We present a forcing-informed (FI) resolvent analysis framework to identify input-output relations for statistically stationary self-sustained unsteady flows. The central idea of this method is to inform the resolvent operator about the spatiotemporal structures of the nonlinear terms that act as exogenous forcing with respect to the mean flow. To construct the FI resolvent operator, we estimate the basis vectors for the input subspace spanned by forcing snapshots and, similarly, for the output subspace, from simulation data. The extracted FI response and forcing modes are expressed through the estimated bases of the output and input subspaces, respectively, and the singular values of the FI resolvent operator correspond to the actual output amplitudes. These properties ensure that the extracted modes are consistent with the actual self-sustained flow fields. Additionally, the forcing snapshots can be used to construct the linear operator, enabling a fully data-driven FI resolvent analysis. The proposed framework is validated using the Stuart-Landau oscillator and demonstrated for a two-dimensional cylinder wake and a three-dimensional transitional boundary layer. We successfully identify the gains and the corresponding pairs of forcing and response modes, even at frequencies where the nonlinear amplification mechanism is crucial. Furthermore, leveraging the balance between the time-averaged energy amplification/attenuation by the linear operator and nonlinear forcing, we introduce a nonlinear energy transfer map that identifies the spatial domains where the extracted forcing mode injects or removes fluctuation energy, thereby providing key physical insight into the self-sustaining mechanisms.

Optical forces in focused-beam traps are generally nonconservative, yet the controlled use of this nonconservative component for airborne single-particle dynamics remains limited. We demonstrate a dual-beam optical trap in which a single aerosol can be switched between localized confinement and sustained orbital motion by tuning the relative positions of two counter-propagating foci. The axial separation controls the onset of nonconservative circulation, while the lateral offset tunes the projected orbit size and causes a monotonic change in the rotation frequency. T-matrix optical force calculations and Langevin simulations support this interpretation by showing that finite axial misalignment activates a circulating force component, whereas near-zero axial separation gives a confinement-dominated force field. Experiments confirm the predicted switching behavior through mean-square displacement and frequency measurements. We further show that the projected orbit geometry provides a particle-dependent observable, with the orbit anisotropy Ay/Ax varying systematically with aerosol diameter. The results provide a compact, low-power platform for controlled orbital dynamics of single airborne particles and for future aerosol measurements based on nonequilibrium trajectory observables.

Laminar-to-turbulent transition delay is a key challenge in hypersonic boundary-layer flows. Unstable disturbances-most prominently the first and second modes-trigger the onset of turbulence and pose a fundamental technological barrier to hypersonic transport. While existing control strategies target the second mode, simultaneous mitigation of the first mode has long appeared physically impossible. A new flow-control concept is introduced in which phase relations between wall pressure and velocity fluctuations are tailored using subsurface phonon engineering to control both modes concurrently. The outcome is substantial drag reduction and alleviation of the extreme thermal loads associated with turbulence.

Extending earlier work in Physics-informed Meta-instrument for eXperiments (PiMiX) [1], PiMiX~2.0 is an artificial-intelligence (AI)-enhanced data-fusion and analysis framework that integrates multi-experiment multi-modal radiographic imaging and tomography (RadIT) with physics-informed reasoning and agentic AI workflows. The framework supports automated data ingestion, multimodal image processing from one or more experiments, three-dimensional (3D) and time-resolved three-dimensional (4D) reconstruction, and physics-aware interpretation of experimental observations. The PiMiX agents are designed for deployment on desktop and laptop systems commonly used in experimental workflows, while remaining scalable to high-performance computing environments for computationally intensive tasks. By coupling RadIT instrumentation and measurements with geometry, physics, computation, and statistical inference, PiMiX 2.0 aims to accelerate RadIT data processing, knowledge extraction, improve reproducibility, and enable more integrated analysis and workflows in high-temperature plasmas, nuclear fusion, advanced manufacturing, other static and dynamic experiments.

Probabilistic weather forecasting is undergoing rapid transformation with artificial intelligence (AI). In traditional numerical weather prediction, computing power can limit how well ensemble forecasts approximate the unknown statistical distribution of future states. AI models facilitate larger ensembles and are trained with probabilistic considerations, ideally leading to better uncertainty quantification. Forecasts from these state-of-the-art models are often considered well-calibrated. However, here we show that the statistical coverage of such models, the ultimate measure of calibration, can struggle, especially on extreme events. To address this shortcoming, we employ conformal prediction, a class of statistical methods that mathematically guarantees coverage under no distributional assumptions, unlike previous post-processing techniques. We apply online conformal prediction to temperature and precipitation forecasts (including extremes) of three leading global weather models, GenCast, NeuralGCM, and AIFS-ENS, ensuring calibrated uncertainty at no expense to other probabilistic metrics. This post-processing method can be applied to any forecasting model.

Diamond hosts optically active point defects central to quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form new defects whose configurational landscape is poorly charted, as subtle energy differences govern the competing minima and pathways. Here we build an interstitial-focused dataset by active learning and benchmark three machine-learning interatomic potentials -- GAP, NEP and the equivariant MACE -- against density functional theory for energies, forces and migration barriers. MACE reproduces the reference energetics and relative stabilities, whereas the others can misorder the ground states. Annealing molecular dynamics with the validated potentials uncovers a series of previously unreported carbon interstitial clusters, from di- to octa-interstitials -- several introducing in-gap states of interest as colour centres -- and shows that their metastability is governed by kinetically accessible pathways rather than energetic ordering. These results chart the interstitial defect landscape and accelerate defect discovery for quantum technologies.

This experimental work explores the flow field around a three-dimensional expansion-compression geometry on a slender cone at Mach 5 and 8 using high-frequency pressure sensors, high-framerate schlieren, temperature-sensitive paint, shear-stress measurements and oil-flow visualizations. The $7^\circ$ cone geometry has a hyperbolic slice acting as an expansion corner which is then followed by a $30^\circ$ finite-span compression ramp. The freestream Reynolds number was varied so that the boundary layer approaching the expansion corner was either laminar, transitional or turbulent. At laminar or early transitional conditions, the separation shock locks onto the expansion corner and the separation region encompasses most of the slice, with the separation shear layer flapping at a preferred frequency. As Reynolds number is increased, the separation shock moves downstream onto the slice, the separation bubble shrinks, and the shear layer flapping frequency increases while its amplitude drops. In all cases, large-scale low-frequency breathing motions are observed. The strong relaminarization across the expansion corner at Mach 8 prevents the shock/boundary-layer interaction from reaching truly turbulent conditions and fundamentally changes its behavior on this non-canonical geometry.

Spatial patterns in observed climate trends remain poorly understood. Here we argue that a warming of land relative to ocean has shaped observed surface temperature and atmospheric circulation trends, including the negative Inter-Decadal Pacific Oscillation (IPO)-like tendency across the Pacific basin. Observed and modeled trends display an overall decline in sea level pressure over the faster-warming land relative to ocean, with a spatial pattern that resembles the seasonal cycle and the response to land heating in idealized climate model experiments. Coupled climate model simulations with historical forcing underestimate the land-sea warming ratio. It is only in the early response of abrupt CO2 quadrupling climate model simulations that climate models are able to recreate the observed land-sea warming ratio, in which case a strengthening of oceanic surface highs and a negative IPO-like surface warming pattern over the Pacific comparable to observed trends are seen. We propose that discrepancies between modeled and observed trends in many climate variables may be explained by the underestimation of the land-sea warming ratio by climate models. Determining the cause of this discrepancy has the potential to constrain projections of future climate change as the underlying mechanism causing climate models to underestimate the land-sea warming ratio discrepancy will set the persistence of this problem.

Neuroevolution Potential (NEP) is one of the most efficient machine-learned interatomic potential frameworks for large-scale atomistic simulations. However, its original training strategy remains computationally demanding, limiting systematic exploration of model architectures and training protocols. Here, we present TorchNEP, a PyTorch-based implementation of NEP that combines analytically derived gradients, adaptive optimization, and a two-stage training strategy. TorchNEP accelerates training by more than two orders of magnitude while maintaining full compatibility with existing NEP models. We further show that the improvement in predictive accuracy primarily originates from the two-stage training protocol rather than the optimization algorithm itself. Across diverse benchmark datasets, TorchNEP consistently improves force and stress predictions while maintaining comparable or improved energy accuracy. Benchmark evaluations on elemental and alloy systems demonstrate enhanced predictive performance for both atomic configurations and key materials properties. Furthermore, we show that increasing model complexity does not necessarily improve predictive performance despite reducing training errors. Overall, TorchNEP provides an efficient and flexible training framework for developing more accurate and robust machine-learned interatomic potentials.

The expansion of the FIFA World Cup to 48 teams in 2026 introduces structural challenges in tournament design. To populate a 32-team knockout bracket from 12 groups of four, the current FIFA rules select the eight best third-placed teams using a global ranking across all groups. This global coupling creates several major problems: a combinatorial explosion of 495 possible bracket configurations; a fundamentally biased and unequal selection of third-placed qualifiers; lack of a clear path for group winners; vulnerability to collusion and ranking manipulation; and no guarantee of same-group separation beyond the first knockout round. We propose a simple unified solution called the four-section bracket (FSB) rule: split the 12 groups into four sections of three groups. All group winners, runners-up, and the two best third-placed teams in each section advance. Group winners remain in their home sections as local anchors, while lower-ranked qualifiers are transferred to other sections according to a fixed, symmetric rule. This structure guarantees same-group separation until the semifinal, protects the top eight group winners with a predictable knockout path, and reduces bracket complexity from 495 configurations to just one invariant topology per section, recovering the symmetry of the traditional 32-team format. We show substantial improvements in competitive fairness and scheduling predictability.

The electronegativity equalization principle provides a simple framework to describe charge redistribution, yet its conventional formulation is limited to a first-order description based on chemical potential equalization. In this work, we introduce 'multi-equalization,' a generalized framework that extends this concept by incorporating higher-order responses within Conceptual Density Functional Theory. This approach represents molecules as sets of flexible electron density partitions, allowing different electronic descriptions (e.g., atomic densities or localized orbitals) to be treated within a unified formalism. We demonstrate that correlations between energy and density derivatives with respect to the number of electrons lead to the simultaneous equalization of multiple descriptors, including chemical hardness and Fukui indices. A constructive algorithm is introduced to determine the optimal density partitions satisfying these multi-equalization conditions. This scheme provides a consistent description of both global charge transfer and local reactivity, overcoming the intrinsic limitations of traditional electronegativity equalization models. Notably, the inclusion of density response functions enables local hardness equalization, introducing spatial resolution into reactivity descriptions. Under multi-equalization, local reactivity descriptors become constrained functionals of the global electron density. This framework establishes a deeper connection between charge equalization models and formal density functional theory, offering a theoretically grounded route toward improved predictions of molecular reactivity.

Modeling the circadian impact of seasonal clock changing requires precise synchronization between solar and social time. This report critiques a recent study that associated disease prevalence in the United States with seasonal clock exposure. We identify a fundamental computational error in which a sign reversal of the longitudinal offset effectively inverted the US East-West axis, cross-correlating local health data with the circadian burden of hypothetical locations on the opposite side of a time zone. We outline the methodology for a correct modelization of the circadian process in the context of US geography.

Modern stellarators are typically designed by optimizing the shape of the plasma boundary surface, with the parameters taken to be Fourier amplitudes. Many promising optimization algorithms such as Bayesian methods require bound constraints on the parameters and are most efficient when each parameter is scaled similarly to the others. With the typical Fourier parameterization, it is unclear how to set these bounds: wide constraints lead to self-intersecting boundaries and frequent failures of the MHD equilibrium calculation, while tight bound constraints limit expressiveness. To address these issues, here we propose two new parameter spaces for stellarator optimization. Both begin with a dataset of existing stellarator boundaries. In the first approach, a quantile transformation is applied to each Fourier degree of freedom, mapping the data distribution to a uniform distribution on the unit interval. In the second approach, principal component analysis (PCA) is applied to points on the boundaries, followed by a quantile transformation. For both approaches, the transformed variables become the degrees of freedom, naturally bounded to [0, 1]. The PCA method has the additional benefit of dimensionality reduction, with high expressiveness for a small number of parameters. The methods are demonstrated via Bayesian optimization for good alpha-particle confinement with guiding-center tracing inside the optimization loop, using asynchronous parallelization. These optimizations yield stellarator configurations with excellent fast-particle confinement in fields that can be far from quasisymmetric or quasi-isodynamic.

Tropes are recurring narrative devices in television and film. We carry out a computational analysis of tropes in the sitcom Friends, using human-curated trope annotations from TVTropes, episode transcripts, and IMDb ratings. Because automatic trope detection remains challenging, we treat existing trope annotations as a curated analytical layer and focus on their downstream narrative and semantic functions. We first examine the relationship between episode-level trope frequency and audience reception. We find a statistically significant positive association between trope count and weighted IMDb ratings, although the modest explanatory power suggests that more than trope density alone explains audience evaluation. We then connect trope annotations to dialogue transcripts and represent trope-related dialogue using TF-IDF-based semantic features. Using PCA and k-means clustering, we group 1,954 distinct tropes into 15 semantically interpretable clusters. Chi-square analyses show that the six main characters are unevenly distributed across these clusters, with character-specific trope profiles that are broadly consistent with their established narrative identities. Finally, we project trope clusters into the ousiometric power-danger space to examine their semantic organization. The results show that "Physical and Sexual Comedy" occupies a region associated with relatively high danger, while "Revelation, Surprise, and Reaction" occupies a region associated with relatively high power. Overall, our work demonstrates a way to operationalize trope measurement and shows that identifiable trope clusters can provide holistic "distant reading" descriptions of characters and stories.

Coherent vacuum-ultraviolet (VUV) lasers are indispensable for precision measurement, quantum optics, and materials science. Recent high-resolution spectroscopy of the Th-229 nuclear clock transition near 148 nm highlights the urgent demand for intense, narrow-linewidth VUV lasers for advancing metrology and testing fundamental physics. However, existing VUV generation schemes typically require enhancement cavities [C. Zhang et al., Opt. Lett. 47, 5591-5594 (2022)], atomic resonances [Q. Xiao et al., Nature 650, 852-856 (2026)], or random quasi-phase-matched nonlinear crystals [V. Lal et al., Optica 12, 1971-1974 (2025)]. Here, we demonstrate a VUV frequency comb via cascaded frequency doubling of a 2400 nm Cr:ZnS comb to its 16th harmonic in nonlinear crystals. The final stage employs a bulk-grown, spatially uniform quasi-phase matched (QPM) crystal developed by IPG, combining VUV transparency, high $χ^2$ nonlinearity, and power scalability. Using this QPM crystal we generate a VUV frequency comb with 40 $μ$W average power (1 nW per mode at 80 MHz mode spacing) with a conversion efficiency order of magnitude higher than other known methods. These results establish a scalable route to compact VUV sources via direct frequency doubling, opening a path toward a robust continuous-wave nuclear clock laser.

A generalized notion of degenerate wormholes is introduced, defined by the vanishing of the metric determinant g at the throat. It is described by the polynomial, g^2 modified Einstein field equations. Building on this framework, we show that both the Einstein Rosen bridge and the Klinkhamer defect wormhole are exact vacuum solutions of the g^2 modified equations, valid globally including at the degenerate throat, while the Klinkhamer configuration additionally admits traversable geometries with b>2M, where b sets the length scale of the wormhole throat and M is a mass parameter. In contrast, standard Morris Thorne and thin shell wormholes, governed by the conventional (non regularized) Einstein equations, are intrinsically non degenerate and necessarily supported by exotic stress energy. Within a unified regularized system with matter, both thin shell and Klinkhamer wormholes appear as two qualitatively distinct classes of states: non degenerate with exotic matter versus degenerate with vacuum sharing the Einstein Rosen bridge as a common limiting configuration. This unified viewpoint clarifies why classical null energy condition no go theorems apply only to the non degenerate sector and suggests the possibility of stationary degenerate traversable wormholes that do not require NEC violation.

According to Born (\emph{Atomic Physics, page 103}), spectra are \emph{``quantum phenomena, which from a classical standpoint are perfectly unintelligible''}. However we illustrate results on classical calculations of infrared spectra of ionic crystals (actually LiF) which show that the situation is much more complex. Indeed it turns out that: 1) At room temperature and at higher ones (up to 1060 K) the classical computations reproduce the experimental data, even better than the \emph{presently available} quantum ones do; 2) At lower temperatures (even at 7.5 K), the classical computations reproduce pretty well the data, if one accepts the idea advanced in 1916 by Nernst (the inventor of the third principle) that zero-point energy has room in classical physics too. It is eventually pointed out that the mentioned results might be regarded as a first step towards an implementation of the Einstein Classical Program, which aims at deducing quantum physics (admittedly the correct theory) from a realistic theory. In fact, we are considering the Einstein classical program in the extreme version in which the realistic theory is just (\emph{essentially, see below}) classical electrodynamics of matter in bulk, involving phase space orbits, solutions of Newton equations. An Appendix is devoted to illustrate the Nernst approach, which concerns also the relation between equipartition and Planck's law.

Modern microscopy systems integrate heterogeneous hardware devices that require dedicated software for coordination. However, high-performance C++ implementations of microscopy control software remain scarce. We present ScopeOne, a C++ and Qt-based microscopy control software built on the MicroManager hardware abstraction layer. Through process isolation and shared memory, ScopeOne achieves simultaneous multi-camera preview with real-time image processing, while maintaining full compatibility with the μManager device ecosystem.

Superheavy and hyperheavy nuclei are one of the frontiers of nuclear structure nowadays, while also for many actinides rather limited experimental information exists. Therefore, theoretical methods providing parameter-independent predictions for these nuclei are of particular interest. Such a method is the proxy-SU(3) approximation to the shell model, which has been adequately tested against experimental data in medium-mass and heavy nuclei up to the rare earth region, and has been found to provide reliable, parameter-independent predictions for the collective deformation variables beta and gamma. Within the proxy-SU(3) approach, the SU(3) symmetry of the 3-dimensional harmonic oscillator, which is destroyed beyond the sd shell by the strong spin-orbit interaction, is restored through a unitary transformation. For each nucleus, the most symmetric irreducible representation (irrep) allowed by the Pauli principle and the short-range nature of the nucleon-nucleon interaction, called the highest-weight (hw ) irrep in mathematical language, is found to suffice, except in cases in which the hw irrep turns out to be completely symmetric, so that the next highest weight (nhw) irrep has also to be included. In this article we provide a full collection of the hw and nhw irreps, as well as of the corresponding parameter-free predictions for the deformation variables beta and gamma, for all atomic nuclei ranging from Z=82, N=82 to Z=126, N=258. Several cases exemplifying the use of the collected results for studying the prolate to oblate shape transition, mirror symmetries, as well as the evolution of the collective variables along the valley of stability are also considered.

The J-PARC E16 experiment aims to search for signatures of chiral symmetry restoration. It studies in-medium modifications of vector mesons that decay via the dielectron channel. The measurements use a high-intensity 30 GeV proton beam with C and Cu targets at rates up to 10 MHz. To achieve this, the experiment upgrades its tracking, by introducing innermost detector modules constructed with the same technology and procedures as the modules of the Silicon Tracking System (STS) of the Compressed Baryonic Matter (CBM) experiment at Facility for Antiproton and Ion Research (FAIR). A total of 15 modules were assembled, tested, characterized and then installed in the E16 detector setup. The detector was commissioned in a beam test experiment at Tsukuba, where the detector modules could be exposed to a 3 GeV electron beam. In preparation for the beam test the modules were characterized and calibrated, and performance studies were accomplished to assess the quality of the setup. During beamtime, three modules were operated and illuminated in two planes by the electron beam. This paper presents the results of the construction, characterization, commissioning, and operation of the E16-STS modules in beam test experiments.

The ATLAS experiment presents an observation of a centrality-dependent dijet transverse momentum imbalance in O+O and Ne+Ne collisions at a nucleon-nucleon center-of-mass energy of 5.36 TeV at the Large Hadron Collider. The measurement uses 8.0 nb$^{-1}$ of O+O and 1.0 nb$^{-1}$ of Ne+Ne data collected in 2025, together with 386 pb$^{-1}$ of \textit{pp} data at the same energy used as a reference. The dijet momentum balance is quantified using the ratio of the sub-leading jet transverse momentum to that of the leading jet, $x_J$. For dijets produced azimuthally back-to-back, the self-normalized $x_J$ distributions exhibit increasingly large deviations from the \textit{pp} reference as collisions become more central, corresponding to an increasing overlap of the colliding nuclei. The observed centrality dependence is consistent with medium-induced partonic energy loss in O+O and Ne+Ne collisions, demonstrating that such effects persist in collision systems considerably smaller than Pb+Pb and Xe+Xe. These results establish a new regime for investigating the path-length dependence of jet quenching and constrain the onset of quark-gluon plasma effects in small nuclear collision systems.

The ALICE Collaboration presents the first measurements of the jet-energy flow ($Δp_{\rm T}$) observable in proton-proton and heavy-ion collisions. Jets are excellent probes for the quark$-$gluon plasma, a deconfined state of matter produced in heavy-ion collisions. The jet-energy flow observable characterizes the radial distribution of energy from the jet axis in an infrared and collinear-safe way and is sensitive to medium-induced parton-shower modifications. Inclusive charged jets are measured in Pb$-$Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV for the transverse-momentum interval 60$-$80 GeV/$c$. For pp collisions at $\sqrt{s}=13$ TeV, measurements include the 40$-$60 and 60$-$80 GeV/$c$ intervals, where the latter serves as the reference for investigating medium-induced modifications. Results show that most parton energy is concentrated in the jet core, with a clear suppression of energy flow in heavy-ion collisions at larger radii (significance 3.5$-$4.5$σ$) indicating a narrowing of the energy flow. While all models -- PYTHIA 8, HERWIG, JEWEL, and JETSCAPE -- reproduce the pp results with only small deviations in the tails, the relative modification in Pb$-$Pb collisions is well described by JEWEL without recoil. Conversely, JEWEL with recoil (medium response) and JETSCAPE show significant deviations, exhibiting increasing or more constant trends with radius that are disfavored by the data.

The ALICE Collaboration presents the first measurements of ${\rm D}^0$-tagged jet angularities in proton$-$proton (pp) collisions at $\sqrt{s} = 5.02$ TeV. Jet angularities are powerful substructure observables that characterize the angular and momentum distributions of particles within jets via tunable weighting parameters. Varying the angular parameter in jet angularities allows for a systematic probe of the sensitivity to collinear and soft radiation, enabling the study of flavor-dependent fragmentation and hadronization through comparisons of jets initiated by different partons. This paper reports ${\rm D}^0$-tagged and inclusive (gluon-dominated) charged-particle jet angularities with a resolution parameter $R=0.4$ in the low jet transverse momentum range ($10 < p_{\rm T}^{\rm ch. \, jet} < 20$ GeV/$c$), where charm-quark mass effects are most significant. At low angular weight, which emphasizes collinear radiation, ${\rm D}^0$-tagged jets exhibit smaller angularity values than inclusive jets. This provides evidence for the radiation suppression from massive quarks -- a phenomenon known as the QCD dead-cone effect. As the angular weight increases, giving more emphasis to wide-angle radiation, the difference between ${\rm D}^0$-tagged and inclusive jet distributions decreases. This indicates that the modification is concentrated within the jet core rather than its edge. PYTHIA 8 simulations qualitatively reproduce both the angularity of ${\rm D}^0$-tagged and inclusive charged-particle jets, but reproduce the ${\rm D}^0$-tagged jet distributions better than those of inclusive jets, offering a powerful new constraint for models. These results provide insight into flavor-dependent fragmentation and establish an essential baseline for future studies of jet modifications in the quark-gluon plasma produced in heavy-ion collisions.

Ultra-relativistic heavy-ion collisions create a hot and dense medium of deconfined quarks and gluons, the quark$-$gluon plasma (QGP), in which parton energy loss ("jet quenching") is a key probe of hot medium properties. While parton energy loss has been firmly established in large systems such as Pb$-$Pb and Au$-$Au collisions, no unambiguous direct evidence exists in smaller systems such as high-multiplicity p$-$Pb and pp collisions. To probe the onset of parton energy loss at intermediate system size, measurements of neutral-pion production are presented in this Letter for oxygen$-$oxygen (OO) and proton$-$oxygen (pO) collisions recorded with the ALICE detector in July 2025, relative to a pp baseline. The nuclear modification factor $R_{\rm OO}$ is suppressed relative to unity with a transverse-momentum dependence similar to that observed in Pb$-$Pb collisions, consistent with a previous CMS measurement in OO collisions with charged particles. As $R_{\rm OO}$ contains contributions from both cold and hot nuclear matter effects, $R_{\rm pO}$ is also presented in order to constrain cold nuclear matter (CNM) contributions. $R_{\rm pO}$ is found to be compatible with unity, indicating that CNM effects alone cannot account for the suppression observed in $R_{\rm OO}$. Final-state effects are isolated using the measured double ratio $R_{\rm OO} \left/ R_{\rm OO}^2 \right.$, which largely cancels CNM contributions and exhibits a significant suppression relative to expectations without energy loss at a $4.9σ$ level. Theoretical models incorporating parton energy loss via different mechanisms predict a significant suppression of the $R_{\rm OO} \left/ R_{\rm OO}^2 \right.$ relative to unity, consistent with the data. These findings establish parton energy loss in OO collisions, extending experimental evidence for jet quenching to the smallest nuclear system studied to date.

In the last years, an anomalously high spreading of West Nile virus (WNV) has been observed in Italy, with particularly high peaks of infections in southern Lazio, Campania and Veneto regions. The main disease vector for WNV is represented by Culex pipiens mosquitoes, which spread human infections through their bites. Here, we investigate WNV fever epidemic diffusion during summer season 2025 in Italy through a computational approach based on a quantum version of the Game of Life (GOL) cellular automaton model. Specifically, human dynamics evolves according to the GOL rules, while stochastic dynamics of disease vectors, i.e., mosquitoes, as well as their interaction with humans, simultaneously occur. We show that this model fits the curves of cumulative infected individuals with high accuracy, either at local and average-regional level, with only optimization of mosquito birth and removal rates parameters. Furthermore, leveraging model flexibility, we show that changes in model parameters values elucidate system response to environmental variations. For instance, we quantify, e.g., the impact of mosquito spreading containment measures or sudden mosquito increasing abundance due to climatic and ecological changes. Overall, we provide a general, quantitative description of WNV infection spreading in Italy which could represent a supportive tool to test different environmental scenarios and could be useful to devise strategies for decision makers to monitor disease vector dynamics and to control consequent virus diffusion.

This paper develops a general framework for analyzing multi-agent systems with feedback loops between agents actions and collective observations. The framework is built on two fundamental agent-level variables: power, which measures agent influence on collective outcomes, and response functions, which determine how agents react to observations. We derive how macroscopic properties, including total power, useful power, entropy, order, fragility, and mobility, emerge from these two variables of heterogeneous agents. To study the trade off between growth and resilience, we introduce a system-level utility function parameterized by a risk-appetite coefficient and derive an optimal degree of order that balances productivity, stability, and adaptability. The analysis suggests that stronger synchronization can increase collective output but may also increase systemic fragility and reduce mobility. We further argue that order, entropy, information, and useful energy are task-dependent and system-relative concepts whose meanings depend on the objectives of the system. By measuring and designing agent power distributions and response functions, it may be possible to better understand, predict, and optimize collective behavior and identify the conditions under which collective intelligence and optimal order emerge.

Kerr frequency combs have recently emerged as an exciting new photonic technology, with applications across science and engineering. Their formation within driven optical resonators that possess a Kerr nonlinearity is enabled through the rich landscape of localized nonlinear dissipative structures intrinsic to these systems. This article offers a comprehensive review of the physics that underpins these nonlinear comb-generating structures. Particular attention is placed on bright temporal cavity solitons and nonlinear switching waves -- the canonical stable comb-generating states in the anomalous and normal dispersion regimes, respectively. Written as both a review and tutorial, the article also includes an in-depth treatment of the numerical methods required to simulate driven Kerr resonators, alongside a comprehensive discussion of the laboratory techniques used to experimentally realize and characterize Kerr combs.

From ancient Mesopotamia to modern cities, dense human settlements coincide with bursts of economic productivity, cultural innovation, and social change. But how does packing people more tightly together alter social organization in ways that reshape collective outcomes? Here, I use a minimal agent-based model to isolate the effect of population density, holding population size and individual behavior fixed while varying only how closely individuals are placed in space. In the model, individuals form social ties gradually, favoring those nearby and those already well-connected. Under these simple rules, varying population density alone is sufficient to reorganize social network structure: sparse populations develop locally clustered communities, while denser ones form globally integrated networks with shorter social distances and a tightly interconnected core of popular individuals. This structural transition occurs sharply over a narrow range of densities and is governed by whether physical proximity or social popularity dominates tie formation. Simulating contagions on these networks reveals that the consequences of this shift depend on what is spreading. Simple contagions (e.g., information or disease) reach a majority of individuals more quickly in denser populations. Complex contagions (e.g., social norms or collective behaviors) do not spread faster, but instead achieve broader and more reliable adoption as density increases. Together, these results show that population density can act as a structural force independent of the economic and behavioral mechanisms typically invoked to explain why cities are engines of change.

Collective oscillations in neuronal systems often arise from interactions between excitatory and inhibitory populations rather than from recurrent coupling within a single ensemble. Motivated by the coexistence of strongly and partially synchronized regimes in such systems, we study the Kuramoto Sakaguchi model on a bipartite network. Despite its minimal structure, the model exhibits rich collective dynamics, including both continuous and discontinuous transitions from full synchrony to partial synchrony (PS). In the PS regime, global oscillations fail to entrain one of the two populations, whose oscillators display quasiperiodic dynamics with an average frequency that can significantly deviate from that of the global field, as observed in neuronal networks. We show that this PS state constitutes an example of self-organized quasiperiodicity, arising here in the canonical Kuramoto Sakaguchi model despite its purely linear global coupling.

Large-scale power outages are typically caused by cascading failures. These unfold dynamically through complex interactions between network dynamics and individual component failures. In contrast, the study of cascading failures in physics has focused on analyzing line overloads in the quasi-static regime. We introduce a new model that integrates the dynamics of node and line failures with a paradigmatic oscillator model for power grid synchronization. This enables us to investigate the collective cascading behavior of coupled failures for the first time. We study the impact of nodal robustness, the ability of nodes to tolerate transient disturbances, and inertia, the ability of nodes to resist frequency deviations, on cascade sizes. We discover two novel mechanisms driving system fragility: i) While low inertia is widely considered a major challenge for power grids, we find that high inertia can amplify cascade sizes unless accompanied by appropriate adjustments of other dynamical properties. ii) Further, we find that an increase in the robustness of individual nodes can paradoxically lead to larger cascades. This latter phenomenon constitutes a novel type of Braess's paradox. Understanding such counterintuitive collective effects may become central for achieving resilient future power grids.