Papers

We address the problem of how individuals can integrate efficiently their private behavior with information provided by others within a group. To this end, we consider the model of collective search introduced in [https://doi.org/10.1103/PhysRevE.102.012402], under a minimal setting with no olfactory information. Agents combine a private exploratory behavior and a social imitation consisting in aligning to their neighbors, and weigh the two contributions with a single “trust" parameter that controls their relative influence. We find that an optimal trust parameter exists even in the absence of olfactory information, as was observed in the original model. Optimality is dictated by the need to explore the minimal region of space that contains the target. An optimal trust parameter emerges from this constraint because it it tunes imitation, which induces a collective mechanism of inertia affecting the size and path of the swarm. We predict the optimal trust parameter for cohesive groups where all agents interact with one another. We show how optimality depends on the initialization of the agents and the unknown location of the target, in close agreement with numerical simulations. Our results may be leveraged to optimize the design of swarm robotics or to understand information integration in organisms with decentralized nervous systems such as cephalopods.

Determinations of the Hubble constant based on the local distance ladder remain in significant tension with early-Universe inferences from the cosmic microwave background. While this tension is often discussed in terms of new physics or unmodeled systematics, the role of the assumed priors on the model parameters has received comparatively little attention. Recently, Desmond et al. (2025) pointed out that the commonly adopted flat prior on distance moduli upweights smaller distances and systematically favors high inferred values of the Hubble constant. Motivated by this observation, we perform a comprehensive Bayesian recalibration of the distance ladder, applying physically motivated priors uniformly to all distances, including the Milky Way Cepheids, which are incorporated directly into the joint fit. Together with a conservative treatment of the Gaia EDR3 residual parallax offset, the Hubble constant shifts from $H_0 = 73.0 \pm 1.0 \, \mathrm{km/s/Mpc}$ to $H_0 = 70.6 \pm 1.0 \, \mathrm{km/s/Mpc}$, reducing the Hubble tension from $5 \, \sigma$ to $2 \, \sigma$. Our results show that the assumed priors – often treated as innocuous defaults – may play a central role in the Hubble tension. Because all local distance ladders rely on the calibration of distances, similar prior-driven effects are expected to arise across distance-ladder methods.

The regeneration of oral and craniofacial bone defects ranging from minor periodontal and peri-implant defects to large and critical lesions imposes a substantial global health burden. Conventional therapies are associated with several limitations, highlighting the development of a unique treatment strategy, such as tissue engineering. A well-designed scaffold for bone tissue engineering should possess biocompatibility, biodegradability, mechanical strength, and osteoconductivity. For this purpose, mesoporous silica nanoparticles (MSNs) were synthesized and incorporated at different ratios (10, 20, and 30%) into alginate/chitosan (Alg/Chit)-based porous composite scaffolds fabricated through the freeze-drying method. The MSN incorporation significantly improved the mechanical strength of the scaffolds while showing a negligible decreasing effect on the porosity. All of the samples showed desirable swelling behaviors, which is beneficial for cell attachment and proliferation. The MSN-containing scaffolds indicated a decreased hydrolytic degradation in an MSN percentage-dependent manner. The fabricated scaffolds did not depict cytotoxic characteristics. The Alg/Chit/MSN30 scaffolds not only showed noncytotoxic properties, but also increased the cell viability significantly compared to the control group. The biomineralization properties of the MSN-containing nanocomposite scaffolds were significantly higher than the Alg/Chit composite, suggesting the potential of these nanoparticles for bone tissue engineering applications. Taken together, it is concluded that the Alg/Chit/ MSN30 scaffolds are considerable substances for bone tissue regeneration, and MSN has a great tissue engineering potential in addition to its extensive biomedical applications.

Quantum mechanics provides extraordinarily accurate probabilistic predictions, yet the framework remains silent on what distinguishes quantum systems from definite measurement outcomes. This paper develops a measurement-theoretic framework for projective gating. The central object is the $\beta$-bound, an inequality that controls how much probability assignments can drift when gating and measurement fail to commute. For a density operator $\rho$, projector $F$, and effect $E$, with gate-passage probability $s = {\rm Tr}(\rho F)$ and commutator norm $\varepsilon = \|[F, E]\|$, the symmetric partial-gating drift satisfies $|\Delta p_F(E)| \leq 2 \sqrt{(1 - s)/s} \cdot \varepsilon$. The constant 2 is sharp. We introduce two diagnostic quantities: the coherence witness $W(\rho, F) = \|F \rho (I - F)\|_1$, measuring cross-boundary coherence, and the record fidelity gap $\Delta_T(\rho_F, R)$, measuring expectation-value change under symmetrisation. Three experimental vignettes demonstrate falsifiability: Hong–Ou–Mandel interferometry, atomic energy-basis dephasing, and decoherence-induced classicality. The framework is operational and interpretation-neutral, compatible with Everettian, Bohmian, QBist, and collapse approaches. It provides quantitative structure that any interpretation must accommodate, along with a template for experimental tests.

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

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

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

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

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

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

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

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

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

A growing variety of optically accessible spin qubits have emerged in recent years as key components for quantum sensors, qubits, and quantum memories. However, the scalability of conventional spin-based quantum architectures remains limited by direct microwave delivery, which introduces thermal noise, electromagnetic cross-talk, and design constraints for cryogenic, high-field, and distributed systems. In this work, we present a unified framework for RF-over-fiber (RFoF) control of optically accessible spins through RFoF optically detected magnetic resonance (ODMR) spectroscopy of nitrogen-vacancy (NV) centers in diamond. The RFoF platform relies on an electro-optically modulated telecom-band laser that transmits microwave signals over fiber and a high-speed photodiode that recovers the RF waveform to drive NV center spin transitions. We obtain an RFoF efficiency of 1.81% at 2.90~GHz, corresponding to $P_{\mathrm{RF,out}}=-0.7$~dBm. The RFoF architecture provides a path toward low-noise, thermally isolated, and cryo-compatible ODMR systems bridging conventional spin-based quantum sensing protocols with emerging distributed quantum technologies.

In this work, we present Multimodal Equivariant Inverse Design Network (MEIDNet), a framework that jointly learns structural information and materials properties through contrastive learning, while encoding structures via an equivariant graph neural network (EGNN). By combining generative inverse design with multimodal learning, our approach accelerates the exploration of chemical-structural space and facilitates the discovery of materials that satisfy predefined property targets. MEIDNet exhibits strong latent-space alignment with cosine similarity 0.96 by fusion of three modalities through cross-modal learning. Through implementation of curriculum learning strategies, MEIDNet achieves ~60 times higher learning efficiency than conventional training techniques. The potential of our multimodal approach is demonstrated by generating low-bandgap perovskite structures at a stable, unique, and novel (SUN) rate of 13.6 %, which are further validated by ab initio methods. Our inverse design framework demonstrates both scalability and adaptability, paving the way for the universal learning of chemical space across diverse modalities.

Origami structures have been an active area of research due to their high volume-to-mass ratio, packability, and shape adaptability. Previously-developed actuated foldable structures have either embedded linear actuators into the structure, or have attached non-folding rotary motors to the exterior structure. Further, in prior work, actuators directly embedded in the structure of folding media all contributed to linear or folding motion, not to continuous rotary motion. On the macro-scale there has not yet been a folding continuous rotary actuator. This paper details the development and testing of the first macro-scale origami rotary motor that can be folded flat, and then unfurled to operate. Using corona discharge for torque production, the prototype motor achieved an expansion ratio of over 3.8:1, reached a top speed of 1720 rpm when driven at -30 kV, and exhibited a maximum motor torque over 0.25 mNm with an active component torque density of 0.11 Nm/kg.

Exact unstable solutions of the Navier-Stokes equations are thought to underpin the dynamics of turbulence, but are usually computed in minimal computational domains. Here, we extend this dynamical systems approach to spatially extended turbulent flows featuring multiple interacting 'substructures', and show how new simple invariant solutions can be constructed by spatial tiling of exact solutions from small-box calculations. Candidate solutions are found via gradient-based optimization of a scalar loss function which targets autorecurrence in spatially-masked regions of the flow. We apply these ideas to a vertically-extended Kolmogorov flow, where we first identify large numbers of relative periodic orbits (RPOs) which are combinations of high-dissipation, small-box solutions with laminar patches. We then show that vertically-stacked combinations of pairs of distinct small-box RPOs can form robust guesses for dynamically-relevant two-tori in the larger domain. Finally, we show how our optimization procedure can identify 'turbulent' trajectories which locally shadow a small-box RPO for multiple periods in a subdomain. These small-box combinations are possible as the flow spends prolonged periods in a regime where it can be effectively considered as a pair of weakly-coupled small-box systems, due to shielding effects associated with higher-dissipation flow structures.

We report on the observation of coherent molecular vibrations launched by the breaking of a molecular bond. The methyl radical, which is produced by $267\,\mathrm{nm}$ photodissociation of methyl iodide, is excited to high levels in its $\nu_2$ “umbrella" vibrational mode by the dissociation. The ensuing coherent vibrational dynamics are observed by measuring ultrafast time-dependent changes in the x-ray transition energy from the C$1s$ to the singly-occupied valence orbital. Due to symmetry, the real space vibrational motion appears predominantly in the x-ray energy shift at the difference frequencies of the $\nu_2$ progression, although the fundamental frequencies of the $\nu_2$ mode are also observed. By constructing a fully quantum-mechanical model of the dynamics the coherent superposition is rigorously characterized and the real-space motion of the radicals is reconstructed. The retrieved trajectories are dominated by pronounced quantum beating governed by the high degree of coherent excitation and the strong negative anharmonicity of the $\nu_2$ mode.

Accurate prediction of wake dynamics behind hydrofoils is critical for mitigating vortex-induced vibrations and improving the performance of hydraulic machinery. Conventional turbulence modeling approaches often struggle to capture the unsteady, coherent structures governing wake behavior, particularly for slender hydrofoils operating at high Reynolds numbers. This study addresses this limitation by combining scale-resolving numerical simulations, including high-resolution Large Eddy Simulation (LES), with Particle Image Velocimetry (PIV) measurements to investigate the turbulent wake of a symmetric, blunt trailing-edge hydrofoil operating at zero angle of attack. The flow was analyzed at a Reynolds number of approximately 7.5x10e5, i.e. close to the onset of wake-structure interaction effects. LES was performed using a fine mesh of approximately 500 million nodes to resolve near-wall and wake dynamics beyond the experimental field of view, while PIV measurements provided time-resolved velocity fields downstream of the trailing edge. Proper Orthogonal Decomposition (POD) was applied to the PIV data to extract dominant coherent structures and quantify their contribution to the turbulent kinetic energy. POD analysis reveals that energy is distributed across many modes, with the leading mode capturing the primary wake dynamics and higher modes forming coupled oscillatory pairs associated with von Karman vortex shedding. PIV-LES agreement shows that central wake measurements combined with numerical simulations enables full wake reconstruction and validates modeling for vibration-relevant hydrofoil dynamics.

Periodically poled lithium niobate (PPLN) is a widely used nonlinear optical device for second harmonic generation (SHG). Despite its wide adoption in commercial systems, its bandwidth for SHG is fundamentally limited by the quasi-phase matching condition. This can be overcome by aperiodic or chirped PPLN structures; however, such devices are typically custom-fabricated and not readily available commercially. In this study, we investigate an alternative approach to achieving broadband SHG by using a standard PPLN crystal containing multiple fixed poling periods. Broadband operation is realized by angle tuning the crystal relative to input beam in free-space. The effect of angle tuning is examined over a range of incident angles, and an 1.6x enhancement in SHG bandwidth is demonstrated. These results suggest a practical and efficient strategy for broadband SHG using standard PPLN devices.

We propose a method to suppress collisional loss in strongly dipolar, rotationally excited ultracold molecules using a combination of static (dc) and microwave (ac) electric fields. By tuning two excited pair molecular rotational states into a Förster resonance with a dc field, simultaneously driving excited rotational transitions with an ac field removes all long-range bound states, allowing near complete suppression of all two- and three-body collisional loss channels. While permitting tunable dipolar and anti-dipolar interactions, this bound-state-free ac/dc scheme is not subject to photon-changing collisions that are the primary source of two-body loss in shielding with two microwave fields, used to achieve the first molecular Bose-Einstein condensate [Bigagli et al., Nature 631, 289 (2024)]. Using NaCs as a representative example for strongly dipolar molecules, close-coupling calculations are performed to show that bound-state-free shielding can achieve ratios of elastic-to-loss rates $\gtrsim 10^{6}$ at 100 nK, with currently accessible ac and dc field generation technologies. This work opens new opportunities for realizing large, long-lived samples of strongly interacting degenerate molecular gases with tunable long-range interactions.

Excited-state intramolecular proton transfer (ESIPT) is a fundamental photochemical process in which photoexcitation induces proton transfer within a molecule, leading to the formation of a tautomeric excited state. It was observed experimentally that the 3-hydroxychromone (3-HC) system exhibits two distinct proton-transfer time scales upon excitation to the lowest "bright" singlet excited state: an ultrafast component on the femtosecond time scale and a slower one on the picosecond time scale, largely insensitive to solvent effects. Up to now, the microscopic origin of the second time constant has only been hypothesised. Here, using mixed quantum-classical non-adiabatic dynamics simulations, we explicitly observe the two ESIPT time constants and we rationalise the origin of the second time scale by the presence of a competitive out-of-plane hydrogen torsional motion. Comprehensive analysis of the excited-state potential energy surfaces and nonadiabatic trajectories enables us to construct an explicit reaction network for 3-HC, delineating the interplay between canonical ESIPT and torsion-mediated pathways. This unified mechanistic framework reconciles the coexistence of ultrafast and slower ESIPT components, offering new insights into the non-adiabatic excited-state dynamics of the system.

Reconstructing high-resolution sea surface temperatures (SST) from staggered SST measurements is essential for weather forecasting and climate projections. However, when SST measurements are sparse, the resulting inferred SST fields are rather inaccurate. Here, we demonstrate the ability of Sparse Discrete Empirical Interpolation Method (S-DEIM) to reconstruct the high-resolution SST field from sparse in situ observations, without using a model. The S-DEIM estimate consists of two terms, one computed from instantaneous in situ observations using empirical interpolation, and the other learned from the historical time series of observations using recurrent neural networks (RNNs). We train the RNNs using the National Oceanic and Atmospheric Administration's weekly high-resolution SST dataset spanning the years 1989-2021 which constitutes the training data. Subsequently, we examine the performance of S-DEIM on the test data, comprising January 2022 to January 2023. For this test data, S-DEIM infers the high-resolution SST from 100 in situ observations, constituting only 0.2% of the high-resolution spatial grid. We show that the resulting S-DEIM reconstructions are about 40% more accurate than earlier empirical interpolation methods, such as DEIM and Q-DEIM. Furthermore, 91% of S-DEIM estimates fall within $\pm 1^\circ$C of the true SST. We also demonstrate that S-DEIM is robust with respect to sensor placement: even when the sensors are distributed randomly, S-DEIM reconstruction error deteriorates only by 1-2%. S-DEIM is also computationally efficient. Training the RNN, which is performed only once offline, takes approximately one minute. Once trained, the S-DEIM reconstructions are computed in less than a second. As such, S-DEIM can be used for rapid SST reconstruction from sparse streaming observational data in real time.

The gain-loss coupling in optical cavities induces exceptional points (EPs), where two optical modes coalesce. The large modal overlap near an EP intensifies gain competition, favoring single-mode lasing. Recent studies further revealed self-modulation closer to the EP that transforms the lasing mode into a frequency comb. Such EP-enabled comb formation suggests a previously unaccounted-for mechanism that overcomes the strong gain competition and drives a second mode to threshold. Here, using a Bloch coupled-mode theory derived from first principles, we show that the second threshold arises from dynamical couplings among the population inversion, the lasing field, and a dark cavity mode. The lasing-inversion coupling produces extra EPs, whose spectral structure governs switching among single-mode lasing and frequency combs with different repetition rates. This above-threshold mode-switching mechanism enables new opportunities for tunable photonic systems, including adaptive optical communication links and dual-comb spectroscopy.

Land-atmosphere exchange is mediated by turbulent fluxes that can be quantified using eddy-covariance (EC) measurements. EC has been widely used to measure ecosystem-scale vertical exchange between atmosphere and vegetation, and to test and refine atmospheric turbulence theories with the aim to improve the representation of turbulent fluxes in numerical models. Traditionally, research has focused on idealized, homogeneous and flat surfaces, but recent work increasingly targets turbulent exchange in complex, heterogeneous environments under non-ideal conditions, where challenges include advective fluxes, mesoscale circulations between contrasting surface types, and non-stationary nighttime turbulence. Here, we introduce the open-source R package Reddy, which combines multiple EC analysis methods into a single modular tool. Reddy enables users to tailor post-processing choices to site-specific conditions, supports station management and facilitates detailed scientific analyses. The package is accompanied by extensive documentation and a suite of Jupyter notebooks that provide hands-on introductions to EC data processing. We demonstrate Reddy using measurements from three Norwegian sites: (1) a morning transition following a strongly stably stratified night at an alpine tundra valley, (2) spectral and ogive analysis before and after an ice-cover transition at a boreal lake, and (3) fitting flux-variance relations at a permafrost-affected palsa peatland. Reddy extends existing EC software and helps moving towards a more holistic turbulence data analysis framework for heterogeneous, real-world environments.

Humans can infer mechanical outcomes by learning from a few observations. This capacity for mechanics intuition is acquired in a data-efficient manner. Here, we propose a reinforcement learning framework to mimic this process, in which an agent encodes continuous physical observation parameters into its state and is trained via episodic switching across closely related observations. With merely two or three similar observations, the agent acquires robust mechanics intuition that generalizes over wide parameter ranges beyond the training data. Our method is demonstrated on the brachistochrone, a large-deformation elastic plate, and the quantum harmonic oscillator. We explain this generalization through a unified theoretical view: it is associated with cross-parameter Bellman consistency encouraged by episodic switching across neighboring task parameters, promoting approximate stationarity of the Bellman residual with respect to physical variations. This is consistent with a smooth policy that tracks a low-dimensional solution manifold underlying the continuum of tasks. Our work identifies episodic switching as a practically effective and theoretically motivated route to artificial mechanics intuition and suggests a computational analogy to data-efficient generalization in biological learners.

Each cycle of a quantum computation requires a quantum state initialisation. For semiconductor-based quantum platforms, initialisation is typically performed via slow microwave processes and usually requires cooling to temperatures where only the lowest quantum level is occupied. In silicon, boron atoms are the most common impurities. They bind holes in orbitals including an effective spin-3/2 ground state as well as excited states analogous to the Rydberg series for hydrogen. Here we show that initialisation temperature demands may be relaxed and speeds increased over a thousand-fold by importing, from atomic physics, the procedure of optical pumping via excited orbital states to preferentially occupy a target ground state spin. Spin relaxation within the orbital ground state of unstrained silicon is too fast to measure for conventional pulsed microwave technology, except at temperatures below 2 K, implying a need not only for fast state preparation but also fast state readout. Circularly polarised ~10 THz photon pulses from a free electron laser meet both needs at temperatures above 3 K: a 9 ps pulse enhances the population of one spin eigenstate for the "1s"-like ground state orbital, and the second interrogates this imbalance in spin population. Using parameters given by our data, we calculate that it should be possible to initialise 99% of spins for boron in strained silicon within 250 ps at 3 K. The speedup of both state preparation and measurement gained for THz rather than microwave photons should be explored for the many other solid state quantum systems hosting THz excitations potentially useful as intermediate states.

The first high-power (> 3 MW) H-mode experiments using a double-null X-point-target (XPT) divertor configuration have been performed on MAST-U. The XPT geometry is obtained by combining a large strike point radius, similar to the Super-X divertor (SXD), with an additional X-point near the separatrix in the baffled outer divertor chambers and leads to additional exhaust benefits over the SXD. The broader electron density profile near the secondary X-point leads to additional plasma-neutral interactions, evidenced by a broader hydrogenic emission profile, and resulting in larger power and ion sinks. The increase in plasma-neutral interactions also leads to lower target electron temperatures and heat fluxes. These benefits appear to extend to transients, and preliminary evidence of improved ELM buffering in the XPT is presented. These results showcase how multiple alternative divertor configuration strategies can be combined to improve momentum, power, and particle losses, which may be required for the challenging exhaust conditions of future reactors.

We analyze the spectral properties of high-order harmonic radiation with photon energies extending beyond the regular cutoff energy in standard high-order harmonic generation. The extension of the regular harmonic cutoff results from infrared (IR)-driven recombination of valence photoelectrons into a cationic core hole created by extreme-ultraviolet (XUV) excitation of inner-shell electrons into the transient valence hole in a combined XUV+IR configuration [Buth et al., Opt. Lett. 36, 3530 (2011)]. We show that the microscopic dipole phase at the extended harmonic frequencies is sensitive to the relative IR-XUV delay and IR intensity, whereas the corresponding signal intensity drops significantly for chirped XUV pulses with poor temporal coherence. We discuss the impact of such sensitivity on the macroscopic harmonic radiation, whereby decoherence among the dipole emitters may lead to further signal suppression.

A new approach to incorporating coupling elements into a generalized coupled mode theory is presented. The simplest model of coupling of a structured waveguide with an external RF power source and load through loops and transmission lines was used. Even such a simple model significantly complicated the system of coupled equations. It turned into a coupled integro-differential system of the Barbashin type with degenerate kernels. Since the integral kernels are degenerate, this system is reduced to three independent systems of differential equations. Instead of solving a system of coupled integro-differential equations, we need to find solutions to three systems of ordinary differential equations. Two systems describe the distribution of the field excited by one loop and the specified value of the excitation current in it. In the first system the loop is located at the section's input, and in the second, at the section's output. The third system does not depend on the loop parameters at all. It describes the distribution of the field excited by an electron beam in a section without loops. Based on the developed analytical model, the computer code was developed for matching the loop couplers for the uniform accelerating sections of X-band. The calculation results were used to simulate the non-uniform section. Without additional matching, we obtained an input reflection coefficient of 8E-3.