Papers

How long a technological civilization remains active, and what determines whether it collapses or persists, is a central question for both projecting humanity's future and assessing the prevalence of detectable intelligence in the galaxy. We model collapse-recovery dynamics across ten plausible futures for Earth-originating civilization using a hybrid deterministic-stochastic simulation over a 1000-year window. The duty cycle, defined as the fraction of its total lifespan that a civilization is technologically active, ranges from ~0.38 to 1.00, with trajectory outcomes shaped by the interplay of governance structure, resource pressure, and hazard exposure. Several model parameters map onto actionable resilience levers, and modest improvements can qualitatively alter long-term trajectories. Sensitivity analysis reveals that the resource depletion rate and the post-collapse recovery fraction are consistently the most impactful levers across scenarios, suggesting that reducing resource consumption may be at least as important as mitigating existential hazards for avoiding civilizational collapse. We discuss implications for Earth's civilizational resilience and for the search for extraterrestrial technosignatures. We also derive an effective detectability duration that accounts for intermittent civilizational activity, and show that the apparent absence of extraterrestrial signals may reflect the prevalence of low-duty-cycle civilizations rather than the rarity of intelligent life.

The computation of excited states within the Complete Active Space Self-Consistent Field (CASSCF) framework remains a significant challenge in quantum chemistry, both theoretically and algorithmically. In this work, we extend the Kähler manifold formalism introduced in Part I of this series to the CASSCF theory, and draw a geometrical connection from the time-dependent CASSCF equations to state-specific and linear response methodologies for excited states. This is achieved by first investigating the underlying CASSCF manifold and identifying its Kähler structure, which is complicated by the nontrivial coupling of CI and orbital degrees of freedom. Building on these theoretical findings, we derive the CASSCF linear response equations in a straightforward manner, and develop a robust state-specific method that relies solely on first-order derivatives of the CASSCF energy functional. Numerical results on representative molecular systems-water, formaldehyde, and ethylene-demonstrate the effectiveness of the proposed state-specific method, while revealing the difficulty of reliable identification of excited states due to nonlinearity induced by the CASSCF theory.

Matter and external stimuli interact dynamically almost everywhere in nature. Under an applied field, a material seldom stays in the state in which it was characterized, and its properties change with time. Angle-resolved experiments are the standard way to resolve direction-dependent properties, however, current theoretical tools for data analysis are built for static states, leaving dynamic behaviors largely neglected. Here, we develop a generalized “input-coefficient-output” (ICO) approach that constructs spatial operation matrices and symmetry-reduced coefficient matrices across diverse physical systems, as we verify for nonlinear optics, elasticity, electrostriction and magnetostriction, and a Python program is released for convenient application. Using angle-resolved polarized second-harmonic generation (SHG) as an example, we focus on the dynamic modeling of material properties under angle-dependent stimuli, construct dynamic matrix equations that capture the time-dependent evolution of material responses, and propose three dynamic models and a point-to-point data fitting method. For the SHG experiments performed on $T_d-$WTe$_2$, the discrete dynamic model captures the asymmetric characteristics of the measured polar pattern with a significantly improved coefficient of determination ($R^2$ increasing from 0.567 to 0.773), whereas conventional static models are constrained to a symmetric curve. The evolution operator extracted from the dynamic model connects the macroscopic measurement to microscopic intermediate states during their interaction with stimuli. This work provides a practical matrix framework for dynamic modeling of material properties, paving the way for exploring complex nonlinear physics under realistic experimental conditions.

JUNO is designed to determine the neutrino mass ordering with an energy resolution of 3% at 1 MeV. In the real detector, however, deformations of the central stainless-steel structure during installation lead to deviations of the photomultiplier tube (PMT) positions from their design values. Based on the limited survey data of the PMTs and the stainless-steel truss, we perform a correlation analysis of the measured points and propose a method to predict the positions of all PMTs. Using the resulting realistic geometry, we demonstrate that the detector deformation has a negligible effect on the energy reconstruction. In contrast, inaccuracies in the assumed geometry can introduce vertex biases of up to 40 mm. Incorporating the realistic geometry into the calibration-based PMT response model removes this bias and preserves the stability of the reconstruction algorithms.

Multilayered thin-film structures are frequently encountered in industrial applications, where accurate thermal property characterization is essential for performance optimization. These films, typically ranging from nanometers to micrometers in thickness, often exhibit anisotropic thermal conductivity and non-bulk heat capacity, which are challenging to measure. In this study, we introduce a variable-spot-size and multi-frequency square-pulsed source (SPS) method for the simultaneous determination of anisotropic thermal conductivities, heat capacities, and interfacial thermal conductance in multilayered systems. By leveraging a broad modulation frequency range (1 Hz to 10 MHz) and tunable laser spot sizes, the SPS method enhances sensitivity to different thermal parameters across layers. We validate this approach on a silicon-on-insulator (SOI) sample comprising a 1.59 um Si layer, 1.03 um SiO2 layer, and a silicon substrate with a 122 nm aluminum (Al) transducer. The SPS method successfully extracts seven key thermal parameters, including the in-plane and cross-plane thermal conductivities and heat capacity of the Si film, the thermal conductivity and heat capacity of the SiO2 layer, the thermal conductivity of the substrate, and the interfacial thermal conductance between Al and Si. Temperature-dependent measurements from 80 to 500 K showed excellent agreement with literature values and first-principles predictions, confirming the method's accuracy and reliability. These results demonstrate the SPS method as a powerful tool for comprehensive thermal characterization of complex multilayered structures, with implications for both fundamental research and practical applications.

Accurate characterization of heat transport across solid-liquid interfaces is essential for thermal management in micro and nanoscale systems. Yet existing techniques often require prior knowledge of liquid properties, which complicates the simultaneous resolution of interfacial and bulk behaviors, and lose sensitivity once interfacial conductance exceeds 100 MW m-2 K-1. Here we present a differential square pulsed source (DSPS) method that provides simultaneous, non-contact measurement of liquid thermal conductivity, volumetric heat capacity, and solid-liquid interfacial conductance without any predefined material parameters. Dual frequency excitation combined with in-situ substrate referencing enables property extraction from multilayer structures, and numerical simulations show a typical uncertainty of about 8 % in interfacial conductance, confirming robustness. The protocol is validated for a wide spectrum of liquids, including oils, lubricants, aqueous electrolytes, and pure water, with excellent agreement with literature values for bulk properties. Analysis of the data set clarifies how vibrational spectrum mismatch, ionic layering, and related interfacial phenomena govern heat transfer, and demonstrates that oleophilic hexadecyl silane modification of aluminum increases interfacial conductance by a factor of sixteen. The results reveal that conductance can be strongly tuned through surface wettability and chemical functionalization, offering direct guidelines for interface engineering. Because the approach is readily extendable to soft materials such as thermal interface gels, it promises broad applicability in emerging interface-dominated thermal technologies.

Solid-liquid interfacial thermal conductance (ITC) critically influences heat transport in microfluidic, electronic, and energy systems, yet most optical thermometry techniques are limited to specific metal-liquid interfaces. In this work, we introduce a universal broadband square-pulsed thermometry method that enables simultaneous quantification of ITC across a wide range of arbitrary solid-liquid interfaces, while also providing accurate measurements of nanoscale liquid-film thickness. To validate the method, we applied it to Al-water interfaces, yielding ITC values in the range of 50-55 MW m^(-2) K^(-1), consistent with prior studies. The technique also reveals markedly lower ITCs for glass-water (9.9 MW m^(-2) K^(-1)) and Si-water (5.7 MW m^(-2) K^(-1)), and further measurements on Al-silicone oil (~10 MW m^(-2) K^(-1)) and PMMA-silicone oil (~0.4 MW m^(-2) K^(-1)) extend the validation to highly viscous nonpolar liquids and polymer-liquid interfaces. These results highlight the capability of the method to capture thermal transport differences across diverse solid-liquid combinations. Further comparisons with acoustic/diffuse mismatch models and molecular dynamics simulations, together with theoretical analysis, highlight the influence of vibrational mismatch, wettability, and surface condition on interfacial thermal transport. This broadly applicable technique enables rapid, quantitative characterization of solid-liquid interfacial thermal transport, with broad implications for interfacial heat transfer science and technology.

Light-matter interactions are governed by conservation laws of energy and momentum. For harmonic generation in crystalline solids, energy conservation imposes that $m$ incoming photons with energy $\hbar \omega_0$ are combined to form one photon at energy $m\hbar \omega_0$. Linear momentum conservation governs phase matching, whereas angular momentum conservation connects the angular momentum carried by photons to the discrete rotational symmetry of the crystal lattice. As a consequence, circular harmonic generation exerts a torque on the lattice and, conversely, a macroscopic rotation of the crystal induces a nonlinear rotational Doppler shift. These cornerstone laws of nonlinear optics rely on macroscopic symmetry arguments, and therefore provide little insight into the microscopic origin of angular momentum transfer. Here we uncover a direct connection between angular momentum conservation in nonlinear optics and the electronic quantum geometry, by proving that the transferred angular momentum from light to the crystal is proportional to the local Berry curvature at one optical resonance. This relation is encoded in the nonlinear harmonic circular dichroism, which we measure experimentally in an atomically thin semiconductor. With this, we extend our understanding of nonlinear optics, and we establish a method for the all-optical control and read-out of the local Berry curvature.

Vortex $\gamma$ photons in superposition states have important applications in photonuclear, high-energy, and strong-field physics. However, their controlled generation in the $\gamma$-ray regime remains a great challenge. Here, we put forward a novel method for the generation of vortex $\gamma$ photon in superposition states, with controllable orbital angular momentum (OAM) separation $\Delta\ell^\prime$ and modal weights, via nonlinear Compton scattering driven by multifrequency circularly polarized laser fields. We develop a strong-field quantum electrodynamics (QED) framework to reveal the underlying mechanism and calculate the radiation probabilities. In our method, the superposition arises from interference between energy-degenerate multiphoton pathways carrying distinct OAM. For two-frequency fields, the OAM separation follows $\Delta\ell'=\nu\mp1$ (upper/lower sign for equal/opposite helicities), and modal weights are tunable by laser intensities, with $\nu$ the frequency ratio. Vortex $\gamma$ photons in controllable superposition states from our method have significant applications in strong-field QED and nuclear photonics.

Physics-Informed Neural Networks (PINNs) recast PDE solving as an optimisation problem in function space by minimising a residual-based objective, yet many applications require additional derivative-based relations that are just as fundamental as the governing equations. In this paper, we present Derivative-Constrained PINNs (DC-PINNs), a general framework that treats constrained PDE solving as an optimisation guided by a minimum objective function criterion where the physics resides in the minimum principle. DC-PINNs embed general nonlinear constraints on states and derivatives, e.g., bounds, monotonicity, convexity, incompressibility, computed efficiently via automatic differentiation, and they employ self-adaptive loss balancing to tune the influence of each objective, reducing reliance on manual hyperparameters and problem-specific architectures. DC-PINNs consistently reduce constraint violations and improve physical fidelity versus baseline PINN variants, representative hard-constraint formulations on benchmarks, including heat diffusion with bounds, financial volatilities with arbitrage-free, and fluid flow with vortices shed. Explicitly encoding derivative constraints stabilises training and steers optimisation toward physically admissible minima even when the PDE residual alone is small, providing reliable solutions of constrained PDEs grounded in energy minimum principles.

Accurately modelling many-body quantum transport systems poses a challenge both conceptually and computationally due to the growth of the Hilbert space and the multi-scale nature of the geometries and couplings present in most naturally occurring networks. A compounding complexity of such systems is that the environment typically plays a key role in the transport dynamics. Utilising variational unitary transformations that displace environmental degrees of freedom allows for the deployment of a second-order master equation capable of capturing the dynamics of intermediate and strongly coupled systems, which are ubiquitous in microscopic energy transport systems. However, direct implementations of this approach suffer from fundamental scalability issues due to the complexity of the self-consistent equations required to solve for the variational parameters. Here, we present an efficient partitioning scheme that leverages the inherent multi-scale nature of natural energy transport networks. This enables scaling of the variational polaron framework to quantum energy transport systems, constituting hundreds to thousands of sites. Our work unlocks the physically motivated exploration of large transport networks, for example, those present within light-harvesting complexes and exciton transport in disordered semiconductors.

We investigate the orientation dynamics of a settling spheroid in simple shear flow, combining a deterministic dynamical-systems analysis with a stochastic Fokker-Planck treatment. The dynamics is governed by the competition between the Jeffery torque from the background shear and the inertial torque from settling. For configurations in which gravity lies in the shear plane, the azimuthal dynamics reduces to overdamped motion in a tilted periodic potential controlled by a single effective parameter $\mathcal{R}$ that combines the particle shape anisotropy and the settling strength. A saddle-node bifurcation on an invariant circle (SNIC) at $\mathcal{R}=1$ governs the transition from sustained rotational motion to steady equilibrium, with the rotation period diverging as $(1-\mathcal{R})^{-1/2}$. When gravity is parallel to the vorticity axis, the attractor is a periodic orbit for all settling strengths. The stochastic analysis reveals that noise plays a fundamentally different role depending on whether settling-induced potential barriers are present: in the classical Jeffery problem it diffuses over the orbit constant, whereas with settling it drives Kramers-type phase slips whose rate is exponentially sensitive to the Péclet number, defined as the ratio of diffusive to convective time scales. Langevin simulations confirm the predicted intermittent dynamics, with phase slips becoming progressively rarer as the barrier height or Péclet number increases. Asymptotic results in both the small- and large-$\mathrm{Pe}$ limits, together with numerical solutions of the Fokker-Planck equation at arbitrary $\mathrm{Pe}$, quantify the orientation moments across all regimes.

During the first superior conjunction of the Tianwen-1 Mars probe in October 2021, its downlink signal received by the Wuqing 70-m radio telescope passed within 4.53 solar radii of the Sun. The signal was significantly perturbed by the solar wind, providing a mechanism to probe coronal activity. We analyze the Doppler frequency scintillation spectrum of the solar wind within 10 solar radii to derive a characteristic frequency scintillation parameter. Statistical analysis indicates this parameter increases as the signal path approaches the Sun, with notable anomalies observed on October 5, 13, and 15. Comparisons with SOHO and SDO data reveal strong spatio-temporal correlations between these scintillation anomalies and coronal activity. We demonstrate that this parameter effectively identifies solar phenomena, including coronal streamers, high-speed solar wind, and coronal mass ejections (CMEs). Quantitative analysis confirms a distinct temporal correlation and delay between frequency scintillation and solar wind speed changes, validating the feasibility of spatially localizing solar activity.

The dynamics of water in electrolyte solutions exhibits a striking, ion-specific anomaly: the diffusion coefficient of water is enhanced relative to the neat liquid in chaotropic CsI solutions, yet suppressed in kosmotropic NaCl solutions. This phenomenon, long challenging for classical force-field-based molecular dynamics, is studied here using classical molecular dynamics simulations with a many-body machine-learned force field (MLFF) trained within the MACE equivariant graph neural network framework. The force field is trained on energies, forces, and stresses computed at the density functional theory level with the revPBE-D3 exchange–correlation functional, which provides a reliable balance between accuracy and computational efficiency for aqueous systems. Simulations of NaCl and CsI aqueous solutions at ambient conditions over a concentration range of 0.89–3.56 mol/kg reproduce the experimentally observed anomalous diffusion and show a quantitative improvement over previous results obtained with the DeePMD framework, trained on the same theory, particularly for NaCl solutions. This improvement is traced to a stronger Na$^{+}$–water interaction in the first hydration shell and the non-negligible retarding contribution of the second hydration shell of Na$^{+}$. For CsI solutions, the water acceleration is shown to be primarily driven by the anion I$^{-}$, whose diffuse and weakly structured hydration shell facilitates rapid water exchange with the bulk. These results are rationalised through a shell-decomposition analysis of time-dependent water diffusivities and ion–oxygen potentials of mean force providing a coherent microscopic picture of the acceleration–retardation mechanism in the studied aqueous electrolytes.

Ultrasound-guided drug and gene delivery (USDG) is a promising non-invasive approach for targeted therapeutic applications. Mechanical properties of encapsulated microbubbles (EMBs), which serve as contrast agents, strongly affect their specific interactions with ultrasound and are thus critical to the success and efficiency of USDG. Accurate calibration of high-fidelity particle-based models of EMB capsid mechanics is computationally challenging because direct Bayesian inference with dissipative particle dynamics (DPD) is prohibitively expensive. We employ a surrogate-accelerated Bayesian calibration workflow that combines deep neural network (DNN) surrogates, transitional Markov chain Monte Carlo sampling, and hierarchical regularization across EMB diameters. Using this framework, we develop two data-informed DPD models of commercial EMB agents, i.e., Definity and SonoVue, and perform inference of force field parameters based on published compression experiments for Definity and indentation experiments for SonoVue, each spanning three distinct diameters. The inferred posteriors show that key model parameters, such as the stretching stiffness and bending modulus, are consistently constrained by the available data. The presented methodology can be used to derive bespoke, data-informed models for a wide range of ultrasound contrast agents, including encapsulated gas vesicles, EMBs with diverse capsids consisting of lipids, proteins, or polymers, and functionalized with ligands.

Finite-element simulations of optical cavities are presented, showing frequency splittings in the resonance spectrum. These can be directly compared to and understood by recent theory and experiments presented in van Exter et al. (2022, Phys. Rev. A 106, 013501), Koks et al. (2022, Phys. Rev. A 105, 063502) and Post et al. (2025, Phys. Rev. A 112, 033537). They provide strong evidence for the nonparaxial theory, but point out limitations of predicted mirror-shape corrections. The simulations also provide model-independent predictions of modal losses for optical cavities.

To describe the interactions in magnetically soft particle systems either numerical full-field methods or dipole models are used. Whereas the former are computationally challenging, simple dipole interactions are largely underestimating the actual forces when particles get closer. Based on the full 2-body solution, an analytic approximation scheme for many-body full-field interactions is developed. The concept is formulated in terms of an improved operator that is equivalent to the classical dipole form. The full interaction operator allows to describe cluster formation and dispersion among particles in applied magnetic fields very compactly and highly efficient. In view of its simple 'dipole-like' form, the implementation is straightforward in many areas where magnetically soft objects are used.

Heat transport exhibits distinct regimes ranging from ballistic propagation to diffusive relaxation, traditionally described by disparate theoretical frameworks. Here, we introduce a unified first-order operator formulation in which temperature and heat flux are treated as a coupled state vector, yielding a minimal dynamical closure of heat transport. The resulting generator is intrinsically non-Hermitian and gives rise to a spectral structure governed by an exceptional point that separates overdamped diffusion from underdamped wave-like propagation. In this framework, Fourier law emerges as a singular limit of a hyperbolic dissipative system, while the Cattaneo equation arises naturally as the minimal hydrodynamic closure of kinetic theory. We show that the exceptional point induces nonanalytic spectral transitions, nonmodal transient dynamics, and a breakdown of conventional modal decomposition. The theory further generalizes to anisotropic media, where direction-dependent exceptional surfaces enable intrinsic steering of heat flow. Our results establish a unified non-Hermitian dynamical framework for heat transport and reveal exceptional-point physics as a fundamental organizing principle underlying thermal dynamics across scales.

This study numerically investigates two-dimensional Rayleigh-Benard convection subjected to horizontal oscillation of the bottom plate, with Prandtl number Pr=4.3, Rayleigh numbers Ra ranging from 5e6 to 1e8, and oscillation frequencies f between 0.0001 and 0.5. The imposed oscillation breaks the up-down symmetry of the classical system, inducing a strong frequency-dependent response in global heat transport, with the maximum Nusselt number enhancement exceeding 60% compared to the uncontrolled case. Central to this control efficiency is a phase-locking mechanism: at the optimal frequency, the intrinsic response time of the large-scale circulation (LSC), quantified by the sign-recovery of volume-averaged angular momentum, locks precisely to the wall oscillation period, enabling perfectly synchronized LSC reversals. Deviations from this optimal condition lead to a marked mismatch; the LSC response time becomes substantially longer when frequency exceeds the optimum and significantly shorter when frequency falls below it. In contrast, boundary layer velocities simply follow the wall oscillations and fail to distinguish control efficiency. Fourier mode analysis reveals that at the optimal frequency, a single-roll mode remains dominant throughout the cycle, facilitating efficient plume transport, whereas higher frequencies yield incomplete reversals and lower frequencies produce a double-roll structure that diminishes heat-transfer efficiency. This frequency-locking mechanism is shown to persist for optimal controls across the entire investigated Rayleigh number range, thus offering robust insight for active control strategies in thermally driven turbulent flows.

In nonlinear systems, small perturbations are conventionally attributed to negligible nonlinearity, justifying linear approximations. Here, we uncover a notable exception to this paradigm in an electrokinetic (EK) flow. Using a novel dual frequency excitation scheme with two high frequency AC electric fields ($> 10^{5}$ Hz), we efficiently excite flow perturbations at a difference frequency ($\Delta f$) four orders of magnitude lower. This approach reveals a strong nonlocal energy transfer mechanism mediated purely by the nonlinearity of the electric body force, enabling precise, clean flow control free from electrode polarization artifacts. Unexpectedly, these small, nominally linear velocity and electric conductivity fluctuations exhibit power law spectra. With increasing electric Rayleigh number, the scaling exponents agree quantitatively with predictions for fully developed EK turbulence by the Quad cascade process theory. This observation not only implies multiple flow state transitions even at low excitations, but also indicates that intrinsic nonlinearity regulates perturbations even in the linear regime, necessitating a fundamental re examination of linear approximations in electrohydrodynamics and other nonlinear systems.

This study examines the impact of additive and multiplicative noise on both a single leaky integrate-and-fire (LIF) neuron and a trained spiking neural network (SNN). Noise was introduced at different stages of neural processing, including the input current, membrane potential, and output spike generation. The results show that multiplicative noise applied to the membrane potential has the most detrimental effect on network performance, leading to a significant degradation in accuracy. This is primarily due to its tendency to suppress membrane potentials toward large negative values, effectively silencing neuronal activity. To address this issue, input pre-filtering strategies were evaluated, with a sigmoid-based filter demonstrating the best performance by shifting inputs to a strictly positive range. Under these conditions, additive noise in the input current becomes the dominant source of performance degradation, while other noise configurations reduce accuracy by no more than 1%, even at high noise intensity. Additionally, the study compares the effects of common and uncommon noise across neuron populations in hidden layer, revealing that SNNs exhibit greater robustness to common noise. Overall, the findings identify the most critical noise mechanisms affecting SNNs and provide practical approaches for improving their robustness.

Polarization of light can form skyrmionic textures, akin to nonlinear solitons in condensed matter, yet their disparate physical context has motivated extensive debate regarding their stability. Here we show that the topological charge of such structures (skyrmion number) changes when an arbitrarily small perturbation splits coalescent phase singularities. In a superposition of two vortex beams, the skyrmion number generally only depends on the higher order topological charge $\lrr{Q_{\rm sk}=\max\lr{\ell_2,\ell_1}}$ rather than the difference of charges of the vortices in superposition $\lrr{Q_{\rm sk}=\ell_2-\ell_1}$, which only holds in the absence of perturbation. These results have significant implications for polarization structures with wavelength-scale localization and those experiencing complex aberrations.

Binary droplet collisions are ubiquitous in dense sprays. Traditional deterministic models cannot adequately represent transitional and stochastic behaviors of binary droplet collision. To bridge this gap, we developed a probabilistic model by using a machine learning approach, the Light Gradient-Boosting Machine (LightGBM). The model was trained on a comprehensive dataset of 33,540 experimental cases covering eight collision regimes across broad ranges of Weber number, Ohnesorge number, impact parameter, size ratio, and ambient pressure. The resulting machine learning classifier captures highly nonlinear regime boundaries with 99.2% accuracy and retains sensitivity in transitional regions. To facilitate its implementation in spray simulation, the model was translated into a probabilistic form, a multinomial logistic regression, which preserves 93.2% accuracy and maps continuous inter-regime transitions. A biased-dice sampling mechanism then converts these probabilities into definite yet stochastic outcomes. This work presents the first probabilistic, high-dimensional droplet collision model derived from experimental data, offering a physically consistent, comprehensive, and user-friendly solution for spray simulation.

Solar jets are dynamic collimated plasma flows in the solar atmosphere that play crucial roles in coronal heating and solar wind acceleration. Their complex and diverse morphologies pose significant challenges for developing universal algorithms for automatic identification and extraction, particularly for on-disk jets affected by projection effects and background contamination. We present SJET, an interactive tool for solar jet feature extraction using multiple algorithms developed in Python that integrates five thresholding algorithms with morphological operations. SJET implements a novel method for identifying start and end points based on circular regions that objectively determines jet propagation direction by exploiting morphological asymmetry, combined with modeling the axis using quadratic Bézier curves for accurate extraction of geometric parameters including length, width, curvature, and deflection angles. Validation analyses using Solar Orbiter/EUI high-resolution image and SDO/AIA observations demonstrate SJET's effectiveness across different observational conditions, with good agreement compared to traditional analysis methods, though the tool's accuracy remains dependent on user-defined threshold parameters and region of interest selection. SJET provides a solution to method inconsistency in solar jet research through standardized processing workflows, establishing a technical foundation for large-sample statistical studies.

In this perspective article, we discuss bacterial populations as a model system of active matter. It allows for the exploration and characterization of various phases of active matter and brings rich implications for both physics and biology. Specifically, we focus on active gas, active liquid, active glass and active liquid crystal states observed in bacterial populations and describe how these differ from their thermal counterparts. A few future directions are also discussed that will deepen the physical interest in active matter as a new type of material, with its implications for several life phenomena observed in bacterial populations and other biological systems.

Smith Purcell radiation has emerged as a crucial platform for investigating light-matter interactions and developing compact, tunable light sources that span from microwaves to X-rays. In classical theory, it is believed that Cherenkov radiation exhibits an energy threshold for electrons, while Smith Purcell radiation is considered free of such a threshold. Although quantum theory suggests there is an emission cutoff in Smith-Purcell radiation, the behavior of this radiation near the threshold remains understudied. In this article, we address this gap by examining the behavior of Smith-Purcell radiation near the threshold from quantum perspectives. Specifically, we derive a quantum energy threshold based on energy-momentum conservation, providing a rigorous limit for the onset of Smith Purcell radiation. Furthermore, we find that around the threshold the incident electron emits a photon and subsequently reverses its direction of motion. Additionally, we establish a classical energy threshold below which the classical theory breakdown by applying the Duane Hunt limit to Smith Purcell radiation. Accordingly, quantum theory is required when the electron energy falls between the classical and quantum thresholds. Our findings enrich the understanding of Smith Purcell radiation and provide valuable insights for developing low energy driven and heralded quantum light sources.

Comparison of two probability density/mass functions (PDF/PMFs) is ubiquitous in various forms of scientific analysis, including machine learning, optimization problems, and hypothesis tests. A copious amount of distance metrics have already been proposed and are regularly being used in this regard. In this document, we report a data-driven systematic comparison among a few of such metrics. The metrics considered here are Hellinger distance, Wasserstein distances (1D), $\sqrt{JS}$ distance, $L_\infty$ norm, Kolmogorov-Smirnov distance, and Fisher-Rao metric. We perform this comparison using electron and photon events from a decaying \iso{Kr}{83} isotope, collected through an HPGe spectrometer operating under cryo-vacuum conditions. To accomplish this, first, a dimensionless Parameter of Interest (PoI) was established, then PDF/PMFs were generated from the data, and finally the stabilities of the PoI under various criteria, such as sample size, discretization length, and normalizing functions, were studied and the results were summarized. In this report, we also propose a list of properties that a normalizing function should have and utilize them in the comparison.

The forthcoming Hyper-Kamiokande experiment is expected to achieve unprecedented sensitivity to neutrino oscillations. Meeting these precision goals requires substantially larger Monte Carlo datasets than previous experiments to satisfy stringent systematic-uncertainty requirements. While traditional maximum-likelihood reconstruction provides high-quality event reconstruction, its per-event computational cost makes reconstruction of large, systematically varied Monte Carlo samples increasingly impractical. We demonstrate a neural-network-based reconstruction approach for the Hyper-Kamiokande far detector using simulated data with the experiment's detector geometry. Single-particle events are generated with kinetic energies from the Cherenkov threshold up to 2 GeV above threshold. The resulting PMT charge and timing information are mapped to $274\times144$ five-channel images that serve as inputs to ResNet models in the WatChMaL framework. These models (i) classify events into four particle hypotheses ($e$, $\mu$, $\gamma$, $\pi^{0}$) and (ii) regress the interaction vertex, direction, and momentum of electrons and muons. The regression models achieve momentum resolutions of $1.27\%$ for muons and $2.21\%$ for electrons, angular resolutions of $0.91^\circ$ and $1.60^\circ$, and vertex resolutions of $13.6\,\mathrm{cm}$ and $17.0\,\mathrm{cm}$, better than those reported for traditional methods. The classifier improves $e$-$\mu$, $e$-$\gamma$, and $e$-$\pi^{0}$ separation relative to the traditional baseline, with ROC AUC values of $0.9999995$, $0.642$, and $0.9659$. Our networks achieve inference times of 1-2 ms per event on a single GPU, yielding speed-ups of $3.2\times10^{4}$-$5.2\times10^{4}$ relative to likelihood-based reconstruction.

The sodium dodecyl sulfate (SDS) surfactant is widely used in various applications, such as household products (e.g., shampoos, toothpaste, detergents, and cleaning products) and food manufacturing (e.g., emulsifiers). To investigate its properties via computer simulation, various models have been developed, including coarse-grained (CG) models that are suitable for capturing a surfactant's self-assembly and fundamental properties for aqueous systems with a surfactant, such as surface tension. Here, we present a CG model for SDS/water systems for many-body dissipative particle dynamics (MDPD), which is based on the MDPD–Martini force field (FF). In the model, charged groups, namely, the SDS sulfate headgroup and the sodium cation, are explicitly modeled following the standard mapping of the Martini force field for molecular dynamics (MD), while the remaining interactions have been obtained from previous MDPD–Martini models for lipid systems, thus demonstrating their transferability. Various relevant system properties, such as the coherent scattered intensity and surfactant distribution at the liquid–vapor surface, are investigated, and results are compared to those obtained by MD simulations and experiments at different surfactant concentrations. Our findings indicate that MDPD–Martini models can offer a credible alternative to MD–Martini models for systems with explicit charges as shown here for SDS. Moreover, MDPD–Martini models reproduce nicely the experimental surface tension isotherm, in contrast to MD simulations. In view of the transferability of the MDPD–Martini interactions, the model parameters of this study can be tested and used to simulate a wider range of soft-matter systems.

Magnetic skyrmions and higher-order topological spin textures offer rich opportunities for multi-level information encoding, yet their deterministic stabilization and transformation under geometric confinement at room temperature remain poorly understood. Here, we demonstrate that geometric confinement acts as a robust and universal control parameter that governs a hierarchical transformation pathway of chiral spin textures in Pt/Co/W multilayer micro-tracks. As the confinement increases, extended labyrinth domains fragment into isolated skyrmions, followed by the systematic suppression of skyrmion pairs and the preferential stabilization of compact higher-order textures. We find that confinement strongly enhances the formation of skyrmioniums via recombination and promotes their subsequent evolution into uniform skyrmion bags by capturing additional skyrmions. Statistical analysis reveals a confinement-driven redistribution of topological populations, with skyrmion bags emerging as the dominant state in the narrowest tracks. Supported by micromagnetic simulations, our results establish geometric confinement as a deterministic selector of complex topological textures and reveal a previously unexplored route for engineering higher-order skyrmionic states at room temperature. These findings provide a scalable materials strategy for multistate skyrmion-based spintronic and memory architectures.