Papers

Conditional probability density functional theory has recently been used to derive the temperature dependence of the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) for the exchange-correlation (XC) free energy. We implement and systematically benchmark thermal PBE within Kohn-Sham density functional theory calculations of warm dense matter. Comparisons with the local density approximation (LDA) and PBE functionals, as well as thermal LDA, show that thermal PBE significantly improves the description of warm dense matter properties, including energies, forces, pressures, and electronic charge densities. In particular, thermal PBE exhibits close agreement with path integral Monte Carlo (PIMC) reference data at negligible additional computational cost. This work demonstrates the practical utility of thermal PBE as an accurate semilocal functional for simulations in the warm dense regime.

Accurate prediction of molecular correlation energies from first principles requires resolving the {O}(N^4) electron repulsion integral (ERI) tensor. Existing graph neural network approaches to the electronic structure problem often compress this tensor into low-rank scalar features, discarding higher-order interaction structures relevant to electron correlation. In this work, we demonstrate that tensor factorization of the ERI naturally induces a structured bipartite message-passing architecture that preserves access to higher-order interaction structure more effectively than compressed orbital representations. By utilizing the density-fitted Cholesky decomposition of the ERI tensor, we derive a bipartite graph network that models orbital degrees of freedom and auxiliary interaction nodes as distinct sets, maintaining interaction topology at a reduced theoretical complexity of {O}(N^3). Evaluated on 132 geometries of six diatomic molecules with Full Configuration Interaction (FCI) reference energies, our factorized representation achieves an in-distribution Mean Absolute Error (MAE) of 0.0296 Ha under five-fold cross-validation, a substantial improvement over compressed-integral baselines. Leave-one-molecule-out validation reveals that zero-shot generalization varies by nearly a factor of four across molecular species and correlates with the structural similarity of the held-out molecule's orbital environment to the training distribution, rather than with nuclear charge asymmetry alone.

When searching for communities in networks, domain experts may have some prior expectations about the size of communities. Yet, community detection methods normally do not optimize communities under cluster size constraints. Multi-resolution techniques allow users to indirectly control the average community size through changing a resolution parameter, but this practice does not control the size of individual communities. We here study the problem of size-constrained community detection, where the size of all communities is limited to a user-specified range of values, in the context of modularity optimization. We propose a heuristic for modularity optimization under community size constraints. To demonstrate the reliability of our proposed heuristic, we also formulate an exact integer optimization model and use its results as a baseline. Our analysis based on synthetic benchmarks and real networks demonstrate the issues with the currently common practice of changing resolution parameters and reveal the advantages of the proposed methods as a principled way of obtaining size-constrained communities. The proposed method is publicly available in the Python Leiden algorithm package.

The shortest path multiplicity, here denoted by $\mu$, is an important metric of complex networks. For real networks $\mu$ is high and it correlates with the network community structure. Since local network evolution induces network communities, it is possible that a high shortest path multiplicity is the natural expectation of local evolution rules. Here I demonstrate, by means of numerical simulations and exact results, that this is indeed the case. For random graphs with arbitrary degree distributions $p_k$, $\langle\mu\rangle\sim \langle k(k-1)\rangle / (\langle k\rangle e)$, growing with the network size when $p_k\sim k^{-\gamma}$ and $\gamma\leq3$. For networks generated by stochastic local rules, $\langle\mu\rangle$ increases with the network size and it does so faster than what is observed in their randomized versions. Furthermore, the number of communities increases with the network size and the correlation with $\langle \mu\rangle$ follows.

We present a multi-diagnostic electromagnetic analysis of a dayside magnetosheath interval observed by the Magnetospheric Multiscale (MMS) mission on 2015 December 28 at high ion beta ($\beta_i = 14.4$), using burst-mode measurements. We combine the scale-dependent electric-to-magnetic field ratio, the parallel electric field, the magnetic compressibility, the wave-normal angle and circular-polarization measure, and the ion–electron frozen-in residuals. The electric-to-magnetic field ratio rises with wavenumber with a log–log slope of +0.92, whereas the ideal advection contribution remains flat, supporting dispersive fluctuations. A wave-normal angle of $75^\circ$ and a circular-polarization measure centred near zero support quasi-perpendicular propagation, while the compressibility and the ion temperature anisotropy do not support mirror-mode dominance. A continuous broken power-law fit gives $f_{\rm break} = 0.48 \pm 0.06$ Hz, corresponding to $k_\perp d_i = 0.85 \pm 0.11$ and $k_\perp \rho_i = 3.23 \pm 0.42$. Because $d_i$ and $\rho_i$ are well separated at this $\beta_i$, the break is more consistent with the ion inertial scale than with the ion gyroscale. Four-spacecraft phase differencing independently supports the Doppler conversion around the break. Above the break, the kinetic-range index is $-2.86 \pm 0.06$ (stat) $\pm 0.18$ (fit window), closer to the weakly damped dispersive limit than to the strongly damped limit, with a fluid-scale index of $-1.81 \pm 0.08$ below it. The observations are consistent with quasi-perpendicular KAW-like turbulence, but do not uniquely identify the mode; the spectral index constrains the cascade rather than the dissipation mechanism.

In the context of renewable energies, wind energy appears as a sustainable alternative to address current environmental and energy challenges. This work studies the synchronization and stability of a network of wind turbines subjected to strong disturbances, by integrating a realistic modeling of wind variability by using the Ornstein-Uhlenbeck stochastic process. The dynamics of each wind turbine are described by a Kuramoto-type equation, while synchronization is analyzed through the time evolution of the phases. Stability is studied by analyzing the basin of attraction to the synchronous solution, namely the set of initial conditions leading to the stable synchronous state. Simulations carried out on various models ranging from an isolated wind turbine with constant power to an isolated wind turbine with variable wind power, reveal that the stability of the system is strongly influenced by inertia, damping, wind speed, wind fluctuation rate, correlation time, and coupling strength. Physically, these parameters control the balance between injected mechanical power, energy dissipation, grid-induced restoring forces, and the temporal structure of wind fluctuations, thereby determining the ability of the wind turbine to absorb perturbations and maintain synchronization under fluctuating wind conditions.

The coupling between surfactant-laden droplet dynamics and electric fields plays an important role in liquid-handling technologies such as digital microfluidics. We develop an energetic variational framework for the coupled electrohydrodynamics of surfactant-laden droplets, incorporating two-phase Stokes flow, insoluble surfactant transport on a moving interface, and electrostatic effects. Based on Onsager's principle, the governing equations are derived by minimizing the Rayleighian, defined as the sum of the rate of change of the free energy and the dissipation functional, subject to the incompressibility constraint. This formulation simultaneously yields the Stokes equations in each bulk phase, the interfacial stress-balance condition incorporating Marangoni and Maxwell stresses, the electrostatic equation, the surface transport equation for the surfactant concentration, and the moving contact-line dynamics. By replacing the viscous dissipation functional with Rayleigh dissipation, we further derive a reduced model describing the evolution of surfactant-laden droplets driven by motion by mean curvature. For sessile droplets represented as graph surfaces, the model reduces to a one-dimensional coupled electrohydrodynamic model for the liquid height, surfactant concentration, and electrostatic potential. A first-order implicit-explicit numerical scheme is proposed for the reduced system, and numerical results illustrate the coupled effects of surfactant transport and electric fields on driving droplet deformation and migration.

Subsurface geomodeling plays a critical role in reservoir characterization, uncertainty quantification, and subsurface flow prediction. However, integrating heterogeneous sources of geological information, including conceptual geological descriptions, sparse well observations, and spatial prior constraints, remains a significant challenge for traditional geostatistical and data-driven geomodeling approaches. In this study, we present FMSIM, a multi-modal conditional flow matching framework for subsurface facies model generation. FMSIM utilizes a deep learning formulation to learn a velocity field that transports samples from a simple prior distribution to a complex geological facies distribution. Global geological semantic information is incorporated through a learned semantic representation framework and a learned prior model, while local hard constraints are enforced via an iterative projection strategy during sampling to ensure 100% fidelity to well observations. Additionally, a temporal guidance gating mechanism is introduced to regulate the influence of spatial probability maps, balancing large-scale trend alignment with fine-scale geological variability. Benefiting from the framework design, the model enables efficient and stable training with a simple loss function. The framework's fully convolutional architecture also demonstrates promising generalization to moderately larger grid sizes not seen during training without retraining. Results on a synthetic fluvial channel dataset indicate that FMSIM captures complex non-stationary geological features and produces geologically consistent realizations under multi-modal conditioning. This approach offers a flexible tool for incorporating conceptual geological knowledge, sparse observational data, and spatial priors into probabilistic subsurface geomodeling workflows.

We derive the Lorentz transformation from spacetime symmetries and continuity, without assuming the invariance of the speed of light. Spacetime homogeneity yields an additive coordinate map; continuity along four linearly independent inertial worldlines, together with additivity, establishes linearity throughout spacetime. Spatial isotropy and the assumed continuous local one-parameter group structure of collinear boosts yield velocity reciprocity and determine a generalized Lorentz-type family parameterized by $\kappa$. The velocity law and the empirical identification of its invariant speed with the coordinate speed of light in vacuum select the Lorentzian branch, fixing $\kappa=-1/c^2$ and recovering the Lorentz transformation.

Ultrasound computed tomography is emerging as a promising safe and accessible modality for soft-tissue medical imaging, with full waveform inversion playing a key role in unlocking its full potential for high-resolution, quantitative reconstructions. Frequency domain full waveform inversion (FDFWI) for reconstructing spatial maps of acoustic properties in the musculoskeletal system is highly sensitive to the quality of low-frequency signals, making the final imaging outcome vulnerable to issues such as inappropriate initial models and strong scatterings related to bones. To address these challenges, we propose a hybrid full waveform inversion (HFWI) algorithm that incorporates a traveltime inversion algorithm based on the generalized Rytov approximation into the FDFWI framework. This hybrid strategy enhances early-stage inversion quality and substantially reduces sensitivity to the initial model, all while maintaining computational efficiency. Importantly, HFWI achieves results comparable to those obtained using well-constructed initial models, without incurring extra computational cost, thus enabling accurate imaging under realistic, bandwidth-limited conditions. In addition, we introduce a near real-time strategy to update first-arrival traveltimes based on forward-scattered phase variations without requiring extra wavefield simulations. Numerical simulations, as well as in vitro and in vivo experiments confirm the robustness and efficiency of the proposed approach. HFWI also shows promise to extend to more complex scenarios of musculoskeletal parametric reconstruction.

Altermagnets combine magnetic compensation with spin-momentum-locked splitting in the absence of spin-orbit coupling, yet existing descriptions, formulated primarily in terms of spin symmetry and lattice geometry, provide limited insight into the electric-structure perspective of its angular harmonic form. Here, we identify a wavefunction-level framework for altermagnetism in two-dimensional square lattices. Using symmetry-adapted polynomial wavefunctions, we show that the harmonic structure of momentum-space spin splitting is inherited from the geometry of the electronic wavefunctions which can be selected by crystal fields. Identical orbital sectors preserve conventional antiferromagnetic degeneracy, whereas intertwined linear and quadratic wavefunctions generate d-wave and g-wave altermagnetic anisotropies, respectively. Tight-binding analysis connects this hierarchy to inequivalent same-spin hopping channels. First-principles calculations on the g-wave mcm-type reticular material platforms confirm high-symmetry-linear degeneracy together with finite generic-k splitting. Our results establish a hierarchy linking wavefunction geometry, orbital realization, microscopic hopping anisotropy, and altermagnetic electronic structure.

The $\textit{Parker Solar Probe}$ ($\textit{PSP}$) mission has observed near-continuous power in parallel ion cyclotron waves (PICWs) in the young, fast solar wind. These waves are unlikely to be directly produced by the turbulent cascade and are likely born of a local instability; yet, they are observed to both cool – and heat – the plasma. We propose that these observations can be self-consistently explained as the natural consequence of PICWs propagating in the inhomogeneous solar wind after they have been driven unstable. In this work, we argue that strong proton heating by a turbulent cascade of oblique ICWs will result in PICWs being driven unstable in a process known as quasi-linear focusing. Because the power in the turbulent cascade is concentrated at scales above the turbulent transition region, PICWs will be driven unstable within a range of wave numbers parallel to the background magnetic field, $k_\parallel$, that is bounded from above by $k_{\parallel\rm P}^*$, corresponding to the start of the transition region. As unstable PICWs propagate away from the sun to regions of lower proton density, their $k_\parallel$, multiplied by the proton inertial length $d_{\rm p}$, increases. Eventually, the $k_\parallel d_{\rm p}$ of the PICWs becomes larger than $k_{\parallel\rm P}^*d_{\rm p}$ and the waves damp, heating the solar wind. We call this effect `cyclotron breaking', in analogy with ocean waves breaking on the shore. We then discuss the testable predictions of the theory, including a distinct heating signature in which PICWs cool fast protons and heat slow protons at any given heliocentric distance $r$. Finally, we conjecture that cyclotron breaking can lead to net heating by PICWs if the power emitted as PICWs decreases sufficiently rapidly with $r$ that local emission of PICWs is overwhelmed by the local damping of PICWs generated closer to the sun.

We introduce "Courant", a Perceiver-based encoder-processor-decoder surrogate model that has latent features exhibiting adaptive specialization and local support in the physical space, enabling functionality akin to an adaptive hp-refinement scheme, an attribute that is highly desirable in traditional numerical solvers and scientific machine learning broadly. The proposed architecture combines a shared random Fourier feature coordinate embedding, state-adapted latent queries, and a light-weight decoder. Courant is trained end-to-end with steady or transient simulation data and only a standard L_2 prediction loss in the physical space, achieving competitive accuracy on benchmarks. We demonstrate that Courant's inductive biases yield latents that are interpretable by design: they develop multiscale geometric specialization in the simulation domain and track coherent structures in the time-dependent case, acting analogously to time-evolving spatial basis functions and allowing for decoding a compact, geometry-anchored, partition-of-unity-like decomposition of the simulated field.

Here, we demonstrate an on-chip cryogenic microwave frequency-comb on a planar superconducting resonator fabricated on Strontium titanate(STO), a quantum paraelectric material. The material's Kerr-type nonlinearity arising from the quantum paraelectric phase, an underexplored state, enables pronounced resonance shifts, Duffing-like bifurcations and comb generation via cascaded foure-wave mixing. Our results establish STO resonators as a low power, cryogenically compatible platforms for nonlinear microwave photonics, with applications in scalable quantum control and on-chip frequency synthesis.

A novel silicon photonic CWDM filter design is proposed and experimentally demonstrated. The design has achieved flat-top transmission across all dies on a wafer, with a device footprint of 48*25 {\mu}m2, an insertion loss of 0.24 $\pm$ 0.18 dB, and a channel central wavelength standard deviation of 0.77 nm.

Strong localization of light in 3D dielectric disorder is challenging to establish because localized contributions can be hidden by diffusive leakage and finite-sample loss. We perform full-wave time-domain Maxwell simulations of dense high-index dielectric slabs and analyze late times after the diffusive component escapes. Transmission develops a non-exponential tail, and an effective diffusion coefficient approaches localized-dynamics scaling. Late-time spectra show narrow resonances with sub-unity mean Thouless conductance and Poisson-like spacings. Time- and frequency-resolved near fields reveal compact non-propagating modal domains separated by persistent low-intensity channels and interacting weakly. These converging signatures constitute finite-system evidence for resolved 3D Anderson localization, with the late-time field forming a self-organized, interference-defined confinement pattern.

Accurate representation of trees is essential for predictive urban micrometeorological simulations, but explicitly resolving detailed tree geometry is computationally prohibitive. Trees are therefore commonly represented as porous media, often with a constant drag coefficient even when spatially heterogeneous area-density distributions are introduced. This limits transferability across inflow conditions and can increase sensitivity to grid resolution, particularly in the grayscale regime where a tree is represented by only a few computational cells. We propose a fractal-based variable-drag model for porous-media tree representations, in which the drag coefficient is prescribed cell-wise as $C_D=C_D(n_{\mathrm{eff}},Re_{\mathrm{eff}})$. Here, $n_{\mathrm{eff}}$ is the cell-effective branching order representing unresolved local morphological complexity, and $Re_{\mathrm{eff}}$ is the cell-effective Reynolds number representing the local flow regime. The model is assessed using steady Reynolds-averaged Navier–Stokes simulations of a porous fractal tree over systematic sweeps of grid resolution and inflow velocity. Model performance is evaluated primarily using aerodynamic porosity, which measures the bulk momentum attenuation induced by the tree. The proposed model produces a plausible aerodynamic response and improves robustness to grid resolution compared with constant-$C_D$ models. It also captures the variation of bulk drag across inflow conditions without empirical retuning. Notably, this whole-tree response is recovered through local cell-wise quantities, $n_{\mathrm{eff}}$ and $Re_{\mathrm{eff}}$. These results demonstrate that morphology- and flow-dependent drag provides a practical route to improving porous-media tree modeling.

Force-matched water models provide a practical route from first-principles reference data to long classical and path-integral molecular simulations. Previous flexible four-site potentials in the spirit of q-TIP4P/F showed that fitting analytic models to density-functional-theory forces can reproduce key structural features of liquid water while retaining the efficiency required for quantum-nuclear sampling. Here we introduce two refinements aimed at making this strategy more accurate and more reproducible for molecular simulation. First, the production fit is based on PBE0 hybrid-functional reference forces and therefore includes the Hartree–Fock exact-exchange contribution in the electronic-structure target. Second, the parametrization is formulated as a separable nonlinear least-squares problem in which all linear force-field amplitudes are eliminated analytically for every trial set of nonlinear shape parameters. The resulting force-matching protocol lowers the dimension of the nonlinear search, reduces compensation between heterogeneous parameters, and enables a controlled comparison of Lennard-Jones and Buckingham oxygen–oxygen repulsion terms. Applied to CP2K reference forces for liquid water, the projected optimization reproduces target force distributions and yields radial distribution functions close to first-principles and neutron-scattering benchmarks. The Buckingham representation gives a more flexible short-range repulsive wall than a purely Lennard-Jones form, and the final flexible model remains stable in path-integral simulations. The method provides a transparent workflow for deriving simulation-ready water potentials from accurate hybrid density-functional reference forces.

Compressed sensing provides an efficient framework for reconstructing wave signals from reduced measurements. For multi-channel buoy data, the three displacement components exhibit intrinsic correlations, as wave motion contributes simultaneously to all directions according to linear wave theory. Meanwhile, conventional compressed sensing methods based on $\ell_1$-shrinkage tend to underestimate signal energy when sparsity is not strictly satisfied, leading to biased spectral estimation. This paper introduces a group sparsity constraint to promote physically consistent sparse representations across channels. An energy constraint is proposed in the form of a soft lower bound, enabling an isotropic rescaling of the recovered spectrum while preserving its sparse structure. Considering a large volume of buoy data, we demonstrate through a series of experiments that the proposed approach enables compression by retaining a subset of original measurements.

The Versatile ULtra-Compact Accelerator-based Neutron source (VULCAN) project is developing a compact accelerator-driven neutron source (CANS) optimised for neutron diffractometry in industrial and university settings. Central to VULCAN is a novel target-moderator-reflector (TMR) assembly optimised to convert a 35 MeV pulsed electron beam into short neutron pulses (FWHM < 20 {\mu}s) in the 1.5-3.5 Å wavelength range. To validate the simulation-driven design process, a prototype TMR was developed for testing at CERN's CLEAR facility, and this paper presents the design, installation, and results of the first experimental campaign. While moderated neutron pulses were successfully detected, significant discrepancies were observed between the experimental and simulated energy spectra. Potential causes are discussed and recommendations for follow-up measurements are provided.

High-power inductively coupled plasma (ICP) wind tunnels are widely used to reproduce high-enthalpy environments relevant to atmospheric entry and hypersonic testing. Despite their importance, radiative heat transfer in ICP facilities is commonly neglected or modeled using simplified optically thin assumptions, and the impact of non-equilibrium radiation on plasma dynamics remains poorly quantified. In this work, a loosely coupled, multi-physics framework is developed to systematically investigate radiative cooling effects in the 350 kW Plasmatron X facility at the University of Illinois Urbana-Champaign. The approach self-consistently couples a magnetohydrodynamic plasma framework with a spectral radiative transport solver, eliminating the need for optically thin or empirical models. Simulations are performed for nitrogen and air plasmas over a wide range of operating pressures (1-101 kPa) and powers (100-350 kW). The results reveal a strong pressure dependence of radiative losses, with radiation contributing negligibly at low pressures, but becoming a dominant energy sink at elevated pressures. At atmospheric pressure, radiative losses account for up to approximately 32% and 22% of the input power for nitrogen and air plasmas, respectively, leading to substantial reductions in core plasma temperatures. Nitrogen plasmas consistently exhibit higher radiative losses than air as a result of increased concentrations of radiatively active species and higher electron number densities. Pressure-power maps of radiative heat loss relative to input power are constructed to quantify combined operating effects and to provide guidance for facility operation and modeling fidelity. Finally, an assessment of self-absorption demonstrates that the Plasmatron X torch operates predominantly in an optically thin regime, even at the highest power and pressure conditions considered.

Active flows are central to mixing and transport across living systems. While Newtonian fluids remain laminar, diffusive and predictable at the microscale, living fluids like dense bacterial suspensions can exhibit highly chaotic flows like active turbulence, with anomalous transport capabilities. The underlying spatiotemporally persistent structures that drive mixing in active flows, however, remain uncharted. Using Lagrangian Coherent Structures, we now uncover networks of attracting and repelling hyperbolic surfaces. We study changes in the distribution and spectra of Finite-Time Lyapunov Exponent fields in response to increasing activity. Despite the dominance of vorticity in the flow, extreme forward and backward time chaotic mixing is found to originate from straining regions, emphasizing the role of saddles. Fractal dimensions of ridges reveal a morphological simplification of LCS networks with increasing activity, while retaining isotropic crossing. Throughout our work, we also probe a hitherto unasked question-Are signatures of Lagrangian irreversibility manifest in attracting and repelling LCS? To the contrary, we find there is a striking time-symmetry. Our work takes the first steps towards linking flow structures in active turbulence to invariant mixing surfaces. These findings will crucially help in designing activity modulation protocols to seed or inhibit flow structures, and thence mixing, in a bid to tame active turbulence for varied applications.

We measure cross-boundary collaboration in an open-source software (OSS) ecosystem by reconstructing the bipartite contributor-repository graph of 464 cybersecurity projects and 11,372 contributors active over October 2001-May 2022 (Rawsec Cybersecurity Inventory). Louvain community detection identifies 163 non-singleton communities; per-community contributor count scales superlinearly with repository count (n_contributors ~ n_repos^1.4), and community formation follows a logistic trajectory saturating around 2018. Three patterns support a recognition/repeat-relationship account of cross-boundary work. First, cross-community work concentrates in a thin carrier layer: only nine canonical humans span seven or more communities at the commit level, authoring 14% of 4,015 inter-community merged pull requests; the top 50 cross-community contributors produce 54%. Second, boundary friction is a recognition cost, not a fixed boundary property: inter-community pull-request acceptance rises from 42% at breadth k=1 to 87% at k=5-9, with median latency compressing from 147 h to 49 h. Third, community survival is cohort-structured: per-cohort residualisation hazard rises an order of magnitude between pre-2010 and 2018 cohorts, and external community reach predicts survival mainly through size, leaving late cohorts under-served despite a stable carrier layer. The corpus predates mainstream LLM coding assistants; this baseline of carrier-layer thinness, friction gradient, and cohort hazard informs debates on social coding as a template for digital societies and on what AI-mediated OSS ecosystems should not optimise away.

Cross-functional fine-tuning of machine-learning interatomic potentials (MLIPs) is often treated as a relabeling problem, where configurations generated at one density-functional level are relabeled using a higher-fidelity target functional. However, the resulting training data may be drawn from the wrong equilibrium distribution, because the statistical weights of configurations change across exchange–correlation functionals. Here we address this distribution mismatch using a target-distribution-guided workflow based on self-learning hybrid Monte Carlo (SLHMC), in which trial configurations are proposed by a machine-learning potential and accepted or rejected using target-functional density-functional-theory energies. Using rutile TiO$_2$ as a test system, we fine-tune the MACE-MP-0 foundation potential toward PBE, r$^2$SCAN, and HSE06 target functionals. The resulting adapted potentials reproduce target-anchored nearest-neighbor Ti–O distributions, radial distribution functions, and the NPT cell metrics examined here more accurately than the foundation-model and off-target relabeling controls considered in this work. In particular, HSE06-guided fine-tuning improves structural and thermodynamic properties that are difficult to access with direct hybrid-functional molecular dynamics because of the computational cost of exact exchange. These results indicate that target-distribution coverage is an essential component of cross-functional MLIP transfer, and that accurate target-level labels alone may be insufficient when the configurational distribution is mismatched.

We study a coevolutionary public goods game on a dynamic hypergraph, where an individual's payoff directly determines the number of hyperedges it can join. In the proposed mechanism, nodes adjust their participation according to the group payoffs of hyperedges, and hyperedges that remain occupied only by defectors for a sufficiently long time collapse and are rebuilt by selecting new members based on the current payoffs of nodes. This adaptive rule captures the performance-driven reorganization of group interactions in evolving collective systems. Using Monte Carlo simulations, we show that the cooperation fraction and average hyperdegree may converge to steady states with stochastic fluctuations or exhibit persistent oscillations, depending on the parameter regime. The steady-state outcomes are strongly nonmonotonic with respect to the structural adaptation parameters: cooperation is sustained only when the rate of link formation is properly balanced. If structural adaptation is too fast, frequent contacts between cooperators and defectors destroy cooperative clusters; if it is too slow, cooperators lack sufficient structural support to expand. This differs from the conventional expectation in static settings that larger benefit parameters always facilitate cooperation. We further introduce spectral entropy to quantify the regularity of the oscillatory dynamics and identify limit-cycle behavior in the phase space in certain regimes. These results suggest that adaptive higher-order restructuring can both promote and destabilize cooperation, offering insight into oscillatory cooperation and recurrent prosperity-decline cycles in real group-structured systems.

Microswimmers are often found in heterogeneous and crowded environments within narrow conduits under external flow conditions, enabling them to perform interesting translational and rotational maneuvers, such as swimming in the upstream direction, following walls, and oscillatory motion. Studying such systems helps us understand the motility behaviors of microswimmers (pushers, pullers, or neutrals) and develop applications such as targeted drug delivery. To study the motion of microswimmers in a channel flow with the presence of hard, monodisperse spherical colloids, we adopted the spherical squirmer model to represent the microswimmers, along with a mesoscale simulation framework, multi-particle collision dynamics (MPCD), to represent the background fluid. In the absence of colloids, a squirmer in a microchannel flow develops an increased probability of moving away from the walls and oscillates between the walls as the flow speed increases compared to the squirmer speed, with a dominant upstream orientation near the walls. However, the presence of the colloids makes the pusher swim towards the center of the channel and upstream direction, and the puller swim away from the center of the channel at low flow speeds. At high flow speeds, the flow carries all the squirmers, resulting in a dominant upstream direction in the channel center. We observe that this leads to a decrease in the local velocity of the squirmer in the flow direction for pusher, neutral, and puller-type squirmers. We also observe that, for a constant colloidal packing fraction, the local velocity magnitude of the puller along the flow direction is less than that of the pusher.

Sodium bismuth titanate (NBT) is a promising oxide-ion conductor,but its electrical conductivity is highly sensitive to small changes in A-site stoichiometry and processing history.This sensitivity can reduce sample-to-sample reproducibility.Here we examine how precursor mixing controls structural uniformity and ionic transport in Na0.52Bi0.47TiO3 ceramics.Dry grinding,wet grinding with ethanol,and ball milling were compared by X-ray diffraction,electron microscopy,energy-dispersive spectroscopy,Eu3+ photoluminescence excitation spectroscopy,and electrochemical impedance spectroscopy.All processed powders and ceramics form the perovskite NBT phase within the detection limit of XRD.However,the microstructure,surface A-site cation ratio,Eu3+ excitation spectra,and electrical response change strongly with the mixing route.Continuous monitoring of Eu3+ excitation spectra at different emission wavelengths reveals different distributions of local Eu3+ environments.Larger spectral-shape variations are consistent with lower structural uniformity and stronger local distortion.Dry-ground samples show higher bulk conductivity than wet-ground samples,whereas wet-ground samples show much lower grain-boundary resistance.At 600 \u2103,the dry-60 min sample reaches a bulk conductivity of 13.54 mS cm-1,while wet-30 min shows the highest grain-boundary conductivity of 13.72 mS cm-1.These results suggest a processing-driven trade-off between bulk defect generation and grain-boundary blocking.Based on this processing understanding,Ca was introduced at the A site in Na0.52Bi0.47-xCaxTiO3.The x=0.04 sample reaches 8.35 mS cm-1 at 500 \u2103 and 18.98 mS cm-1 at 600 \u2103.

A unidirectional reduction of the deep-water surface gravity wave problem is derived in physical space using real variables. By employing a near-identity canonical transformation, cubic interactions are eliminated from the Hamiltonian, resulting in a bidirectional system analogous to the 1D Zakharov equation. An exact factorization of the linear dynamics then selects the rightward-propagating branch, which is carried to second order in the wave steepness, yielding a single nonlocal evolution equation with full linear dispersion. The model admits an exact monochromatic solution whose reconstructed surface elevation is the third-order Stokes wave, and a multiple-scales analysis recovers the Dysthe envelope equation complete with its nonlocal mean-flow term. The equation also admits exact breather solutions, whose profiles agree closely with those of the super compact equation. Dropping the sub-leading nonlinear terms that vanish on the resonant manifold provides a more compact variant suitable for analytical study. Numerical comparisons show that both formulations track the fully nonlinear Euler dynamics closely through modulational-instability recurrence and broadband focusing up to moderate wave steepness.

Three-dimensional (3D) photonic integration offers a pathway to overcome the fundamental scaling limitations of planar platforms by enabling enhanced routing flexibility for compact, low-loss, and highly interconnected photonic circuits. In this work, we fabricate 3D photonic circuits combining high-confinement air-clad waveguides for compact routing with low-confinement polymer-clad waveguides for robust single-mode operation within a monolithic platform. Efficient mode transition between polymer-clad and air-clad waveguides is demonstrated with a loss of 0.25 dB per interface. We also realize compact, Euler S- and U-shaped bends with minimal bending radii of 10 $\mu$m and losses as low as 0.5 dB and 0.4 dB, respectively, along with compact adiabatic air-clad splitters exhibiting a splitting loss of 0.6~dB over a length of 52 $\mu$m. Finally, full fabrication of a compact hybrid circuit is demonstrated, highlighting the feasibility and scalability of the approach. Our work represents a significant step in 3D photonic integration for applications including optical neural networks, photonic wire bonding and their potential for novel integrated photonic applications.

Chip-integrated photon-pair sources based on spontaneous parametric down-conversion (SPDC) have emerged as a promising solution for scalable quantum light generation. Thin-film lithium tantalate (TFLT) is a compelling $\chi^{(2)}$ platform, combining strong nonlinearity with a high optical-damage threshold, weak photorefractive response, and ferroelectricity that enables quasi-phase matching. However, SPDC-based photon-pair generation on TFLT has not yet been demonstrated. Here, we combine high-quality periodic poling with low-loss nanophotonic waveguides to realize photon-pair sources on TFLT in both traveling-wave and resonant configurations. In periodically poled straight waveguides, we achieve broadband photon-pair generation with high efficiency ($2.1~\mathrm{GHz}~\mathrm{mW}^{-1}$) and coincidence-to-accidental ratio (up to $3.8\times10^{5}$). We further confirm high-purity single-photon operation via heralded second-order correlation ($g^{(2)}_\mathrm{H}(0) = 0.0018 \pm 0.0002$) and high-fidelity time-energy entanglement through Franson interference (visibility of $98.9 \pm 0.5\%$). In periodically poled racetrack resonators, we map out a broad quantum frequency comb spanning the telecom C- and L-bands. By isolating individual frequency-correlated pairs, we measure a high spectral brightness of $11~\mathrm{GHz}~\mathrm{mW}^{-1}~\mathrm{GHz}^{-1}$. These results are competitive with the state of the art across $\chi^{(2)}$ integrated platforms, positioning TFLT as a strong contender for integrated quantum light sources, with applications in wavelength-multiplexed quantum communications and photonic quantum information processing.