Parrondo's paradox, where the alternation of two losing strategies can produce a winning outcome, has recently been demonstrated in continuous-time quantum walks (CTQWs) through periodic defect modulation. We extend this phenomenon to aperiodic protocols. We show that deterministic, non-repetitive defect switching can enhance quantum spreading in CTQWs compared to the defect-free case. Furthermore, we establish that the degree of this enhancement is strongly influenced by the autocorrelation and persistence characteristics of the applied aperiodic sequence. Our findings indicate that aperiodic defect modulation reliably maintains Parrondo's effect and provides new ways to control wavepacket properties in CTQWs.

This work investigates the expressive power of quantum circuits in approximating high-dimensional, real-valued functions. We focus on countably-parametric holomorphic maps $u:U\to \mathbb{R}$, where the parameter domain is $U=[-1,1]^{\mathbb{N}}$. We establish dimension-independent quantum circuit approximation rates via the best $n$-term truncations of generalized polynomial chaos (gPC) expansions of these parametric maps, demonstrating that these rates depend solely on the summability exponent of the gPC expansion coefficients. The key to our findings is based on the fact that so-called ``$(\boldsymbol{b},ε)$-holomorphic'' functions, where $\boldsymbol{b}\in (0,1]^\mathbb N \cap \ell^p(\mathbb N)$ for some $p\in(0,1)$, permit structured and sparse gPC expansions. Then, $n$-term truncated gPC expansions are known to admit approximation rates of order $n^{-1/p + 1/2}$ in the $L^2$ norm and of order $n^{-1/p + 1}$ in the $L^\infty$ norm. We show the existence of parameterized quantum circuit (PQC) encodings of these $n$-term truncated gPC expansions, and bound PQC depth and width via (i) tensorization of univariate PQCs that encode Chebyšev-polynomials in $[-1,1]$ and (ii) linear combination of unitaries (LCU) to build PQC emulations of $n$-term truncated gPC expansions. The results provide a rigorous mathematical foundation for the use of quantum algorithms in high-dimensional function approximation. As countably-parametric holomorphic maps naturally arise in parametric PDE models and uncertainty quantification (UQ), our results have implications for quantum-enhanced algorithms for a wide range of maps in applications.

Although used extensively in everyday life, time is one of the least understood quantities in physics, especially on the level of quantum mechanics. Here we use an experimental method based on spin- and angle-resolved photoemission spectroscopy from spin-degenerate dispersive states to determine the Eisenbud-Wigner-Smith (EWS) time delay of photoemission. This time scale of the quantum transition is measured for materials with different dimensionality and correlation strength. A direct link between the dimensionality, or rather the symmetry of the system, and the attosecond photoionisation time scale is found. The quasi 2-dimensional transition metal dichalcogenides 1T-TiSe$_2$ and 1T-TiTe$_2$ show time scales around 150 as, whereas in quasi 1-dimensional CuTe the photoionisation takes more than 200 as. This is in stark contrast with the 26 as found for 3-dimensional pure Cu. These results provide new insights into the role of symmetry in quantum time scales and may provide a route to understanding the role of time in quantum mechanics.

The method of Q-cumulants has been shown as a powerful tool to study the fine details of the azimuthal anisotropies in high-energy nucleus-nucleus collisions. A new method for the fast calculation of arbitrary order Q-cumulant $v_{n}\{2k\}$ values, based on the partition of a non-negative integer l $\le$ m for calculation of the 2m-particle azimuthal correlations is presented in this paper. Unlike the standard Q-cumulants method in which the calculation of high-order multi-particle calculations is impractical, the newly proposed method enables easy calculation. The validity of the method is proven via a toy model that uses the elliptic power distribution to simulate anisotropic emission of particles. The method enables the study of fine details of the $v_{2}$ distribution, such as higher-order central moments of the $v_{2}$ distribution, as well as the hydrodynamic behavior of the Quark-Gluon Plasma.

Accurate measurements of fundamental cosmological parameters, especially the Hubble constant (H_0) and present-day matter density (Ω_{m0}), are crucial for constraining dark energy (DE) evolution. We analyze the sensitivities of cosmological observables (H(z), D_L(z), E_{G}) to Ω_{m0}, w_0, and w_an under different parametrizations. Our results show observables are far more sensitive to Ω_{m0} than to DE equation of state parameters (e.g., at z \sim 0.5, H(z)'s Ω_{m0} sensitivity is \sim 0.7 vs. w_a's \sim 0.04). This hierarchy mandates high-precision Ω_{m0} measurements to accurately constrain time-varying DE. We also find DE parameter sensitivity highly depends on parametrization; the standard CPL form shows low sensitivity to w_a, but ω(z) = w_0 + w_a \ln(1+z) significantly enhances it. Our analysis of DESI DR1/DR2 data confirms these theoretical limits: standalone DESI data primarily provides only upper limits for w_a, underscoring insufficient constraining power for a definitive time-varying DE detection. While combined datasets offer tighter constraints, interpretation requires caution due to parametrization influence. We further confirm this point using simulated Supernovae MCMC data. In conclusion, improving Ω_{m0} precision and adopting optimized parametrizations are imperative for future surveys like DESI to fully probe dark energy's nature.

Gaussian processes are a powerful class of non-linear models, but have limited applicability for larger datasets due to their high computational complexity. In such cases, approximate methods are required, for example, the recently developed class of Hilbert space Gaussian processes. They have been shown to significantly reduce computation time while retaining most of the favorable properties of exact Gaussian processes. However, Hilbert space approximations have so far only been developed for uni-dimensional outputs and manifest (known) inputs. Thus, we generalize Hilbert space methods to multi-output and latent input settings. Through extensive simulations, we show that the developed approximate Gaussian processes are indeed not only faster, but also provide similar or even better uncertainty calibration and accuracy of latent variable estimates compared to exact Gaussian processes. While not necessarily faster than alternative Gaussian process approximations, our new models provide better calibration and estimation accuracy, thus striking an excellent balance between trustworthiness and speed. We additionally illustrate our methods on a real-world case study from single cell biology.

Environmental sensing is an important research topic in the integrated sensing and communication (ISAC) system. Current works often focus on static environments, such as buildings and terrains. However, dynamic factors like rainfall can cause serious interference to wireless signals. In this paper, we propose a system called RainfalLTE that utilizes the downlink signal of LTE base stations for device-independent rain sensing. In articular, it is fully compatible with current communication modes and does not require any additional hardware. We evaluate it with LTE data and rainfall information provided by a weather radar in Badaling Town, Beijing The results show that for 10 classes of rainfall, RainfalLTE achieves over 97% identification accuracy. Our case study shows that the assistance of rainfall information can bring more than 40% energy saving, which provides new opportunities for the design and optimization of ISAC systems.

The paper deals with analysis and design of sliding mode control systems modeled by finite-dimensional integro-differential equations. Filippov method and equivalent control approach are extended to a class of nonlinear discontinuous integro-differential equations and to a class of control systems modeled by infinite-dimensional differential equations in Banach spaces. Sliding mode control algorithms are designed for distributed input delay systems and for a heat control system.

Noether's theorem, which connects continuous symmetries to exact conservation laws, remains one of the most fundamental principles in physics and dynamical systems. In this work, we draw a conceptual parallel between two paradigms: the emergence of exact invariants from continuous symmetries, and the appearance of approximate invariants from discrete symmetries associated with reversibility in symplectic maps. We demonstrate that by constructing approximating functions that preserve these discrete symmetries order by order, one can systematically uncover hidden structures, closely echoing Noether's framework. The resulting functions serve not only as diagnostic tools but also as compact representations of near-integrable behavior. The second article applies the method to global dynamics, with a focus on large-amplitude motion and chaotic systems. We demonstrate that the approximate invariants, once averaged, accurately capture the structure of resonances and the boundaries of stability regions. We also explore the recovery of exact invariants in integrable cases, showing that the method reproduces the correct behavior when such structure is present. A single unified function, derived from the map coefficients, yields phase portraits, rotation numbers, and tune footprints that closely match numerical tracking across wide parameter ranges. Comparisons with the Square Matrix method reveal that while both approaches satisfy local constraints, our technique provides greater accuracy and robustness in resonant and strongly nonlinear regimes. These results highlight the method's practical power and broad relevance, offering a compact, analytic framework for organizing nonlinear dynamics in symplectic maps with direct applications to beam physics and beyond.

Noether's theorem, which connects continuous symmetries to exact conservation laws, remains one of the most fundamental principles in physics and dynamical systems. In this work, we draw a conceptual parallel between two paradigms: the emergence of exact invariants from continuous symmetries, and the appearance of approximate invariants from discrete symmetries associated with reversibility in symplectic maps. We demonstrate that by constructing approximating functions that preserve these discrete symmetries order by order, one can systematically uncover hidden structures, closely echoing Noether's framework. The resulting functions serve not only as diagnostic tools but also as compact representations of near-integrable behavior. The first article establishes the formal foundations of the method. Using the symmetric form of the map as a flexible test case, we benchmark the perturbative construction against established techniques, including the Lie algebra method for twist coefficients. To resolve the inherent ambiguity in the perturbation series, we introduce an averaging procedure that naturally leads to a resonant theory -- capable of treating rational rotation numbers and small-denominator divergences. This enables an accurate and structured description of low-order resonances, including singular and non-singular features in the quadratic and cubic Hénon maps. The approach is systematic, requiring only linear algebra and integrals of elementary functions, yet it yields results in striking agreement with both theory and numerical experiment. We conclude by outlining extensions to more general maps and discussing implications for stability estimates in practical systems such as particle accelerators.

In this note we observe that the categorical structure of a flop occurs for some well-known non-commutative resolutions of a nodal curve. We describe the flop-flop spherical twists, and give a geometric interpretation in terms of Landau--Ginzburg models. The resolutions are all weakly crepant but not strongly crepant, and we formulate an intermediate condition that distinguishes the smaller ones.

Thermal radiative transfer (TRT) presents significant computational challenges due to the stiff, nonlinear coupling between radiation and material energy, particularly in multigroup, high-fidelity transport models. In this work, we develop an efficient nonlinear acceleration framework for TRT based on the Second Moment (SM) method. Our approach couples high-order discrete ordinates transport to a gray, diffusion-based low-order system that implicitly resolves the stiff absorption-emission physics, isolating this stiffness from the high-order system. The resulting algorithm alternates between transport sweeps and a Newton-type solution of the coupled low-order and material energy balance equations, utilizing nonlinear temperature elimination for improved robustness. Crucially, our approach is the first moment-based TRT algorithm with a symmetric and positive definite (SPD) low-order system enabling scalable linear solves via algebraic multigrid-preconditioned conjugate gradient. We investigate both consistent and independent low-order discretizations within a discontinuous Galerkin framework and assess their performance on one and two-dimensional gray and multigroup benchmark problems.

Rapid uniformly-rotating neutron stars are expected to be formed for instance in the collapse of some massive stars, the accretion of compact object binaries, and double neutron star mergers. The huge amount of the rotational energy has been widely believed to be the source of some cosmic gamma-ray bursts and superluminous supernovae. Benefited from the constraints on the equation of state of the neutron star matter set by the latest multi-messenger data, the chiral effective field theory and perturbative quantum chromodynamics, here we present the maximum gravitational mass as well as the rotational energy for a neutron star at a given spin period. Our nonparametric equation of state analysis reveals that the critical Keplerian configurations ($Ω_{\rm kep}^{\rm crit}=1.02_{-0.07}^{+0.06}\times 10^{4}~{\rm rad/s}$) can sustain maximum gravitational masses of $M_{\rm kep}^{\rm crit}=2.73 \pm 0.09 M_\odot$ with corresponding rotational energy reaching $E_{\rm rot,kep}^{\rm crit}=2.36^{+0.24}_{-0.22}\times 10^{53}$ erg. However, the maximum rotational energy that can be feasibly extracted from a neutron star is limited to $1.40^{+0.14}_{-0.13}\times 10^{53}$ erg, which holds for a baryon mass of $2.66^{+0.10}_{-0.09}M_\odot$. All these parameters, obtained via the nonparametric reconstruction of the equation of state, are at the $68.3\%$ confidence level and the adoption of a quarkonic model yields rather similar results. These findings are found to have already set some intriguing constraints on the millisecond magnetar interpretation of some exciting data.

In this study, we consider a topological derivative-based imaging technique for the fast identification of short, linear perfectly conducting cracks completely embedded in a two-dimensional homogeneous domain with smooth boundary. Unlike conventional approaches, we assume that the background permittivity and permeability are unknown due to their dependence on frequency and temperature, and we propose a normalized imaging function to localize cracks. Despite inaccuracies in background parameters, application of the proposed imaging function enables to recognize the existence of crack but it is still impossible to identify accurate crack locations. Furthermore, the shift in crack localization of imaging results is significantly influenced by the applied background parameters. In order to theoretically explain this phenomenon, we show that the imaging function can be expressed in terms of the zero-order Bessel function of the first kind, the crack lengths, and the applied inaccurate background wavenumber corresponding to the applied inaccurate background permittivity and permeability. Various numerical simulations results with synthetic data polluted by random noise validate the theoretical results.

An elimination tree of a connected graph $G$ is a rooted tree on the vertices of $G$ obtained by choosing a root $v$ and recursing on the connected components of $G-v$ to obtain the subtrees of $v$. The graph associahedron of $G$ is a polytope whose vertices correspond to elimination trees of $G$ and whose edges correspond to tree rotations, a natural operation between elimination trees. These objects generalize associahedra, which correspond to the case where $G$ is a path. Ito et al. [ICALP 2023] recently proved that the problem of computing distances on graph associahedra is NP-hard. In this paper we prove that the problem, for a general graph $G$, is fixed-parameter tractable parameterized by the distance $k$. Prior to our work, only the case where $G$ is a path was known to be fixed-parameter tractable. To prove our result, we use a novel approach based on a marking scheme that restricts the search to a set of vertices whose size is bounded by a (large) function of $k$. On the negative side, we show that it is unlikely that FPT algorithms exist on a natural generalization of graph associahedra, namely hypergraphic polytopes, by proving that computing distances on them is W[2]-hard parameterized by the distance. We also prove that, on hypergraphic polytopes, the distance cannot be approximated in polynomial time within a factor $c \cdot \log(|V|+|\mathcal{E}|)$ for some constant $c > 0$ unless P = NP, where $H=(V, \mathcal{E})$ is the input hypergraph. This result strengthens the hardness result of Cardinal and Steiner [Combin. Theory 2025], who proved that the problem cannot be approximated within a factor $(1 + \varepsilon)$ for some absolute constant $\varepsilon > 0$ unless P = NP. Finally, we rule out the existence of polynomial kernels parameterized by the number of vertices of the input hypergraph, a parameter for which the problem is easily seen to be FPT.

Many businesses depend on legacy systems, which often use outdated technology that complicates maintenance and updates. Therefore, software modernization is essential, particularly data migration between different database schemas. Established methodologies, like model transformation and ETL tools, facilitate this migration; they require deep knowledge of database languages and both the source and target schemas. This necessity renders data migration an error-prone and cognitively demanding task. Our objective is to alleviate developers' workloads during schema evolution through our DAMI-Framework. This framework incorporates a domain-specific language (DSL) and a parser to facilitate data migration between database schemas. DAMI-DSL simplifies schema mapping while the parser automates SQL script generation. We assess developer experience in data migration by conducting an empirical evaluation with 21 developers to assess their experiences using our DSL versus traditional SQL. The study allows us to measure their perceptions of the DSL properties and user experience. The participants praised DAMI-DSL for its readability and ease of use. The findings indicate that our DSL reduces data migration efforts compared to SQL scripts.

Any micropolar fluid containing magnetic particles, such as blood or ferrofluids, subjected to an external magnetic field experiences a magnetic torque due to the misalignment between particle magnetization and the magnetic field. This effect, known as micromagnetorotation (MMR), remains underexplored in blood flows where erythrocyte magnetization is often neglected. To investigate this, two transient OpenFOAM solvers were developed: epotMicropolarFoam, for incompressible, laminar magnetohydrodynamic (MHD) micropolar flows, and epotMMRFoam, which extends it by incorporating MMR. Both solvers use the PISO algorithm for pressure-velocity coupling and adopt the low magnetic Reynolds number approximation. Micropolar effects are modeled by including the microrotation-vorticity difference in the momentum equation and solving the internal angular momentum equation. In epotMMRFoam, the MMR term is added to this equation, and a constitutive equation for magnetization is also solved. Validation against analytical MHD micropolar Poiseuille flow showed excellent accuracy (error less than 2 percent). Including MMR led to notable reductions in velocity (up to 40 percent) and microrotation (up to 99.9 percent), especially under strong magnetic fields and high hematocrit. Without MMR, magnetic effects were minimal due to the low electrical conductivity of blood. Simulations of 3D MHD artery and 2D MHD aneurysm flows confirmed these findings. In aneurysm geometries, MMR suppressed vortex cores, indicating strong stabilizing and shear-dampening effects. These solvers show high potential for biomedical applications such as magnetic hyperthermia and targeted drug delivery.

To understand the emergence of macroscopic irreversibility from microscopic reversible dynamics, the idea of coarse-graining plays a fundamental role. In this work, we develop a unified inferential framework for macroscopic states, that is, coarse descriptions of microscopic quantum systems that can be inferred from macroscopic measurements. Building on quantum statistical sufficiency and Bayesian retrodiction, we characterize macroscopic states through equivalent abstract (algebraic) and explicit (constructive) formulations. Central to our approach is the notion of observational deficit, which quantifies the degree of irretrodictability of a state relative to a prior and a measurement. This leads to a general definition of macroscopic entropy as an inferentially grounded measure of asymmetry under Bayesian inversion. We formalize this structure in terms of inferential reference frames, defined by the pair consisting of a prior and a measurement, which encapsulate the observer's informational perspective. We then formulate a resource theory of microscopicity, treating macroscopic states as free states and introducing a hierarchy of microscopicity-non-generating operations. This theory unifies and extends existing resource theories of coherence, athermality, and asymmetry. Finally, we apply the framework to study quantum correlations under observational constraints, introducing the notion of observational discord and deriving necessary and sufficient conditions for their vanishing in terms of information recoverability. This work is dedicated to Professor Ryszard Horodecki on the occasion of his 80th birthday, in deep admiration and gratitude for his pioneering contributions to quantum information theory.

We construct Symmetry Topological Field Theories (SymTFTs) for continuous subsystem symmetries, which are inherently non-Lorentz-invariant. Our framework produces dual bulk descriptions -- gapped foliated and exotic SymTFTs -- that generate gapless boundary theories with spontaneous subsystem symmetry breaking via interval compactification. In analogy with the sandwich construction of SymTFT, we call this Mille-feuille. This is done by specifying gapped and symmetry-breaking boundary conditions. In this way we obtain the foliated dual realizations of various models, including the XY plaquette, XYZ cube, and $ϕ$, $\hatϕ$ theories. This also captures self-duality symmetries as condensation defects and provides a systematic method for generating free theories that non-linearly realize subsystem symmetries.

We are concerned with infinite Prandtl number Rayleigh--Bénard convection with Navier-slip boundary conditions. The goal of this work is to estimate the average upward heat flux measured by the nondimensional Nusselt number $Nu$ in terms of the Rayleigh number $Ra$, which is a nondimensional quantity measuring the imposed temperature gradient. We derive bounds on the Nusselt number that coincide for relatively small slip lengths with the optimal Nusselt number scaling for no-slip boundaries, $Nu\lesssim Ra^{1/3}$; for relatively large slip lengths, we recover scaling estimates for free-slip boundaries, $Nu\lesssim Ra^{5/12}$.

Spontaneous reporting system databases are key resources for post-marketing surveillance, providing real-world evidence (RWE) on the adverse events (AEs) of regulated drugs or other medical products. Various statistical methods have been proposed for AE signal detection in these databases, flagging drug-specific AEs with disproportionately high observed counts compared to expected counts under independence. However, signal detection remains challenging for rare AEs or newer drugs, which receive small observed and expected counts and thus suffer from reduced statistical power. Principled information sharing on signal strengths across drugs/AEs is crucial in such cases to enhance signal detection. However, existing methods typically ignore complex between-drug associations on AE signal strengths, limiting their ability to detect signals. We propose novel local-global mixture Dirichlet process (DP) prior-based nonparametric Bayesian models to capture these associations, enabling principled information sharing between drugs while balancing flexibility and shrinkage for each drug, thereby enhancing statistical power. We develop efficient Markov chain Monte Carlo algorithms for implementation and employ a false discovery rate (FDR)-controlled, false negative rate (FNR)-optimized hypothesis testing framework for AE signal detection. Extensive simulations demonstrate our methods' superior sensitivity -- often surpassing existing approaches by a twofold or greater margin -- while strictly controlling the FDR. An application to FDA FAERS data on statin drugs further highlights our methods' effectiveness in real-world AE signal detection. Software implementing our methods is provided as supplementary material.

Recent experiments demonstrate all-electric spinning of levitated nanodiamonds with embedded nitrogen-vacancy spins. Here, we argue that such gyroscopically stabilized spin rotors offer a promising platform for probing and exploiting quantum spin-rotation coupling of particles hosting a single spin degree of freedom. Specifically, we derive the effective Hamiltonian describing how an embedded spin affects the rotation of rapidly revolving quantum rotors due to the Einstein-de Haas and Barnett effects, which we use to devise experimental protocols for observing this coupling in state-of-the-art experiments. This will open the door for future exploitations of quantum spin rotors for superposition experiments with massive objects.

Software testing is crucial for ensuring software quality, yet developers' engagement with it varies widely. Identifying the technical, organizational and social factors that lead to differences in engagement is required to remove barriers and utilize enablers for testing. While much research emphasizes the usefulness of software testing approaches and technical solutions, less is known about why developers do (not) test. This study investigates the first-hand experience of developers with software testing. The study illuminates how developers' opinions about testing and their testing behavior changes. Through analysis of personal evolutions of practice, we explore when and why testing is used. Employing socio-technical grounded theory (STGT), we construct a theory by systematically analyzing data from 19 in-depth, semi-structured interviews with software developers. Allowing interviewees to reflect on how and why they approach software testing, we explore perspectives that are rooted in their contextual experiences. We develop eleven categories of circumstances that act as conditions for the application and adaptation of testing practices and introduce three concepts that we then use to present a theory of emerging testing strategies (ETS) that explains why developers do (not) use testing practices. This study reveals a new perspective on the connection between testing artifacts and collective reflection of practitioners, and it embraces. It has direct implications for practice %and contributes to the groundwork of socio-technical research which embraces testing as an experience in which human- and social aspects are entangled with organizational and technical circumstances.

In this paper, we present a completely rigorous formulation of Kohn-Sham density functional theory for spinless fermions living in one dimensional space. More precisely, we consider Schrödinger operators of the form $H_N(v,w) = -Δ+ \sum_{i\neq j}^N w(x_i,x_j) + \sum_{j=1}^N v(x_i)$ acting on $\wedge^N \mathrm{L}^2([0,1])$, where the external and interaction potentials $v$ and $w$ belong to a suitable class of distributions. In this setting, we obtain a complete characterization of the set of pure-state $v$-representable densities on the interval. Then, we prove a Hohenberg-Kohn theorem that applies to the class of distributional potentials studied here. Lastly, we establish the differentiability of the exchange-correlation functional and therefore the existence of a unique exchange-correlation potential. We then combine these results to provide a rigorous formulation of the Kohn-Sham scheme. In particular, these results show that the Kohn-Sham scheme is rigorously exact in this setting.

Absolutely maximally entangled (AME) states of multipartite quantum systems exhibit maximal entanglement across all possible bipartitions. These states lead to teleportation protocols that surpass standard teleportation schemes, determine quantum error correction codes and can be used to test performance of current term quantum processors. Several AME states can be constructed from graph states using minimal quantum resources. However, there exist other constructions that depart from the stabilizer formalism. In this work, we present explicit quantum circuits to generate exemplary non-stabilizer AME states of four subsystems with four, six, and eight levels each and analyze their capabilities to perform quantum information tasks.

We study the robustness of topological phases on aperiodic lattices by constructing *-homomorphisms from the groupoid model to the coarse-geometric model of observable C*-algebras. These *-homomorphisms induce maps in K-theory and Kasparov theory. We show that the strong topological phases in the groupoid model are detected by position spectral triples. We show that topological phases coming from stacking along another Delone set are always weak in the coarse-geometric sense.

The effective transport of heat and mass is crucial to both industrial applications and physiological processes. Recent research has evaluated the benefit of using flexible reeds for triggering the vortex induced vibration to enhance mixing, as opposed to traditional techniques like rigid blender or static meshes. Inspired by the soft, porous, and moving fish gill lamellae, we proposed a new concept of thermal dispenser that prescribes active pitching motion to the leading edge of an otherwise passively flapping perforated panel. Experimental measurements revealed drastic differences between the steady leaky flow wake behind a statically deflected perforated panel and the periodic shedding wakes with complex vortex structure transitions behind an actuated perforated panel with or without chord-wise flexibility. A semi-empirical simulation of the thermal convection and diffusion takes the experimentally obtained velocity as input and yields the temperature results. Vortex dynamics, Lagrangian coherent structures, and thermal mixing behaviors were analyzed and compared to elucidate the effects of kinematics, perforation, and flexibility on the wake mode transitions, lateral entrainment mixing, and overall heating. Our work provides a foundational understanding of the fluid-structure interactions of perforated bendable panels under active control which has not been described before in the intermediate Reynolds number range. It provides insights for developing an innovative bio-inspired heat or mass dispenser potentially suitable for subtle and small scale applications.

Many-body quantum systems can exhibit collective effects that enhance the sensitivity of parameter estimation protocols. An example is provided by resonantly driven two-level atoms subject to collective dissipation, which can display a transition between a stationary phase and a time-crystal one. Previous work has shown that the light emitted in the time-crystal phase can be harnessed for parameter estimation using continuous monitoring protocols, such as photon counting or homodyne detection, which under ideal conditions yield a quadratic enhancement of sensitivity with the number of particles. In this work, we explore what is the minimal information about the emission field that needs to be accessed in order to resolve collective effects and exploit them for parameter estimation. We show that, for short probing times, a single-time measurement of the emission field already captures the collective behavior emerging at the nonequilibrium transition. In contrast, within the time-crystal phase, exploiting collective effects requires at least two-time measurements. To this end, we introduce a family of correlated intensity measurements that extract the relevant information and can be implemented using an interferometric setup. While the ultimate sensitivity bound remains size independent, as recently established within the framework of noisy quantum metrology, our analysis shows that these protocols utilize collective effects to yield a transient increase in sensitivity with particle number.

We study the evolution of a concentrated vortex advected by a smooth, divergence-free velocity field in two space dimensions. In the idealized situation where the initial vorticity is a Dirac mass, we compute an approximation of the solution which accurately describes, in the regime of high Reynolds numbers, the motion of the vortex center and the deformation of the streamlines under the shear stress of the external flow. For ill-prepared initial data, corresponding to a sharply peaked Gaussian vortex, we prove relaxation to the previous solution on a time scale that is much shorter than the diffusive time, due to enhanced dissipation inside the vortex core.

Epidemic spreading often occurs in spatially heterogeneous environments, yet how quenched heterogeneity reshapes its onset and critical dynamics remains poorly understood. The diffusive epidemic process, a minimal reaction-diffusion model whose absorbing-state transition is controlled by the relative diffusion of healthy and infected species, provides a natural setting for this question. Using a new single-seed algorithm that effectively simulate infinite systems for the infected individuals, we find that effective global diffusion rates can be used to predict disorder relevance and we identify two distinct infinite-disorder fixed points. Notably, we find that disorder in diffusion rates is qualitatively different from that in reaction rates as it can even induce a total suppression of the active phase, a phenomenon not observed with other types of disorder. These results establish mobility disorder as a distinct route by which quenched heterogeneity qualitatively reorganizes spreading dynamics, with implications for systems ranging from cell polarity to epidemic propagation in heterogeneous media.