Papers

The Lie group SE(3) of isometric orientation preserving transformation is used for modeling multibody systems, robots, and Cosserat continua. The use of these models in numerical simulation and optimization schemes necessitates the exponential map, its right-trivialized differential (often referred to as tangent operator), as well as higher derivatives in closed form. The $6\times 6$ matrix representation of the differential, $\mathbf{dexp}_{\mathbf{X}}:se\left( 3\right) \rightarrow se\left( 3\right) $ , and its first derivative were reported using a $3\times 3$ block partitioning. In this paper, the differential, its first and second derivative, as well as the Jacobian and Hessian of the evaluation maps, $\mathbf{dexp}_{\mathbf{X}}\mathbf{Z}$ and $\mathbf{dexp}_{\mathbf{X}}^{T}% \mathbf{Z}$, are reported avoiding the block partitioning. For all of them, higher-order approximations are derived. Besides the compactness, the advantage of the presented closed form relations is their numerical robustness when combined with the local approximation. The formulations are demonstrated for computation of the deformation field and the strain rates of an elastic Cosserat-Simo-Reissner rod.

Lensless optical imaging eliminates the need for refractive optics, enabling compact and low-cost cameras with a large field-of-view, supporting point-of-care diagnostics and industrial monitoring. Practical deployments, however, remain constrained by ill-posed image reconstruction pipelines that require multiple measurements, careful calibration or object-specific training, thus limiting robustness and scalability. In this work, we introduce a single-shot lensless imaging framework that reconstructs complex objects from only a single recorded intensity pattern using a genetically programmed iterative algorithm. Our method couples a wave-propagation model with an adaptive meta-optimisation strategy to jointly estimate the object amplitude, object phase, and effective object-detector distance. Experiments demonstrate high-fidelity recovery of amplitude objects, including a USAF target and 2~$\mu$m silicon beads on a glass slide, as well as a phase-dominant biological sample consisting of U2OS cells on a glass slide. Across multiple object types, wavelengths, and propagation distances, the same learned policy maintains high reconstruction quality with minimal retuning, indicating strong out-of-distribution generalisation. As a practical demonstration, the framework is integrated with a $\beta$-amyloid-based optical digital bead assay under wide field-of-view acquisition. The resulting platform combines single-shot capture, compact hardware, and accurate reconstruction of complex fields, enabling rapid, portable assays in which throughput, alignment tolerance, and cost are critical.

The performance of a quadrupole mass filter (QMF) is highly sensitive to deviations from ideal electrode geometry. In this work, we investigate the effect of small inward and outward tilting of cylindrical rods on the resolution and transmission characteristics of a QMF. Such geometric perturbations introduce an axial variation in the radial confinement potential, resulting in Mathieu parameters that vary along the ion trajectory. To examine this effect, the ion stability diagram is computed using a Runge Kutta (RK45) method with axially-varying Mathieu parameters. The modified stability region exhibits shift and contraction depending on the magnitude and nature of rod inclination. The evolution of higher order field components, particularly the dodecapole term, is analyzed along the axial direction. Ion trajectory simulations are performed using SIMION to evaluate the corresponding changes in QMF transmission characteristics in the first stability zone of operation. While simulations at fixed operating conditions indicate a transmission resolution trade off at small tilting angles leading to resolution enhancement, analysis at constant peak transmission reveals that even slight deviations from the parallel configuration lead to a degradation in resolution. These results highlight the critical role of minute geometric imperfections in QMF operation and provide insights into tolerance limits and design optimization for improved mass filter performance.

Capillary rise is one of the classical problems in fluid mechanics and is traditionally described by Jurin's law, which balances capillary suction against hydrostatic pressure. Here we extend this classical result to active fluids, materials that generate internal stresses through microscopic energy consumption. Using the continuum theory of active nematics, we show that activity modifies the normal stress balance at the liquid-gas interface through an additional active normal stress contribution. This leads to a generalized active Jurin's law, which can be written in dimensionless form as \(H_{\infty} = 1 - \mathrm{Ja}_a \xi_0\), where \(H_{\infty}\) is the dimensionless active Jurin height at equilibrium, \(\mathrm{Ja}_a\) is an active Jurin number comparing active stress to capillary pressure, and \(\xi_0\) characterizes the alignment of active constituents at the meniscus. The theory predicts that extensile and contractile active fluids can either enhance or suppress capillary rise depending on the magnitude of activity and the interfacial alignment state. From this relation we construct a phase diagram in the \((\mathrm{Ja}_a,\xi_0)\) plane that delineates regimes of activity-enhanced rise, activity-suppressed rise, and complete suppression of the classical capillary state. When orientational order depends on confinement and flow, the coupling between activity and capillarity produces nonlinear equilibrium conditions that may admit multiple steady heights; linear stability analysis reveals that the overdamped dynamics selects a single stable state, whereas the inertial extension allows the possibility of activity-induced bistability. These results show that internally generated stresses fundamentally reshape one of the most classical capillary transport problems.

AI is increasingly used to accelerate engineering design by improving decision-making and shortening iteration cycles. Application to marine propeller design, however, remains challenging due to scarce training data and the lack of widely available pretrained models. We address this gap with a physics-based data generation pipeline and a generative-AI framework for direct performance-to-design generation tailored to marine propellers. First, we build a database of over 20,000 four- and five-bladed propeller geometries, each accompanied by simulated open-water performance curves. On top of this dataset, we develop a three-module design framework: (1) A Conditional Generation Model that proposes candidate geometries conditioned on design specifications such as target thrust, power, and diameter. (2) A Performance Prediction Model, implemented as a neural-network surrogate, that predicts thrust, torque, and efficiency in milliseconds, enabling rapid evaluation of generated designs. (3) A design refinement stage that applies evolutionary optimization to enforce practical constraints such as required thrust under power limits and bounds on blade-area ratio and thickness. Experimental results over a range of operating conditions show that the framework can generate hydrodynamically plausible propeller designs that match prescribed performance targets while substantially reducing design-iteration time relative to the traditional expert-guided refinement. Latent diffusion-based generator produces more diverse designs under the same conditions than the conditional variational autoencoder, suggesting a stronger capacity for design-space exploration with diffusion models. By coupling physics-based data synthesis with modular AI models, the proposed approach streamlines the propeller design cycle and reduces reliance on expensive high-fidelity simulations to final validation stages.

Scalable, low-dissipation memory operating below 4 K is a critical requirement for superconducting and quantum computing systems. Existing cryogenic memory technologies rely on CMOS derivatives or hybrid architectures that incur leakage, refresh overhead or limited compatibility with superconducting logic. Here we demonstrate a superconducting tunnelling magnetoresistance device that functions as a non-volatile cryogenic memory element across the full superconducting temperature range. By integrating a de Gennes spin valve with a superconducting tunnel junction in a current perpendicular-to-plane geometry, we realise exchange-field control of the superconducting energy gap. This produces two magnetically switchable gap voltages and robust quasiparticle tunnelling magnetoresistance down to 0.25 K.The device operates at millivolt bias with nanowatt-level read power and zero standby dissipation. Its vertical junction architecture and Nb-based materials platform enable compatibility with superconducting logic and scalable cryogenic memory arrays.

We present a Bethe-Salpeter equation (BSE) solver based on a self-consistent $GW$ reference evaluated on the Matsubara frequency axis, referred to as BSE@sc$GW$. The self-consistent $GW$ starting point provides a robust quasiparticle description and reduces sensitivity to the initial mean-field reference compared to one-shot $GW$-based approaches. We further introduce a dynamical correction to the static Casida formulation via a plasmon-pole model. This scheme incorporates simple dynamical screening effects while retaining the efficiency of an effective eigenvalue problem. The resulting dynamically corrected BSE@sc$GW$ yields excitation energies in close agreement with high-level wavefunction-based benchmarks for both singlet and triplet excitations of small molecules. Overall, the accuracy of the dynamic BSE@sc$GW$ approach arises from the combination of a well-converged single-particle reference and the inclusion of frequency-dependent screening effects.

Recently, Ryu et al. generalized Einstein's three coefficients for absorption, stimulated emission, and spontaneous emission between two quantum levels to a set of four spectra between two broadened bands. The spectra obey generalized Einstein relationships at thermal equilibrium; Einstein's relations are obtained as an approximation for line spectra. Here, the generalized Einstein relation between absorption and stimulated emission dipole-strength spectra is applied to investigate optical lineshape models. Lineshapes for the Bloch model, the stochastic model, and the semi-classical Brownian oscillator model do not obey the generalized Einstein relation and therefore fail to satisfy detailed balance with Planck blackbody radiation. The quantum Brownian oscillator model treats a harmonic quantum vibration that is bi-linearly coupled to a thermal bath of quantum harmonic oscillators which generate damping and a random force. The two-state quantum Brownian oscillator lineshape model provides lineshapes for transitions between two displaced, but otherwise identical, harmonic potential energy surfaces on which the same quantum vibration is coupled to the same thermal bath of quantum harmonic oscillators. The absorption and stimulated emission lineshapes were calculated using the quantum Brownian oscillator model in under-damped, critically damped, and over-damped cases. The thermal and reorganization energy were each varied from much less to greater than the vibrational quantum of energy. All quantum Brownian oscillator lineshapes obey the generalized Einstein relation within the numerical precision of the calculation (14 to 30 digits), suggesting this lineshape model is compatible with detailed balance. The formula giving the electric-dipole transition cross-section in terms of these lineshapes is presented.

Diamond is an outstanding semiconductor for extreme electronics, yet reproducible n-type doping remains a long-standing challenge. Here we demonstrate stable n-type single-crystal diamond grown in a single step by a precisely controlled boron-hydrogen-phosphorus co-doping strategy. Hall measurements yield electron concentrations up to 1.0*1019 cm-3 with a resistivity as low as 0.249 ohmic.cm. Secondary-ion mass spectrometry shows that tri-elements doping is the key for achieving n-type conductivity as the electron density exceeds the incorporated phosphorus concentration and is the same level of that of hydrogen and boron concentrations, supporting a donor mechanism beyond an isolated substitutional phosphorus or just boron-hydrogen co-doping. Temperature-dependent photoluminescence (PL) reveals this tri-elements codoping method induces the impurity band, and the donor level is quite shallow around 61.6 meV, consistent with the temperature dependent resistance measurements. Moreover, the co-doped diamond also exhibits strong ultraviolet emission near 270-285 nm, and the internal quantum efficiency is estimated to be 69.4%, while the undoped diamond or only boron doped diamond shows negligible UV emission. These results establish a practical route to low-resistance high luminous n-type diamond and its based chips.

We find that waves develop in a time-decaying mixing layer of viscoelastic fluid, leading the mean-flow to yo-yo. This is in sharp contrast with Newtonian fluids, where laminar mixing layers evolve monotonically. We combine direct numerical simulations with a theoretical analysis of the energy budget for the flow to uncover the underlying physical mechanism. The yo-yoing of the mean-flow is shown to be driven by the elastic polymers injecting energy into the fluid and, in turn, being rotated by the large-scale mean shear. We then provide the mathematical model of the problem and solve it analytically, finding wave solutions with non-linear dispersion predicting the period of the yo-yoing and the parameter range where it occurs. As decaying mixing layers are one of the simplest and canonical examples of unsteady flows, the phenomenon identified here explains the anomalies recently observed in experiments of unsteady viscoelastic flows in complex geometries.

Heat waste is a bottleneck in the development of green information technologies and much effort has been devoted to suppress the heating effect in both electronic and spintronic devices. Here we take an alternative approach and show that controllable heating at the nanoscale can actually benefit information processing. In particular, we study a hybrid nanostructure consisting of a metallic square frame and an antiferromagnetic (AFM) thin film and show that the plasmonic heating can reversibly switch two perpendicularly-oriented AFM domains without the assistance of magnetic fields and electric currents. The required switching energy is at the order 1 nJ, three to six orders of magnitude lower than the current-driven AFM switching. The physical mechanism arises from the thermal-induced strain fields inside the frame, which couple to and manipulate the magnetic orientation via magnetoelastic effect. The strain field direction can be well controlled by selectively exciting the longitudinal and transverse plasmon modes by varying the polarization of the waves, which readily allows for a reversible switching of the AFM vector. Our findings provide tremendous opportunities for optically manipulating the magnetism with ultralow energy consumption and may further promote the interdisciplinary study of photonics, acoustics and spintronics.

Access to safe drinking water remains a major challenge in rural areas of developing countries. This study investigates the feasibility of a simple, low-cost solar photocatalytic reactor coated with a commercial titanium dioxide (TiO$_2$) pigment for the disinfection of well water contaminated with fecal coliforms. A TiO$_2$ film was deposited on a glass plate using a straightforward acetone slurry method and exposed to natural sunlight in Antananarivo, Madagascar. The efficiency was compared with the conventional SODIS method (solar disinfection without catalyst). Water samples from ten different wells were characterized for physicochemical and bacteriological quality. After 10 minutes of solar exposure, the photocatalytic reactor reduced presumptive fecal coliform counts below the method detection limit ($<$1~CFU/100~mL) in all ten samples tested, whereas the SODIS control reduced the initial count by approximately 51% in a representative sample. While disinfection kinetics varied slightly with water turbidity and pH, inactivation below the detection limit was consistently achieved. Pearson correlation analysis showed no statistically significant linear correlation between the inactivation rate constant and any measured water-quality parameter (all $p > 0.4$). The results demonstrate that even a non-uniform, low-purity TiO$_2$ coating significantly accelerates bacterial inactivation under solar radiation, offering a promising and affordable household-scale treatment technology for low-resource settings. However, longer-term validation, catalyst stability, dark-control assessment, and leaching studies are still required before widespread implementation can be recommended.

Intrinsic channel noise is an important source of variability in neuronal dynamics, but its state-dependent and boundary-constrained nature can be difficult to represent numerically. Here, we investigate the effects of bounded multiplicative noise applied to the slow M-current gating variable in a five-dimensional conductance-based model of a CA1 pyramidal neuron. We employ a full-truncation semi-implicit Euler scheme to control boundary violations and perform Monte Carlo parameter sweeps, time-step checks, burst-detection sensitivity analyses, and conductance perturbations. In subthreshold regimes, noise induces firing and produces an intermediate-noise minimum in the coefficient of variation, consistent with coherence resonance. In the deep subthreshold regime, the burst rate exhibits moderate Arrhenius-like scaling over a restricted low-noise interval, supporting an interpretation in terms of noise-activated escape. Near the numerically identified onset of sustained oscillations, temporal coherence is particularly sensitive to noise intensity. In the suprathreshold regime, strong multiplicative noise accelerates firing through premature stochastic exits from the hyperpolarized recovery branch, although this behavior does not follow classical Kramers scaling. Control simulations using clipped additive Gaussian noise produce qualitatively different dynamics, indicating that the state dependence and boundary behavior of the diffusion term materially affect the observed transitions. The main trends remain robust under the tested variations in numerical resolution, burst-detection threshold, slow timescale, and M-current conductance. These results characterize model-specific effects of boundary-constrained channel noise and motivate future comparisons with discrete-state ion-channel models.

Experimental science usually relies on laboratory procedures that, after finitely many steps, terminate with numerical reports on physical quantities. This paper argues that such procedures can be understood as algorithmic once the protocol, background conditions, and reporting rules are fixed. Assuming an explicit physical Church–Turing bridge principle, a reproducible experiment therefore computes a map from admissible inputs to outputs, and the corresponding function exists in the sense appropriate to those outputs. Furthermore, computable analysis allows us to explain why this conclusion is compatible with finite-precision measurement since in this case what matters is a systematic approximation to a requested accuracy, not the production of exact real numbers in a single step. Neither protocol dependence nor stochasticity undermines the existence claim. Rather, they specify which map is realized by a given protocol and what additional assumptions are required for stronger claims about a single protocol-independent quantity. The paper therefore separates three questions that are often conflated: whether the function exists, whether it is computable, and when results obtained under different protocols may be treated as measurements of the same quantity.

We propose a general approach, named by us hyperstatistics, to treat complex systems, in which Boltzmann-Gibbs statistics breaks down at the domain level of the system. Hyperstatistics preserves the concavity of the nonadditive $q$-entropy. We obtain analytical closed-form expressions for the here proposed ($q, n$)-generalized Boltzmann factor $B_q^{n}$ considering uniform, $\gamma$, Log-normal, F, and the $q$-$\gamma$ probability distribution functions. Remarkably, for all investigated distribution functions, $B_q^{n}$ reduces to a $q$-exponential-type function. To demonstrate the applicability of hyperstatistics, we use a table top experiment of the discharge of a capacitor considering $\gamma$-distributed relaxation times, the pressure decay over time associated with the pumping of $^4$He lines of a closed cycle cryostat, midrapidity data for $\textit{p}$-Pb collisions at the LHC, as well as data set for acceleration distribution in turbulent systems. Furthermore, we deduce the power-law-like $\textit{universal}$ dielectric response using a $q$-generalized $\gamma$-distribution function proposed in this work. Hyperstatistics is applicable to systems with inherent non-Boltzmann-Gibbsian statistics at the domain level. We demonstrate that at the domain level hyperstatistics and superstatistics are equivalent, while at the macroscopic level hyperstatistics recovers superstatistics for $q \to 1$ because the Boltzmann-Gibbs character of the domains is restored, as well as Boltzmann-Gibbs statistics for $n \to \infty$.

Due to climatic changes, excessive grazing, and deforestation, semi-arid and arid ecosystems are vulnerable to desertification and land degradation. As aridity increases, vegetation cover often self-organizes into spatial patterns before collapsing to bare soil. While recent theoretical work has established that spatially heterogeneous yet isotropic environments induce a smooth hysteresis loop – yielding either periodic (hexagonal) patterns during degradation or disordered (clustered) patterns during recovery – empirical validation of this physical footprint at a global scale has been lacking. Here, we present an extensive empirical validation using remote sensing across eight distinct global ecosystems, coupled with historical bio-climatic databases. We demonstrate that the spatial morphology of vegetation patches acts as a direct physical footprint of the ecosystem's historical aridity trend. Our results show that ecosystems experiencing increasing aridity display periodic arrays with a defined wavelength, whereas those recovering under decreasing aridity exhibit scale-free clustering. This framework provides a non-destructive, robust satellite-based indicator for diagnosing whether a dryland ecosystem is on a degradation or recovery pathway.

Tantalum is a promising platform for superconducting quantum circuits, yet coherence times remain limited by dielectric losses from interfacial two-level systems (TLS), exacerbated by native oxide regrowth. Here, we implement molecular surface passivation using self-assembled organic monolayers on freshly etched tantalum and silicon in coplanar waveguide resonators. Surface characterization by contact angle, XPS, FTIR and TEM confirm the formation of ordered, nanometer-thick films that suppress oxide formation. Microwave measurements in the ~5-9 GHz range reveal internal quality factors up to 1.8x10^6 in the single-photon regime at 100 mK, representing a ~140% improvement over untreated devices with native oxide. Power and temperature dependent measurements attribute this enhancement to reduced TLS-induced losses. These results demonstrate that molecular passivation effectively engineers low-loss interfaces and provides a scalable route toward high-coherence superconducting quantum devices.

Scaling photonic quantum-information platforms requires arrays of superconducting nanowire single-photon detectors (SNSPDs) for feedforward control, in which optical operations are conditioned on Bell-state measurements relying on photon-coincidence detections. On-chip superconducting cryotron electronics, performing logic on detector outputs and driving optical modulators, could reduce latency and room-temperature interconnect complexity for feedforward schemes. To date, no cryotron circuits designed for this purpose have been demonstrated. We demonstrate a bias-programmable logic gate based on three nanocryotrons (nTrons) that implements selectable AND (coincidence), XOR (odd-parity), and OR functions. It operates on two electrical pulses at 4.2 K, with bit-error rates below $10^{-3}$, bias margins up to $\pm21.7\%$, and operation extending to 25 MHz over narrower bias windows. It performs coincidence and odd-parity detection on two SNSPDs' outputs with bit-error rates below $3.2 \times 10^{-2}$. As proof-of-concept, we show that nTrons can drive capacitive loads up to 1.15 V, potentially enabling compatibility with electro-optic modulators in feedforward schemes.

The coupling between propagating surface plasmon polaritons (SPPs) in Au nanostructures and the exciton transitions of cyanine dye J-aggregates has been examined using leakage radiation microscopy. Real space images of the nanostructures give the propagation lengths of the leaky SPP modes, and Fourier space images yield their dispersion curves. The dispersion curves show an avoided crossing when the structures are coated with J-aggregates, with a Rabi splitting of approximately 30 meV. The lifetimes of the coupled states were calculated by combining the measured propagation lengths with the group velocities obtained from the dispersion curves. The lifetimes decrease from ~50 fs for the bare Au nanostructures, to ~10 fs in the avoided crossing region for the coupled J-aggregate/Au nanostructure system. Analytical Holstein-Tavis-Cummings model calculations and finite element simulations of the coupled system show that the decrease in lifetime is primarily due to energy dissipation into dark states associated with the J-aggregates.

A fiber-based photonic lantern can couple an array of single-mode optical fibers to the guided modes of a multimode fiber, with the mapping between the single-mode fibers and guided modes fully described by a complex-valued transfer matrix. Recent experimental studies have reported strong wavelength-dependent evolution of this matrix in non-mode-selective photonic lanterns, yet a quantitative physical explanation for this behavior has not previously been demonstrated. Here, we present direct measurements of the wavelength-dependent encoding transfer matrix of a photonic lantern across the range 1525 nm to 1575 nm using off-axis holographic imaging, enabling high-fidelity recovery of both amplitude and phase. Beyond measurement, we introduce a physically grounded propagation model and numerical simulation that quantitatively reproduces the observed wavelength evolution and provides a unified physical explanation for behavior reported in prior experimental work. The model identifies differential modal phase accumulation in the multimode section as the dominant mechanism governing spectral evolution and shows that increasing the length of the multimode end systematically accelerates the phase evolution of the transfer matrix with wavelength. These results establish a direct and predictive link between photonic lantern geometry and spectral response, providing a design framework for tailoring lanterns either to enhance sensitivity to closely spaced wavelengths or to enforce uniform response over broad bandwidths for spectroscopic and imaging applications.

Most reinforcement-learning (RL) controllers used in continuous control are architecturally centralized: observations are compressed into a single latent state from which both value estimates and actions are produced. Biological control systems are often organized differently. Insects, in particular, coordinate navigation, heading stabilization, memory, and context-dependent action selection through distributed circuits rather than a single monolithic controller. Motivated by this contrast, we study an RL policy architecture that decomposes control into interacting modules for sensory encoding, heading representation, sparse associative memory, recurrent command generation, and local motor control, with a learned arbitration mechanism that allocates motor authority across modules. The model is evaluated on a two-dimensional navigation task that require simultaneous food seeking, obstacle avoidance, and predator escape. In a six-seed predator-navigation experiment trained with Proximal Policy Optimization (PPO) for 75 updates, the modular policy achieves the strongest final mean performance among the tested controllers, with final episodic return $-2798.8\pm964.4$ versus $-3778.0\pm628.1$ for a centralized gated recurrent unit (GRU) and $-4727.5\pm772.5$ for a centralized multilayer perceptron (MLP). The modular policy also attains the lowest final value loss and stable PPO optimization statistics while driving module-assignment entropy to $0.0457\pm0.0244$, indicating highly selective control allocation. These results suggest that distributed control can serve as a useful inductive bias for RL problems involving dynamically competing behavioral objectives.

This study derives conservative and skew-symmetric formulations of the incompressible flow equations in a terrain-following sigma-coordinate system that preserve key structural properties of the Cartesian formulation. Unlike conventional formulations based on the direct application of the sigma-transformation to Cartesian equations, in which metric-induced terms disrupt the intrinsic structure of the governing equations, the proposed formulations are designed to avoid these structural inconsistencies. A conservative form is derived in a manner consistent with general conservation laws, and its modified eigenstructure is analyzed relative to the Cartesian counterpart. A skew-symmetric formulation is then derived by introducing a new set of variables, yielding a form that is energy-conserving for the Euler equations and energy-bounded for the Navier-Stokes equations. Finally, we discuss characteristic-based boundary conditions to ensure energy boundedness of the system.

We present the first 3D global kinetic simulations of the interacting magnetospheres of pre-merger binary neutron stars. The stars, whose magnetic moments are anti-aligned, twist the field lines connecting them, leading to periodic eruptions. Each eruption consists of an expanding magnetic flux tube with a reconnecting current sheet trailing behind it, topologically analogous to coronal mass ejections. We predict two novel classes of electromagnetic precursor signals powered by the efficient dissipation of magnetic energy in these periodically forming trailing current sheets. First, particles accelerated in the sheets produce nonthermal gamma-ray signals peaking at $\sim16\,\mathrm{MeV}$, which escape minutes to seconds before merger while the sheets are still optically thin to pair production, with modest characteristic luminosities of $L_\mathrm{obs}\gtrsim 10^{42}\,\mathrm{erg/s}$, detectable only for nearby mergers. Second, merging plasmoids in the sheets could produce fast radio burst-like transients in the final seconds before merger, with characteristic luminosities $L_\mathrm{radio}\sim 10^{38-40}\,\mathrm{erg/s}$. These coherent radio precursors would be detectable by upcoming instruments, either in untargeted surveys by wide-field instruments such as CHORD, or through targeted follow-up of gravitational-wave early-warning alerts with instruments such as DSA or SKA-mid.

An exact solution is presented for the resistance of an orifice in a 2D membrane separating two infinitely large conductive reservoirs and obstructed by an infinitely long cylinder. The solution is obtained by constructing a curvilinear coordinate system that captures the symmetry of the obstructed system with constant-coordinate surfaces mapping the system boundaries, and by integrating the resistive contributions of infinitesimally thin equipotential slices. As commonly done when assessing the resistance of fluidic channels of finite length, the exact expression of the obstructed 2D orifice can be used as the access region of obstructed cylindrical channels and will thus find use in single molecule sensing applications.

The energy density method is generalized to include spin polarization with the full formalism derived based on spin-density functional theory, which aims at decomposing the total energy into well-defined atomic energies. The method involves two steps: (1) decomposing the total energy into spin-polarized energy density functions in real space, and (2) integrating these energy densities over chosen gauge-invariant volumes for uniquely defined atomic energies, whose summation over all the atoms restores the DFT total energy up to a constant difference. This method is numerically implemented into the Vienna ab initio simulation package for the projector augmented-wave method, and is showcased with two applications. In the first application, we model the paramagnetic face-centered cubic Fe using spin special quasirandom structures; the spin energies are fit to spin cluster expansions and a deep neural network. In the second application, we calculate the atomic energy distributions of dilute magnetic semiconductor Ni-doped GaN with different dopant distances and spin configurations. This method extracts additional useful information for the study of magnetic systems with density functional theory.

Background: The classroom discourse analysis has been transformed by the growing use of audio-video multimodal data, which demands analytical methods that balance interpretive depth with computational scalability. Methods: This study introduces the Audio Video Verbal Analysis (AVVA) framework, adapted from the Verbal Analysis method to integrate qualitative interpretation with quantitative modelling. Unlike fully multimodal learning analytics approaches, AVVA focuses on verbatim transcripts with essential interactional modalities. Findings: The framework embeds triangulation as a core design strategy across ten methodological steps, strengthening validity and analytical rigour. A comprehensive validation scheme addresses fundamental challenges in temporal observational research: Phi Ceiling for low-frequency variables (via Base Rate Filtering), estimation uncertainty (via bootstrap confidence intervals), and the Modifiable Temporal Unit Problem, where measured associations depend on observational window size. Four-criterion stability assessment (sign consistency, confidence interval overlap, zero exclusion, magnitude stability) classifies variable pairs into interpretable patterns: grain-invariant, scale-specific, or multi-scale, etc. structures across temporal grain sizes. Its application to 23 hours of classroom recordings illustrates its practical viability and its potential to yield meaningful insights. Contribution: The framework thus provides a scalable pathway for transforming rich classroom discourse into analysable datasets.

Spatially periodic systems of coupled bosons are governed by on-site interactions and tunneling between sites, opening a rich phase space of many-body behavior. Here, we explore nanophotonic control of collective many-body light states in a driven-dissipative Kerr microresonator. We demonstrate a non-equilibrium Mott insulator to superfluid transition that arises from the interplay of spatially local Kerr interactions that generate and mediate interference among discrete frequency modes. A photonic-crystal (PhC) lattice bandgap inscribed on the resonator controls linear mode coupling while preserving self-mode Kerr interactions. By increasing the PhC bandgap, we suppress nonlinear cross-mode coupling to access the Mott-insulator phase, wherein the soliton spectrum forms a flattop frequency comb with large and uniform power per mode. In contrast, reducing the PhC bandgap restores cross-mode coupling and drives a delocalized superfluid regime characterized by long-range phase coherence and a spectrum with non-uniform power distribution. Our work shows that many-body physics creates collective states in driven-dissipative systems, enabling advances in programmable photonics and quantum-optical computing.

The electronic and thermoelectric (TE) transport properties of 13 n-type and p-type half-Heusler alloys are computationally examined using Boltzmann transport. The electronic scattering times resulting from all relevant phonon interactions and ionized impurity scattering (IIS) are fully accounted for using ab initio extracted parameters. We find that at room temperature the average peak TE power factors (PF) of all materials we examine reside between 5 and 10 mW/mK$^2$. We also find that IIS in combination with the long range polar optical phonon (POP) scattering are more influential in determining the electronic transport and PF over all other non-polar phonon interactions (acoustic and optical phonon transport). In fact, the combination of POP and IIS determines the thermoelectric power factor of the half-Heuslers examined on average by about 65%. The results highlight the crucial impact of Coulombic scattering process (POP and IIS) on the TE properties of half-Heusler alloys and provide profound insight for understanding transport, which can be applied widely in other complex bandstructure materials. In terms of computation expense, the computationally cheaper POP and IIS provide an acceptable first-order estimate of the power factor of these materials, while the non-polar contributions, which require more expensive ab initio calculations, could be of secondary importance.

Neuronal function requires precise spatial organization of mitochondria to meet localized energetic demand. However, the physical constraints governing mitochondrial transport in axons remain poorly defined. Bidirectional motor-driven trafficking inherently introduces the potential for collisions, but the implications of these interactions for transport failure and structural damage are not understood. Here, we develop an agent-based model that couples mitochondrial motility, morphology, and lifecycle dynamics to a deformable axonal boundary. We show that mitochondrial traffic jams emerge from a force balance between active propulsion and steric interactions, and that their severity is governed by organelle shape and mechanical properties. Elongated, mechanically rigid mitochondria remain aligned and are transported rapidly, whereas flexible, low-aspect-ratio mitochondria are prone to jamming and accumulation. Incorporating fission and fusion dynamics reveals that fission amplifies transport disruption by generating collision-prone populations, while fusion restores transport by producing anisotropic structures that navigate crowded environments more efficiently. Importantly, we find that sustained jamming generates mechanical stress on the axonal membrane, leading to deformation and swelling. Together, these results establish a physical framework linking mitochondrial dynamics to axonal integrity and provide testable predictions for how dysregulated fission-fusion balance can drive transport failure and structural pathology in neurons.

We introduce a modal representation for Lagrangian trajectories in turbulence, termed Lagrangian Proper Orthogonal Decomposition (LPOD). An ensemble of particle trajectories is used to construct velocity time series, which are normalized independently for each trajectory to isolate fluctuations. Principal Component Analysis is then applied to the resulting dataset, with temporal instances defining the feature space. The method is tested on trajectories from both direct numerical simulations of homogeneous isotropic turbulence and three-dimensional particle-tracking experiments, showing that the leading modes exhibit similar structures and energy distributions in both cases. Truncated reconstructions are obtained by combining modes and coefficients, rescaling the fluctuations, and integrating in time. For trajectories of the order of the integral time scale, single-particle dispersion and curvature statistics are accurately reproduced using a limited number of modes (c.a. 10), whereas capturing the tails of acceleration distributions requires a larger set (c.a. 30-60). Longer trajectories require progressively more modes for accurate reconstruction. These results suggest a possible route to data-driven generation of synthetic particle trajectories via stochastic sampling of the modal Lagrangian dynamics.