Papers

In this work, high-quality Ge-doped (-201) $\beta$-Ga$_2$O$_3$ thin films were heteroepitaxially grown on c-plane sapphire substrates with offcut angles of 0 deg, 2 deg, 6 deg, and 8 deg using low-pressure chemical vapor deposition (LPCVD). Increasing sapphire offcut promoted step-flow growth, resulting in improved terrace alignment, reduced surface roughness, and enhanced crystalline quality. Phase-pure monoclinic $\beta$-Ga$_2$O$_3$ with strong (-201) preferential orientation was confirmed by X-ray diffraction and Raman spectroscopy, while X-ray photoelectron spectroscopy revealed near-stoichiometric composition with an O/Ga ratio of 1.48. Electrical transport properties exhibited a strong dependence on substrate offcut angle, with room-temperature Hall mobility increasing from 15 to 117 cm$^2$/V s as the offcut angle increased from 0 deg to 6 deg, across carrier concentrations spanning $1.43 \times 10^{17}$ to $2.75 \times 10^{18}$ cm$^{-3}$. The 6 deg offcut sample achieved a room-temperature mobility of 117 cm$^2$/V s at a carrier concentration of $1.43 \times 10^{17}$ cm$^{-3}$ and a peak low-temperature mobility of 337 cm$^2$/V s at 128 K with a carrier concentration of $8.96 \times 10^{16}$ cm$^{-3}$, representing the highest reported room-temperature and low-temperature mobilities for Ge-doped $\beta$-Ga$_2$O$_3$ films grown on sapphire substrates. Carrier concentration and mobility data were analyzed using charge-neutrality and Boltzmann transport models incorporating donor activation together with polar optical phonon, ionized impurity, neutral impurity, acoustic deformation potential, and dislocation scattering mechanisms. The fitting revealed shallow donor activation energies of 12.5-19 meV, a deeper donor level at 80 meV, low acceptor compensation ($< 5 \times 10^{15}$ cm$^{-3}$), and threading dislocation densities on the order of $10^9$ cm$^{-2}$.

In this work, Ge-doped $\beta$-Ga$_2$O$_3$ homoepitaxial films were grown on native (010) $\beta$-Ga$_2$O$_3$ substrates using low-pressure chemical vapor deposition (LPCVD). Controlled $n$-type doping was achieved with room-temperature carrier concentrations ranging from $7.4\times10^{17}$ to $2.57\times10^{18}\ \mathrm{cm}^{-3}$ and corresponding electron mobilities of 105-62 cm$^2$/V$\cdot$s. The films exhibited smooth surface morphology with RMS roughness values of 2.94-3.97 nm, while X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy confirmed phase-pure $\beta$-Ga$_2$O$_3$ with excellent crystalline quality and near-stoichiometric composition. Temperature-dependent Hall measurements on the film with a room-temperature carrier concentration of $7.4\times10^{17}\ \mathrm{cm}^{-3}$ and mobility of 105 cm$^2$/V$\cdot$s yielded a peak electron mobility of 234 cm$^2$/V$\cdot$s at 116 K, while charge-neutrality and transport modeling revealed a dominant shallow donor level with an activation energy of 14 meV, confirming efficient electrical activation of Ge donors. Vertical Ni/$\beta$-Ga$_2$O$_3$ Schottky barrier diodes fabricated using the Ge-doped drift layer exhibited good rectifying behavior with a turn-on voltage of 0.74 V, an ideality factor of 1.32, a Schottky barrier height of 1.02 eV, and a specific on-resistance of 2.49 m$\Omega\cdot$cm$^2$. Capacitance-voltage measurements yielded a net donor concentration of $7.7\times10^{17}\ \mathrm{cm}^{-3}$ and a Schottky barrier height of 1.13 eV, in good agreement with Hall and current-voltage measurements. These results demonstrate that LPCVD enables controllable Ge doping while maintaining high structural and electronic quality, establishing LPCVD-grown Ge-doped $\beta$-Ga$_2$O$_3$ as a promising platform for future high-voltage power electronic devices.

Quantum advantage in real-time spin dynamics should be assessed against the strongest relevant classical substitutes, not merely against the qubit nature of the microscopic system. We develop a physics-based diagnostic for this boundary by reducing a qubit spin model to a spin-Landau–Lifshitz (LL) classical sector and organizing the residual quantum sector as controlled corrections. The control parameter is graph coordination: we study a spin star with \(d\) leaves and \(O(1/d)\) hub–leaf couplings. In its homogeneous form the star benchmarks the transition from LL-substitutable dynamics to genuinely quantum, discrete-sector interference; in its fully driven form, with time-dependent fields and bilinear couplings, it is the basic message-passing primitive for tree and loopy spin structures. For coherent-state return amplitudes we prove exact leaf elimination and derive a continuous-time \(1/d\) hierarchy. L0 is a driven one-spin weak-mean-field theory, while G1 is a Gaussian nonlocal-in-time influence correction coupling leaf two-time kernels to the hub weak two-point function. On bounded finite-time windows away from zeros of the boundary amplitudes, the hierarchy gives \(\log\mathcal A-\log\mathcal A_{\rm L0}=O(1/d)\) and \(\log\mathcal A-\log\mathcal A_{\rm L0}-\Delta_{\rm G1}=O(1/d^2)\); numerical tests on fully driven anisotropic ensembles give slopes \(-1.05\) and \(-2.03\). Static, inhomogeneous, aligned, and fully driven stars provide validation rungs, and comparison with a temporal matrix-product influence-matrix baseline delineates complementary regimes. Unlike rank compression on a Trotter grid, the hierarchy is ordered by a physical parameter, formulated in continuous time, and each truncation level is itself a physical theory, with the LL sector as the high-coordination limit.

Machine learning interatomic potentials (MLPs) have revolutionized atomistic modeling, offering the potential to replace traditional methods like Density Functional Theory (DFT). However, inference time of MLPs is orders of magnitude slower than that of classical force fields, hindering real-world applications for biomolecular systems that require timescales of microseconds and beyond. Implicit solvent MLPs can address this issue, but are faced with data challenges associated with coarse-grained modeling. Consequently, previous approaches relied on empirical force field data, thereby inherently limiting the MLP's accuracy. Here, we introduce the Transferable Water Implicit Network (TWIN), an implicit water MLP parametrized entirely by an Equivariant Graph Neural Network and trained solely on ab initio and experimental labels. We demonstrate TWIN's transferability across drug-like molecules, peptides, and proteins, achieving excellent results on ab initio and experimental crystallographic and NMR benchmarks, consistently outperforming previous machine-learning-based implicit solvent or coarse-grained models. Furthermore, TWIN closely matches DFT-based explicit solvent MLPs while providing a two-order-of-magnitude faster timestep evaluation, paving the way for efficient ab initio-level modeling of biomolecular systems in aqueous environments.

We investigate the detection and accommodation of sensor faults for a proton exchange membrane electrolyzer coupled to a DC/DC converter powered by renewable energy sources. The proposed method for detecting and accommodating the sensor fault is model-free and is based on the concept of ultra-local model that is becoming classic in control engineering. The existing literature on active control tolerant to sensor fault dedicated to this question shows that no previous work has addressed this topic. Our approach mitigates the effect of sensor fault on closed-loop behavior and guarantees the stability and performance of the overall system. Numerical simulations under variations in renewable energy sources validate our approach.

Mode-selective PT-symmetry is the manifestation of Vernier-assisted cross-injection between oscillators with unequal delays. PT-symmetry has been proposed as a mechanism for achieving low-noise single-mode operation in optoelectronic oscillators, but existing formulations are typically based on matched delay loops and frequency-independent coupling, leading to symmetry transitions that are global in frequency and therefore not intrinsically mode selective. A more general formulation of PT-symmetric time-delay oscillators is developed in which the coupling operator is allowed to be dispersive. This permits frequency-selective PT-symmetry transitions and removes the requirement for matched delay loops. Two mathematically equivalent but physically distinct realisations are derived. The first corresponds to coupled gain-loss loops connected by a dispersive coupler, while the second corresponds to a pair of equal-gain oscillators coupled through symmetric cross-injection with unequal delays. Numerical simulations confirm the predicted behaviour and demonstrate strong sidemode suppression while preserving the low phase-noise characteristics of large-delay oscillators. The results establish a direct connection between PT-symmetry, cross-injection architectures, and Vernier oscillator design.

The spreading and rupture of local distribution of surfactant on a slippery inclined thin film flow in the presence of external shear is explored in this article. The surfactant can be adsorbed at the free surface or can be dissolved in the bulk. The surfactant concentrations are governed by advection and diffusion equations for bulk as well as the interface. Moreover, the adsorption-desorption rates of the bulk and interface surfactants are regulated by sorption kinetics rates. The van der Waals forces are considered for rupture dynamics. The lubrication approximation method is used to derive the evolution equations of film thickness and interface surfactant concentration. Two different scenarios are considered for sorption kinetics rates: (i) rapid and (ii) slow in the case of the spreading phenomenon. Then, two cases are considered related to the distribution of the surfactant in case of rapid sorption kinetics. The slippery bottom helps more fluid to flow and the capillary ridge to gain height for rapid sorption kinetics, and the external shear force amplifies the thinning of the film. However, in the case of slow sorption kinetics, the adsorption-desorption takes place at a slow rate, and the transient period results in a reduced Marangoni gradient at the interface. This leads to a pulse-type character in the film thickness profile. The external shear force reduces the pulse height, whereas a slippery surface at the bottom increases the pulse height. On the other hand, van der Waals forces are considered to be the major factor behind the rupture mechanism. The linear stability analysis depicts that external shear force destabilizes the flow, but the slip parameter displays a dual effect based on the Bond number, capillary number, and Hamaker constant.

We develop a two-axis interpolation framework for the O$(n)$ universality family, treating the spatial dimension $D$ and the spin-component number $n$ as independent continuous parameters connecting exact limiting solutions. On the spatial axis, anchoring between the Onsager solution at $D=2$ and mean-field theory at $D\to\infty$ yields a closed-form prediction for the 3D Ising critical coupling that agrees well with Monte Carlo benchmarks $K_c = 0.2204$ (benchmark: $0.22165$) with no adjustable parameters. Wilson–Fisher-constrained polynomial interpolation gives $\nu=2/3$, $\beta=31/96$, and $\eta=35/864$ at $D=3$ (benchmarks: $0.6299$, $0.3265$, $0.0362$), and reproduces conformal-bootstrap results across $3 \le D < 4$. On the spin axis, we establish a necessary compatibility criterion: two-anchor interpolation succeeds only for observables that vary monotonically between the anchor values. The critical coupling $K_c(n)$ violates this criterion because the Heisenberg value falls below the spherical limit, whereas the correlation-length exponent $\nu(n)$ satisfies it. A perturbative $1/n^2$ expansion yields $\nu(3) = 0.7493$ (benchmark: $0.7112$), and propagation through exact scaling relations gives $\beta(3) = 0.3797$ (benchmark: $0.3689$) and $\gamma(3) = 1.489$ (benchmark: $1.396$), without introducing additional parameters. The framework naturally extends to non-integer spin, producing the prediction $\nu(2.5) = 0.7143$ for the O$(2.5)$ universality class. These results establish dimensional and spin interpolation as a unified and predictive approach to critical phenomena, while clarifying the structural conditions under which interpolation succeeds.

Although neural ordinary differential equations (NODEs) are a powerful framework for learning continuous-time dynamics, they generally do not preserve essential qualitative properties, such as positivity. We propose a structure-preserving Neural ODE framework based on nonstandard finite difference (NSFD) discretization. The learned dynamics are parameterized by nonnegative production and destruction rates, yielding an explicit, differentiable update that integrates seamlessly into standard automatic differentiation pipelines. We prove that the resulting scheme unconditionally preserves positivity for arbitrary time-step sizes while retaining first-order consistency. We outline an extension based on Patankar-type discretizations that preserves conservation laws exactly. Numerical experiments on an SIR epidemic model show that our approach generates physically meaningful trajectories, remains robust under coarse discretizations, and outperforms conventional NODEs in preserving the qualitative structure of the learned dynamics.

Transition state (TS) search is a crucial step in understanding chemical reactivity and mechanisms, yet conventional algorithms remain computationally intensive and heavily reliant on initial guesses, user s expertise, and chemical intuition. While recent machine learning approaches have shown promise, they demand either large training datasets or geometric interpolation between known endpoints, limiting their generality. In this work, we introduce a TS search model based on the soft actor-critic model, an advanced reinforcement learning algorithm in which an agent learns to navigate potential energy surfaces directly from local energetic and curvature information starting from a given reactant and its corresponding product. By formulating the search as a sequential decision-making process in internal coordinates, the agent adaptively proposes chemically meaningful structural updates through a reward function designed to promote movement towards saddle point regions. Without labelled trajectories or prescribed reaction pathways, the method successfully identifies TS geometries for standard benchmark reactions, operating directly on realistic molecular potential energy surfaces. These results highlight the potential of RL as a general strategy for reducing dependence on initial guesses and enabling scalable, automated reaction discovery across diverse chemical systems.

The electrical conductivity $\sigma$ of the plasma filament left behind by an ultrashort pulsed laser (USPL) optical pulse after it is geometrically then self-focused in air via the Kerr effect is measured by attenuation of a 3.2 GHz TE$_{10}$ mode within an S-band waveguide through which the filament passes, taking into account the characteristic radius $R$ of the filament, as determined by fast camera visible light imaging. Models of the major air constituents' ionization rate $W_{i}$ vs. local laser intensity $I$, and of temperature $T$ and mean axial electron momentum $\left\langle p_{{z}}\right\rangle $ vs. peak laser intensity $I_{0}$ are then used to infer a hypothetical steady state filament's $I_{0}$, $T$, major species particle densities, and assumed axially invariant current time integral $Q$ and current decay rate $\nu$ after pulse passage. $Q$ is independently measured via the filament's self-emission signal in the waveguide for comparison. The theoretical far field microwave radiation pattern due to the actual axial variation in $Q=Q\left( z\right)$ is compared favorably to published measurements. A much lower upper bound on $\nu$ is inferred once such radiation is taken into account. Results are presented along a $30$ cm long filament at a broad range of atmospheric pressures.

Ehrenfest dynamics is a widely used mixed quantum–classical approach for nonadiabatic molecular dynamics, whereas thawed Gaussian wavepacket dynamics provides an efficient semiclassical description of adiabatic nuclear quantum dynamics. Here we describe thawed Gaussian Ehrenfest dynamics (TGED), which unifies and generalizes these two methods to capture both electronic nonadiabaticity and nuclear quantum effects within a single framework. The fully variational formulation of TGED is derived by applying the time-dependent variational principle to a Hartree product of electronic and Gaussian nuclear wavepackets. Replacing the effective locally quadratic molecular potential obtained from this variational treatment by alternative effective locally quadratic potentials yields an infinite family of TGED methods, of which we present several members. We analyze the limiting cases of the general formalism and show, in particular, that it reduces to conventional Ehrenfest dynamics in the classical limit for the nuclei and to thawed Gaussian wavepacket dynamics in the absence of electronic coupling. Finally, we present explicit geometric integrators for the entire family of methods and identify the conditions under which the different approximations become exact.

This paper presents the electrical model, key equations, and practical construction of a small transmitting magnetic loop antenna built from unusually large 100 mm diameter copper tubing. The large conductor surface area and wide-area transitions to the vacuum capacitors were designed to minimize resistive losses. The frequency range from 1.8 MHz to 31 MHz is unusually wide. Frequency, impedance matching, and azimuth are all adjusted automatically by servo motors. Indoor losses originate predominantly from near-field coupling to the environment rather than from the antenna itself. Temperature-rise measurements confirm that the bulk of the dissipated power is absorbed by the environment, not by the antenna components. The conducted H-field measurements demonstrate good agreement between the measured H-field and the theoretical free-space H-field calculated from the antenna geometry and an estimated loop current. The loop current was estimated from the measured antenna bandwidth and the applied transmit power. The antenna was developed for indoor operation where outdoor installation is not possible.

Luminescence thermometry has evolved through decades of research focused on optimising materials and on extracting temperature information from isolated spectral features such as luminescence intensity ratios, bandwidth, line shift and excited-state lifetime. Despite extensive material development, these conventional methods remain fundamentally limited by construction: only a small subset of pre-selected spectral features is exploited, while the bulk of the temperature-relevant information encoded in the full spectrum is systematically discarded. A paradigm shift is presented here, Luminescence Thermometry 2.0 (LT 2.0), implemented through the newly developed SpectraSensML platform, in which machine learning regression operates on the entire emission spectrum to deliver temperature readout. The approach is demonstrated on a Yb3+-doped phosphor emitting in the near-infrared biological transparency window across 125 to 700 K. Nineteen regression algorithms drawn from four families, namely tree ensembles, physics-aware regression models, kernel and instance methods, and neural networks, are systematically benchmarked. A sensor-fusion estimator that combines the first three principal components reaches an root-mean-square error of 0.36 K, a seven-fold improvement over the best luminescence intensity ratio variant. Single-component approaches are shown to be quantitatively sub-optimal: multi-component regressors that exploit the first three principal components reduce the temperature uncertainty by close to an order of magnitude. The structural reason behind the failure of decision-tree ensembles on unseen temperatures is explained: their piecewise-constant predictions cannot interpolate beyond training set-points. The open-source SpectraSensML application used to obtain the results is released alongside the manuscript to enable reproducible community benchmarks.

Network security is widely recognized as a key application of quantum technology. However, its large-scale deployment is hindered by the need for tight coordination between fundamentally different quantum and classical processing steps in conventional protocols. This requirement introduces strong cross-layer interdependencies that conflict with the modular, layered architectures enabling scalability in modern communication networks. Here, we present an alternative strategy that confines all quantum interventions to the physical layer and remains transparently compatible with existing network abstractions. This is achieved by directly embedding quantum features and classical information within the same optical field using bright squeezed light. Physical-layer signals are analyzed using a cumulative sum (CUSUM) method to enable quantum-enhanced threat detection. We validate the practicality of this approach through field deployment over a metropolitan-scale fiber network and further demonstrate network-level security functionalities enabled by physical-layer quantum-enhanced thread detection. These results establish a practical, scalable framework for seamlessly integrating quantum-enhanced security into large-scale communication infrastructure.

We analyze chemical short-range order in equiatomic fcc NiCoV using molecular-dynamics snapshots generated with a machine-learned interatomic potential. Radial distribution functions identify stable coordination shells, while shell-resolved Warren-Cowley parameters and bond probabilities reveal continued chemical ordering after the radial structure has largely converged. The dominant signal is V-V avoidance in the first shell and V-V enrichment in the second shell, consistent with an L1$_2$-like local ordering tendency, while the third-shell response remains weak. Lagged Jensen-Shannon diagnostics show that bond statistics relax more slowly than the RDF. Principal component analysis of per-replica-centered bond probabilities resolves three collective modes: a V-sublattice ordering amplitude, a Ni-Co redistribution mode, and a Co-V exchange-like mode. These results show that scalar RDF convergence can miss slow chemical relaxation, and that shell-resolved bond statistics provide a compact route for tracking SRO development in multicomponent alloys.

We demonstrate quantum amplitude estimation (QAE) as a route to quadratic speedup for Monte Carlo-type expectation values in nuclear engineering. Using QPE-based QAE, we study two examples: a discrete fission-neutron-yield expectation and a U-238 resonance integral under a $1/E$ slowing-down spectrum. The toy problem is implemented as a gate-level Qiskit circuit, while the resonance-integral example is simulated through an exact eigendecomposition of the Grover operator to avoid state-preparation decomposition bottlenecks. In both cases, the squared error scales as $O(1/T^2)$ with the number of oracle calls $T$, compared with the classical Monte Carlo scaling $O(1/N)$. For the U-238 example, QAE recovers the resonance integral to approximately $0.03%$ relative error with $m=14$ phase-estimation qubits.

Roaming reactions, in which a dissociating fragment moves through a flat region of the potential surface rather than down the minimum-energy path, lie outside the assumptions of conventional transition state theory. The phase-space theory of roaming – unstable periodic orbits and their invariant manifolds organizing transport – has been developed for the Chesnavich model of $\mathrm{CH_4^+}\to\mathrm{CH_3^+}+\mathrm{H}$, which is cylindrically symmetric and reduces to two degrees of freedom (2-DoF). We construct and analyze a three-degree-of-freedom (3-DoF) extension. From the rigid-body formulation of Ezra and Wiggins, we break the symmetry with an azimuthal coupling respecting the three-fold ($C_3$) symmetry of the methyl fragment, obtaining a family $H_b$ whose planar reduction at $b=0$ is the 2-DoF model exactly and which is genuinely 3-DoF for $b>0$. This activates the out-of-plane degree of freedom at once: with the physical planar-top inertia ratio $I_z=2I_x$, arbitrarily weak coupling makes the periodic orbit on the roaming shelf transversely unstable, opening an escape route out of the reaction plane. Apart from a narrow elliptic window $0.58\lesssim b\lesssim0.63$, the instability persists across the range studied, changing type through a period-doubling at $b_c\approx0.63$. Because a periodic orbit cannot anchor a dividing surface in three degrees of freedom, we construct the objects that do – three three-dimensional normally hyperbolic invariant manifolds, one per transition state – at $b=0$, and prove that every compact interior piece of each persists for sufficiently small $b>0$. At $E=0.5\ \mathrm{kcal\,mol^{-1}}$ the coupling lowers the direct non-reactive fraction of a microcanonical ensemble of incoming trajectories by $0.032$ and raises the two roaming fractions by $0.040$; the effect decreases as the energy increases.

We investigate functional expansion tallies as a reduced-basis representation for predicting inter-cycle correlations in Monte Carlo transport. Using an analytic two-dimensional isotropic scattering-chain benchmark with reflective boundaries, we compare a conventional discrete-cell Markov-chain estimator with a Galerkin reduced-order model built directly from Monte Carlo tallies of basis-function products. The reduced model estimates integrated autocorrelation time without first constructing a large discrete transition matrix. For the benchmark problem, the cosine basis converges rapidly to the exact result, while polynomial bases show systematic convergence with increasing order. Compared with discrete binning, the reduced-basis approach achieves lower bias at comparable or lower solve cost, suggesting that functional-expansion representations can provide an efficient path toward correlation prediction, uncertainty quantification, and future variance-reduction methods in Monte Carlo criticality calculations.

Simultaneous detection of the spectral and polarization properties of light is highly desirable for integrated imaging and photonic technologies but typically requires complex multi-component architectures. Here, we demonstrate that the intrinsic dielectric anisotropy of layered insulating CrCl3 enables ultrabroadband polarization-resolved photodetection spanning wavelengths from 300 to 1700 nm. The photoresponse is governed by long-lived ligand-field excitons, whose microsecond-scale lifetime produces a photoconductive gain exceeding 4.5 x 10^4. By combining wavelength-, polarization-, and angle-resolved optoelectronic measurements, we reveal that distinct ligand-field and higher-energy excitonic transitions possess different optical dipole orientations, leading to excitation-energy-dependent rotation of the in-plane polarization axis. Furthermore, oblique illumination activates out-of-plane optical dipoles, while competing excitonic transitions with distinct dipole orientations drive wavelength-dependent rotation and reversal of the polarization anisotropy. Together, these effects produce a highly tunable degree of polarization ranging from -90% to +75%, establishing intrinsic three-dimensional vectorial light-matter interactions in a layered magnetic van der Waals insulator. These findings establish dielectric anisotropy and excitonic dipole engineering as powerful design principles for compact ultrabroadband polarization-sensitive photodetectors and multifunctional van der Waals photonic systems.

Sub-terahertz electromechanics offers a promising route to probe mechanical quantum motion at experimentally friendly Kelvin temperatures. Traditionally, high-frequency mechanical resonators rely on advanced microfabrication to shape complex microstructures, while bulk crystals have been largely overlooked due to their large inertia and challenging transduction at such frequencies. Here we show that bulk lithium niobate can host mechanically accessible modes near 100 GHz when coupled via plug-and-play three-dimensional microwave cavities. This approach enables efficient, non-contact excitation of centimeter-scale, milligram-mass vibrational modes across 7.0–110 GHz, with mechanical quality factors up to 30,000 at W band. Furthermore, using a frequency-tunable superconducting niobium cavity at 4 K, we demonstrate strong coupling between a microwave cavity mode and multiple mechanical modes, enabling coherent energy exchange between microwave photons and mechanical phonons with cooperativity up to 16.6 at 110 GHz. These results establish a versatile platform for accessing massive high-frequency mechanical modes and for precision tests of mechanical quantum physics at elevated temperatures.

Soil harbors much of Earth's bacterial life. The activity of these bacteria governs plant growth, carbon and nitrogen cycling, and the response of land to a changing climate. Understanding this activity is difficult, however: soil is structurally and chemically heterogeneous and optically opaque, and its bacteria not only respond to their surroundings but continually reshape them, a two-way feedback that most idealized experiments and theories overlook. Here we review how this dynamic feedback governs the physics of bacterial motility, growth, and sensing in soil across three scales – the single pore, the mesoscale of many pores, and the broader landscape.

Can a population of people not individually inclined to harm others nonetheless produce harmful collective outcomes, purely because of the institutional structure they inhabit? Social scientists have long argued yes, but existing accounts are largely qualitative and provide no precise condition distinguishing safe institutions from unsafe ones. We develop a threshold cascade model in which agents have positive activation thresholds, harmful behavior is irreversible, and the institution exerts both standing pressure and peer influence along a weighted network. We give a necessary and sufficient condition, checkable from the institution's structure and its members' thresholds, for resistance to any shock up to a given size. The criterion extends to signed influence, in which some peer effects counteract harm, and yields a convex optimization formulation for least-cost repair. It also reveals a sharp frontier between functionality and safety. An institution can coordinate its members and remain safe if and only if the exposure that coordination creates stays below the weakest member's net threshold. A further tension arises when coordination requires responsiveness to peer influence, which can make it impossible to prevent the most exposed group from cascading. We then analyze a mean-field model of two groups differing in how easily their members are pushed into harm. When one group is unstable in isolation but the system is stable under full mixing, disproportionate within-group influence creates a sharp homophily threshold beyond which the harm-free state becomes unstable. In the model, identical treatment of both groups does not generally equalize their cascade robustness.

Recently, research in nanophotonics has turned toward Mie resonances in voids on the surface of high-refractive-index materials. The optical Kerr effect (OKE) in high-index membrane metasurfaces with Mie void lattices is investigated using three-dimensional finite-difference time-domain (FDTD) simulations, with gallium phosphide (GaP) as a model material. The effective nonlinear refractive index is extracted for empty spherical and truncated-cone (frustum) voids in a high-index slab. Metasurfaces with isolated Mie void resonances yield only modest effective OKE enhancement, up to a factor of ten relative to bulk GaP. Mie void resonances in GaP metasurfaces are observable when the separation between voids exceeds approximately 220 nm; otherwise, modes in the high-index material between the voids prevail. A much stronger response arises from the later modes developing in the high-index regions between closely spaced voids. While the nonlinear figure of merit of Mie-void metasurfaces is limited for applications relying solely on energy-density enhancement, the open-cavity geometry offers advantages for hybrid systems that require access to the confined field, such as quantum emitters or nonlinear materials infiltrated into the voids.

The Mahabharata and Ramayana constitute the two major Sanskrit epics of ancient India. Traditionally they are viewed quite differently, with the former being seen as more historical and the latter more poetic. We perform network analysis on both texts in an attempt to gather quantitative information on interrelationships between characters, thereby offering quantitative insight into such separate classifications. We also compare with the Iliad in an attempt to see if structural similarities exist that underlie several narrative similarities.

This study employs the Large Eddy Simulation (LES) to investigate secondary flows and near-wall turbulence induced by two types of surface-mounted longitudinal ribs, namely rectangular and triangular, in a channel flow. The friction Reynolds number, based on friction velocity and channel height H, is set at 220. The rib aspect ratio W/h, where W and h represent the width and height of the rib, is 2, and the rib spacing, S is 0.6H. The results indicate formation of two counter-rotating vortices between the adjacent ribs for both the cases considered. The roughness function is higher with the rectangular rib as compared to that of the triangular rib. At the location of the mid-plane on the rectangular ribs, the wall shear stress is relatively lower as compared to that of the location of mid-plane between the ribs. Conversely, for the case of triangular rib, the opposite pattern is observed. Normal Reynolds stress exhibits strong anisotropic behaviour near the wall for both the cases, overlapping above 0.4H. Between 0.4H and 0.8H, the variation in normal Reynolds stresses is linear. Near the wall, higher production of turbulent kinetic energy (TKE) and normal Reynolds stresses are observed with the triangular rib as compared to those of the rectangular rib. The ratio of production to dissipation is unity in the log-law region.

This manuscript summarizes the recent developments in EELS quantification flow as will be implemented in the CEOS Panta Rhei and TEMDM software. This should serve as a technical reference for the algorithms used in the software.

This study experimentally and numerically investigates the electrohydrodynamic (EHD) interaction produced by a surface dielectric barrier discharge (SDBD) plasma actuator at atmospheric pressure. The non-thermal dielectric barrier discharge generates ionic wind, which is characterized using a symmetric annular actuator composed of concentric ring and disk electrodes. Unlike conventional linear SDBD actuators that primarily produce tangential airflow, this annular configuration generates a predominantly vertical ionic-wind jet. The effects of electrode diameter D and thickness delta on the induced wind velocity perpendicular to the electrode plane are systematically examined. The experimental results show a maximum wind velocity of 3.42 m s^{-1} for an optimized electrode configuration with D = 32 mm and delta = 0.06 mm. Numerical plasma-fluid simulations support the experimental trends and provide spatial distributions of airflow velocity, electrohydrodynamic volumetric force, electron temperature, and gas pressure in the plasma region. Additional diagnostics based on ozone concentration measurements and Schlieren imaging show that electrodes with larger diameters, particularly 22 and 32 mm, enhance the height and development of the vertical flow, while increasing electrode diameter also promotes ozone production. The results demonstrate an important trade-off between ionic-wind performance and reactive byproduct generation. These findings provide practical guidance for optimizing annular dielectric barrier discharge plasma actuators for active flow control, air purification, ozone-assisted disinfection, and biomedical plasma applications.

In this paper, we provide a systematic investigation of SO(2) theory to machine learning interatomic potentials (MLIPs) and identify the limitations of conventional SO(2) Linear architectures relative to SO(3) Clebsch-Gordan Tensor Products (CGTP). Building on these insights, we propose direct Cartesian construction and recursive Clebsch-Gordan construction of Wigner D-matrices and introduce two novel interaction building blocks. First, we propose the Edge Complex Product Basis based on Generalized Asymmetric Contraction, a new formulation for many-body expansion that directly constructs higher-order interactions on edges through complex-valued equivariant multiplications. Second, we introduce Radial Rotary Complex Attention(RRA), which enhances extrapolation performance and surpasses existing attention vector formulations. We also introduce several improvements to the Atomic Cluster Expansion module. Building on these advances, we train our models on OMat24, sAlex, and MPTrj, and introduce TECE-OAM-RRA-1.0, which achieve state-of-the-art (SOTA) performance on the Matbench Discovery.

Enzymatic catalysis can generate nonequilibrium fluctuations, but how these couple to tracer motion at larger length scales depends on physical context. Here, we investigate colloidal tracers in two configurations: passive particles dispersed in an enzymatically active solution, and enzyme-decorated particles where catalysis occurs directly at the tracer surface. We combine differential dynamic microscopy (DDM), which probes ensemble-averaged long-time diffusion, with optical tweezer (OT) measurements of short-time force fluctuations, and compare several complementary metrics for quantifying activity-induced enhancement. For 1 $\mu$m tracers, we observe activity-induced enhancements in both configurations, with the strongest effects for enzyme-decorated particles, which exhibit enhanced diffusion and increased non-thermal force fluctuations. For 200 nm tracers, enhancements are more subtle and method-dependent: DDM detects modest increases in diffusion for bare particles, while corresponding signatures are not resolved by the OT. These results demonstrate that enzymatic activity can be transduced from molecular to microscale motion and forces, but that the apparent magnitude and detectability of enhancement depend strongly on tracer size, localization of activity, the timescales probed by the measurement, and the metric used to quantify enhancement. More broadly, understanding how enzyme activity modifies transport and fluctuations across scales is important for interpreting nonequilibrium dynamics in active soft matter, intracellular transport, and chemically crowded biological environments.