of the systems increases linearly with time, with growth exponent β=1, at variance with a random deposition of monomers which presents a sublinear behavior (β=1/2). The linear behavior is due to the segmented growth process, as we show using a simple analytical model.Single-file diffusion exhibits anomalously slow collective transport when particles are able to immobilize by binding and unbinding to the one-dimensional channel within which the particles diffuse. We have explored this system for short porelike channels using a symmetric exclusion process with fully stochastic dynamics. We find that for shorter channels, a non-Fickian regime emerges for slow binding kinetics. In this regime the average flux 〈Φ〉∼1/L^3, where L is the channel length in units of the particle size. We find that a two-state model describes this behavior well for sufficiently slow binding rates, where the binding rates determine the switching time between high-flux bursts of directed transport and low-flux leaky states. Each high-flux burst is Fickian with 〈Φ〉∼1/L. Longer systems are more often in a low-flux state, leading to the non-Fickian behavior.We study the maximum response of network-coupled bistable units to subthreshold signals focusing on the effect of phase disorder. We find that for signals with large levels of phase disorder, the network exhibits an enhanced response for intermediate coupling strength, while generating a damped response for low levels of phase disorder. We observe that the large phase-disorder-enhanced response depends mainly on the signal intensity but not on the signal frequency or the network topology. We show that a zero average activity of the units caused by large phase disorder plays a key role in the enhancement of the maximum response. With a detailed analysis, we demonstrate that large phase disorder can suppress the synchronization of the units, leading to the observed resonancelike response. Cytoskeletal Signaling inhibitor Finally, we examine the robustness of this phenomenon to the unit bistability, the initial phase distribution, and various signal waveform. Our result demonstrates a potential benefit of phase disorder on signal amplification in complex systems.We report intermittent large-intensity pulses that originate in Zeeman laser due to instabilities in quasiperiodic motion, one route follows torus-doubling to chaos and another goes via quasiperiodic intermittency in response to variation in system parameters. The quasiperiodic breakdown route to chaos via torus-doubling is well known; however, the laser model shows intermittent large-intensity pulses for parameter variation beyond the chaotic regime. During quasiperiodic intermittency, the temporal evolution of the laser shows intermittent chaotic bursting episodes intermediate to the quasiperiodic motion instead of periodic motion as usually seen during the Pomeau-Manneville intermittency. The intermittent bursting appears as occasional large-intensity events. In particular, this quasiperiodic intermittency has not been given much attention so far from the dynamical system perspective, in general. In both cases, the infrequent and recurrent large events show non-Gaussian probability distribution of event height extended beyond a significant threshold with a decaying probability confirming rare occurrence of large-intensity pulses.Recent advances show that neural networks embedded with physics-informed priors significantly outperform vanilla neural networks in learning and predicting the long-term dynamics of complex physical systems from noisy data. Despite this success, there has only been a limited study on how to optimally combine physics priors to improve predictive performance. To tackle this problem we unpack and generalize recent innovations into individual inductive bias segments. As such, we are able to systematically investigate all possible combinations of inductive biases of which existing methods are a natural subset. Using this framework we introduce variational integrator graph networks-a novel method that unifies the strengths of existing approaches by combining an energy constraint, high-order symplectic variational integrators, and graph neural networks. We demonstrate, across an extensive ablation, that the proposed unifying framework outperforms existing methods, for data-efficient learning and in predictive accuracy, across both single- and many-body problems studied in the recent literature. We empirically show that the improvements arise because high-order variational integrators combined with a potential energy constraint induce coupled learning of generalized position and momentum updates which can be formalized via the partitioned Runge-Kutta method.We study the formation of solitons of microwave self-induced transparency (M/W-SIT) which occurs under cyclotron resonance interaction of an electromagnetic pulse with an initially rectilinear magnetized electron beam. Taking into account the relativistic dependence of the gyrofrequency on the particle energy for electromagnetic wave propagating with a phase velocity different from the speed of light (i.e., far from the autoresonance conditions), such a beam can be considered as a medium of nonisochronous unexcited oscillators. Thus, similar to passing light pulses in the two-level medium, for sufficiently large amplitude and duration the incident electromagnetic pulse decomposes into one or several solitons. We find analytically the generalized solution for the M/W-SIT soliton with amplitude and duration determined, besides the soliton velocity, by the frequency self-shift parameter. The feasibility and stability of the obtained solutions are confirmed in numerical simulations of a semibounded problem describing propagation and nonlinear interaction of an incident electromagnetic pulse.