We prove that the purely imaginary square well generates an infinite number of bound states with real energies. In the strong-coupling limit, our exact PT symmetric solutions coincide, utterly unexpectedly, with their textbook, well known Hermitian predecessors.

We study a model for nonperturbative unitarization of the four-point contact scalar amplitude in four dimensions. It is defined through an infinite sum of planar diagrams, constructed using two-particle unitarity and crossing symmetry. We reformulate the problem in terms of a set of nonlinear integral equations obeyed by the single and double discontinuities of the amplitude. We then solve them using a neural-network ansatz trained by minimizing a physics-informed loss functional. We obtain a one-parameter family of amplitudes, which exhibit rich structure: sizeable particle production, nontrivial emergent Regge behavior, Landau curves, a logarithmic decay at high energy and fixed angle. Finally, we go beyond the two-particle-reducible setup by treating the multi-particle data -- supported above the multi-particle Landau curves due to multi-particle unitarity -- as a dynamical variable. We demonstrate that it can be tuned to suppress low-spin particle production -- a phenomenon we call Aks screening -- at the cost of generating larger and oscillatory double spectral density in the multi-particle region.

We take a step toward answering a long-standing question in the asymptotic expansion of Feynman integrals: how to systematically determine the regions in the Expansion-by-Regions technique for multiscale processes? Focusing on generic massless wide-angle scattering, we provide an all-order momentum-space prescription for facet regions, which generally dominate -- and in most cases exhaust -- the contributions in a given asymptotic expansion. This extends the Euclidean-space picture, where regions correspond to specific subgraphs, to the complexities of Minkowski space. Our results are derived from a novel analytical approach combining graph theory and convex geometry; as a key byproduct, we uncover for the first time the algebraic structure underlying momentum modes (collinear, soft, and their hierarchies).

We study the long-time behavior of a particle in $\mathbb{R}^d$, $d \geq 2$, subject to molecular diffusion and advection by a random incompressible flow. The velocity field is the divergence of a stationary random stream matrix $\mathbf{k} $ with positive Hurst exponent $γ> 0$, so the resulting random environment is multiscale and self-similar. In the perturbative regime $γ\ll 1$, we prove quenched power-law superdiffusion: for a typical realization of the environment, the displacement variance at time $t$ grows like $t^{2/(2-γ)}$, the scaling predicted by renormalization group heuristics. We also identify the leading prefactor up to a random (quenched) relative error of order $γ^{\frac12}\left| \log γ\right|^3$. The proof implements a Wilsonian renormalization group scheme at the level of the infinitesimal generator $\nabla \cdot (νI_d + \mathbf{k} ) \nabla$, based on a self-similar induction across scales. We demonstrate that the coarse-grained generator is well-approximated, at each scale $r$, by a constant-coefficient Laplacian with effective diffusivity growing like $r^γ$. This approximation is inherently scale-local: reflecting the multifractal nature of the environment, the relative error does not decay with the scale, but remains of order $γ^{\frac12}\left| \log γ\right|^2$. We also prove anomalous regularization under the quenched law: for almost every realization of the drift, solutions of the associated elliptic equation are Hölder continuous with exponent $1 - Cγ^{\frac12}$ and satisfy estimates which are uniform in the molecular diffusivity $ν$ and the scale.

We construct a quantum Schwinger-Keldysh (SK) effective field theory for the diffusive hydrodynamics of a conserved scalar field. Quantum corrections within the SK framework are guided by fluctuation-dissipation relations, enforced via a dynamical Kubo-Martin-Schwinger (KMS) symmetry. We find that the KMS symmetry necessarily generates fluctuation contributions in the SK effective action at all orders in the noise field, thereby giving rise to intrinsically non-Gaussian noise. We use our results to compute one-loop quantum corrections to the two-point density-density retarded correlation function, leading to a quantum generalization of hydrodynamic long-time tails. Our results apply at arbitrarily high orders in $\hbar$. The one-loop results for retarded correlation functions have been expressed in terms of a family of polynomials. We also provide a closed-form expression for the one-loop results at leading order in the wavevector expansion.

As the number of detected gravitational wave (GW) events increases with the improved sensitivity of the observatories, detecting strongly lensed pairs of events is becoming a real possibility. Identifying such lensed pairs, however, remains challenging due to the computational cost and/or the reliance on prior knowledge of source parameters in existing methods. This study investigates a novel approach, Optimal Cross-Correlation Analysis for Multiplets (OCCAM), applied to strain data from one or more detectors for Compact Binary Coalescence (CBC) events identified by GW searches, using an optimal, mildly model-dependent, low computation cost approach to identify strongly lensed candidates. This technique efficiently narrows the search space, allowing for more sensitive, but (much) higher latency, algorithms to refine the results further. We demonstrate that our method performs significantly better than other computationally inexpensive methods. In particular, we achieve 97 percent (80 percent) lensed event detection at a pairwise false positive probability of approximately 13 percent (7 percent) for a single detector with LIGO design sensitivity, assuming an SNR greater than or equal to 10 astrophysically motivated lensed and unlensed populations. Thus, this method, using a network of detectors and in conjunction with sky-localisation information, can enormously reduce the false positive probability, making it highly viable to efficiently and quickly search for lensing pairs among thousands of events, including the sub-threshold candidates.

Much of the century-old debate surrounding the status of thermodynamics in relativity has centered on the search for a suitably relativistic temperature; recent works by Chua (2023) and Chua and Callender (forthcoming) have suggested that the classical temperature concept -- consilient as it is in classical settings -- 'falls apart' in relativity. However, these discussions typically assume an unproblematic Lorentz transformation for -- specifically, the Lorentz invariance of -- the pressure concept. Here I argue that, just like the classical temperature, the classical concept of pressure breaks down in relativistic settings. I discuss how this might suggest a new thermodynamic limit -- a u --> 0 limit -- without which an unambiguous thermodynamic description of systems doesn't emerge.

It has been shown that a very old black hole can tunnel into a white hole through the emission of a large baby universe. This process can be modeled by a genus-one geometry corresponding to a single baby universe emission, with a tunneling probability proportional to \( t^{2} e^{-2S(E)} \), where \( t \) denotes the black hole age and \( S(E) \) its entropy at energy \( E \). The growth of this probability at late times raises the question of its behavior near \( t \sim e^{S} \). A natural possibility is that the full genus expansion, together with its non-perturbative completion, leads to saturation of the tunneling probability. Motivated by this idea, the present analysis employs a non-perturbative bulk inner product in place of the perturbative one and shows that, at late times, the probabilities of realizing firewall geometries and smooth geometries approach constant values.

Accurate reconstruction of atmospheric wind fields is essential for applications such as weather forecasting, hazard prediction, and wind energy assessment, yet conventional instruments leave spatio-temporal gaps within the lower atmospheric boundary layer. Unmanned aircraft systems (UAS) provide flexible in situ measurements, but individual platforms sample wind only along their flight trajectories, limiting full wind-field recovery. This study presents a framework for reconstructing four-dimensional atmospheric wind fields using measurements obtained from a coordinated UAS swarm. A synthetic turbulence environment and high-fidelity multirotor simulation are used to generate training and evaluation data. Local wind components are estimated from UAS dynamics using a bidirectional long short-term memory network (Bi-LSTM) and assimilated into a physics-informed neural network (PINN) to reconstruct a continuous wind field in space and time. For local wind estimation, the bidirectional LSTM achieves root-mean-square errors (RMSE) of 0.064 and 0.062 m/s for the north and east components in low-wind conditions, increasing to 0.122 to 0.129 m/s under moderate winds and 0.271 to 0.273 m/s in high-wind conditions, while the vertical component exhibits higher error, with RMSE values of 0.029 to 0.091 m/s. The physics-informed reconstruction recovers the dominant spatial and temporal structure of the wind field up to 1000 m altitude while preserving mean flow direction and vertical shear. Under moderate wind conditions, the reconstructed mean wind field achieves an overall RMSE between 0.118 and 0.154 m/s across evaluated UAS configurations, with the lowest error obtained using a five-UAS swarm. These results demonstrate that coordinated UAS measurements enable accurate and scalable four-dimensional wind-field reconstruction without dedicated wind sensors or fixed infrastructure.

Corrections to vacuum black hole solutions of general relativity (GR) are considered in an effective field theory (EFT) framework, perturbatively in EFT coefficients, focusing on the Schwarzschild solution of GR. We find dominant corrections to the Schwarzschild metric in all orders in the derivative expansion far away from the horizon. These corrections can be summed up in a closed form through EFT coefficients up to all orders in derivatives and to the second order in curvature. It occurs that such a summation is convergent only for localizable theories, making a direct connection between the graviton scattering amplitudes properties and the applicability of a perturbative treatment of an EFT of gravity. We further apply our results to logarithmic form-factors which appear in the 1-loop effective action for GR in four dimensions. We find out that the corresponding corrections to the Schwarzschild metric are stronger than those from the tree-level EFT operators. The developed framework can be extended to account for the corrections to the other BH solutions in GR, such as the Kerr metric.

Understanding urban mobility requires models that capture how people interact with and navigate the built environment. We present a scalable, generalizable agent-based framework in which daily schedules emerge from the interplay between mandatory (e.g., work, school) and flexible (e.g., errands, food, leisure) activities, driven by evolving individual needs. The results of our model are validated against empirical patterns from the 2017 U.S. National Household Travel Survey, including activity distributions, origin-destination flows, and trip-chain length distributions. We introduce a normalized similarity metric to quantify agreement between simulated and empirical patterns. Most cities achieve scores above 0.80, demonstrating strong alignment without the need for city-specific calibration. The model scales efficiently to over 20 million agents, enabling full-population simulations of large metropolitan areas. This combination of universality and scalability enables scenario analysis for infrastructure stress testing, disaster recovery, innovation diffusion, and disease spread in urban systems.

In high-energy physics, estimating anti-neutron parameters (position and momentum) using the electromagnetic calorimeter (EMC) is crucial but challenging. To conquer this challenge, we propose Vision Calorimeter (ViC), a framework that migrates visual object detectors to analyze particle images. The motivation lies in introducing a physics-inspired heat-conduction operator (HCO) into the detector's backbone and head to handle the discrete and sparse patterns of these images. Implemented via the Discrete Cosine Transform, HCO extracts frequency-domain features, bridging the distribution gap between natural and particle images. Experiments demonstrate that ViC significantly outperforms conventional methods, reducing the incident position prediction error by 46.16% (from 17.31° to 9.32°) and providing the first baseline result with an incident momentum regression error of 21.48%. This study underscores ViC's great potential as a reliable particle detector for high-energy physics. Code is available at https://github.com/yuhongtian17/ViC.

Interacting spin systems in solids underpin a wide range of quantum technologies, from quantum sensors and single-photon sources to spin-defect-based quantum registers and processors. We develop a quantum-computer-aided framework for simulating such devices using a general electron spin resonance Hamiltonian incorporating zero-field splitting, the Zeeman effect, hyperfine interactions, dipole-dipole spin-spin terms, and electron-phonon decoherence. Within this model, we combine Gray-encoded qudit-to-qubit mappings, qubit-wise commuting aggregation, and a multi-reference selected quantum Krylov fast-forwarding (sQKFF) hybrid algorithm to access long-time dynamics while remaining compatible with NISQ and early fault-tolerant hardware constraints. Numerical simulations demonstrate the computation of autocorrelation functions up to $\sim100$ ns, together with microwave absorption spectra and the $\ell_1$-norm of coherence, achieving 18-30$\%$ reductions in gate counts and circuit depth for Trotterized time-evolution circuits compared to unoptimized implementations. Using the nitrogen vacancy center in diamond as a testbed, we benchmark the framework against classical simulations and identify the reference-state selection in sQKFF as the primary factor governing accuracy at fixed hardware cost. This methodology provides a flexible blueprint for using quantum computers to design, compare, and optimize solid-state spin-qubit technologies under experimentally realistic conditions.

Partial differential equation (PDE) simulations are fundamental to engineering and physics but are often computationally prohibitive for real-time applications. While generative AI offers a promising avenue for surrogate modeling, standard video generation architectures lack the specific control and data compatibility required for physical simulations. This paper introduces a geometry aware world model architecture, derived from a video generation architecture (LongVideoGAN), designed to learn transient physics. We introduce two key architecture elements: (1) a twofold conditioning mechanism incorporating global physical parameters and local geometric masks, and (2) an architectural adaptation to support arbitrary channel dimensions, moving beyond standard RGB constraints. We evaluate this approach on a 2D transient computational fluid dynamics (CFD) problem involving convective heat transfer from buoyancy-driven flow coupled to a heat flow in a solid structure. We demonstrate that the conditioned model successfully reproduces complex temporal dynamics and spatial correlations of the training data. Furthermore, we assess the model's generalization capabilities on unseen geometric configurations, highlighting both its potential for controlled simulation synthesis and current limitations in spatial precision for out-of-distribution samples.

The sound speed of dark energy encodes fundamental information about the microphysics underlying cosmic acceleration, yet remains essentially unconstrained by existing observations. We demonstrate that a lunar-based laser interferometer, such as the proposed Laser Interferometer Lunar Antenna (LILA), can directly probe the sound speed of dark energy by measuring the real-time evolution of horizon-scale gravitational potentials. Operating in the ultra-low-frequency gravitational band inaccessible from Earth, LILA is sensitive to scalar metric perturbations sourced by dark energy dynamics. Using both fluid and effective field theory descriptions, we develop a complete framework linking dark energy sound speed to observable strain signatures. We construct a likelihood pipeline and Fisher forecasts, showing that LILA can either detect clustering dark energy or exclude broad classes of models with unprecedented sensitivity. This establishes lunar interferometry as a novel and powerful probe of the physics driving cosmic acceleration.

We investigate a single magnetic impurity in the presence of strong spin-orbit coupling and single-ion anisotropy. We show that at sufficiently strong coupling there exists a finite temperature window, before the moment is completely screened, where the magnetic anisotropy of the system flips: the hard-axis becomes the easy-axis or vice versa. We derive this rigorously for a single impurity using numerical renormalization group calculations as well as Nozieres' strong-coupling limit and discuss its relevance to heavy-fermion compounds which order magnetically along the hard-direction. We show that the coexistence of Curie-like response and Kondo fluctuations is stabilized along the initially hard direction leading to the anisotropy switch.

Disorder is ubiquitous in quantum materials, and its interplay with topology can generate phases absent in the clean limit. Using the Haldane model as a minimal setting, we show that disorder not only shifts topological boundaries but also stabilizes a topological Anderson insulator (TAI) between trivial and Chern insulating regimes. Employing the local Chern marker as a real-space topological probe, we map the full phase diagram and demonstrate that the TAI forms a finite domain bounded by trivial and Anderson insulators. Multifractal analysis of low-energy eigenstates at the boundary reveals universal critical spectra, independent of whether disorder generates or destroys topology. These results place topology, localization, and criticality within a unified framework and provide clear benchmarks for real-space diagnostics of disordered topological phases.

Open quantum systems interact with their environment, leading to nonunitary dynamics. We investigate the thermodynamics of linear Open Quantum Walks (OQWs), a class of quantum walks whose dynamics is entirely driven by the environment. We define an equilibrium temperature, identify a population inversion near a finite critical value of a control parameter, analyze the thermalization process, and develop the statistical mechanics needed to describe the thermodynamical properties of linear OQWs. We also study nonequilibrium thermodynamics by analyzing the time evolution of entropy, energy, and temperature, while providing analytical tools to understand the system's evolution as it converges to the thermalized state. We examine the validity of the second and third laws of thermodynamics in this setting. Finally, we employ these developments to shed light on dissipative quantum computation within the OQW framework.

We present a study of extended Ly$α$ emission around four quasars hosted by dusty starbursts, which are composite systems thought to represent a transitional stage in quasar evolution. To extract faint CGM emission in the presence of bright point sources, we introduce {\it CubeCarve}, a dual-channel deconvolution algorithm that separates unresolved quasar emission from spatially extended structure. This approach enables reliable recovery of \Lya\ emission projected onto the quasar position without introducing subtraction artifacts. Using {\it CubeCarve}, we find that the \Lya\ surface brightness profiles of these systems are, on average, fainter and shallower than those of quasars of similar bolometric luminosities. We also find that the total integrated \Lya\ luminosities of the nebulae are lower in systems whose host galaxies exhibit brighter far-infrared emission. These results suggest that the CGM conditions in composite systems differ from those in the broader quasar population. Our study highlights both the physical diversity of quasar CGM environments and the effectiveness of {\it CubeCarve} for recovering diffuse emission in modern IFU datasets.

We investigate the effect of translational and rotational inertia on motility-induced phase separation in underdamped active dumbbells and identify the emergence of four distinct kinetic temperatures across the coexisting phases-unlike in overdamped systems. We find that the dilute, gas-like phase consistently exhibits a higher translational kinetic temperature than the dense, liquid-like phase, with this difference amplified by increasing the rotational inertia. Rotational kinetic temperatures display a similar trend, with the dense phase remaining colder than the dilute phase; however, in this case the temperature difference grows with translational inertia and activity, while becoming practically independent of rotational inertia. This counterintuitive behavior arises from the interplay of activity-driven collisions with both translational and rotational inertia in the coexisting phases. Our results highlight the critical role of translational and rotational inertia in shaping the kinetic temperature landscape of motility-induced phase separation and offer new insights into the nonequilibrium thermodynamics of active matter.

Positron Emission Tomography (PET) is a powerful medical imaging technique, but the design and evaluation of new PET scanner technologies present significant challenges. The process is typically divided into three major stages: 1. detector design and simulation, 2. image reconstruction, and 3. image interpretation. Each of these stages requires significant expertise, making it difficult for individuals or small teams to manage all three at once. AIRPET (AI-driven Revolution in Positron Emission Tomography) is a web-based platform designed to address this challenge by integrating all phases of PET design into a single, accessible, and AI-assisted workflow. AIRPET provides an interface to large language models (LLMs) for assisted geometry creation and an interface for basic PET image reconstruction with the potential for further expansion. Here we introduce AIRPET and outline its current functionality and proposed additions.

We propose non-supersymmetric analogues of 6d N=2 Type II/heterotic dualities via a quotient of a K3 surface: an Enriques surface. We start from Type~II strings on a K3 surface and construct orbifold theories using an involution of K3. We extract the massless and tachyonic spectra and identify the moduli spaces locally. We further reinterpret the constructions as Type 0A/0B strings compactified on an Enriques surface, and argue that the theories are dual to recently constructed non-supersymmetric heterotic asymmetric orbifolds.

Interaction quenches in strongly correlated electron systems provide a powerful route to probe nonequilibrium many-body dynamics. For the Hubbard model, nonequilibrium dynamical mean-field theory has revealed coherent post-quench oscillations, dynamical crossovers, and long-lived transient regimes. However, these studies are largely restricted to spatially homogeneous dynamics and therefore neglect the role of spatial structure formation during ultrafast evolution. Here we investigate interaction quenches in the half-filled Hubbard model using a real-space time-dependent Gutzwiller framework. We show that homogeneous nonequilibrium dynamics is generically unstable: even arbitrarily weak spatial fluctuations grow dynamically and drive the system toward intrinsically inhomogeneous states. Depending on the interaction strength, the post-quench evolution exhibits spatial differentiation, nucleation, and slow coarsening of Mott-like domains. Our results establish spatial self-organization as a generic feature of far-from-equilibrium correlated matter and reveal a fundamental limitation of spatially homogeneous nonequilibrium theories.

In a one-dimensional ferromagnet-superconductor nanowire, magnetism can suppress superconductivity except where the Zeeman field is suppressed, for example domain wall superconductivity (DWS) near magnetic domain walls or multi-domain-averaged superconductivity (MDAS) in multi-domain states where the net magnetization over the coherence length averages to nearly zero. Here we study full-shell InAs/EuS/Al nanowires using scanning SQUID magnetometry and transport, and find superconductivity in the Al shell only when the EuS is in a multi-domain state, consistent with both DWS and MDAS, and absent in the saturated single-domain state. Scanning SQUID magnetometry further shows that the EuS magnetic texture is position dependent and reconfigurable by small changes in external magnetic field, including moving a well-defined domain wall at $\approx$5.5 $μ$m/mT with sub-mT fields, implying that any associated localized superconducting region would likewise be movable. Such magnetic texture controlled superconductivity along a nanowire may be useful for topological qubits, Andreev spin qubits, superconducting logic, and memory devices.

Confinement strongly influences electrochemical systems, where structural control has enabled advances in nanofluidics, sensing, and energy storage. In electric double-layer capacitors (EDLCs), or supercapacitors, energy density is governed by the accessible surface area of porous electrodes. Continuum models, built on first-principles transport equations, have provided critical insight into electrolyte dynamics under confinement but have largely focused on pores with straight walls. In such geometries, a fundamental trade-off emerges: wider pores charge faster but store less energy, while narrower pores store more charge but charge slowly. Here, we apply perturbation analysis to the Poisson-Nernst-Planck (PNP) equations for a single pore of gradually varying radius, focusing on the small potential and slender aspect ratio regime. Our analysis reveals that sloped pore walls induce an additional ionic flux, enabling simultaneous acceleration of charging and enhancement of charge storage. The theoretical predictions closely agree with direct numerical simulations while reducing computational cost by 5-6 orders of magnitude. We further propose a modified effective circuit representation that captures geometric variation along the pore and demonstrate how the framework can be integrated into pore-network models. This work establishes a scalable approach to link pore geometry with double-layer dynamics and offers new design principles for optimizing supercapacitor performance.

The origin of the Hubble tension remains one of the central open problems in modern cosmology, with competing explanations invoking either early-Universe physics, late-time modifications of cosmic expansion, or unresolved observational systematics. In this Letter we propose a new, purely geometric null test of the late-time expansion history that is exactly independent of the Hubble constant. By combining strong-lensing time-delay distances with gravitational-wave standard-siren luminosity distances, we construct a dimensionless ratio that depends only on the redshift dependence of the expansion rate and can be both predicted from early-Universe data and measured directly at late times, without relying on the cosmic distance ladder or the sound horizon. We show that the comparison between the early- and late-time determinations of this ratio provides a transparent consistency test of the standard cosmological expansion. When combined with an independent standard-siren measurement of $H_{0}$, this framework allows one to unambiguously distinguish between early- and late-time origins of the Hubble tension. With the forthcoming detection of lensed gravitational-wave standard sirens, the proposed test provides a timely and robust framework for probing this long-standing cosmological puzzle.

We present a framework for generative machine learning that leverages the holographic principle of quantum gravity, or to be more precise its manifestation as the anti-de Sitter/conformal field theory (AdS/CFT) correspondence, with techniques for deep learning and transport theory. Our proposal is to represent the flow of data from a base distribution to some learned distribution using the bulk-to-boundary mapping of scalar fields in AdS. In the language of machine learning, we are representing and augmenting the flow-matching algorithm with AdS physics. Using a checkerboard toy dataset and MNIST, we find that our model achieves faster and higher quality convergence than comparable physics-free flow-matching models. Our method provides a physically interpretable version of flow matching. More broadly, it establishes the utility of AdS physics and geometry in the development of novel paradigms in generative modeling.

Plexcitonic assemblies are hybrid materials composed of a plasmonic nanoparticle and molecular or semiconducting emitters whose electronic transitions are strongly coupled to the plasmonic mode. This coupling hybridizes the system modes into upper and lower polariton branches. The strength of the interaction depends on the number of emitters and on their orientation and spatial arrangement relative to the metallic surface. These structural factors have profound consequences for the ensuing photoexcited dynamics. Despite the extensive spectroscopic work on plexcitonic systems, direct understanding of the molecular geometry at the metal interface remains limited. In this work, we present a comprehensive structural characterization of one of the most widely studied plexcitons formed by the cyanine dye 5,5',6,6'-tetrachloro-1,1'-diethyl-3,3'-di(4-sulfobutyl)-benzimidazolocarbocyanine (TDBC) and silver nanoprisms using a combination of NMR, THz-Raman spectroscopy, and DFT calculations. By comparing the signals from the monomeric and aggregated forms of TDBC with that of the plexciton, we identify shared spectral fingerprints that reveal how molecular packing is modified when the aggregate adsorbs on the silver surface. We observe Raman modes specific to plexciton systems, and identify NOESY cross-peaks in the aliphatic region, that along with THz-Raman modes in the 10-400 cm$^{-1}$ region are sensitive indicators of aggregation geometry and adsorption. We find that isolated TDBC monomers adopt an asymmetric conformation in which both sulfobutyl chains lie on the same side of the chromophore, while J-aggregates adopt a symmetric up-down alternation of the chains from molecule to molecule. This work establishes the molecular geometry of a prototypical TDBC-silver plexciton, providing a structural benchmark for understanding geometry-dependent photophysics in hybrid exciton-plasmon systems.

In the aftermath of large-scale disasters, the scarcity of resources and the paralysis of infrastructure raise severe challenges to effective post-disaster recovery. Efficient coordination between shelters and victims plays a crucial role in building community resilience, yet the evolution of two-layer behavioral feedback between these two groups through network coupling remains insufficiently understood. Here, this study develops a two-layer network to capture the cross-layer coupling between shelters and victims. The upper layer uses a post-disaster emergency resource redistribution model within the framework of the public goods game, while the lower layer adopts a cooperative evolutionary game to describe internal victim interactions. Monte Carlo simulations on scale-free networks reveal threshold effects of incentives: moderate public goods enhancement and subsidies promote cooperation, whereas excessive incentives induce free-riding. In contrast, credible and well-executed punishment effectively suppresses defection. Targeted punishment of highly connected shelters significantly enhances cooperation under resource constraints. A comparative analysis using a network generated from the actual coordinates of Beijing shelters confirms the model's generality and practical applicability. The findings highlight the importance of calibrated incentives, enforceable sanctions, and structural targeting in fostering robust cooperation across organizational and individual levels in post-disaster environments.

Nonlinear magnetic response driven by time-periodic magnetic fields offers a distinct route to probe spin-resolved quantum geometry beyond conventional electric-field-driven nonlinear effects. While linear magnetic responses depend on the Zeeman quantum geometric tensor, the influence of generalized spin-rotation quantum geometries on nonlinear responses has not been established. Here, we develop a microscopic quantum-kinetic framework to elucidate how the Zeeman and spin-rotation quantum geometric tensors govern nonlinear gyrotropic magnetic transport in two-dimensional systems. We derive second-order gyrotropic magnetic currents and reveal a distinct geometric separation: the off-diagonal sector is controlled by the Zeeman symplectic and metric connections, whereas the diagonal sector is dictated by the spin-rotation quantum metric and Berry curvature. This identifies the spin-rotation quantum geometric tensor as a fundamental geometric quantity unique to the nonlinear regime. Applying our theory to massless Dirac fermions, hexagonally warped topological insulator surface states, tilted massive Dirac fermions, and parity-time symmetric CuMnAs, we demonstrate how specific symmetries selectively activate conduction and displacement channels. Our findings link spin-resolved quantum geometry to nonlinear magnetic transport, offering design principles for engineering tailored nonlinear magnetic responses in optoelectronic and spintronic devices.