We propose a thermodynamic cycle similar to the Otto cycle with a two-qubit working substance such that one of its isochoric strokes is provided by a controllable quantum measurement. We show that the measurement device can affect the working substance in four possible ways, as well as the energy exchanges between the working substance and an external work source during the isentropic strokes take place in two possible ways. By these considerations, we obtain eight thermal machines, each of which works individually in engine, accelerator, or refrigerator modes. We show that, for half of the obtained thermal machines, the applied quantum measurement causes their respective performances to go higher than the corresponding regular Otto limits, while the remainder go lower than the mentioned limits. Interestingly, it is shown that the performance of each thermal machine explicitly depends on the strength parameters of applied measurement, which in turn are related to coupling constants of the working substance and measurement device. These findings highlight that the role of generalized measurements as nonequilibrium quantum resources is not always constructive in powering the thermal machines.

Previous studies about directed transport of active matter have overlooked the restoring torques arising from confinement that significantly impact the collective behavior. We numerically investigated the transport of active particles experiencing restoring torques (coupling the steric and confining forces) in a periodic channel. It is demonstrated that restoring torques can significantly enhance the rectification, and can induce current reverse. With a fixed positive asymmetry parameter, the scaled average velocity shows a peak function in the negative direction as the restoring torque increases, and after current reversal, it presents a peak function in the positive direction. When the restoring torque is an intermediate value, current reverse can occur by varying the self-propulsion speed or packing fraction. Our results may contribute to a better understanding of the effects of confinement with restoring torques on the collective behavior of active matter, and provide a theoretical foundation for related experimental investigations of active matter transport.

We study the lower bound of the entropy production in a one-dimensional underdamped Langevin system constrained by a time-dependent parabolic potential. We focus on minimizing the entropy production during transitions from a given initial distribution to a given final distribution taking a given finite time. We derive the conditions for achieving the minimum entropy production for the processes with normal distributions, using the evolution equations of the mean and covariance matrix to determine the optimal control protocols for stiffness and center of the potential. Our findings reveal that not all covariance matrices can be given as the initial and final conditions due to the limitations of the control protocol. This study extends existing knowledge of the overdamped systems to the underdamped systems.

As dynamic and adaptable immune system sentinels, macrophages can adopt various functional phenotypes in response to environmental stimuli. The transitions between these states, pro-inflammatory ($M1$), anti-inflammatory ($M2$), and mixed $M1/M2$, are crucial for immune regulation and the pathogenesis of many diseases. Therefore, understanding the parameter domains that enable the coexistence of these phenotypes and the critical values that indicate shifts in macrophage phenotypes is vital. By examining two-parameter phase diagrams and one-parameter bifurcation diagrams, distinct regions of monostability and multistability are identified as cytokine signaling levels ($\mathrm{IFN}\gamma$ and IL-4) change, revealing tipping points for phenotype transitions. All three phenotypes can coexist when the cytokine signals are balanced. The study explores the resilience of macrophage phenotypes through basin stability measures and stochastic potential, highlighting the preservation of a phenotype amid environmental perturbations. Furthermore, it illustrates phenotype shifts using critical transition theory and the predictive power of early warning signals (EWSs) based on critical slowing down. EWSs are derived from stochastic trajectories generated via Monte Carlo simulation with the Gillespie algorithm, incorporating intrinsic noise. It is shown that EWSs are more effective in capturing transitions from $M1\to M1/M2$ and $M1/M2\to M2$ than in detecting the direct transition from $M2\to M1$.

Ion fast ignition by laser-driven ion beams is an interesting approach within the inertial confinement fusion scheme to achieve nuclear fusion. In the ion fast ignition, once the precompression of the target is finished, a sphere of fully ionized deuterium-tritium (DT) is obtained. In this work, we have investigated the following stages: heating of the DT sphere by a monoenergetic ion beam and the preliminary evolution of the plasma. To this end, we have carried out a systematic numerical study in terms of the beam parameters (ion fluence and projectile energy), the temperature fields obtained in the plasma, the self-heating, ignition and burning gains, as well as the characteristic lengths and temperatures of the heated plasma and the hot spots generated during the beam-plasma interaction, considering three beams of ions with different charges (${\mathrm{p}}^{+},{\mathrm{C}}^{6+}$, and ${\mathrm{V}}^{23+}$). This study allows us to determine the beam parameter space in which hot spots are generated and ignition is achieved. In addition, we have also studied the effect of the plasma corona surrounding the core and the presence of impurities in the plasma on the parameters mentioned above. Since in both cases the slowing down of the ion beam is increased with respect to the pure DT ideal core, the beam parameter space is shifted toward higher values of the ion fluence and projectile energy.

The observations of linear and nonlinear physical processes are subject to random errors, which can be represented by a wide variety of probability distributions. In contrast, most estimation and inference techniques rely on a Gaussian assumption, which may limit our ability to make model-based predictions. There is a need for data assimilation methods that can capture and leverage the higher moments of these physical processes for state estimation and forecasting. In this paper, we develop the generalized unscented transform (GenUT), which uses a minimal number of sample points to accurately capture elements of the higher moments of most probability distributions. Constraints can be analytically enforced on the sample points while guaranteeing at least second-order accuracy. The GenUT is widely applicable to non-Gaussian distributions, which can substantially improve the assimilation of observations of nonlinear physics, such as the modeling of infectious diseases.

We study the Mpemba effect on a nonequilibrium Markov chain that imitates the active motion of random walkers in a discrete energy landscape. The broken detailed balance, rendered by the activity, gives rise to a unique anomalous relaxation in the system which is distinctly different than the typical equilibrium systems. We observe that the activity can both suppress or induce the Mpemba effect. Furthermore, we report an oscillatory Mpemba effect where the relaxation trajectories, emanating from the hot and cold initial conditions, cross each other multiple times, and this occurs due to the emergence of complex eigenvalues in the relaxation spectrum due to the activity. Our work reveals a possible pathway for studying the Mpemba effect in active living systems where broken detailed balance is crucial to achieve many biological functions.

The depth of a potential well plays a critical role in noise-assisted rate processes. Prevailing qualitative understanding suggests that the sizes of the fluctuations relative to the size of the basin of attraction in the bifurcation diagram dictate the possibility of noise-driven cellular fate transitions regulated by multistable switches. However, the quantitative relation between the size of basins of attraction and the depth of the wells in the pseudopotential energy of the dynamical systems is unknown. We show that, in multistable switches due to saddle-node bifurcations, the depth of the wells follows power-law scaling with the size of the basins of attraction, with the scaling exponent, $\alpha$, ranging between $2.5$ and $3.0$ across various models and parameter combinations. Power-law scaling also holds for the well depth with the distance from the bifurcation point, with the scaling exponent, $\beta$, ranging between $1.4$ and $1.8$. By investigating various models of bi- and tristability with random parameter sampling, we report median scaling exponents of $\overline{\alpha }=2.85\pm 0.12$ and $\overline{\beta }=1.5\pm 0.08$. Scaling laws provide a route to determine the well depth, in relative scale, from the bifurcation diagram, bypassing the challenging task of direct calculation of pseudopotential energy in multidimensional dynamical systems.

The mechanisms of self-sustained oscillations of brain rhythms have been studied for a long time and it is revealed that the emergence of a pacemaker loop takes a key role for these rhythms. However, it is unclear how this pacemaker loop plays a role in the resting state of the brain, where the characteristic slow-wave activities show a multi-scaled feature and can switch easily between different dynamics states. To study this problem, herein we present a neural model of pacemaker looplike network, with a weak temporal electrical coupling to mark the resting state of the brain. We find that different dynamics patterns can be generated by this model, including the disorder, traveling chimera state, chimera state, and synchronization. Interestingly, we observe a sensitive switching effect between the region of traveling chimera state and that of chimera state, which may provide new insights to the mechanism of quickly switching between different rhythms of the brain in the resting state. Further, we introduce an index $Q$ to describe the fluctuations of the local order parameter of network and conjecture that there is a new regularity caused by the fluctuations. We find that $Q$ is optimally dependent on the matching of parameters and thus confirms the conjecture. Moreover, we show that the observed traveling chimera state is robust to different forms of temporal couplings.

The present study demonstrates the existence of oscillation revival in complex systems, contingent on the population size or heterogeneity level of coupling feedback impurity (imperfections or defects). The findings reveal that by manipulating such heterogeneity, the stability of amplitude death can effectively eliminate oscillation quenching in a network of interconnected oscillators, in sharp contrast to homogeneous coupling, which has the propensity to cause oscillations cessation. It offers a wide range of possible technological applications in which a spatial system must continuously oscillate to function correctly with just a few interventions at certain network nodes.

Defying the median voter theorem, party polarization has spread globally, especially in the United States. As concerns grow over its risks to democracy, political science has probed its causes, revealing two paradoxes: while polarization between U.S. political parties has undeniably increased since the 1970s, the corresponding rise in issue polarization among voters remains contested; moreover, the growing number of politically relevant issues would be expected to counteract party polarization rather than reinforce it. To examine these findings theoretically, we analyze a mathematical model of bipartisan elections where voters and parties interact in a multidimensional ideological space. We derive equations that determine the critical threshold of expected voter support needed for a party to find it strategically advantageous to position itself at the center or outside of it. This threshold fundamentally increases with the decrease of the effective dimension of the ideological space, while issue polarization among voters plays a secondary role. Thus, the model points to both paradoxes being explainable if ideological dimensionality is causally linked to polarization. The model reveals a Curie point: below this threshold, parties polarize as in a ferromagnetic phase, with voters acting like aligned spins; above it, they shift toward the center, akin to a paramagnetic phase.

Various time series in natural and social processes have been found to be bursty. Events in the time series rapidly occur within short time periods, forming bursts, which are alternated with long inactive periods. As the timescale defining bursts increases, individual events are sequentially merged to become small bursts and then bigger ones, eventually leading to the single burst containing all events. Such a merging pattern has been depicted by a tree that fully reveals the hierarchical structure of bursts, thus called a burst tree. The burst-tree structure can be simply characterized by a burst-merging kernel that dictates which bursts are merged together as the timescale increases. In this work, we develop the maximum likelihood estimation method of the burst-merging kernel from time series, which is successfully tested against the time series generated using several model kernels. We also apply our method to some empirical time series from various backgrounds. Our method provides a useful tool to precisely characterize the time series data, hence enabling to study their underlying mechanisms more accurately.

We investigate the phase transition to synchronization in a multiplex network of adaptively coupled Kuramoto oscillators with frequency disorders. We demonstrate how the frequency disorder in a specific layer initiates nucleation across the layers of the multiplex networks. We find that the multiplex network mostly exhibits an abrupt single-step phase transition with occasional gradual multistep phase transition under uniform frequency distribution in all the layers. However, the layers with frequency disorder determine the nucleation in the other layers with uniform frequency distribution, regulating their phase transitions. Notably, the intermediate range of interlayer coupling strength significantly lowers the intralayer coupling strength required for synchronization. In particular, we find that desynchronization transitions are manifested due to both abrupt single-step and gradual multistep transitions in an intermediate range of the interlayer coupling strength, corroborating that such desynchronization phase transitions are specific to multilayer networks. We elucidate the distinct synchronization transitions resulting from heterogeneous nucleation using synchronization index and snapshots of coupling weights. Further, we deduce the evolution equations by reducing the full model to cluster level description using the collective coordinate framework. The transitions observed in the reduced model accurately capture those observed in the full model.

DNA replication is a fundamental process required for duplicating the genome before cell division. However, how epigenetic information is preserved during replication remains poorly understood. DNA replication occurs within specific domains, with replication initiation following a time-regulated pattern. After replication, chromatin must be refolded to restore the 3D structure of the parental chromatin. In this work, we investigate the nonequilibrium problem of how chromatin folds in 3D soon after replication, using stochastic simulation methods, to generate the contact probability map for a specific replicating domain. Our findings indicate that the initial post-replication chromatin structure is influenced by the location of replication origins, the nonequilibrium nature of the replication process, and the speed of the replication fork. Specifically, replication fork speed can significantly impact the organization of newly synthesized chromatin, affecting contact density and enabling the formation of euchromatin-like and heterochromatin-like structures.

Transposons are small, self-replicating DNA sequences found in every branch of life. Often, one transposon will parasitize another, forming a tiny intracellular ecosystem. In some species these ecosystems thrive, while in others they go extinct, yet little is known about when or why this occurs. Here, we present a stochastic model for these ecosystems and discover a transition from stable coexistence to population collapse when the propensity for a transposon to replicate comes to exceed that of its parasites. Our model also predicts that replication rates should be low in equilibrium, which appears to be true of many transposons in nature.

Friction, defined as the resistance to relative motion between two contacting objects, has historically been regarded as a consequence of mechanical energy dissipation. However, the precise mechanism by which it operates in the context of phonon excitation has not been fully elucidated. In this study, we present a theoretical framework based on the atomistic Green's function method that connects friction-excited phonons to the resulting friction force. Our analysis reveals that phonons are primarily excited at bi-washboard frequencies and their harmonics when relative motion occurs in arbitrary directions. The theoretical predictions are validated through detailed molecular dynamics simulations and further supported by experimental evidence. A key finding of our work is the identification of a spectral function that links the normalized number densities of friction-excited phonons to their frequencies. This relationship is intrinsic to the friction system and remains independent of specific factors such as sliding velocity or direction. Our discovery enables a quantitative explanation of anisotropic friction, nonmonotonic velocity dependence of friction, and fluctuations in friction, all without relying on additional assumptions. By resolving the spectral characteristics of phonon excitations, this study provides systematic insights into the fundamental nature of friction.

We show that the inequality in the divergent acoustic energy release rate in quasistatically compressed nanoporous materials can be used as a precursor to failure. A quantification of the inequality in the evolution of the energy release rate using social inequality (such as Gini and Kolkata) indices can predict large bursts of energy release. We also verify similar behavior for simulations of viscoelastic fiber bundle models that mimic the strain-hardening dynamics of the samples. The results demonstrate experimental applicability of the precursory signal for fracture with a diverging energy release rate using social inequality indices.

By exact projection in phase space, we derive the generalized Langevin equation (GLE) for convolution-time-filtered observables. We employ a general convolution filter that directly acts on arbitrary phase-space observables and can involve low-pass, high-pass, band-pass, and band-stop components. The derived filter GLE has the same form and properties as the ordinary GLE but exhibits modified potential, mass, and memory friction kernel. Our filter-projection approach has diverse applications and can be used to (i) systematically derive temporally coarse-grained models by low-pass filtering, (ii) undo data smoothing inherent in any experimental measurement process, (iii) decompose data exactly into slow and fast variables that can be analyzed separately and each obey Liouville dynamics. The latter application is suitable for removing slow transient or seasonal (i.e., periodic) components that do not equilibrate over simulation or experimental observation time scales and constitutes an alternative to nonequilibrium approaches. We derive integral formulas for the GLE parameters of filtered data for general systems and filters. For the special case of a Markovian system and a Debye low-pass filter, we derive the filter GLE memory kernel in closed form and show that low-pass smoothing of Markovian data induces exponentially decaying memory.

We theoretically investigate the finite-time performance of a quantum thermochemical engine utilizing a harmonically trapped one-dimensional (1D) Bose gas in the quasicondensate regime as the working fluid. The proposed engine operates in an Otto cycle, where the unitary work strokes are simulated through a quench of the interatomic interactions of the 1D Bose gas. In the work strokes, the working fluid is treated as a closed quantum many-body system that undergoes dynamic evolution, beginning from an initial thermal equilibrium state at a nonzero temperature. On the other hand, during the thermalization strokes, the working fluid is treated as an open many-body quantum system in diffusive contact with a thermal reservoir, allowing particle exchange alongside the transfer of heat. Using a $c$-field approach, we demonstrate that the engine's operation is enabled by the chemical work done on the working fluid through the flow of particles from the hot reservoir. We examine the finite-time performance of the proposed quantum thermochemical engine in two extreme regimes: (i) the out-of-equilibrium regime (sudden quench), which yields near-maximum power (due to fast driving of the system) while compromising efficiency, and (ii) the quasistatic (near-adiabatic) limit, which approaches maximum efficiency but generates zero power output due to slow driving of the system. Notably, we show that using chemical work allows the engine to achieve efficiencies close to the near-maximum (quasistatic) limit, even in the out-of-equilibrium regime, while maintaining high power output. Thus, in the out-of-equilibrium operational regime, our proposed engine provides a favorable trade-off between efficiency and power output. We also draw connections to previous research, particularly the case of an adiabatic engine cycle operating at zero temperature. We show that this zero-temperature scenario establishes an upper bound on the efficiency and work of our proposed thermochemical engine operating at nonzero temperatures.

Universality is a fundamental concept in modern physics. For the $q$-state Potts model, the critical exponents are merely determined by the order-parameter symmetry $S_q$, spatial dimensionality and interaction range, independent of microscopic details. In a simplest and mean-field treatment, i.e., the Potts model on complete graph (CG), the phase transition is further established to be of percolation universality for the range of $0<q<2$. By simulating the CG Potts model in the random-cluster representation, we numerically demonstrate such a hyperuniversality that the critical exponents are the same for $0<q<2$ and, moreover, the Ising system ($q=2$) exhibits a variety of critical geometric properties in percolation universality. On the other hand, many other universal properties in the finite-size scaling (FSS) theory, including Binder-like ratios and distribution function of the order parameter, are observed to be $q$ dependent. Meanwhile, we have made improvements to the Monte Carlo algorithms for efficiently simulating the CG Potts model. Our finding provides valuable insights for the study of critical phenomena in finite spatial dimensions, particularly when the FSS theory is utilized.