Successful intracellular delivery of nucleic acids (NAs) hinges on many factors, one of them being NAs’ efficacious escape from endosomes. As competent NA vectors, $p\mathrm{H}$-responsive gemini surfactants (GSs) might achieve high efficacy by facilitating endosomal escape. However, how the GSs assist the escape remains debated as many proposed mechanisms still lack experimental support, which hinders replication and further improvement of the efficient delivery. Here, via UV, fluorescence spectroscopy, and small-angle neutron scattering (SANS), we examined a $p\mathrm{H}$-responsive GS's and a $p\mathrm{H}$-unresponsive GS's capabilities to compact DNA and withstand binding competition, and their interactions with model endosomal and lysosomal membranes, at varied $p\mathrm{Hs}$. Acidification-driven enhancement of DNA-compaction capability and of stability against binding competition were found specific to the $p\mathrm{H}$-responsive GS. Alongside the $p\mathrm{H}$-responsive GS's structural perturbation to the membranes as observed with SANS, the features suggest that $p\mathrm{H}$-responsive GSs facilitate endosomal escape by releasing excess GS molecules from DNA-GS complexes upon acidification in endosome maturation, with the released GS molecules disrupting endosomal and lysosomal membranes and thereby assisting the escape. A general design principle for NA vectors is proposed on the basis of this experimental finding.

We study the interactions of two or more solitary waves in the Adlam-Allen model describing the evolution of a (cold) plasma of positive and negative charges, in the presence of electric and transverse magnetic fields. In order to show that the interactions feature an exponentially repulsive nature, we elaborate two distinct approaches: (a) using energetic considerations and the Hamiltonian structure of the model, and (b) using the so-called Manton method. We compare these findings with results of direct simulations, and we identify adjustments necessary to achieve a quantitative match between them. Additional connections are made, such as with solitons of the Korteweg–de Vries equation. New challenges are identified in connection to this model and its solitary waves.

In this paper, the prototypical process of the normal impact of dense agglomerates is investigated using the discrete element method–boundary element method coupled simulations. The agglomerate consists of 50 charged particles with the surface energy equal to $10\mathrm{mJ}/{\mathrm{m}}^2$. The particles are assumed to be tribocharged and follow an exponential charge distribution, while the varying levels of coupled polarization are also considered. Simulation results reveal that the presence of the electrostatic interactions due to particle charging and polarization could drive more pronounced re-agglomeration after the collision, which effectively reduces the degree of agglomerate fragmentation. Moreover, when quantifying the collision outcomes using the fragmentation ratio, the influence of the electrostatic force is most significant at a moderate incident velocity. This is because, at such incident velocities, the impact is violent enough to break the agglomerate, but many ejected fragments are usually at low velocities and are attracted back by the long-range electrostatic force. Furthermore, the electrostatic force between same-sign particles even becomes attractive when particles are strongly polarized, leading to qualitative changes in particle dynamics. Finally, by comparing the collision outcomes under different incident velocities, the contact interactions are found to prevail when particles are still bounded in the agglomerate, while the electrostatic interaction becomes dominant after particles detach from each other.

Applying constant tensile stress to a piece of amorphous solid results in a slow extension, followed by an eventual rapid mechanical collapse. This “creep” process is of paramount engineering concern, and as such was the subject of study in a variety of materials, for more than a century. Predictive theories for ${\tau }_w$, the expected time of collapse, are incomplete, mainly due to its dependence on a bewildering variety of parameters, including temperature, system size, tensile force, but also the detailed microscopic interactions between constituents. The complex dependence of the collapse time on all the parameters is discussed below, using simulations of strip of amorphous material. Different scenarios are observed for ductile and brittle materials, resulting in serious difficulties in creating an all-encompassing theory that could offer safety measures for given conditions. A central aim of this paper is to employ scaling concepts, to achieve data collapse for the probability distribution function (pdf) of $ln{\tau }_w$. The scaling ideas result in a universal function which provides a prediction of the pdf of $ln{\tau }_w$ for out-of-sample systems, from measurements at other values of these parameters. The predictive power of the scaling theory is demonstrated for both ductile and brittle systems. Finally, we present a derivation of universal scaling function for brittle materials. The ductile case appears to be due to a plastic necking instability and is left for future research.

Boundary conditions for the Boltzmann equation are investigated on the basis of a kinetic model for gas-surface interactions. The model takes into account gas and physisorbed molecules interacting with a surface potential and colliding with phonons. The potential field is generated by fixed crystal molecules, and the interaction with phonons represents the fluctuating part of the surface. The interaction layer is assumed to be thinner than the mean free path of the gas and physisorbed molecules, and the phonons are assumed to be at equilibrium. The asymptotic kinetic equation for the inner physisorbate layer is derived and used to investigate gas distribution boundary conditions. To be more specific, a model of the boundary condition for the Boltzmann equation is derived on the basis of an approximate iterative solution of the kinetic equation for the physisorbate layer, and the quality of the model is assessed by detailed numerical simulations, which also clarify the behavior of the molecules in the layer.

We explore the effect of stochastic resetting on the first-passage properties of the Feller process. The Feller process can be envisioned as space-dependent diffusion, with diffusion coefficient $D\left(x\right)=x$, in a potential $U\left(x\right)=x(\frac{x}{2}-\theta )$ that owns a minimum at $\theta$. This restricts the process to the positive side of the origin and therefore, Feller diffusion can successfully model a vast array of phenomena in biological and social sciences, where realization of negative values is forbidden. In our analytically tractable model system, a particle that undergoes Feller diffusion is subject to Poissonian resetting, i.e., taken back to its initial position at a constant rate $r$, after random time epochs. We addressed the two distinct cases that arise when the relative position of the absorbing boundary ($x_a$) with respect to the initial position of the particle ($x_0$) differ, i.e., for (a) $x_0<x_a$ and (b) $x_a<x_0$. Utilizing the Fokker-Planck description of the system, we obtained closed-form expressions for the Laplace transform of the survival probability and hence derived the exact expressions of the mean first-passage time $\langle T_r\rangle$. Performing a comprehensive analysis on the optimal resetting rate ($r^{★}$) that minimize $\langle T_r\rangle$ and the maximal speedup that $r^{★}$ renders, we identify the phase space where Poissonian resetting facilitates first-passage for Feller diffusion. We observe that for $x_0<x_a$, resetting accelerates first-passage when $\theta <{\theta }_c$, where ${\theta }_c$ is a critical value of $\theta$ that decreases when $x_a$ is moved away from the origin. In stark contrast, for $x_a<x_0$, resetting accelerates first-passage when $\theta >{\theta }_c$, where ${\theta }_c$ is a critical value of $\theta$ that increases when $x_0$ is moved away from the origin. Our study opens up the possibility of a series of subsequent works with more case-specific models of Feller diffusion with resetting.

We study a $p$-spin model with ferromagnetic coupling and quenched random crystal fields for $p\ge 3$ for spin-1 systems. We find that the model has lines of first-order transitions at finite temperature $\left(T\right)$ for all $p\ge 3$. For bimodal distribution of the random crystal field these lines meet at a triple point for weak strength of the crystal field $\left(\mathrm{\Delta }\right)$. Beyond a critical strength of $\mathrm{\Delta }$, they do not meet and one of the lines ends at a critical point $\left(T_c\right)$. Interestingly, we find that on increasing $T$ from $T_c$, keeping other parameters fixed, the system undergoes one more transition which is first order in its character. The system thus exhibits a Gardner-like transition for a range of parameters for all finite $p\ge 3$. For $p\to \infty$ the model behaves differently and there is only one random first-order transition at $T=0$.

We introduce heat engines working in the nanoregime that allow one to extract a finite amount of deterministic work. Using the resource theory approach to themodynamics, we show that the efficiency of these cycles is strictly smaller than Carnot's, and we associate this difference with a fundamental irreversibility that is present in single-shot transformations. When fluctuations in the extracted work are allowed there is a trade-off between their size and the efficiency. As the size of fluctuations increases so does the efficiency and optimal efficiency is attained for unbounded fluctuations, while a certain amount of deterministic work is drawn from the cycle. Finally, we show that when the working medium is composed of many particles, by creating an amount of correlations between the subsystems that scale logarithmically with their number, Carnot's efficiency can also be approached in the asymptotic limit along with deterministic work extraction.

Hypergraphs and simplical complexes both capture the higher-order interactions of complex systems, ranging from higher-order collaboration networks to brain networks. One open problem in the field is what should drive the choice of the adopted mathematical framework to describe higher-order networks starting from data of higher-order interactions. Unweighted simplicial complexes typically involve a loss of information of the data, though having the benefit to capture the higher-order topology of the data. In this work we show that weighted simplicial complexes allow one to circumvent all the limitations of unweighted simplicial complexes to represent higher-order interactions. In particular, weighted simplicial complexes can represent higher-order networks without loss of information, allowing one at the same time to capture the weighted topology of the data. The higher-order topology is probed by studying the spectral properties of suitably defined weighted Hodge Laplacians displaying a normalized spectrum. The higher-order spectrum of (weighted) normalized Hodge Laplacians is studied combining cohomology theory with information theory. In the proposed framework we quantify and compare the information content of higher-order spectra of different dimension using higher-order spectral entropies and spectral relative entropies. The proposed methodology is tested on real higher-order collaboration networks and on the weighted version of the simplicial complex model “Network Geometry with Flavor.”

In this work, we investigate the temporal evolution of the degree of a given vertex in a network by mapping the dynamics into a random walk problem in degree space. We analyze when the degree approximates a preestablished value through a parallel with the first-passage problem of random walks. The method is illustrated on the time-dependent versions of the Erdős-Rényi and Watts-Strogatz models, which were originally formulated as static networks. We have succeeded in obtaining an analytic form for the first and the second moments of the first-passage time and showing how they depend on the size of the network. The dominant contribution for large networks with $N$ vertices indicates that these quantities scale on the ratio $N/p$, where $p$ is the linking probability.

Hydrogen evolution in acidic aqueous electrolytes was recently found to be characterized by a carpet of microbubbles covering the microelectrode and feeding the growth of the main bubbles by coalescence. Besides this, oscillatory behavior of the main bubbles was observed prior to departure. Extending earlier studies, this work delivers the forces acting on the main bubble more accurately by taking into account further geometric and electrochemical details measured during experiments. Combining simulation work and measurements makes it possible to confirm the role of an attractive electrical (Coulomb) force caused by the adsorption of hydrogen ions at the bubble interface and to obtain a better understanding of the bubble dynamics observed.

For renewal-reward processes with a power-law decaying waiting time distribution, anomalously large probabilities are assigned to atypical values of the asymptotic processes. Previous works have revealed that this anomalous scaling causes a singularity in the corresponding large deviation function. In order to further understand this problem, we study in this article the scaling of variance in several renewal-reward processes: counting processes with two different power-law decaying waiting time distributions and a Knudsen gas (a heat conduction model). Through analytical and numerical analyses of these models, we find that the variances show an anomalous scaling when the exponent of the power law is $-3$. For a counting process with the power-law exponent smaller than $-3$, this anomalous scaling does not take place: this indicates that if we only consider the standard deviation from the expectation, any anomalous behavior will not be detected. In this case, we argue that anomalous scaling appears in higher order cumulants. Finally, many-body particles interacting through soft-core interactions with the boundary conditions employed in the Knudsen gas are studied using numerical simulations. We observe that the variance scaling becomes normal even though the power-law exponent in the boundary conditions is $-3$.

The Mpemba effect is the phenomenon in which the system with high initial temperature cools faster than the system with low initial temperature when all other conditions are the same. A theoretical model of the Mpemba effect through the canonical first-order phase transition is proposed in this paper, which shows that in the cooling processes, the path of the first-order phase transition of the system with the high initial temperature does not pass through any metastable state, while the path of the first-order phase transition of the system with the low initial temperature passes through a metastable state, which leads to the occurrence of the Mpemba effect. Then an example of the theoretical model is given in the Blume-Emery-Griffiths model. The Monte Carlo algorithm is adopted to calculate the estimated times for both systems with different initial temperature to cool down and undergo a first-order phase transition. The simulation results demonstrate a Mpemba effect in the system. Moreover, the evolution paths of the first-order phase transitions of the systems with high and low initial temperatures are given, respectively. The theoretical model presented here may help explain the Mpemba effect in water.

We investigate how site dilution, as would be introduced by immunization, affects the properties of the active-to-absorbing nonequilibrium phase transition in the paradigmatic susceptible-infectious-recovered (SIR) model on regular cubic lattices. According to the Harris criterion, the critical behavior of the SIR model, which is governed by the universal scaling exponents of the dynamic isotropic percolation (DyIP) universality class, should remain unaltered after introducing impurities. However, when the SIR reactions are simulated for immobile agents on two- and three-dimensional lattices subject to quenched disorder, we observe a wide crossover region characterized by varying effective exponents. Only after a sufficient increase of the lattice sizes does it become clear that the SIR system must transition from that crossover regime before the effective critical exponents asymptotically assume the expected DyIP values. We attribute the appearance of this exceedingly long crossover to a time lag in a complete recovery of small disconnected clusters of susceptible sites, which are apt to be generated when the system is prepared with Poisson-distributed quenched disorder. Finally, we demonstrate that this transient region becomes drastically diminished when we significantly increase the value of the recovery rate or enable diffusive agent mobility through short-range hopping.

Some mosquitoes are vectors for disease transmission to human populations. Aedes aegypti, the main vector for dengue in Argentina, mainly breeds in artificial containers as it is strongly adapted to urban environments. This highlights the relevance of understanding human social behavior to design successful vector control campaigns. We developed a model of mosquito populations that considers their main biological and behavioral features and incorporates parameters that model human behavior in relation to water container disposal. We performed extensive numerical simulations to study the variability of adult and aquatic mosquito populations when various protocols are applied, changing the effectiveness and frequency of water bucket disposal and the delay in the availability of water containers for breeding. We found an effectiveness threshold value above which it is possible to significantly limit mosquito dispersal. Interestingly, a nonsynchronized discard frequency, more attainable by human populations, was more efficient than a synchronized one to reduce the aquatic mosquito population. Scenarios with random delays in the availability of water containers indicate that it is not decisive to have a fixed time delay for the entire population, which is more realistic as it mimics a wider range of human behaviors. This simple model could help design dengue prevention campaigns aiming at mosquito population control.

The motion of colloidal species on an out-of-thermal equilibrium surface is studied experimentally by optical microscopy. Water droplets of size in the micrometer range, spontaneously formed at a spherical-like interface between water and oil, are the colloidal species. The interface appears as a convex meniscus when putting water on oil with an added nonionic surfactant. Since the water density is greater than that of oil, the interface is produced into the oil. The spontaneously formed water droplets move attached to the interface while still growing from submicrometer sizes to a few micrometers. Although the dynamic nature of the process, with both the interface and the particles still changing, produces heterogeneities in the system, anomalous diffusion was not observed. The motion of the droplets has a well-identified Brownian component with a Gaussian distribution of steps due to the thermal agitation of the media surrounding the droplets and a drift component due to the effect of gravity.

We study closed systems of particles that are subject to stochastic forces in addition to the conservative forces. The stochastic equations of motion are set up in such a way that the energy is strictly conserved at all times. To ensure this conservation law, the evolution equation for the probability density is derived using an appropriate interpretation of the stochastic equation of motion that is not the Itô nor the Stratonovic interpretation. The trajectories in phase space are restricted to the surface of constant energy. Despite this restriction, the entropy is shown to increase with time, expressing irreversible behavior and relaxation to equilibrium. This main result of the present approach contrasts with that given by the Liouville equation, which also describes closed systems, but does not show irreversibility.

Stochastic resetting causes kinetic phase transitions, whereas its underlying physical mechanism remains to be elucidated. We here investigate the anomalous transport of a particle moving in a chaotic system with a stochastic resetting and a rough potential and focus on how the stochastic resetting, roughness, and nonequilibrium noise affect the transports of the particle. We uncover the physical mechanism for stochastic resetting resulting in the anomalous transport in a nonlinear chaotic system: The particle is reset to a new basin of attraction which may be different from the initial basin of attraction from the view of dynamics. From the view of the energy landscape, the particle is reset to a new energy state of the energy landscape which may be different from the initial energy state. This resetting can lead to a kinetic phase transition between no transport and a finite net transport or between negative mobility and positive mobility. The roughness and noise also lead to the transition. Based on the mechanism, the transport of the particle can be tuned by these parameters. For example, the combination of the stochastic resetting, roughness, and noise can enhance the transport and tune negative mobility, the enhanced stability of the system, and the resonant-like activity. We analyze these results through variances (e.g., mean-squared velocity, etc.) and correlation functions (i.e., velocity autocorrelation function, position-velocity correlation function, etc.). Our results can be extensively applied in the biology, physics, and chemistry, even social system.

The role of waning immunity in basic epidemic models on networks has been undervalued while being noticeably fundamental for real epidemic outbreaks. One central question is which mean-field approach is more accurate in describing the epidemic dynamics. We tackled this problem considering the susceptible-infected-recovered-susceptible (SIRS) epidemic model on networks. Two pairwise mean-field theories, one based on recurrent dynamical message-passing (rDMP) theory and the other on the pair quenched mean-field (PQMF) theory, are compared with extensive stochastic simulations on large networks of different levels of heterogeneity. For waning immunity times longer than or comparable with the recovering time, rDMP outperforms PQMF theory on power-law networks with degree distribution $P\left(k\right)\sim k^{-\gamma }$. In particular, for $\gamma >3$, the epidemic threshold observed in simulations is finite, in qualitative agreement with rDMP, while PQMF leads to an asymptotically null threshold. The critical epidemic prevalence for $\gamma >3$ is localized in a finite set of vertices in the case of the PQMF theory. In contrast, the localization happens in a subextensive fraction of the network in rDMP theory. Simulations, however, indicate that localization patterns of the actual epidemic lay between the two mean-field theories, and improved theoretical approaches are necessary to understanding the SIRS dynamics.