Glasses are traditionally characterized by their rugged landscape of disordered low-energy states and their slow relaxation towards thermodynamic equilibrium. Far from equilibrium, dynamical forms of glassy behavior with anomalous algebraic relaxation have also been noted, for example, in networks of coupled oscillators. Due to their disordered and high-dimensional nature, such systems have been difficult to study theoretically, but data-driven methods are emerging as a promising alternative that may aid in their analysis. Here, we characterize glassy dynamics using the dynamic mode decomposition, a data-driven spectral computation that approximates the Koopman spectrum. We show that the gap between oscillatory and decaying modes in the Koopman spectrum vanishes in systems exhibiting algebraic relaxation, and thus, we propose a model-agnostic signature for robustly detecting and analyzing glassy dynamics. We demonstrate the utility of our approach through both a minimal example of a one-dimensional ODE and a high-dimensional example of coupled oscillators.
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Although light nuclear clusters are known to form abundantly in warm and dilute nuclear matter, their role in hot and dense nuclear matter remains unclear due to the lack of experimental indication for their modifications by the Mott effect under such conditions. To address this issue, we resort to intermediate-energy heavy-ion collisions, where light clusters are mainly produced in the transiently formed hot and dense matter. A kinetic approach, which includes dynamically the formation and dissociation of light clusters, is employed to deduce the strength of the Mott effect and the α-particle fraction in hot and dense nuclear matter from the light-nuclei yields measured by the FOPI Collaboration in central Au+Au collisions at energies of 0.25A to 0.6A  GeV. We find an unexpectedly abundant α clustering in this environment, which will have profound implications for modeling the nuclear equation of state and describing supernovae and neutron star mergers.
Nsp15 is an endoribonuclease, highly conserved among coronaviruses, that helps the virus evade detection in host cells by cleaving uridine-rich viral RNA sequences. Those sequences would otherwise trigger immune response pathways. Its essential role in pathogenesis and highly conserved nature make Nsp15 an attractive target for therapeutic intervention. While its crystal structure and uridine specificity are well-established, the influence of RNA structural dynamics and divalent cations on Nsp15 activity remains less understood. Leveraging single-molecule Pulsed Interleaved Excitation (PIE)-FRET and Fluorescence Correlation Spectroscopy (FCS) in combination, we developed an assay to track RNA cleavage by Nsp15 variants in real time and monitor the conformational dynamics of hybrid RNA/DNA substrates. Using our methodology and analysis strategies, we obtained clear indicators of RNA cleavage with both PIE-FRET and FCS data analysis. Our assay also revealed signatures of unique dynamic behavior in uridine-containing RNA substrates, indicating that divalent cations enhance substrate flexibility, which is associated with a faster observed reaction rate of RNA cleavage by Nsp15 in the presence of Mn2+. We propose that divalent cations induce a conformation that may be more favorable for cleavage in single-stranded RNA substrates that mediate the efficient nuclease activity of Nsp15.
The study of dense matter has been greatly advanced by progress in theoretical high-energy simulations and modern observational astronomy. Neutron stars serve as natural laboratories for probing matter under extreme density and strong gravitational fields. While polytropic equations of state are widely employed, conventional models based on a single polytropic index cannot adequately represent the stratified, layered nature of realistic compact-star interiors. To address this limitation, we develop a composite relativistic polytropic model in which the polytropic index varies smoothly with radius. By coupling the Einstein field equations with a generalized composite polytropic equation of state, we derive the composite Tolman-Oppenheimer-Volkoff (CTOV) system. The resulting nonlinear equations are solved using a Monte Carlo-based numerical integration method, which efficiently handles stiffness while enabling probabilistic exploration of the parameter space and natural uncertainty quantification. Our results demonstrate that increasing the relativistic parameter σ significantly reduces both the Emden function and the enclosed mass function, producing more compact stellar configurations. Sharper core-envelope transitions (ε = 0.01) yield systematically higher compactness than smoother transitions (ε = 0.03). The derived mass-radius relations reproduce the observed diversity of neutron stars, successfully matching both low-mass, large-radius systems such as PSR J0030 + 0451 and high-mass compact pulsars such as PSR J1614-2230. Importantly, the maximum-mass analysis shows that stiff composite configurations (nc = 1, ne = 2, xc = 0.7) can support gravitational masses up to Mmax ≈ 3.47 M[Formula: see text] for ε = 0.01 and Mmax ≈ 2.71 M[Formula: see text] for ε = 0.03, with corresponding minimum radii in the range Rmin ≈ 10.6-13.2 km, consistent with current observational constraints. These findings confirm that composite polytropes provide a flexible, physically motivated framework for modeling stratified compact stars and for constraining the dense-matter equation of state.
We review the key observations and theories relevant to the origin and evolution of the Galilean satellites. Key observations include: the potentially undifferentiated nature of Callisto; the increasing ice fraction with semi-major axis; the present-day existence of the Laplace resonance; the potential resurfacing of Ganymede mid-way through its evolution; and the metal-enriched nature of Jupiter's envelope. The most widely accepted theory for the formation of the satellites is the so-called "starved disk" model, although newer alternatives including decretion disks and pebble accretion have also been proposed. Models that allow slow satellite formation in a cold disk are preferred, based on the density progression and Callisto's apparent differentiation state. Major model uncertainties include the angular momentum distribution of the material infalling to the circumplanetary disk, the source of the solids, and the thermal and viscosity structure of the disk. We identify six outstanding questions, some of which will be answered by JUICE, Europa Clipper and Tianwen-4. A major difficulty in answering some questions is overprinting of primordial characteristics by later events.
Silk protein ionomer-based layer-by-layer (LBL) nanocoating strategy was utilized to preserve the viability, metabolic activity and differentiation potential of human mesenchymal stem cells (hMSCs) in an in vitro inflamed osteoarthritis (OA) tissue niche. These temporary ionomer coatings for cell protection were functionalized with cytokines to impart immune-modulatory behavior. The cytokine-functionalized ionomer nanocoatings polarized neutral M0 macrophages into M1 and M2 subtypes, depending on the specific cytokine used, thereby reducing synovial inflammation in an in vitro setup. This approach would potentially abrogate the need for ex vivo polarization before transplantation during immunotherapy. The modular nature of these silk ionomer nanocoatings in tailoring cell surface stability was further validated by extending the cell coatings to 3D spheroids and also by introducing tyramine to the cationic silk ionomer to facilitate covalent crosslinking in addition to the electrostatic interactions between the cell surface and the ionomers. These results demonstrate the versatile and modular nature of silk-based ionomer cell nanocoatings, allowing control at material, cellular, and spheroid levels to tune coating stability and inflammatory responses. These systems uniquely impart immunomodulatory function, providing a biomaterial platform for cell-based immune-regenerative therapy, such as for OA and other inflammatory diseases.
Bilayer graphene cavities where electrons are confined within finite graphene flakes provide an alluring platform not only for the future nanoelectronic devices owing to the tunable energy gap but also for investigating the quantum nature of chaos due to the trigonal warping of their Fermi surface. Here we demonstrate that rotating the cavity boundary relative to the underlying lattice structure drives a quantum transition from nearly integrable dynamics to chaotic regime, observed as a concomitant crossover of eigenvalue statistics and eigenstate profiles. Complementing the full quantum treatment, we examine the classical backbone of this onset of chaos by employing semiclassical ray dynamics. Our results position bilayer graphene cavities as a promising venue for investigating and engineering quantum-chaotic behavior in graphene-based devices.
Multiphase gas-ranging from cold molecular clouds ( ≲ 100 K) to hot, diffuse plasma ( ≳ 10 6 K) is a defining feature of the interstellar, circumgalactic, intracluster, and intergalactic media. Accurately simulating its dynamics is critical to improving our understanding of galaxy formation and evolution, however, due to their multi-scale and multi-physics nature, multiphase systems are highly challenging to model. In this review, we provide a comprehensive overview of numerical simulations of multiphase gas in and around galaxies. We begin by outlining the environments where multiphase gas arises and the physical and computational challenges associated with its modeling. Key quantities that characterize multiphase gas dynamics are discussed, followed by an in-depth look at idealized setups such as turbulent mixing layers, cloud-wind interactions, thermal instability, and turbulent boxes. The review then transitions to less idealized and/or larger-scale simulations, covering radiative supernovae bubbles, tall box simulations, isolated galaxy models including dwarf and Milky Way-mass systems, and cosmological zoom-in simulations, with a particular focus on simulations that enhance resolution in the halo. Throughout, we emphasize the importance of connecting scales, extracting robust diagnostics, and comparing simulations to observations. We conclude by outlining persistent challenges and promising directions for future work in simulating the multiphase Universe.
Understanding tropical land temperature response to rising atmospheric CO2 in the past is crucial for better constraining future climate projections. However, the evolution of regional land temperatures on paleoclimate timescales remains uncertain due to the paucity of precise records. Here we reconstructed temperatures across the last deglaciation using nucleation-assisted microthermometry in a stalagmite from central-eastern South America. We show that cave temperatures increased by 5.8 ± 0.3 °C (2 standard errors of the mean, SEM) from the Last Glacial Maximum to the early Holocene, broadly tracking global atmospheric CO2 and Antarctic temperatures. Our results reveal an abrupt regional warming across the Antarctic Cold Reversal-Younger Dryas (ACR-YD) transition, linked to the weakening of the Atlantic Meridional overturning circulation (AMOC). Notably, the most rapid warming at our cave was still slower than projections of future long-term warming, highlighting the unprecedented nature of the current greenhouse gas forcing.
Light beams possessing orbital angular momentum (OAM) have gained significant interest in areas such as optical manipulation, quantum entanglement, and super-resolved imaging. The OAM per photon of such beams, typically Laguerre-Gaussian beams, is directly proportional to the azimuthal index l. This index is continuous in nature and a non-trivial parameter to measure. In this study, Laguerre-Gaussian beams of differing l are generated through mode conversion using microscopic spiral phase plates. The value of l depends on the refractive index of the medium surrounding the spiral phase plates. Utilising laser speckle, we demonstrate an ultra-precise measurement of the azimuthal index of the generated beams to a precision of 2 × 10-5. In turn, this enables an ultra-precise measurement of the refractive index of the medium surrounding the spiral phase plates, with a best measured precision of 6.4  ×  10-7 refractive index units. Our study interrogates samples of sucrose and haemoglobin, only 300 pL in volume, within a microfluidic channel.
Methane is the simplest hydrocarbon, yet it exhibits an extraordinarily complicated series of crystal phases. Notably, the non-plastic phases have large unit cells with nearly, but not quite, cubic symmetry. Furthermore, although non-polar molecules interact very weakly, their reorganization across phase transitions is very sluggish. Here, we demonstrate that these complex structures can be understood as a simple packing of near-spherical supermolecular clusters of methane molecules: the departure from cubic symmetry arising from the non-spherical nature of the molecules. We use molecular dynamics based on density functional theory calculations to simulate the finite-temperature crystal structures of methane, finding that the complex phase A is based around a 13-molecule regular icosahedron, with 8 additional molecules forming the 21-molecule unit cell. Similarly, phase B is based on a body-centered cubic (bcc) packing of 17-molecule Z16 polyhedra, with the remaining 12 molecules per cell in tetrahedral interstices. We demonstrate that the favored intermolecular separation depends sensitively on molecular orientation, leading to hindered rotation and suppressed entropy. The structures are determined by a trade-off between efficient packing and entropy.
Scroll waves in three-dimensional excitable media rotate around singular filaments, which are topological defects analogous to phase singularities in two dimensions. Methods for precise localization of these filaments are essential for studies of filament dynamics in modeling and experimental studies. Practical applications of these methods include understanding the mechanisms of cardiac arrhythmias, most of which have recently been shown to have a three-dimensional nature. In this study, we propose a novel Jacobian-determinant vector method for filament identification based on the topological current theory. We define a Jacobian-determinant vector field, which we construct so that its magnitude peaks at filament locations, while its direction shows the local filament orientation, guiding further filament detection. We validate the method using the Aliev-Panfilov model across complex filament configurations, including Hopf links and trefoil knots, and demonstrate better performance compared to convolution-based and zero-normal-velocity methods, particularly under noisy and slightly fibrotic environments. This method, thus, provides a useful tool for studying scroll-wave dynamics in both theoretical and applied contexts.
Treatment planning in radiotherapy is inherently a multi-criteria optimization (MCO) problem, as it requires balancing competing clinical goals. Traditionally, the treatment's robustness is not formulated as a part of this decision making problem, but dealt with separately through margins or robust optimization. This work facilitates integration of robustness into multi-criteria optimization using a recently proposed efficient "scenario-free" (s-f) robust optimization approach: Utilizing variance reduction objectives, whose computation is independent of the number of chosen error scenarios, robustness can become part of the multi-criteria decision making process at minimal computational overhead. The s-f approach relies on the fast evaluation of the expected dose distribution and mean variance during optimization independent of the scenario number. This is achieved by precomputation of expected dose influence and total variance influence matrices, which can then be used for repeated solving of subproblems in the two explored MCO approaches: Lexicographic Ordering (LO) and full Pareto Front (PF) approximation. Different prioritization strategies within the LO approach are used to assess the impact of variance reduction on the optimization outcome. A 3-objective PF approximation, including a variance reduction objective, is generated to visualize and analyze trade-offs between the competing objectives. The robust optimization is performed including 100 $\hskip.001pt 100$ scenarios modeling setup and range errors, as well as organ motion, on 3D- and 4DCT lung cancer patient datasets. Robustness analysis is performed to assess and explore the efficacy of all optimization strategies. The s-f approach enabled robust optimization in MCO with computational times comparable to nominal MCO. Both MCO strategies highlighted the interplay between dosimetric and variance reduction objectives. The LO approach showed how prioritization affects plan quality and robustness, while the PF analysis revealed a clear trade-off between robustness and organ-at-risk sparing. The proposed s-f robust optimization approach allowed the efficient application of robust MCO by significantly reducing the required computational time. The reported analysis highlighted the conflicting trade-off nature of plan robustness and dosimetric quality, demonstrating how robust MCO supports a more informed and flexible decision-making process in treatment planning.
Although ice polymorphs commonly feature orientational order and disorder, it is difficult to grasp the nature of partial order. In this study, we report on the hydrogen ordering of ice V using calorimetry at ambient pressure with an isothermal annealing approach. H2O/D2O isotopic substitution underlines the existence of the partially ordered intermediate state between ice XIII (below 113 K) and ice V (above 120 K), which exhibits a large isotope effect on the enthalpy of hydrogen disordering. Combined with the observation of two-staged time evolution of hydrogen order and the significant deuteration-induced slowdown of the ordering kinetics by a factor of 15-60, we propose that this intermediate state bears dynamic disorder. This reflects mutual conversions of ordered configurations taking place, i.e., domain fluctuations between differently ordered configurations. This finding raises a new perspective to characterize partial order, leading to the potential application toward frustrated functional materials.
Structural frustration is believed to play a key role in suppressing crystallization and promoting glass formation, yet experimentally quantifying and understanding the structural frustration in glasses remains highly challenging. Here, we systematically investigate the structural evolution of a prototypical metallic glass-former, Cu46Zr42Al7Y5, during its glass transition and crystallization upon heating, using in-situ high-temperature high-energy synchrotron x-ray diffraction in combination with conventional and ultrafast differential scanning calorimetry. By probing the kinetic interplay between glass transition and crystallization over a broad range of heating rates, we identify a heating-rate-dependent crossover in the nature of the first exothermic process: at high rates it is associated with conventional-like crystallization, whereas at low rates it corresponds to a polyamorphic-like amorphous-amorphous transition. Quantitative analysis of the evolving medium- and short-range order reveals that these structural evolution pathways are governed by the competition between locally favored ordering and extended crystal-like ordering, providing direct experimental evidence that such structural competition can frustrate crystallization and stabilize the glassy state. These findings provide fundamental insights into the intricate relationship between structural frustration and structural stability in metallic glasses.
Understanding the dynamic nature of developmental networks requires live imaging techniques capable of capturing real-time developmental processes in wild-type and mutant embryos as they are exposed to pharmacological perturbations. A peculiar developmental patterning process in early vertebrate embryos is the sequential segmentation of bilateral somites from the unsegmented tail tissue along their major axis. Earlier work discovered that segmentation is instructed by an oscillatory fibroblast growth factor (Fgf)/ERK signaling gradient sourced from the tailbud. Somite segmentation was recapitulated at will in the absence of the molecular oscillator, "the segmentation clock", via pulsatile drug inhibitions. Here, we present a live imaging setup for zebrafish embryos that incorporates a 3D-printed chamber and a programmed syringe pump for precise, automated, periodic drug delivery. The chamber secures the orientation of zebrafish embryos in agarose inserts and incorporates inflow and outflow ports to facilitate controlled drug perfusion. Servo motors controlled by an Arduino were integrated to automate valve switching, achieving fully automated exchange of two different fluids for alternating drug delivery and rinse cycles. Such periodic delivery of an inhibitor drug entrains the Fgf/ERK signaling gradient in the embryonic tail to oscillate in clock-deficient mutants, creating lab-reconstituted somites in otherwise defective embryos. Embryos expressing fluorescent markers can further be imaged at single-cell resolution during perturbations. Overall, this system provides a cost-effective, reproducible platform for investigating vertebrate development and interrogating cellular decision-making under controlled experimental conditions. We anticipate this setup will be broadly beneficial for the biomedical research community interested in controlled drug delivery and in vivo cellular dynamics.
Spin glasses are magnetic materials exhibiting numerous magnetization patterns, that randomly vary both in real space and in time. To date, it is still not well understood what the nature of these spatiotemporal dynamics is, namely if they are completely random or if there are relevant and correlated length and time scales. Here, we demonstrate dynamic heterogeneity in the aging dynamics of elemental neodymium. We used spin-polarized scanning tunneling microscopy in combination with atomistic spin dynamics simulations to image the locally ordered magnetic patterns in the self-induced spin glass state and tracked the induced spatiotemporal dynamics in response to external perturbations. We observed that the real-space magnetization exhibited a coexistence of slow and fast dynamics coinciding with particular length scales. We adapted a wavelet paradigm to identify and correlate local spatial order with its dynamical evolution. These results provide a platform to study dynamics in spin glasses beyond the mean-field limit, linking to a generalized picture of glasses.
Accurate simulation of fluid flow in porous media is a challenging task due to the complexity of pore-space geometries and the computational cost of solving the Navier-Stokes equations. Traditional numerical solvers rely on carefully constructed meshes, often requiring manual intervention, and typically exhibit slow convergence. This difficulty is particularly pronounced in porous media, where the diffusive nature of momentum transport is hindered by intricate solid boundaries. These challenges limit the efficiency of numerical simulations, particularly when repeated evaluations are required. We present a neural-network-based framework for predicting pore-scale velocity fields directly from sample geometry. The method is based on a convolutional encoder-decoder architecture with skip connections, designed to preserve fine-grained structural information. Physical consistency is encouraged through a custom loss function composed of multiple terms: incompressibility, no-flow conditions within solids, periodicity constraints, and agreement with the global tortuosity index. We systematically analyze the influence of weight selection for these loss terms, quantifying their individual contributions to prediction accuracy. Several architectural variants inspired by computer vision are evaluated to identify one providing the best performance and robustness. The generalization ability of the trained network is assessed on samples outside the training distribution, including variations in boundary conditions, obstacle geometry, and porosity. Finally, we demonstrate additional practical applications in which network predictions are used to initialize the Lattice-Boltzmann simulations, a standard fluid dynamics solver, often used in complex boundary problems like porous media and used by us to train the network. We have used network-generated velocity field as a starting point and found that this significantly accelerates LBM solver convergence, achieving improvements in over 90% of cases.
Continuous-variable (CV) quantum key distribution (QKD) allows for quantum secure communication with the benefit of being close to classical coherent communication. In recent years, CV QKD protocols using a discrete number of displaced coherent states have been studied intensively as the modulation can be directly implemented with real devices with finite resolution. Until now, experiments only calculated key rates in the asymptotic regime. Here, we present a CV QKD system using discrete modulation that is especially designed for atmospheric channels. We use polarization encoding to exploit the nonbirefringent nature of the turbulent atmosphere. This allows to expand CV QKD networks beyond the existing fiber backbone. In a laboratory demonstration with a static 3-decibel loss channel, we implemented a recently developed security proof allowing to calculate composable finite-size key rates against independently and identically distributed collective attacks. We applied the full QKD protocol including a quantum random number generator, error correction, and privacy amplification to extract secret keys.