Particle accelerators play a pivotal role in advancing scientific research, yet optimizing beamline configurations to maximize particle transmission remains a labor-intensive task requiring expert intervention. In this work, we introduce RLABC (Reinforcement Learning for Accelerator Beamline Control), a Python-based library that reframes beamline optimization as a reinforcement learning (RL) problem. Leveraging the Elegant simulation framework, RLABC automates the creation of an RL environment from standard lattice and element input files, enabling sequential tuning of magnets to minimize particle losses. We define a comprehensive state representation capturing beam statistics, actions for adjusting magnet parameters, and a reward function focused on transmission efficiency. Employing the Deep Deterministic Policy Gradient (DDPG) algorithm, we demonstrate RLABC's efficacy on two beamlines, achieving transmission rates of 94% and 91%, comparable to expert manual optimizations. This approach bridges accelerator physics and machine learning, offering a versatile tool for physicists and RL researchers alike to streamline beamline tuning.

We have developed PyTIE (Python Topological Indices Expressions) which is defined as the collections of Python packages such as PyTIE D, PyTIE DS, PyTIE SMS DE, and PyTIE SMS DSE, which are open-source software packages and cross-platform Python package designed to expedite the retrieval of results for mathematics, chemistry and chemical engineering researchers within constant time. This open-source tool extends its utility to chemistry and chemical engineering researchers with limited mathematical proficiency. PyTIE facilitates the loading of molecular graphs, specifying parameters such as minimum degree, maximum degree, and the number of vertex pairs (edge partitions). The edge partitions of a molecular graph based on degree sum also plays a crucial role in predicting heat of formation and enthalpy of formation along with DFT techniques. It systematically computes expressions and numerical values for various topological indices, including degree-based and neighborhood degree-based indices, as well as Shannon's entropy, providing visual representations of the results. Emphasizing topological indices for Quantitative Structure-Activity Relationship and Quantitative Structure-Property Relationship analyses, PyTIE proves particularly relevant in these studies. Serving as a Python package, it seamlessly integrates with libraries such as NumPy, math and SymPy offering extensive options for data analysis. The efficiency of PyTIE is demonstrated through illustrative examples in various contexts.

This paper quantitatively analyzes county-level voting patterns in Wisconsin's presidential elections from 2000 to 2024. As a pivotal swing state, Wisconsin has alternated between Democratic and Republican candidates since 2012. Using data from the Wisconsin Elections Commission, we examine vote totals across 72 counties and seven election cycles. Pearson correlations measure similarity in county voting trajectories, while choropleth maps visualize spatial shifts. Results show strong clustering of vote changes: Democratic and Republican gains between 2016 and 2020 were concentrated in southeastern urban and suburban counties, with rural areas showing little change. Correlations reveal a north-south divide, as southern counties exhibit similar trends and northern ones diverge. These findings highlight spatial heterogeneity in electoral dynamics and the decisive role of urban mobilization in statewide outcomes.

This paper presents an electromagnetic investigation of the crosstalk between two bent microstrip lines (MLs) separated by a perforated planar shield. As an extension of our previous study, the effects of various discontinuities in either the MLs or the shield along the coupling path are analyzed through numerical simulations and validated by measurements. The underlying electromagnetic mechanisms are also discussed. Furthermore, multimodal wave theory in a rectangular waveguide is applied to predict crosstalk behavior when the shield contains an aperture. This study aims to conceptually elucidate complex crosstalk phenomena that are difficult to model using circuit theory, and successful predictions of crosstalk behavior are presented for different problem cases.

Radiation chemistry of model systems irradiated with ultra-high dose-rates (UHDR) is key to obtain a mechanistic understanding of the sparing of healthy tissue, which is called the FLASH effect. It is envisioned to be used for efficient treatment of cancer by FLASH radiotherapy. However, it seems that even the most simple model systems, water irradiated with varying dose-rates (DR), pose a challenge. This became evident, as differences within measured and predicted hydrogen peroxide (H2O2) yields (g-values) for exposure of liquid samples to conventional DR and UHDR were reported. Many of the recently reported values contradict older experiments and current Monte-Carlo simulations(MCS). In the present work, we aim to identify possible reasons of these discrepancies and propose ways to overcome this issue. Hereby a short review of recent and classical literature concerning experimental and simulational studies is performed. The studies cover different radiation sources, from gamma rays, high-energy electrons, heavy particles (protons and ions) with low and high linear energy transfer (LET), and samples of hypoxic & oxygenated water, with cosolutes such as bovine-serum albumine (BSA). Results are for additional experimental parameters, such as solvent, sample container and analysis methods used to determine the respective g-values of H2O2. Similarly the parameter of the MCS by the step-by-step approach, or the independent-reaction time (IRT) method are discussed. Here, UHDR induced modification of the radical-radical interaction and dynamics, not governed by diffusion processes, may cause problems. Approaches to test these different models are highlighted to allow progress: by making the step from a purely descriptive discourse of the effects observed, towards testable models, which should clarify the reasons of how and why such a disagreement came to light in the first place.

Spin-dependent dispersion and Rashba effect are manifestations of universal spin orbit interaction associated with the breaking of the spatial inversion symmetry in condensed matter and in optical systems. In sharp contrast to this, we report a spin-split dispersion effect of leaky surface plasmons in an inversion-symmetric one dimensional plasmonic grating system. In our system, the signature of spin-momentum locking and the resulting spin-polarization dependent splitting of dispersion of the surface plasmons are observed through the leakage radiation detected in a Fourier (momentum) domain optical arrangement. The setup enables single-shot recording of the full polarization-resolved dispersion (frequency vs transverse momentum (k)) of the leaky surface plasmons. Momentum domain polarization analysis identified a transverse momentum (k) dependent linear birefringence-linear dichroism effect (referred to as the geometric LB-LD effect) responsible for the observed spin-split dispersion. This unconventional SOI effect is reminiscent of the recently reported LB-LD effect resulting in giant chirality in centrosymmetric crystal, albeit with geometric origin. It is demonstrated that the interplay of the geometrical polarization transformation in focused polarized light and subsequent interaction of the structured field polarization with the plasmonic grating leads to the evolution of strong geometrical phase gradient or spin(circular polarization)-dependent transverse momentum of light resulting in spin-split dispersion. Our study offers a new paradigm of spin-based dispersion engineering and spin-enabled nano-optical functionalities in simple symmetric metasurfaces using geometric LB-LD effect.

Many protein-protein interaction (PPI) networks take place in the fluid yet structured plasma membrane. Lipid domains, sometimes termed rafts, have been implicated in the functioning of various membrane-bound signaling processes. Here, we present a model and a Monte Carlo simulation framework to investigate how changes in the domain size that arise from perturbations to membrane criticality can lead to changes in the rate of interactions among components, leading to altered outcomes. For simple PPI networks, we show that the activity can be highly sensitive to thermodynamic parameters near the critical point of the membrane phase transition. When protein-protein interactions change the partitioning of some components, our system sometimes forms out of equilibrium domains we term pockets, driven by a mixture of thermodynamic interactions and kinetic sorting. More generally, we predict that near the critical point many different PPI networks will have their outcomes depend sensitively on perturbations that influence critical behavior.

In this work, we present the first derivation and implementation of analytic gradient methods for the computation of the atomic axial tensors (AATs) required for simulations of vibrational circular dichroism (VCD) spectra using configuration interaction methods including double (CID) and single and double (CISD) excitations. Our new implementation includes the use of non-canonical perturbed orbitals to improve the numerical stability of the gradients in the presence of orbital near-degeneracies, as well as frozen-core capabilities. We validated our analytic CID and CISD formulations against two new finite-difference approaches. Using this new implementation, we investigated the significance of singly excited determinants and the role of CI-coefficient optimization in VCD simulations by comparisons among Hartree-Fock (HF) theory, second-order Møller-Plesset perturbation (MP2) theory, CID, and CISD theories. For our molecular test set including (P )-hydrogen peroxide, (S )-methyloxirane, (R)-3-chloro-1-butene, (R)-4-methyl-2-oxetanone, and (M )-1,3-dimethylallene we noted sign discrepancies between the HF and MP2 methods compared to that of the new CID and CISD methods for four of the five molecules as well as similar discrepancies between the CID and CISD methods for (M )-1,3-dimethylallene.

Unsteady flows generated when a body approaches or departs from a granular bed arise in swimming, burrowing, and maneuvering devices. Yet, the threshold for grain motion in such transients remains poorly modeled due to the complexity of the flow. In this study, we report laboratory measurements of the onset of erosion when a rigid circular disk is subjected to a single vertical stroke through quiescent water above a granular bed. The stroke length and travel time were varied independently to determine the critical velocity at which the granular bed is eroded for different minimum distances from the bed. Two erosion mechanisms are observed for disk motion towards the bed: during the stroke, the outward squeezing flow erodes grains near the edge, while after stoppage, the starting vortex or associated secondary vortices impinge on the surface. For motion away from the bed, only the early interaction between the inward suction flow and the nascent vortex entrains grains. The resulting dimensionless thresholds clarify the respective roles of radial flows and vortices in transient, impulsively driven erosion.

This article introduces a new 3D magnetohydrodynamic (MHD) equilibrium solver, based on the concept of admissible variations of B, p that allows for magnetic relaxation of a magnetic field in a perturbed/non-minimum energy state to a lower energy state. We describe the mathematical theory behind this method, including ensuring certain bounds on the magnetic energy, and the differential geometry behind transforming to and from a logical domain and physical domain. Our code is designed to address a number of traditional challenges to 3D MHD equilibrium solvers, e.g. exactly enforcing physical constraints such as divergence-free magnetic field, exhibiting high levels of numerical convergence, dealing with complex geometries, and modeling stochastic field lines or chaotic behavior. By using differentiable Python, our numerical method comes with the additional benefits of computational efficiency on modern computing architectures, high code accessibility, and differentiability at each step. The proposed magnetic relaxation solver is robustly benchmarked and tested with standard examples, including solving 2D toroidal equilibria at high-beta, and a rotating ellipse stellarator. Future work will address the integration of this code for 3D equilibrium optimization for modeling magnetic islands and chaos in stellarator fusion devices.

Using a custom-built scanning system, we generated maps of birefringence on reflection at $\lambda=1064$~nm from single-crystal GaAs/Al$_{0.92}$Ga$_{0.08}$As Bragg reflectors (henceforth ``AlGaAs coatings''). Ten coatings were bonded to fused silica substrates and one remained on the epitaxial growth wafer. The average phase difference on reflection between beams polarized along the fast and slow axes of the coating was found to be $\psi = 1.09 \pm 0.18$~mrad, consistent with values observed in high-finesse optical reference cavities using similar AlGaAs coatings. Scans of substrate-transferred coatings with diameters between 18 and 194 millimeters showed birefringence non-uniformity at a median level of $0.1$~mrad. A similar epitaxial multilayer that was not substrate transferred, but remained on the growth wafer, had by far the least birefringence non-uniformity of all mirrors tested at $0.02$~mrad. On the other hand, the average birefringence of the epi-on-wafer coating and substrate-transferred coatings was indistinguishable. Excluding non-uniformity found at the location of crystal and bonding defects, we conclude that the observed non-uniformity was imparted during the substrate transfer process, likely during bonding. Quantifying the impact on the scatter loss in a LIGO-like interferometer, we find that birefringence non-uniformity at the levels seen here is unlikely to have a significant impact on performance. Nonetheless, future efforts will focus on improved process control to minimize and ultimately eliminate the observed non-uniformity.

In this paper, we propose a novel fully programmable linear photonic processor, which we call LightPro, with improved scalability, performance, and footprint. At the heart of LightPro are compact, low-loss, and programmable silicon photonic (SiPh) directional coupler (DC) devices that deploy phase-change material (PCM) for programming the DC's splitting ratio. By thermally inducing phase transitions in the PCM, the coupling coefficient of the DC can be dynamically adjusted to achieve different splitting ratios in the device output. Building on this device foundation, we develop a neural architecture search (NAS) and pruning algorithm to optimize the architecture of the processor for performing MVM operations. Our simulation results show that LightPro achieves up to an 85% reduction in footprint and more than 50% improvement in power consumption. In addition, LightPro is evaluated by performing inference with weight matrices trained on MNIST and linearly separable Gaussian datasets, showing less than a 5% drop in accuracy when scaling up the network. Prototyping results, using a commercial photonic processor (iPronics SmartLight), show LightPro's efficiency and performance (e.g., computational accuracy) compared to conventional photonic MVM hardware, demonstrating the experimental evaluation and feasibility of LightPro for next-generation photonic AI accelerators.

We present a framework for Large Eddy Simulations (LES) with Immersed Boundaries (IBs) to simulate high Reynolds number flows over complex walls. In this approach, which we call Immersed Boundary-Modeled LES (IBMLES), we volume-filter the Navier-Stokes equations to derive the IB bodyforce. This also gives rise to the subfilter stress (SFS) and residual viscous stress tensors, the latter of which is closed, and we expect the SFS can be closed with existing models. We show that the IB bodyforce can be closed by modeling the wall-slip velocity and provide two such models. The first is an algebraic model based on volume filtering the Van-Driest velocity profile, and the second is a slip-length model. We perform an a priori analysis by volume-filtering direct numerical simulation (DNS) data of turbulent channel flow at $\mathrm{Re}_{\tau}=5200$ to inform these models and investigate the behavior of the other terms in this formulation. We find streamwise wall-slip velocity is significant and both models show good agreement with volume-filtered DNS data on average. Slip velocity is non-uniform and retains a signature of inner or large scale flow structures depending on filter size. SFS terms are analogous to those in traditional LES and can likely be modeled with existing SFS models such Dynamic-Smagorinsky. Residual viscous stress is significant, so it must be considered in IBMLES. We perform filtering with multiple filter types and find there is little sensitivity to the choice of filter kernel so long as it abides by the assumptions given in this framework.

Classical linear optics posits that at sufficiently low intensities, light propagation in dielectric media is governed solely by their linear susceptibilities. Here, we demonstrate a departure from this paradigm in high-Q microresonators, where prolonged photon confinement enables rare quantum electrodynamical (QED) events, mediated by the quantum vacuum, to embed distinctive Raman signatures of the coupled analyte into the resonator's linear transmission spectrum despite their absence from the linear susceptibility. We further show that increasing the amount of adsorbed analyte amplifies these Raman fingerprints well above typical noise floors, rendering them experimentally accessible with state-of-the-art photonic architectures and detection schemes. This novel weak-coupling cavity-QED effect offers unique routes to harness extended photon lifetimes and constrained geometries for leveraging vacuum fluctuations in next-generation photonic technologies for chemical and biological sensing and high-precision optical spectroscopy.

We show that both temporal and spatial symmetry breaking in canonical K-type transition arise as organized hydrodynamic structures rather than stochastic fluctuations. Before the skin-friction maximum, the flow is fully described by a periodic, spanwise symmetric, harmonic response to the Tollmien-Schlichting wave, forming a spatially compact coherent structure that produces hairpin packets. This fundamental harmonic response may visually resemble turbulence, but remains fully periodic and delimits the exact extent of the deterministic regime. A distinct regime change occurs after this point; a hierarchy of new (quasi-)periodic and aperiodic space-time structures emerges, followed shortly by anti-symmetric structures that develop similarly despite no anti-symmetric inputs, marking the onset of aperiodicity and spanwise asymmetry. We identify these structures as symmetry-decomposed spectral and space-time proper orthogonal modes that resolve the full progression from deterministic to broadband dynamics. The key insight is that laminar-turbulent transition can be viewed as a sequence of symmetry breaking events, each driven by energetically dominant, space-time coherent modes that gradually turn an initially harmonic flow into broadband turbulence.

The magnetic Rayleigh-Taylor instability (MRTI) governs plasma mixing and transport in a wide range of astrophysical and laboratory systems. Owing to computational constraints, MRTI is often studied using two-dimensional (2D) simulations, but the extent to which 2D captures the true three-dimensional (3D) dynamics remains unclear. In this work, we perform direct numerical simulations of non-ideal, incompressible MRTI in both 2D and 3D, systematically varying the magnetic field strength from weakly to strongly magnetized regimes. We find that the 3D system exhibits richer mode interactions due to the coexistence of interchange, undular, and mixed modes structures that are inherently absent in 2D. The mixing layer in 3D has enhanced small-scale mixing and reduced fluid dispersion compared to 2D, which is characterized by large-scale plumes. Energy diagnostics reveal that the gravitational potential energy released is higher in 2D, primarily because of inefficient mixing and significant fluid dispersion. In contrast, 3D systems display greater energy dissipation and anisotropy, driven by small-scale vortical motions. The non-linear growth of the instability increases monotonically with magnetic field strength in 3D but shows a non-monotonic trend in 2D. Despite these broad differences, the rate of magnetic-to-kinetic energy conversion remains remarkably similar across dimensions, indicating that 2D simulations can meaningfully capture reconnection-driven processes but not the full turbulent evolution. Overall, our results demonstrate that 2D MRTI simulations cannot reliably represent 3D mixing, energy dynamics, or nonlinear growth, highlighting the fundamental importance of three-dimensionality in magnetized plasma instabilities.

Assessing the frequency and intensity of extreme weather events, and understanding how climate change affects them, is crucial for developing effective adaptation and mitigation strategies. However, observational datasets are too short and physics-based global climate models (GCMs) are too computationally expensive to obtain robust statistics for the rarest, yet most impactful, extreme events. AI-based emulators have shown promise for predictions at weather and even climate timescales, but they struggle on extreme events with few or no examples in their training dataset. Rare event sampling (RES) algorithms have previously demonstrated success for some extreme events, but their performance depends critically on a hard-to-identify "score function", which guides efficient sampling by a GCM. Here, we develop a novel algorithm, AI+RES, which uses ensemble forecasts of an AI weather emulator as the score function to guide highly efficient resampling of the GCM and generate robust (physics-based) extreme weather statistics and associated dynamics at 30-300x lower cost. We demonstrate AI+RES on mid-latitude heatwaves, a challenging test case requiring a score function with predictive skill many days in advance. AI+RES, which synergistically integrates AI, RES, and GCMs, offers a powerful, scalable tool for studying extreme events in climate science, as well as other disciplines in science and engineering where rare events and AI emulators are active areas of research.

The duration of isolated attosecond pulses created via high-order harmonic generation is determined by the number of optical cycles in the driving laser. Achieving shorter attosecond soft X-ray pulses requires minimizing the number of cycles while maintaining a high pulse energy. Here, we demonstrate a carrier-envelope-phase-stable, 100-mJ-class sub-cycle mid-infrared laser that produces a supercontinuum coherent soft X-ray with unprecedented bandwidth. The system delivers 50-mJ, 6.7-fs (0.88-cycle) pulses at a center wavelength of 2.26 $\mu$m - over two orders of magnitude more energetic than any previous sub-cycle laser. We applied the system to high-order harmonic generation and compared the results to simulations based on the three-dimensional time-dependent Schrödinger equation to identify unique features of sub-cycle lasers. This work represents a decisive step toward high-energy half-cycle lasers and high-energy single-digit attosecond soft X-ray pulses that can be used to probe matter and light-matter interactions at previously inaccessible temporal resolutions.

Deep learning-based surrogate models offer a computationally efficient alternative to high-fidelity computational fluid dynamics (CFD) simulations for predicting urban wind flow. However, conventional approaches usually only yield low-frequency predictions (essentially averaging values from proximate pixels), missing critical high-frequency details such as sharp gradients and peak wind speeds. This study proposes a hierarchical approach for accurately predicting pedestrian-level urban winds, which adopts a two-stage predictor-refiner framework. In the first stage, a U-Net architecture generates a baseline prediction from urban geometry. In the second stage, a conditional Generative Adversarial Network (cGAN) refines this baseline by restoring the missing high-frequency content. The cGAN's generator incorporates a multi-scale architecture with stepwise kernel sizes, enabling simultaneous learning of global flow structures and fine-grained local features. Trained and validated on the UrbanTALES dataset with comprehensive urban configurations, the proposed hierarchical framework significantly outperforms the baseline predictor. With a marked qualitative improvement in resolving high-speed wind jets and complex turbulent wakes as well as wind statistics, the results yield quantitative enhancement in prediction accuracy (e.g., RMSE reduced by 76% for the training set and 60% for the validation set). This work presents an effective and robust methodology for enhancing the prediction fidelity of surrogate models in urban planning, pedestrian comfort assessment, and wind safety analysis. The proposed model will be integrated into an interactive web platform as Feilian Version 2.

This paper reports an innovative concept of ``plasma fibre" using bright-core helicon plasma, inspired by its spatial and spectral similarities to the well-known optical fibre. Theoretical analyses are presented for both ideal case of step-like density profile and the realistic case of Gaussian density profile in radius. The total reflection of electromagnetic waves near the sharp plasma density gradient and consequently the wave-guide feature could indeed happen if the incident angle is larger than a threshold value. Numerical computations using electromagnetic solver that based on Maxwell's equations and cold-plasma dielectric tensor yield consistent results. The experimental verification and prospective applications are also suggested. The ``plasma fibre" could be functional component that embedded into existing communication systems for special purpose based on its capability of dynamic reconfiguration.

We investigate temporal scattering in lossless Drude media and reveal an overlooked role of the zero-frequency flat band associated with static polarization charge. This flat band forms an exceptional line spanning all wavenumbers and can be directly excited during temporal scattering at photonic time interfaces, generating non-propagating static fields alongside the usual reflected and transmitted waves. Eigenvector coalescence at the corresponding exceptional points leads to two distinctive features absent in previously studied systems: a static mode whose amplitude increases linearly with time, and an additional static component arising from the system's generalized eigenvector. Remarkably, these effects occur without violating total energy conservation, underscoring the Hermitian nature of the dynamics. Our findings present a new physical picture of temporal scattering, sharply distinct from that in dispersionless and Lorentz-dispersive media.

Light is the fundamental medium through which we perceive the world around us. In the modern era, light can not only be used in its raw form but can also be used as a versatile tool. Generally, light fields carry energy and momentum (both linear and angular). Due to the transfer of linear momentum from light to matter, the radiation pressure is exerted, whereas, the intrinsic spin angular momentum (SAM) is associated with the polarization states of light. Light fields embedded with optical orbital angular momentum (OAM) -- also known as optical vortices or phase singular beams -- have truly revolutionized the field of optics and extended our basic understanding of the light-matter interaction process across various scales. Optical vortices -- spatially characterized by the presence of twisted phase fronts and a central intensity null -- have found a myriad of applications starting from microparticle trapping and manipulation to microscopy, optical communication, and quantum information science, among others. Here, we revisit some of the fundamental concepts on optical vortices and discuss extensively on how this new dimension of light i.e., the OAM, has been exploited in both linear and nonlinear optical regimes. We discuss the different types of vortex beams, the techniques used to generate and detect their OAM, and their propagation. Particularly, we put a special emphasis on the utilization of vortex beams in nonlinear regimes to explain different optical phenomena such as the second harmonic generation, parametric down-conversion, and high-order harmonic generation. The generation of vortex beams in the UV to XUV regimes, encoded with higher OAM values, could potentially extend their application range to areas such as high-capacity data transmission, stimulated emission depletion microscopy, phase-contrast imaging, and particle trapping in optical tweezers, among others.

Geometry can fundamentally govern the propagation of light, independent of material constraints. Here, we demonstrate that a fractal phase space, endowed with a non-Euclidean, scale-dependent geometry, can intrinsically induce resonance quantization, spatial confinement, and tunable damping without the need for material boundaries or external potentials. Employing a fractional formalism with a fixed scaling exponent, we reveal how closed-loop geodesics enforce constructive interference, leading to discrete resonance modes that arise purely from geometric considerations. This mechanism enables light to localize and dissipate in a controllable fashion within free space, with geometry acting as an effective quantizing and confining agent. Numerical simulations confirm these predictions, establishing geometry itself as a powerful architect of wave dynamics. Our findings open a conceptually new and experimentally accessible paradigm for material-free control in photonic systems, highlighting the profound role of geometry in shaping fundamental aspects of light propagation.

The remarkable progress of artificial intelligence (AI) has revealed the enormous energy demands of modern digital architectures, raising deep concerns about sustainability. In stark contrast, the human brain operates efficiently on only ~20 watts, and individual cells process gigabit-scale genetic information using energy on the order of trillionths of a watt. Under the same energy budget, a general-purpose digital processor can perform only a few simple operations per second. This striking disparity suggests that biological systems follow algorithms fundamentally distinct from conventional computation. The framework of information thermodynamics-especially Maxwell's demon and the Szilard engine-offers a theoretical clue, setting the lower bound of energy required for information processing. However, digital processors exceed this limit by about six orders of magnitude. Recent single-molecule studies have revealed that biological molecular motors convert Brownian motion into mechanical work, realizing a "demon-like" operational principle. These findings suggest that living systems have already implemented an ultra-efficient information-energy conversion mechanism that transcends digital computation. Here, we experimentally establish a quantitative correspondence between positional information (bits) and mechanical work, demonstrating that molecular machines selectively exploit rare but functional fluctuations arising from Brownian motion to achieve ATP-level energy efficiency. This integration of information, energy, and timescale indicates that life realizes a Maxwell's demon-like mechanism for energy-efficient information processing.

Achieving ultra-high field intensities is paramount for advancing compact plasma accelerators and high-energy-density physics, yet it is fundamentally limited by the constraints of focusing distance and nonlinear efficiency. We report a theoretical model demonstrating a highly efficient, magnetically-assisted pathway for extreme laser energy concentration in under-dense plasma. By tuning an external magnetic field near the cyclotron resonance (Ce=0.7), we show a fundamental, nonlinear enhancement of the relativistic self-focusing (RSF) mechanism. This magnetic enhancement drives the pulse into a catastrophic, coupled collapse over an exceptionally short distance of 1.25 Rayleigh lengths. The dynamics result in simultaneous spatial confinement (fr=0.05) and significant temporal self-compression (ft=0.60 ). Crucially, this combined confinement yields a localized peak intensity amplification factor exceeding 103 compared to the initial state. This work confirms a robust and compact method for generating petawatt-scale power densities and provides a direct, actionable blueprint for next-generation laser-plasma experiments.

Biofilms in porous media critically influence hydraulic properties in environmental and engineered systems. However, a mechanistic understanding of how microbial life controls permeability remains elusive. By combining microfluidics, controlled pressure gradient and time-lapse microscopy, we quantify how motile and non-motile bacteria colonize a porous landscape and alter its resistance to flow. We find that while both strains achieve nearly identical total biomass, they cause drastically different permeability reductions - 78% for motile cells versus 94% for non-motile cells. This divergence stems from motility, which limits biomass spatial accumulation, whereas non-motile cells clog the entire system. We develop a mechanistic model that accurately predicts permeability dynamics from the pore-scale biomass distribution. We conclude that the spatial organization of biomass, not its total amount, is the primary factor controlling permeability.

The wave operator model provides a framework for modeling wave propagation by encoding material parameter distributions into matrix-form operators. This paper extends this framework from lossless to lossy media. We present a derivation of the wave operator solution for the electric field in dissipative environments, which can be decomposed into a closed-form propagation term and a non-closed-form dissipation term. Based on an analysis of the dominant exponential decay within the propagation term, an attenuation compensation strategy is proposed to restore the attenuated data to an approximate lossless state. The performance of this compensation strategy is analyzed and validated through numerical experiments, establishing the theoretical foundation for reduced order model (ROM)-based techniques in lossy media.

X-ray microtomography at synchrotron sources is fundamentally limited by the high radiation dose applied to the samples, which restricts investigations to non-native tissue states and thereby compromises the biological relevance of the resulting data. The limitation stems from inefficient indirect detection schemes that require prolonged exposures. Efforts to extract additional contrast through multimodal techniques, like modulation-based imaging, worsen the problem by requiring multiple tomographic scans. In addition, the techniques suffer from low modulator pattern visibility, which reduces measurement efficiency and sensitivity. We address both the detection efficiency and modulation visibility challenges using a novel setup that combines an X-ray waveguide, a structured phase modulator, and a photon-counting detector. Our approach simultaneously achieves near-theoretical limits in both visibility (95%) and quantum efficiency (98%), thereby enabling dose-efficient multimodal microtomography at single-micrometer resolution. This advance will enable new classes of experiments on native-state biological specimens with the potential to advance biomedical research, disease diagnostics, and our understanding of tissue structure in physiological environments.

High-quality micropillar cavities were grown using molecular-beam epitaxy. Stable continuous-wave lasing at room-temperature was demonstrated for microlasers with semiconductor and hybrid output mirrors. At 300 K, single-mode lasing was demonstrated for micropillars with a diameter of 5 $\mu$m at a wavelength of 960 nm, with a minimum lasing threshold of 1.2 mW and a bare quality-factor exceeding 8000.

Laser cutting is an old and multi-physical process that was quickly adopted by the metallurgical industry. However, this fast industrialisation has had a significant impact on quality control. Several studies have been carried out to characterise and minimise different types of cutting defects. Reviews published between 2008 and 2022 highlight that research often focuses on single-criterion ___quality' approaches, aiming to minimise specific defects such as the Heat-Affected Zone, surface roughness, or kerf geometry. Consequently, efforts have been directed at optimising specific aspects of quality rather than adopting a complete approach. Furthermore, these reviews reveal that cutting quality can be enhanced through the careful selection of laser manufacturing parameters and part parameters. However, while parameters such as material and thickness have been investigated, the influence of part morphology on cutting quality remains underexplored.___ Although some studies have examined the effects of material and thickness, part morphology is often limited to simple segments with varying cutting lengths or angles. While other research has investigated the impact of angle size on cutting quality, no established method exists to systematically determine the influence of each part morphology on cutting quality.______ In response to this gap, the present study proposes to evaluate the criticality of cutting defects, as defined by existing standards, across various part morphologies using a method adapted from Failure Modes, Effects, and Criticality Analysis (FMECA). The objective is to develop a global approach that examines the influence of all morphologies on all types of cutting defects. An industrial application shows that cutting defects, particularly thermal ones, are strongly influenced by morphologies, while thickness affects them irregularly. Improvement priorities target critical defects shaped by both factors. Burrs and adherent slag are critical in angles and arcs, while segments, often used in studies, are less sensitive._________ These observations lead to the definition of design limits. This method offers a detailed analysis of the influence of design data on quality, providing practical tools for improving industrial processes._________

Achieving an optimal biomechanical environment within bone scaffolds is critical for promoting tissue regeneration, particularly in load-bearing anatomical sites where rigid fixation can induce stress shielding and compromise healing. Functionally graded (FG) scaffolds, which incorporate controlled variations in porosity or material properties, have attracted significant attention as a strategy to mitigate stress shielding by promoting more favourable load transfer. In this study, the effects of porosity gradient magnitude (i.e., max-to-min ratio of porosity), gradient resolution, scaffold material properties, and fixation plate rigidity on the distribution of mechanical stimuli within FG scaffolds were systematically investigated. Finite element analyses (FEA) were conducted on a femoral segmental defect model stabilised with a bone plate, and multiple porosity gradient strategies were compared against a corresponding uniform scaffold composed of body-centred cubic (BCC) unit cells. Scaffolds composed of titanium alloy (Ti-6Al-4V), bioactive glass (45S5 Bio-glass), and polylactic acid (PLA) were evaluated to capture a range of material stiffnesses. Introducing porosity gradients consistently enhanced the mean octahedral shear strain within the scaffold, particularly in regions adjacent to the fixation plate affected by stress shielding. The magnitude of mechanical stimulus improvement increased with both greater porosity gradient magnitudes and higher gradient resolution. These improvements were more pronounced in stiffer materials, such as Ti-6Al-4V, emphasising the critical interplay between scaffold material properties and architectural design. These findings highlight the importance of tailoring both porosity profiles and material selection to optimise scaffold mechanics for bone regeneration.

The present investigation is directed at exploring southern polar ionospheric responses to intense space weather events and their correlations with plasma convection and auroral precipitation. The main phases of six geomagnetic storms occurring in the year 2023 (ascending phase of the present solar cycle) are considered for this study. The ionospheric Total Electron Content (TEC) measurements derived from GPS receivers covering the Antarctic region are used for probing the electron density perturbations during these events. Auroral precipitation maps are shown to illustrate the locations of the GPS stations relative to particle precipitation. SuperDARN maps are shown to understand the effects of plasma convection over these locations. Correlation between the enhanced TEC observations with the auroral precipitation (R $\sim$ 0.31) and the plasma convection (R $\sim$ 0.88) reveals that the latter is more responsible for causing significant enhancements in the diurnal maximum values of TEC over the Antarctic region in comparison to the former. Therefore, this work shows correlation studies between two physical processes and ionospheric density enhancements over the under-explored south polar region under strong levels of geomagnetic activity during 2023.

A robust pre-emptive kill switch for cold atom experiments is introduced to significantly reduce costly system reassembly or replacement. The design incorporates upper (alarm) and lower (evaporation) event detection mechanisms based on predefined thresholds. Meanwhile, a duty cycle timing methodology is used to avert unintentional activation of the dispenser in circumstances where pulse signals occur. The circuit employs generic components, a modular design, and formalized logic, ensuring cost-effectiveness, making the design suitable for school laboratories and other research environments. This design is highly versatile and can be applied to other sensitive devices beyond dispensers, such as heating filaments, titanium sublimation pumps, tungsten lamps, and comparable systems.

The Data Assimilation (DA) community has been developing various diagnostics to understand the importance of the observing system in accurately forecasting the weather. They usually rely on the ability to compute the derivatives of the physical model output with respect to its initial condition. For example, the Forecast Sensitivity-based Observation Impact (FSOI) estimates the impact on the forecast error of each observation processed in the DA system. This paper presents how these DA diagnostic tools are transferred to Machine Learning (ML) models, as their derivatives are readily available through automatic differentiation. We specifically explore the interpretability and explainability of the observation-driven GraphDOP model developed at the European Centre for Medium-Range Weather Forecasts (ECMWF). The interpretability study demonstrates the effectiveness of GraphDOP's sliding attention window to learn the meteorological features present in the observation datasets and to learn the spatial relationships between different regions. Making these relationships more transparent confirms that GraphDOP captures real, physically meaningful processes, such as the movement of storm systems. The explainability of GraphDOP is explored by applying the FSOI tool to study the impact of the different observations on the forecast error. This inspection reveals that GraphDOP creates an internal representation of the Earth system by combining the information from conventional and satellite observations.

High-precision measurements of the fine-structure splittings in helium high Rydberg states have been reported, yet corresponding ab initio benchmarks for direct comparison remain unavailable. In this work, we extend the correlated B-spline basis function (C-BSBF) method to calculate the fine-structure splittings of high Rydberg states in $^4$He. The calculations include the $m\alpha^4$- and $m\alpha^5$-order contributions, the singlet-triplet mixing effect, and estimated spin-dependent $m\alpha^6$-order corrections obtained using a $1/n^3$ scaling approximation. High-precision ab initio results are obtained for principal quantum numbers $n=24$-37 with kilohertz-level accuracy and further extended to $n=45$-51 by extrapolation and fitting. The theoretical results show excellent agreement with quantum-defect theory (QDT) predictions and allow direct comparison with experimental measurements. Additionally, the discrepancy observed at $n=34$ is expected to be clarified with improved experimental precision.

In preparation to the CROSS experiment at the Canfranc underground laboratory (Spain) $-$ aiming to search for neutrinoless double-beta ($0\nu\beta\beta$) decay of $^{100}$Mo using low-temperature detectors with heat-scintillation readout $-$ we report on development of a dedicated muon veto system. The need for the muon veto in CROSS is caused by a comparatively high residual cosmic muon flux at the experimental site ($\sim$20 $\mu$/m$^2$/h), being a dominant background in the region of interest (ROI) at $\sim$3 MeV. Thus, we installed the muon veto system around the CROSS low-background setup, forming four lateral, one top, and four bottom sectors. In this paper we describe the design, construction and operation of the CROSS muon veto system, as well as its optimization and validation by comparing dedicated Monte Carlo (MC) simulations of muons with low-temperature measurements in the setup. We demonstrate a stable operation of the veto system with the average trigger rates compatible with MC simulations. Also, we investigated two muon trigger logics based on coincidences with either 2 sectors or a single sector of the veto. The MC study shows that, in combination with the multiplicity cut of thermal detectors, these trigger logics allow to reject 99.2\% and 99.7\% of muon-induced events in the ROI, respectively. Despite a comparatively high dead time ($\sim$18\%) introduced by coincidences with any of nine sectors of the veto $-$ the adopted strategy $-$ the muon-induced background in the ROI of the CROSS experiment can be reduced down to $\sim$2 $\times 10^{-3}$ cnts/keV/kg/yr, i.e., an acceptable level compatible with a high-sensitivity $0\nu\beta\beta$ decay search foreseen in CROSS.

Comparing networks is essential for a number of downstream tasks, from clustering to anomaly detection. Despite higher-order interactions being critical for understanding the dynamics of complex systems, traditional approaches for network comparison are limited to pairwise interactions only. Here we construct a general information theoretic framework for hypergraph similarity, capturing meaningful correspondence among higher-order interactions while correcting for spurious correlations. Our method operationalizes any notion of structural overlap among hypergraphs as a principled normalized mutual information measure, allowing us to derive a hierarchy of increasingly granular formulations of similarity among hypergraphs within and across orders of interactions, and at multiple scales. We validate these measures through extensive experiments on synthetic hypergraphs and apply the framework to reveal meaningful patterns in a variety of empirical higher-order networks. Our work provides foundational tools for the principled comparison of higher-order networks, shedding light on the structural organization of networked systems with non-dyadic interactions.

We demonstrate that recent advances in QED theory of Li-like ions [V. A. Yerokhin et al., Phys. Rev. A 112, 042801 (2025)] enable determinations of absolute nuclear charge radii for heavy elements. By incorporating constraints derived from electron-scattering data, we obtain radii that are independent of the assumed model of the nuclear charge distribution. Our approach is validated for $^{208}$Pb, a well-studied spherical nucleus, and is then applied to $^{209}$Bi, where low-lying nuclear excitations complicate the interpretation of muonic-atom data.

In this paper, we develop an analytical model to investigate the sound generated by the shock-instability interactions (SII) in supersonic round jets, extending our previous two-dimensional planar study to circular configurations. The jet is represented by a vortex sheet, with its motion modeled by the Euler equations. Shock and instability waves are modeled using Pack's approach and the linear stability theory, respectively, while their interaction is calculated by solving an inhomogeneous wave equation. Using the Fourier transform and steepest descent method, we obtain a closed-form solution for the resulting acoustic field. Results due to the interaction between the instability waves and one interaction cell capture the key directivity features of screech reported in experiments and numerical simulations, indicating that the classic monopole assumption may be inadequate. In particular, the screech-tone intensity due to multiple shock cells decays rapidly as the observer angle approaches 180 degrees, which is in better agreement with the experimental data measured by Norum. We further analyze how the instability wave growth rate influences these directivity patterns and examine the sound generation efficiency of the broadband shock-associated noise. Finally, an examination of near-field pressure fluctuations due to the SII reveals that noise is produced primarily via the Mach wave radiation mechanism.

This document is comprised of a collection of consolidated parameters for the key parts of the muon collider. These consolidated parameters follow on from the October 2024 Preliminary Parameters Report. Attention has been given to a high-level consistent set of baseline parameters throughout all systems of the complex, following a 10 TeV center-of-mass design. Additional details of the designs contributing to this baseline design are featured in the appendix. Likewise, explorative variations from this baseline set can be found in the appendix. The data is collected from a collaborative spreadsheet and transferred to overleaf.

We present a numerical investigation of the magnetophoresis of metal ions in porous media under static, nonuniform magnetic fields. The multiphysics simulations couple momentum transport, mass diffusion, and magnetic field equations, with the porous medium modeled using two distinct approaches: a Stokes-based formulation incorporating effective diffusivity, and a Brinkman-based formulation that explicitly accounts for permeability and medium-induced drag. Comparison with recent experimental data [Nwachuwku et al. Submitted, 2025] reveals that the Stokes model partially fails to capture key trends, while the Brinkman model, with permeability accurately reproduces observed transport behavior on various porous media. Our simulations predict that both paramagnetic (MnCl2) and diamagnetic (ZnCl2) ions may form field-induced clusters under magnetic gradients over a range of concentrations of 1mM-100mM and magnetic field gradients of up to 100 T2/m. The dominant driving force is found to be the magnetic gradient (Kelvin) force, while the paramagnetic force from concentration gradients contributes minimally. In binary mixtures, hydrodynamic interactions between paramagnetic and diamagnetic clusters significantly alter transport dynamics. Specifically, paramagnetic clusters can pull diamagnetic clusters along the magnetic field gradient, enhancing diamagnetic migration and suppressing the motion of paramagnetic species. These findings highlight the importance of porous media modeling and interspecies interactions in predicting magnetophoretic transport of ionic mixtures.

Horizontally ($\Omega \perp \mathbf{v}_{\rm{ns}}$) and axially ($\Omega \parallel \mathbf{v}_{\rm{ns}}$) rotating counterflow of superfluid $^4$He (He~II) generated thermally in a square channel is studied using the second sound attenuation technique, detecting statistically steady state and temporal decay of the density of quantized vortex lines $L(t,\Omega)$. The array of rectilinear quantized vortices created by rotation at angular velocity $\Omega$ strongly affects the transient regimes of quantum turbulence characterized by counterflow velocity $\mathbf{v}_{\rm{ns}}$, differently in both geometries. Two effects are observed, acting against each other and affecting the late temporal decay $L(t,\Omega)$. The first is gradual decrease of the decay exponent $\mu$ of the power law $L(t,\Omega) \propto t^{-\mu}$, associated with the fact that under rotation thermal counterflow acquires two-dimensional features, clearly observed in the $\Omega \parallel \mathbf{v}_{\rm{ns}}$ geometry. It exists in the $\Omega \perp \mathbf{v}_{\rm{ns}}$ geometry as well, however, it is screened here by the influence of the effective Ekman layer built within the effective Ekman time $T_{\rm{Ek}}^{\rm{eff}}=H (\nu_{\rm{eff}} \Omega)^{-1/2}$ of order seconds, where $H$ is the characteristic size of the turbulent system along $\mathbf \Omega$ and $\nu_{\rm{eff}}$ is the effective kinematic viscosity of turbulent He~II. For faster rotation rates $L(t,\Omega)$ gradually ceases to display a clear power law. Instead, rounded and ever steeper decays occur, gradually shifted toward shorter and shorter times, significantly shortening the time range for a possible self-similar decay of vortex line density. This effect is not observed in $\Omega \parallel \mathbf{v}_{\rm{ns}}$ geometry, as here the much longer $T_{\rm{Ek}}^{\rm{eff}}$ of order minutes cannot affect the observed $L(t,\Omega)$ decay appreciably.

We develop a wave packet molecular dynamics framework for modeling the structural properties of partially-ionized dense plasmas, based on a chemical model that explicitly includes bound state wavefunctions. Using hydrogen as a representative system, we compute self-consistent charge state distributions through free energy minimization, following the approach of Plummer et al. [Phys. Rev. E 111, 015204 (2025)]. This enables a direct comparison of static equilibrium properties with path integral Monte Carlo data, facilitating an evaluation of the model's underlying approximations and its ability to capture the complex interplay between ionization and structure in dense plasma environments.

A huge interesting progress in the field of organic electronic materials and devices has been observed in the last decade. However, the understanding of these materials is still a challenge to overcome. Most studies in literature focus on active devices such as OTFTs, OLEDs and OPVs. Nevertheless, a complete technology has to have also passive devices in order to allow the design of interesting applications and complex circuits. This paper deals with the development of a complete set of passive devices allowing the fabrication of simple applications such as filters or sensors. The process flow is a fully screen printed technology that uses exclusively organic materials on gold laser ablated flexible substrate. Discrete passive (R, L, C) devices have been processed and characterized. This has permitted the fabrication of RLC low-band pass filters that are dedicated to RF applications, typically around 1GHz. Furthermore, based on these discrete passive components, we have developed a sensitive sensor on flexible substrate for RFID applications. We present the state of the art of our process development for RF applications using organic materials.

Political debate nowadays takes place mainly on online social media, with election periods amplifying ideological engagement. Reddit is generally considered more resistant to polarization and echo chamber effects than platforms like Twitter or Facebook. Here, we challenge this assumption through a case study across the 2016 US presidential election. We use statistical validation techniques to extract ideologically distinct communities of subreddits, in terms of their contributing user base and news consumption, which we use to analyze the dynamics of political debate. We thus reveal clear polarization in both interaction-based and topic-based communities, with clusters of Democratic, Conservative, and Banned subreddits. Election periods intensify cross-group engagement, align Banned and Conservative content, and reduce linguistic diversity within groups. Overall we characterize Reddit as a polarized environment marked by the presence of echo chambers, highlighting network validation as a key method for identifying behavioral and interaction patterns on online social media.

Stability achieved by large angular momentum is ubiquitous in nature, with examples ranging from classical mechanics, over optics and chemistry, to nuclear physics. In atoms, angular momentum can protect excited electronic orbitals from decay due to selection rules. This manifests spectacularly in highly excited Rydberg states. Low angular momentum Rydberg states are at the heart of recent breakthroughs in quantum computing, simulation and sensing with neutral atoms. For these applications the lifetime of the Rydberg levels sets fundamental limits for gate fidelities, coherence times, or spectroscopic precision. The quest for longer Rydberg state lifetimes has motivated the generation, coherent control and trapping of circular Rydberg atoms, which are characterized by the maximally allowed electron orbital momentum and were key to Nobel prize-winning experiments with single atoms and photons. Here, we report the observation of individually trapped circular Rydberg atoms with lifetimes of more than 10 milliseconds, two orders of magnitude longer-lived than the established low angular momentum orbitals. This is achieved via Purcell suppression of blackbody modes at room temperature. We coherently control individual circular Rydberg levels at so far elusive principal quantum numbers of up to $n=103$, and observe tweezer trapping of the Rydberg atoms on the few hundred millisecond scale. Our results pave the way for quantum information processing and sensing utilizing the combination of extreme lifetimes and giant Rydberg blockade.

Photonic integrated circuits are heavily researched devices for telecommunication, biosensing, and quantum technologies. Wafer-scale fabrication and testing are crucial for reducing costs and enabling large-scale deployment. Grating couplers allow non-invasive measurements before packaging, but classical designs rely on long tapers and narrow bandwidths. In this work, we present compact, inverse-designed grating couplers with broadband transmission. We optimized and fabricated arrays of devices and characterized them with a 4f-scanning setup. The nominal design reached simulated efficiencies of 52 %, while measurements confirmed robust performance with up to 32 % efficiency at the target 1540 nm wavelength and 46 % at shifted wavelengths. Without scaling and contour biasing, the measured efficiency at the target wavelength drops to only 4.4 %. Thus, a key finding is that systematic scaling and edge biasing recover up to an eightfold improvement in efficiency. These inverse-designed grating couplers can be efficiently corrected post-design, enabling reliable performance despite fabrication deviations. This approach allows simple layout adjustments to compensate for process-induced variations, supporting wafer-scale testing, cryogenic photonic applications, and rapid design wavelength tuning.

This work introduces a systematic algorithm for generating directed networks with prescribed symmetries by constructing expansions from a given quotient network. The method enables researchers to synthesize realistic network models with controllable symmetry structure, facilitating studies of symmetry-driven dynamics such as cluster synchronization in biological, social, and technological systems.

Numerical simulations of reactive hypersonic flow under thermodynamic and chemical non-equilibrium conditions are presented for the Mars Pathfinder capsule. An 8-species chemical model is employed to simulate Mars' atmosphere. Park's two-temperature model is used to account for the thermal non-equilibrium phenomena. The present work analyzes the impact of different values of the weight factors used in Park's model, aiming to broaden the understanding of the weight factors influence. The code used to simulate the flows solves the Navier-Stokes equations modified to account for reacting gas mixtures. The findings are depicted in terms of the Mach number and temperature modes along the stagnation streamline in a region close to the shock wave. The present analysis also includes results regarding the stagnation point convective heat flux. The results indicate that varying the weight factors yields negligible differences in the shock wave position and stagnation point convective heat flux. The changes in the weight factors cause variations in the maximum temperature mode values in the non-equilibrium region. The results presented are in good agreement with experimental data present in the literature. The present work indicates that Park's two-temperature model weight factors can substantially affect the temperature mode distributions in the flow non-equilibrium region.

Food waste represents a major challenge to global climate resilience, accounting for almost 10\% of annual greenhouse gas emissions. The retail sector is a critical player, mediating product flows between producers and consumers, where supply chain inefficiencies can shape which items are put on sale. Yet how these dynamics vary across geographic contexts remains largely unexplored. Here, we analyze data from Denmark's largest retail group on near-expiry products put on sale. We uncover the geospatial variations using a dual-clustering approach. We identify multi-scale spatial relationships in retail organization by correlating store clustering -- measured using shortest-path distances along the street network -- with product clustering based on promotion co-occurrence patterns. Using a bipartite network approach, we identify three regional store clusters, and use percolation thresholds to corroborate the scale of their spatial separation. We find that stores in rural communities put meat and dairy products on sale up to 2.2 times more frequently than metropolitan areas. In contrast, we find that metropolitan and capital regions lean toward convenience products, which have more balanced nutritional profiles but less favorable environmental impacts. By linking geographic context to retail inventory, we provide evidence that reducing food waste requires interventions tailored to local retail dynamics, highlighting the importance of region-specific sustainability strategies.

The ability to control and understand the phase transitions of individual nanoscale building blocks is key to advancing the next generation of low-power reconfigurable nanophotonic devices. To address this critical challenge, molecular nanoparticles (NPs) exhibiting a spin crossover (SCO) phenomenon are trapped by coupling a quadrupole Paul trap with a multi-spectral polarization-resolved scattering microscope. This contact-free platform simultaneously confines, optically excites, and monitors the spin transition in Fe(II)-triazole NPs in a pressure-tunable environment, eliminating substrate artifacts. Thus, we show light-driven manipulation of the spin transition in levitating NPs free from substrate-induced effects. Using the robust spin bistability near room temperature of our SCO system, we quantify reversible opto-volumetric changes of up to 6%, revealing precise switching thresholds at the single-particle level. Independent pressure modulation produces a comparable size increase, confirming mechanical control over the same bistable transition. These results constitute full real-time control and readout of spin states in levitating SCO NPs, charting a route toward their integration into ultralow-power optical switches, data-storage elements, and nanoscale sensors.

This study evaluates the performance of three reference equations of state (EoS), AGA8-DC92, GERG-2008, and SGERG-88, in predicting the density of regasified liquefied natural gas (RLNG) mixtures. A synthetic nine-component RLNG mixture was gravimetrically prepared. High-precision density measurements were obtained using a single-sinker magnetic suspension densimeter over a temperature range of (250 to 350) K and pressures up to 20 MPa. The experimental data were compared with EoS predictions to evaluate their accuracy. AGA8-DC92 and GERG-2008 showed excellent agreement with the experimental data, with deviations within their stated uncertainty. In contrast, SGERG-88 exhibited significantly larger deviations for this RLNG mixture, particularly at low temperatures of (250 to 260) K, where discrepancies reached up to 3 %. Even at 300 K, deviations larger than 0.4 % were observed at high pressures, within the model's uncertainty, but notably higher than those of the other two EoSs. The analysis was extended to three conventional 11-component natural gas mixtures (labeled G420 NG, G431 NG, and G432 NG), previously studied by our group using the same methodology. While SGERG-88 showed reduced accuracy for the RLNG mixture, it performed reasonably well for these three mixtures, despite two of them have a very similar composition to the RLNG. This discrepancy is attributed to the lower CO2 and N2 content typical in RLNG mixtures, demonstrating the sensitivity of EoS performance to minor differences in composition. These findings highlight the importance of selecting appropriate EoS models for accurate density prediction in RLNG applications.

In this PhD thesis, a method for solving fast and accurately the monoenergetic drift-kinetic equation at low collisionality is presented. The algorithm is based on the analytical properties of the drift-kinetic equation when its dependence on the pitch-angle cosine is represented employing Legendre polynomials as basis functions. The Legendre representation of the monoenergetic drift-kinetic equation possesses a tridiagonal structure, which is exploited by the algorithm presented. The monoenergetic drift-kinetic equation can be solved fast and accurately at low collisionality by employing the standard block tridiagonal algorithm for block tridiagonal matrices. The implementation of the aforementioned algorithm leads to the main result of this thesis: the new neoclassical code MONKES (MONoenergetic Kinetic Equation Solver), conceived to satisfy the necessity of fast and accurate calculations of the bootstrap current for stellarators and in particular for stellarator optimization. MONKES is a new neoclassical code for the evaluation of monoenergetic transport coefficients in stellarators. By means of a convergence study and benchmarks with other codes, it is shown that MONKES is accurate and efficient. The combination of spectral discretization in spatial and velocity coordinates with block sparsity allows MONKES to compute monoenergetic coefficients at low collisionality, in a single core, in approximately one minute. MONKES is sufficiently fast to be integrated into stellarator optimization codes for direct optimization of the bootstrap current (and radial neoclassical transport) and to be included in predictive transport suites.

We analyse the behaviour of the Rayleigh-Taylor instability (RTI) in the presence of a foam. Such a problem may be relevant, for example, to some inertial confinement fusion (ICF) scenarios such as foams within the capsule or lining the inner hohlraum wall. The foam displays 3 different phases: by order of increasing stress, it is first elastic, then plastic, and then fractures. Only the elastic and plastic phases can be subject to a linear analysis of the instability. The growth rate is analytically computed in these 2 phases, in terms of the micro-structure of the foam. In the first, elastic, phase, the RTI can be stabilized for some wavelengths. In this elastic phase, a homogenous foam model overestimates the growth because it ignores the elastic nature of the foam. Although this result is derived for a simplified foam model, it is likely valid for most of them. Besides the ICF context considered here, our results could be relevant for many fields of science.

Strong coupling between quantum emitters and optical cavities is essential for quantum information processing, high-purity single-photon sources, and nonlinear quantum devices. Achieving this regime at room temperature in a compact, deterministic on-chip platform-critical for integration with nanoelectronic circuitry and scalable device architectures-remains a major challenge, mainly due to the difficulty of fabricating cavities with ultra-small mode volumes and precisely positioning quantum emitters. Here, we demonstrate a robust quantum plasmonic device in which colloidal quantum dots (Qdots) are strongly coupled to plasmonic slit cavities using a dielectrophoresis-based positioning technique with real-time photoluminescence (PL) feedback, providing directly resolvable coupled structures that enable parallel device fabrication and straightforward integration with additional optical elements such as waveguides. Our measurements reveal clear PL resolved Rabi splitting at room temperature with pre characterized cavities, with variations across devices that scale with the average number of coupled Qdots. While electrical tuning via the quantum-confined Stark effect is enabled by integrated electrodes, its impact is largely overshadowed by room-temperature spectral diffusion. Our results pave the way for scalable, electrically tunable quantum plasmonic platforms, offering new opportunities for integrated quantum photonic circuits, active light-matter interactions, and room-temperature quantum technologies.

Plasma processing of superconducting radio frequency (SRF) cavities has been an active research effort at Jefferson Lab (JLab) since 2019, aimed at enhancing cavity performance by removing hydrocarbon contaminants and reducing field emission. In this experiment, processing using argon-oxygen and helium-oxygen gas mixtures to find minimum ignition power at different cavity pressure was investigated. Ongoing simulations are contributing to a better understanding of the plasma surface interactions and the fundamental physics behind the process. These simulations, combined with experimental studies, guide the optimization of key parameters such as gas type, RF power, and pressure to ignite plasma using selected higher-order mode (HOM) frequencies. This paper presents experimental data from argon-oxygen and helium-oxygen gas mixture C75 and C100 cavity plasma ignition studies, as well as simulation results for the C100-type cavity based on the COMSOL model previously applied to the C75 cavity.

Marine low clouds play a crucial role in Earth's radiation budget. These clouds efficiently reflect sunlight and drive the magnitude and sign of the global cloud feedback. Despite their relevance, the evolution of shallow cloud decks over the last decades is not well understood. One of the dominant controls of this low cloud cover is the lower tropospheric stability, quantified by the estimated inversion strength (EIS). Here, we quantify how regional EIS depends on local and remote surface temperature, revealing the dynamics controlling the characteristics of shallow clouds. We find that global EIS increases with warming in tropical regions of ascent and decreases with warming in regions of descent, as expected. In addition to the West Pacific Warm Pool, the Atlantic convection regions and the central Pacific are important predictors. Focusing on subtropical ocean upwelling regions in different ocean basins, where the low cloud decks reside, EIS increases with a fairly complex pattern of remote warming and decreases with local warming. The spatial relationship between surface temperature and EIS is robust across different climate models and reanalyses, allowing us to constrain the large spread in estimates of historical EIS trends. In the Southeast Pacific, where historical temperature trends are not well understood, we attribute the observed increased EIS since 1980 entirely to remote warming, indicating that local cooling did not increase stability in this region. Our results put into question the dominance of the West Pacific Warm Pool in controlling low cloud feedbacks in the eastern Pacific and give insights into mechanisms underlying the spatial dependence of radiative feedbacks on surface temperature patterns.

The built-in potential of p-n junctions plays a critical role in charge separation, which is fundamental to the photovoltaic effect. However, the conventional classical theory of photovoltaic effect in p-n junctions typically does not account for the quantitative influence of the built-in potential. In this study, we revisit the classical theory and propose an improved analytic expression of photocurrent by applying more accurate boundary conditions. Our improved expression reveals that the photocurrent comprises of two components: the conventional photocurrent and a previously unrecognized backward photocurrent. Latter reduces the total photocurrent, yet it has not been discussed in prior literature. The essential role of the built-in potential is to suppress this backward current. Furthermore, our improved expression of photocurrent predicts that the photocurrent vanishes under certain forward bias conditions. This prediction is experimentally validated using a commercial silicon solar cell, confirming the direct impact of the built-in potential on photocurrent behavior.

Modern experiments with cold molecular ions have reached a high degree of complexity requiring frequent sample preparation, state initialization and protocol execution while demanding precise control over multiple devices and laser sources. To maintain a high experimental duty cycle and robust measurement conditions, automation becomes essential. We present a fully automated control system for the preparation of trapped state-selected molecular ions and subsequent quantum logic-based experiments. Adaptive feedback routines based on real-time image analysis introduce and identify single molecular ions in atomic-ion Coulomb crystals. By appropriate manipulation of the trapping potentials, excess atomic ions are released from the trap to produce dual-species two-ion strings, here Ca$^+-$N$_2^+$. After mass and state identification of the molecular ion, nanosecond-level synchronization of laser pulses employing the Sinara/ARTIQ framework and real-time data analysis enable quantum-logic-spectroscopic measurements. The present automated control system enables robust, unsupervised operation over extended periods resulting in an increase of the number of experimentation cycles by about a factor of ten compared to manual operation and a factor of about eight in loaded molecules in typical practical situations. The modular, distributed design of the system provides a scalable blueprint for similar molecular-ion experiments.

The new Inner Tracking System (ITS2) of the ALICE experiment began operation in 2021 with the start of LHC Run 3. Compared to its predecessor, ITS2 offers substantial improvements in pointing resolution, tracking efficiency at low transverse momenta, and readout-rate capabilities. The detector employs silicon Monolithic Active Pixel Sensors (MAPS) featuring a pixel size of 26.88$\times$29.24 $\mu$m$^2$ and an intrinsic spatial resolution of approximately 5 $\mu$m. With a remarkably low material budget of 0.36% of radiation length ($X_{0}$) per layer in the three innermost layers and a total sensitive area of about 10 m$^2$, the ITS2 constitutes the largest-scale application of MAPS technology in a high-energy physics experiment and the first of its kind operated at the LHC. For stable data taking, it is crucial to calibrate different parameters of the detector, such as in-pixel charge thresholds and the masking of noisy pixels. The calibration of 24120 monolithic sensors, comprising a total of 12.6$\times$10$^{9}$ pixels, represents a major operational challenge. This paper presents the methods developed for the calibration of the ITS2 and outlines the strategies for monitoring and dynamically adjusting the detector's key performance parameters over time.

We present here an optimisation and demonstration of a wide band instrument capable of measuring localised and directionally alternated magnetic fields below pT in the very high frequency (VHF) range. We take advantage of the magnon-photon hybridization between a yttrium iron garnet (YIG) sphere and a copper resonant cavity to employ a resonant heterodyne detection scheme. The measurement is near instantaneous due to the strong coupling attained between magnons and this http URL this work measurements are reported showing a significant widening of the measurement bandwidth, obtained by tuning the YIG Larmor frequency with a bias magnetic field and adjusting the magnon-photon coupling strength. Minimum sensitivity in the sub pT regime is demonstrated in the range 150 -- 225 MHz at room temperature and expected to go to fT in cryogenic temperatures. Dynamic range is estimated to be above 100 dB. The sensitivity is found to be independent on size, being ready to in-chip miniaturization. Such device can be an important building block to quantum circuits, such as baluns, transducers or signal processing units.

This study investigates into the adsorption sensing capabilities of single-walled (5,5) boron nitride nanotubes (BNNTs) towards environmental pollutant gas molecules, including CH2, SO2, NH3, H2Se, CO2 and CS2. Employing a linear combination of atomic orbital density functional theory (DFT) and spin-polarized generalized gradient approximation (GGA), the investigation reveals the nanotube's robust adsorption behavior without compromising its structural integrity. Thermodynamic and chemical parameters, such as adsorption energy, HOMO-LUMO gap, vertical ionization energy, and vertical electron affinity, highlight the (5,5) BNNTs' potential as efficient absorbents for pollutant molecules. Infrared spectroscopy confirms the formation of distinct BNNT-gas complexes. These findings underscore the promising application of BN nanotubes as absorbents for common gaseous pollutants, essential for developing sensors to enhance indoor air quality.

High-precision micromanipulation techniques, including optical tweezers and hydrodynamic trapping, have garnered wide-spread interest. Recent advances in optofluidic multiplexed assembly and microrobotics demonstrate significant progress, particularly by iteratively applying laser-induced, localized flow fields to manipulate microparticles in viscous solutions. However, these approaches still face challenges such as undesired hydrodynamic coupling and instabilities when multiple particles are brought into close proximity. By leveraging an analytical model of thermoviscous flows, this work introduces a stochastic optimization approach that selects flow fields for precise particle arrangement without relying on rule-based heuristics. Through minimizing a comprehensive objective function, the method achieves sub-micrometer alignment accuracy even in a crowded setting, avoiding instabilities driven by undesired coupling or particle collisions. An autonomously emerging "action at a distance" strategy - placing the laser scan path farther from the manipulated particles over time - exploits the $1/r^2$ decay of thermoviscous flow to refine positioning. Overall, objective function-based model predictive control enhances the versatility of automated optofluidic manipulations, opening new avenues in assembly, micromanufacturing, robotics, and life sciences.

Self-oscillators are intriguing due to their ability to sustain periodic motion without periodic stimulus. They remain rare as achieving such behavior requires a balance of energy input, dissipation and non-linear feedback mechanism. Here, we report a molecular-scale optoelectronic self-oscillatory system based on electrically excited plasmons. This system generates light via inelastic electron tunnelling, where electrons lose their energy to molecules and excite the surface plasmon polaritons that decay radiatively. Time-series imaging of photon emission in gold-naphthalene-2-thiol-EGaIn junctions, together with correlation mapping of individual emission spots, reveal long-lived (~1000 s), low-frequency oscillations (1-20 mHz) interspersed with transient high-frequency (20-200 mHz) bursts. This behavior can be explained by attributing individual emission spots to single-molecule resistors that follow Kirchhoff's circuit laws. Induced by tunnelling current, these individual spots emit in a correlated way, self-sustaining the overall oscillatory emission from the whole junction. Our observation is of great interest as it resonates with a broader understanding of similar molecular-scale dynamic systems such as picocavities, offering exciting potential for optoelectronic and sensing applications.

Imaging through dynamic scattering media, such as biological tissue, presents a fundamental challenge due to light scattering and the formation of speckle patterns. These patterns not only degrade image quality but also decorrelate rapidly, limiting the effectiveness of conventional approaches, such as those based on transmission matrix measurements. Here, we introduce an imaging approach based on second-order correlations and synthetic wavelength holography (SWH) to enable robust image reconstruction through thick and dynamic scattering media. By exploiting intensity speckle correlations and using short-exposure intensity images, our method computationally reconstructs images from a hologram without requiring phase stability or static speckles, making it inherently resilient to phase noise. Experimental results demonstrate high-resolution imaging in both static and dynamic scattering scenarios, offering a promising solution for biomedical imaging, remote sensing, and real-time imaging in complex environments.

The water entry of solid and liquid bodies has been studied for over a century, and various researchers have classified the different behaviors that occur when the gas-filled cavity collapses. Although four main cavity collapse regimes have been described and classified for the water entry of small, dense, hydrophobic spheres, only some of these regimes have previously been seen for other impactors, and the scaling used for spheres is not universal across all impactor types. In this paper, we create a unifying scaling to predict cavity collapse regimes, pinch-off time, and pinch-off depth using modified definitions of the Bond, Weber, and Froude numbers for various impactor types. The scaling is based on the downward cavity velocity and an effective diameter, which considers the drag coefficient of the impactor. The impactors we tested include dense solid spheres, disks, and cones, as well as continuous liquid jets and droplet streams. Data for all of these impactor types and behaviors are plotted together with good collapse. Our results indicate that the hydrodynamic characteristics of the impactor, not simply its geometry, govern the global behavior of the cavity.

In stellarators, achieving effective divertor configurations is challenging due to the three-dimensional nature of the magnetic fields, which often leads to chaotic field lines and fuzzy separatrices. This work presents a novel approach to directly optimize modular stellarator coils for a sharp X-point divertor topology akin to the Large Helical Device's (LHD) helical divertor using a target plasma surface with sharp corners. By minimizing the normal magnetic field component on this surface, we target a clean separatrix with minimal chaos. Notably, this approach demonstrates the first LHD-like helical divertor design using optimized modular coils instead of helical coils. Separatrices are produced with significantly lower chaos than in LHD, demonstrating that a wide chaotic layer is not intrinsic to the helical divertor. Additional optimization methods are implemented to improve engineering feasibility of the coils and reduce chaos, including weighted quadrature and manifold optimization, a method which does not rely on normal field minimization. The results outline several new strategies for divertor design in stellarators, though it remains to achieve these edge divertor features at the same time as internal field qualities like quasisymmetry.

Quantitative phase imaging (QPI) enables visualization and quantitative extraction of the optical phase information of transparent samples. However, conventional QPI techniques typically rely on multi-frame acquisition or complex interferometric optics. In this work, we introduce Quad-Pixel Phase Gradient Imaging ($QP^{2}GI$), a single-shot quantitative phase imaging method based on a commercial quad-pixel phase detection autofocus (PDAF) sensor, where each microlens on the sensor covers a $2\times2$ pixel group. The phase gradients of the sample induce focal spot displacements under each microlens, which lead to intensity imbalances among the four pixels. By deriving the phase gradients of the sample from these imbalances, $QP^{2}GI$ reconstructs quantitative phase maps of the sample from a single exposure. We establish a light-propagation model to describe this process and evaluate its performance in a customized microscopic system. Experiments demonstrate that quantitative phase maps of microbeads and biological specimens can be reconstructed from a single acquisition, while low-coherence illumination improves robustness by suppressing coherence-related noise. These results reveal the potential of quad-pixel PDAF sensors as cost-effective platforms for single-frame QPI.

Selective configuration interaction methods approximate correlated molecular ground- and excited states by considering only the most relevant Slater determinants in the expansion. While a recently proposed neural-network-assisted approach efficiently identifies such determinants, the procedure typically relies on canonical Hartree-Fock orbitals, which are optimized only at the mean-field level. Here we assess approximate natural orbitals - eigenfunctions of the one-particle density matrix computed from intermediate many-body eigenstates - as an alternative. Across our benchmarks for H$_2$O, NH$_3$, CO, and C$_3$H$_8$ we see a consistent reduction in the required determinants for a given accuracy of the computed correlation energy compared to full configuration interaction calculations. Our results confirm that even approximate natural orbitals constitute a simple yet powerful strategy to enhance the efficiency of neural-network-assisted configuration interaction calculations.

Physics teaching in engineering programmes poses discipline-specific demands that intertwine conceptual modelling, experimental inquiry, and computational analysis. This study examines nine teaching competences for physics instruction derived from international and regional frameworks and interpreted within engineering contexts. Nineteen university instructors from the Technological Institute of Toluca completed an open-ended questionnaire; responses were analysed using a grounded theory approach (open and axial coding) complemented by descriptive frequencies. Results indicate stronger development in technical mastery, methodological/digital integration, technology-mediated communication, and innovation (C1, C2, C6, C9), while information literacy for digital content creation/adaptation and digital ethics/safety (C7, C8) remain underdeveloped. A recurrent understanding-application gap was identified, revealing uneven transfer from conceptual awareness to enacted classroom practice. We conclude that advancing physics education for engineers requires institutionally supported, discipline-specific professional development that aligns modelling, laboratory work, and computation with ethical and reproducible digital practices; such alignment can move instructors from adoption/adaptation toward sustained appropriation and innovation in multimodal settings.

Low-dose computed tomography (LDCT) is the current standard for lung cancer screening, yet its adoption and accessibility remain limited. Many regions lack LDCT infrastructure, and even among those screened, early-stage cancer detection often yield false positives, as shown in the National Lung Screening Trial (NLST) with a sensitivity of 93.8 percent and a false-positive rate of 26.6 percent. We aim to investigate whether X-ray dark-field imaging (DFI) radiograph, a technique sensitive to small-angle scatter from alveolar microstructure and less susceptible to organ shadowing, can significantly improve early-stage lung tumor detection when coupled with deep-learning segmentation. Using paired attenuation (ATTN) and DFI radiograph images of euthanized mouse lungs, we generated realistic synthetic tumors with irregular boundaries and intensity profiles consistent with physical lung contrast. A U-Net segmentation network was trained on small patches using either ATTN, DFI, or a combination of ATTN and DFI channels. Results show that the DFI-only model achieved a true-positive detection rate of 83.7 percent, compared with 51 percent for ATTN-only, while maintaining comparable specificity (90.5 versus 92.9 percent). The combined ATTN and DFI input achieved 79.6 percent sensitivity and 97.6 percent specificity. In conclusion, DFI substantially improves early-tumor detectability in comparison to standard attenuation radiography and shows potential as an accessible, low-cost, low-dose alternative for pre-clinical or limited-resource screening where LDCT is unavailable.

Social learning shapes collective search by influencing how individuals use peer information. Empirical and computational studies show that optimal information sharing that is neither too localized nor too diffuse, can enhance resource detection and coordination. Building on these insights, we develop a randomized search model that integrates social learning with area-restricted search (ARS) to investigate how communication distance affects collective foraging. The model includes three behavioral modes: exploration, exploitation, and targeted walk, which are governed by a single parameter, $\rho$, that balances exploration and exploitation at the group level. We quantify how $\rho$ influences group efficiency ($\eta$), temporal variability/burstiness ($B$), and agent variability/equity in resource distribution ($\sigma$), revealing a clear trade-off among these outcomes. When $\rho \to 0$, agents explore independently, maximizing collective exploration. As $\rho$ increases, individuals preferentially exploit patches discovered by others: $\eta$ first rises and then declines, while $B$ shows the opposite trend. Group efficiency is optimized at interior $\rho$ values that balance exploration and exploitation. At the largest $\rho$, equality among agents is highest, but efficiency declines and burstiness is maximized too. Finally, by introducing negative rewards, we examine how social learning mitigates risk.

A generalised concept of the signal-to-noise ratio (or equivalently the ratio of predictable components, or RPC) is provided, based on proper scoring rules. This definition is the natural generalisation of the classical RPC, yet it allows one to define and analyse the signal-to-noise properties of any type of forecast that is amenable to scoring, thus drastically widening the applicability of these concepts. The methodology is illustrated for ensemble forecasts, scored using the continuous ranked probability score (CRPS), and for probability forecasts of a binary event, scored using the logarithmic score. Numerical examples are demonstrated using synthetic data with prescribed signal-to-noise ratios as well as seasonal ensemble hindcasts of the North Atlantic Oscillation (NAO) index. For the synthetic data, the RPC statistic as well as the scoring rule--based ones agree regarding which data sets exhibit anomalous signal-to-noise ratios, but exhibit different variance, indicating different statistical properties. For the NAO data, on the other hand, the results among the different statistics are more equivocal.

We present a path-integral Monte Carlo estimator for calculating the dipole polarizability of interacting Coulomb plasma in the long-wavelength limit, i.e., the optical region. Unlike the conventional dynamic structure factor in reciprocal space, our approach is based on the real-space dipole autocorrelation function and is suited for long wavelengths and small cell sizes, including finite clusters. The simulation of thermal equilibrium in imaginary time has exact Coulomb interactions and Boltzmann quantum statistics. For reference, we demonstrate analytic continuation of the Drude model into the imaginary time and Matsubara series, showing perfect agreement with our data within ranges of finite temperatures and densities. Method parameters, such as the finite time-step and finite-size effects prove only modestly significant. Our method, here carefully validated against an exactly solvable reference, remains amenable to more interesting domains in higher-order optical response, quantum confinements and quantum statistical effects, and applications in plasmonics, heterogeneous plasmas and nonlinear optics, such as epsilon-near-zero materials.

We present force characterizations of two newly developed insect-scale propulsors--one single-tailed and one double-tailed--for microrobotic swimmers that leverage fluid-structure interaction (FSI) to generate thrust. The designs of these two devices were inspired by anguilliform swimming and are driven by soft tails excited by high-work-density (HWD) actuators powered by shape-memory alloy (SMA) wires. While these propulsors have been demonstrated to be suitable for microrobotic aquatic locomotion and controllable with simple architectures for trajectory tracking in the two-dimensional (2D) space, the characteristics and magnitudes of the associated forces have not been studied systematically. In the research presented here, we adopted a theoretical framework based on the notion of reactive forces and obtained experimental data for characterization using a custom-built micro-N-resolution force sensor. We measured maximum and cycle-averaged force values with multi-test means of respectively 0.45 mN and 2.97 micro-N, for the tested single-tail propulsor. For the dual-tail propulsor, we measured maximum and cycle-averaged force values with multi-test means of 0.61 mN and 22.6 micro-N, respectively. These results represent the first measurements of the instantaneous thrust generated by insect-scale propulsors of this type and provide insights into FSI for efficient microrobotic propulsion.

Quantitative computed tomography (QCT) plays a crucial role in assessing bone strength and fracture risk by enabling volumetric analysis of bone density distribution in the proximal femur. However, deploying automated segmentation models in practice remains difficult because deep networks trained on one dataset often fail when applied to another. This failure stems from domain shift, where scanners, reconstruction settings, and patient demographics vary across institutions, leading to unstable predictions and unreliable quantitative metrics. Overcoming this barrier is essential for multi-center osteoporosis research and for ensuring that radiomics and structural finite element analysis results remain reproducible across sites. In this work, we developed a domain-adaptive transformer segmentation framework tailored for multi-institutional QCT. Our model is trained and validated on one of the largest hip fracture related research cohorts to date, comprising 1,024 QCT images scans from Tulane University and 384 scans from Rochester, Minnesota for proximal femur segmentation. To address domain shift, we integrate two complementary strategies within a 3D TransUNet backbone: adversarial alignment via Gradient Reversal Layer (GRL), which discourages the network from encoding site-specific cues, and statistical alignment via Maximum Mean Discrepancy (MMD), which explicitly reduces distributional mismatches between institutions. This dual mechanism balances invariance and fine-grained alignment, enabling scanner-agnostic feature learning while preserving anatomical detail.

Accurately describing the ground state of strongly correlated systems is essential for understanding their emergent properties. Neural Network Backflow (NNBF) is a powerful variational ansatz that enhances mean-field wave functions by introducing configuration-dependent modifications to single-particle orbitals. Although NNBF is theoretically universal in the limit of large networks, we find that practical gains saturate with increasing network size. Instead, significant improvements can be achieved by using a multi-determinant ansatz. We explore efficient ways to generate these multi-determinant expansions without increasing the number of variational parameters. In particular, we study single-step Lanczos and symmetry projection techniques, benchmarking their performance against diffusion Monte Carlo and NNBF applied to alternative mean fields. Benchmarking on a doped periodic square Hubbard model near optimal doping, we find that a Lanczos step, diffusion Monte Carlo, and projection onto a symmetry sector all give similar improvements achieving state-of-the-art energies at minimal cost. By further optimizing the projected symmetrized states directly, we gain significantly in energy. Using this technique we report the lowest variational energies for this Hamiltonian on $4\times 16$ and $4 \times 8$ lattices as well as accurate variance extrapolated energies. We also show the evolution of spin, charge, and pair correlation functions as the quality of the variational ansatz improves.

The classical Maximum-Entropy Principle (MEP) based on Shannon entropy is widely used to construct least-biased probability distributions from partial information. However, the Shore-Johnson axioms that single out the Shannon functional hinge on strong system independence, an assumption often violated in real-world, strongly correlated systems. We provide a self-contained guide to when and why practitioners should abandon the Shannon form in favour of the one-parameter Uffink-Jizba-Korbel (UJK) family of generalized entropies. After reviewing the Shore and Johnson axioms from an applied perspective, we recall the most commonly used entropy functionals and locate them within the UJK family. The need for generalized entropies is made clear with two applications, one rooted in economics and the other in ecology. A simple mathematical model worked out in detail shows the power of generalized maximum entropy approaches in dealing with cases where strong system independence does not hold. We conclude with practical guidelines for choosing an entropy measure and reporting results so that analyses remain transparent and reproducible.

Quantum communication is a growing area of research, with quantum internet being one of the most promising applications. Studying the statistical properties of this network is essential to understanding its connectivity and the efficiency of the entanglement distribution. However, the models proposed in the literature often assume homogeneous distributions in the connections of the optical fiber infrastructure, without considering the heterogeneity of the network. In this work, we propose new models for the quantum internet that incorporate this heterogeneity of node connections in the optical fiber network, analyzing how this characteristic influences fundamental metrics such as the degree distribution, the average clustering coefficient, the average shortest path and assortativity. Our results indicate that, compared to homogeneous models, heterogeneous networks efficiently reproduce key structural properties of real optical fiber networks, including degree distribution, assortativity, and hierarchical behavior. These findings highlight the impact of network structure on quantum communication and can contribute to more realistic modeling of quantum internet infrastructure.

Nanophase metallic iron ( $\mathrm{npFe}^0$ ) is a key indicator of space weathering on the lunar surface, primarily attributed to solar wind irradiation and micrometeoroid impacts. Recent discoveries of hematite ( $\mathrm{Fe}_2 \mathrm{O}_3$ ), a highly oxidized form of iron, in the lunar polar regions challenge the prevailing understanding of the Moon's reducing environment. This study, using ReaxFF molecular dynamics simulations of micrometeoroid impacts on fayalite ( $\mathrm{Fe}_2 \mathrm{SiO}_4$ ), investigates the atomistic mechanisms leading to both reduced and oxidized iron species. Our simulations reveals that the high-temperature and pressure conditions at the impact crater surface produces a reduced iron environment while providing a transient oxygen-rich environment in the expanding plume. Our findings bridge previously disparate observations-linking impact-driven $\mathrm{npFe}^0$ formation to the puzzling presence of oxidized iron phases on the Moon, completing the observed strong dichotomous distribution of hematite between the nearside and farside of the Moon. These findings highlight that micrometeoroid impacts, by simultaneously generating spatially distinct redox environments, provide a formation mechanism that reconciles the ubiquitous identification of nanophase metallic iron ( $\mathrm{npFe}^0$ ) in returned lunar samples with $\mathrm{Fe}^{3+}$ signatures detected by remote sensing. This underscores the dynamic nature of space weathering processes. For a more nuanced understanding of regolith evolution, we should also consider the presence of different generations or types of $\mathrm{npFe}{ }^0$, such as those formed from solar wind reduction versus impact disproportionation.

Neuromorphic computing demands synaptic elements that can store and update weights with high precision while being read non-destructively. Conventional ferroelectric synapses store weights in remnant polarization states and might require destructive electrical readout, limiting endurance and reliability. We demonstrate a ferroelectric MEMS (FeMEMS) based synapse in which analog weights are stored in the piezoelectric coefficient $d_{31,eff}$ of a released Hf$_{0.5}$Zr$_{0.5}$O$_2$ (HZO) MEMS unimorph. Partial switching of ferroelectric domains modulates $d_{31,eff}$, and a low-amplitude mechanical drive reads out the weight without read-disturb in the device yielding more than 7-bit of programming levels. The mechanical switching distribution function follows a Lorentzian distribution as a logarithmic function of partial poling voltage ($V_p$) consistent with nucleation-limited switching (NLS), and the median threshold extracted from electromechanical data obeys a Merz-type field-time law with a dimensionless exponent $\alpha = 3.62$. These relationships establish a quantitative link between mechanical weights and electrical switching kinetics. This mechanically read synapse avoids depolarization and charge-injection effects, provides bipolar weights (well suited for excitatory and inhibitory synapses), directly reveals partial domain populations, and offers a robust, energy-efficient route toward high-bit neuromorphic hardware.

Flare ribbons with parallel and circular morphologies are typically associated with different magnetic reconnection models, and the simultaneous observation of both types in a single event remains rare. Using multi-wavelength observations from a tandem of instruments, we present an M8.2-class flare that occurred on 2023 September 20, which produced quasi-parallel and semi-circular ribbons. The complex evolution of the flare includes two distinct brightening episodes in the quasi-parallel ribbons, corresponding to the two major peaks in the hard X-ray (HXR) light curve. In contrast, the brightening of semi-circular ribbons temporally coincides with the local minimum between the two peaks. Using potential field extrapolation, we reconstruct an incomplete dome-like magnetic structure with a negative polarity embedded within the northwestern part of the semi-circular positive polarity. Consequently, the magnetic configuration comprises two sets of field lines with distinct magnetic connectivities. We suggest that the standard flare reconnection accounts for the two-stage brightening of quasi-parallel ribbons associated with the two HXR peaks. Between the two stages, this process is constrained by the interaction of eruptive structures with the dome. The interaction drives the quasi-separatrix layer reconnection, leading to the brightening of semi-circular ribbons. It also suppresses the standard flare reconnection, resulting in a delayed second HXR peak.

We investigate the evolution of red supergiant (RSG) progenitors of core-collapse (CC) supernovae (SNe) with initial masses between $12-20~M_\odot$ focusing on the effects of enhanced mass loss due to pulsation-driven instabilities in their envelopes and subsequent dynamical ejections during advanced stages of nuclear burning. Using time-dependent mass loss from detailed MESA stellar evolution models, including a parameterized prescription for pulsation-driven superwinds and time-averaged mass loss rates attributed to resulting shock-induced ejections, we construct the circumstellar medium (CSM) before the SN explosion. We calculate resulting CSM density profiles and column densities considering the acceleration of the stellar wind. Our models produce episodes of enhanced mass loss $10^{-4}-10^{-2}~M_\odot~\rm{yr}^{-1}$ in the last centuries-decades before explosion forming dense CSM ($>10^{-15}~\rm{gcm}^{-3}$ at distances $<10^{15}$ cm) -- consistent with those inferred from multi-wavelength observations of Type II SNe such as SN~2023ixf and SN~2020ywx.

Using particle-resolved molecular-dynamics simulations, we compute the phase diagram for soft repulsive spherocylinders confined on the surface of a sphere. While crystal (K), smectic (Sm), and isotropic (I) phases exhibit a stability region for any aspect ratio of the spherocylinders, a nematic phase emerges only beyond a critical aspect ratio lying between 6.0 and 7.0. As required by the topology of the confining sphere, the ordered phases exhibit a total orientational defect charge of +2. In detail, the crystal and smectic phases exhibit two +1 defects at the poles, whereas the nematic phase features four +1/2 defects which are connected along a great circle. For aspect ratios above the critical value, lowering the packing fraction drives a sequence of transitions: the crystal melts into a smectic phase, which then transforms into a nematic through the splitting of the +1 defects into pairs of +1/2 defects that progressively move apart, thereby increasing their angular separation. Eventually, at very low densities, orientational fluctuations stabilize an isotropic phase. Our simulations data can be experimentally verified in Pickering emulsions and are relevant to understand the morphogenesis in epithelial tissues.

This article serves to concisely review the link between gradient flow systems on hypergraphs and information geometry which has been established within the last five years. Gradient flow systems describe a wealth of physical phenomena and provide powerful analytical technquies which are based on the variational energy-dissipation principle. Modern nonequilbrium physics has complemented this classical principle with thermodynamic uncertaintly relations, speed limits, entropy production rate decompositions, and many more. In this article, we formulate these modern principles within the framework of perturbed gradient flow systems on hypergraphs. In particular, we discuss the geometry induced by the Bregman divergence, the physical implications of dual foliations, as well as the corresponding infinitesimal Riemannian geometry for gradient flow systems. Through the geometrical perspective, we are naturally led to new concepts such as moduli spaces for perturbed gradient flow systems and thermodynamical area which is crucial for understanding speed limits. We hope to encourage the readers working in either of the two fields to further expand on and foster the interaction between the two fields.

We study the linear asymptotic stability of stably stratified monotone shear flows for the Boussinesq equations in the periodic channel. By means of the limiting absorption principle, we obtain a precise description of the inviscid damping experienced by the perturbed velocity field and density, with time-decay rates that depend on the local Richardson number $\mathcal{J}(y)$ and split into four stratification regimes (non-stratified, weak, mild, and strong) reflecting qualitative changes in the structure of the Green's function at the critical thresholds $\mathcal{J}(y)=0$ and $\mathcal{J}(y) = \frac14$. The velocity and density decay estimates are later used to prove quantitative sub-linear growth of the vorticity and gradient of density. As a byproduct of our analysis, we show that, under mild hypotheses on the underlying shear-type equilibrium, the spectrum of the linearised Boussinesq operator is purely continuous.

Light pulses offer a faster, more energy-efficient, and direct route to magnetic bit writing, pointing toward a hybrid memory and computing paradigm based on photon transmission and spin retention. Yet progress remains hindered, as deterministic, single-pulse optical toggle switching has so far been achieved only with ferrimagnetic materials, which require too specific a rare-earth composition and temperature conditions for technological use. In mainstream ferromagnet--central to spintronic memory and storage--such bistable switching is considered fundamentally difficult, as laser-induced heating does not inherently break time-reversal symmetry. Here, we report coherent magnetization switching in ferromagnets, driven by thermal anisotropy torque with single laser pulses. The toggle switching behavior is robust over a broad range of pulse durations, from femtoseconds to picoseconds, a prerequisite for practical applications. Furthermore, the phenomenon exhibits reproducibility in CoFeB/MgO-based magnetic tunnel junctions with a high magnetoresistance exceeding 110%, as well as the scalability down to nanoscales with remarkable energy efficiency (17 fJ per 100-nm-sized bit). These results mark a notable step toward integrating opto-spintronics into next-generation memory and storage technologies.

Trigonal tellurium (Te) has attracted researchers' attention due to its transport and optical properties, which include electrical magneto-chiral anisotropy, spin polarization and bulk photovoltaic effect. It is the anisotropic and chiral crystal structure of Te that drive these properties, so the determination of its crystallographic orientation and handedness is key to their study. Here we explore the structural dynamics of Te bulk crystals by angle-dependent linearly polarized Raman spectroscopy and symmetry rules in three different crystallographic orientations. The angle-dependent intensity of the modes allows us to determine the arrangement of the helical chains and distinguish between crystallographic planes parallel and perpendicular to the chain axis. Furthermore, under different configurations of circularly polarized Raman measurements and crystal orientations, we observe the shift of two phonon modes only in the (0 0 1) plane. The shift is positive or negative depending on the handedness of the crystals, which we determine univocally by chemical etching. Our analysis of three different crystal faces of Te highlights the importance of selecting the proper orientation and crystallographic plane when investigating the transport and optical properties of this material. These results offer insight into the crystal structure and symmetry in other anisotropic and chiral materials, and open new paths to select a suitable crystal orientation when fabricating devices.

We report a high-performance thermochromic VO2-based coating prepared by using a three-step process, consisting of magnetron sputter depositions of SiO2 films and V-W films and their postannealing, on standard glass at a low substrate temperature of 350 °C without opening the vacuum chamber to atmosphere. It is formed by four layers of W-doped VO2 nanoparticles dispersed in SiO2 matrix. The coating exhibits a transition temperature of 33 °C with an integral luminous transmittance of 65.4% (low-temperature state) and 60.1% (high-temperature state), and a modulation of the solar energy transmittance of 15.3%. Such a combination of properties, together with the low temperature during preparation, fulfill the requirements for large-scale implementation on building glass and have not been reported yet.

Droplet deformations caused by substrate vibrations are ubiquitous in nature and highly relevant for applications such as microreactors and single-cell sorting. The vibrations can induce droplet oscillations, a fundamental process that requires an in-depth understanding. Here, we report on extensive many-body dissipative particle dynamics simulations carried out to study the oscillations of droplets of different liquids on horizontally vibrating substrates, covering a wide range of vibration frequencies and amplitudes as well as substrate wettability. We categorize the phases observed for different parameter sets based on the capillary number and identify the transitions between the observed oscillation phases, which are characterized by means of suitable parameters, such as the angular momentum and vorticity of the droplet. The instability growth rate for oscillation phase II, which leads to highly asymmetric oscillations and eventual droplet breakup, is also determined. Finally, we characterize the state of the droplet for the various scenarios by means of the particle-particle and particle-substrate contacts. We find a steady-state scenario for phase I, metastable breathing modes for phase II, and an out-of-equilibrium state for phase III. Thus, we anticipate that this study provides much needed insights into a fundamental phenomenon in nature with significant relevance for applications.

This study explores visitor behaviour at The British Museum using data science methods applied to novel sources, including audio guide usage logs and TripAdvisor reviews. Analysing 42,000 visitor journeys and over 50,000 reviews, we identify key drivers of satisfaction, segment visitors by behavioural patterns, examine tour engagement, model spatial navigation, and investigate room popularity. Behavioural clustering uncovered four distinct visitor types: Committed Trekkers, Leisurely Explorers, Targeted Visitors, and Speedy Samplers, each characterised by different levels of engagement and movement. Tour usage analysis revealed high drop-off rates and variation in completion rates across different language groups. Spatial flow modelling revealed that accessibility and proximity, particularly aversion to stairs, shaped visitor paths more than thematic organisation. Room popularity was more strongly predicted by physical accessibility than curatorial content. We propose practical strategies for improving engagement and flow, offering a scalable framework for visitor-centred, data-informed museum planning.

We present a first--principles density functional theory (DFT) study of transition metal (TM = Ti, Cr, Mn, Fe, Co, Ni) functionalized two--dimensional polyaramid (2DPA) to explore their structural, electronic, and magnetic properties. Mechanical parameters, such as bulk modulus, shear modulus, Young's modulus, Poisson's ratio, and Pugh ratio, together with phonon dispersion, confirm the mechanical and dynamic stability of all doped systems. Electronic structure analysis shows strong binding of Co, Cr, Fe, Ni, and Ti with formation energies between --1.15 eV and --2.96 eV, while Mn binds more weakly (--0.67 eV). TM doping introduces new electronic states that reduce the band gap, with Fe-doped 2DPA exhibiting the lowest value of 0.26 eV. The systems display predominantly ferromagnetic ordering, with magnetic moments of 1.14 {\mu}B (Co), 3.57 {\mu}B (Cr), 2.26 {\mu}B (Fe), 4.19 {\mu}B (Mn), and 1.62 {\mu}B (Ti). These results demonstrate that TM--doped 2DPA possesses tunable magnetic and electronic characteristics, highlighting its potential for spintronic applications.

We propose a stochastic branching particle-based method for solving nonlinear non-conservative advection-diffusion-reaction equations. The method splits the evolution into an advection-diffusion step, based on a linearized Kolmogorov forward equation and approximated by stochastic particle transport, and a reaction step implemented through a branching birth-death process that provides a consistent temporal discretization of the underlying reaction dynamics. This construction yields a mesh-free, nonnegativity-preserving scheme that naturally accommodates non-conservative systems and remains robust in the presence of singularities or blow-up. We validate the method on two representative two-dimensional systems: the Allen-Cahn equation and the Keller-Segel chemotaxis model. In both cases, the present method accurately captures nonlinear behaviors such as phase separation and aggregation, and achieves reliable performance without the need for adaptive mesh refinement.

We present a single-photon transduction scheme using 4-wave-mixing and quantum scattering in planar, cooperative Rydberg arrays that is both efficient and highly directional and may allow for terahertz-to-optical transduction. In the 4-wave-mixing scheme, two lasers drive the system, coherently trapping the system in a dark ground-state and coupling a signal transition, that may be in the terahertz, to an idler transition that may be in the optical. The photon-mediated dipole-dipole interactions between emitters generate collective super-/subradiant dipolar modes, both on the signal and the idler transition. As the array is cooperative with respect to the signal transition, an incident signal photon can efficiently couple into the array and is admixed into dipolar idler modes by the drive. Under specific criticality conditions, this admixture is into a superradiant idler mode which primarily decays into a specific, highly directional optical photon that propagates within the array plane. Outside of the array, this photon may then be coupled into existing quantum devices for further processing. Using a scattering-operator formalism we derive resonance and criticality conditions that govern this two-step process and obtain analytic transduction efficiencies. For infinite lattices, we predict transduction efficiencies into specific spatial directions of up to 50%, while the overall, undirected transduction efficiency can be higher. An analysis for finite arrays of $N^2$ emitters, shows that the output is collimated into lobes that narrow as $1/\sqrt{N}$. Our scheme combines the broadband acceptance of free-space 4-wave mixing with the efficiency, directionality and tunability of cooperative metasurfaces, offering a route towards quantum-coherent THz detection and processing for astronomical spectroscopy, quantum-networked sparse-aperture imaging and other quantum-sensing applications.

Empirical complex systems can be characterized not only by pairwise interactions, but also by higher-order (group) interactions influencing collective phenomena, from metabolic reactions to epidemics. Nevertheless, higher-order networks' apparent superior descriptive power -- compared to classical pairwise networks -- comes with a much increased model complexity and computational cost, challenging their application. Consequently, it is of paramount importance to establish a quantitative method to determine when such a modeling framework is advantageous with respect to pairwise models, and to which extent it provides a valuable description of empirical systems. Here, we propose an information-theoretic framework, accounting for how structure affect diffusion behaviors, quantifying the entropic cost and distinguishability of higher-order interactions to assess their reducibility to lower-order structures while preserving relevant functional information. Empirical analyses indicate that some systems retain essential higher-order structure, whereas in some technological and biological networks it collapses to pairwise interactions. With controlled randomization procedures, we investigate the role of nestedness and degree heterogeneity in this reducibility process. Our findings contribute to ongoing efforts to minimize the dimensionality of models for complex systems.

The ground state and excited state resonance dipole-dipole interaction energy between two elongated conducting molecules are explored. We review the current status for ground state interactions. This interaction is found to be of a much longer range than in the case when the molecules are pointlike and nonconducting. These are well known results found earlier by Davies, Ninham, and Richmond, and later, using a different formalism, by Rubio and co-workers. We show how the theory can be extended to excited state interactions. A characteristic property following from our calculation is that the interaction energy dependence with separation ($R$) goes like $f(R)/R^2$ both for resonance and for the van der Waals case in the long range limit. In some limits $f(R)$ has a logarithmic dependency and in others it takes constant values. We predict an unusual slow decay rate for the energy transfer between conducting molecules.

The timing of human labor is among the most critical determinants of neonatal survival, yet the mechanisms that govern the transition from uterine quiescence to coordinated contractions remain elusive. Here we present a dynamical-systems framework that models the pregnant uterus as a spatially extended network of electrically excitable cells regulated by sparse adaptive feedback mimicking hormonal and mechanical influences. This approach reveals how stability during gestation and sensitivity near parturition can be simultaneously maintained through the interplay of control, network structure, and noise. Our analysis shows that spontaneous contractions such as Braxton-Hicks and Alvarez waves are not epiphenomena, but functional components that reduce control effort and preserve responsiveness. Moreover, we identify preterm labor as a boundary-crossing phenomenon arising when control fails to correctly interpret early-warning signals. These results establish a unifying mechanistic theory for labor onset, yield testable predictions, and suggest new therapeutic strategies to mitigate preterm birth risk.

Constrained Spherical Deconvolution (CSD) is widely used to estimate the white matter fiber orientation distribution (FOD) from diffusion MRI data. Its angular resolution depends on the maximum spherical harmonic order ($l_{max}$): low $l_{max}$ yields smooth but poorly resolved FODs, while high $l_{max}$, as in Super-CSD, enables resolving fiber crossings with small inter-fiber angles but increases sensitivity to noise. In this proof-of-concept study, we introduce Spatially Regularized Super-Resolved CSD (SR$^2$-CSD), a novel method that regularizes Super-CSD using a spatial FOD prior estimated via a self-calibrated total variation denoiser. We evaluated SR$^2$-CSD against CSD and Super-CSD across four datasets: (i) the HARDI-2013 challenge numerical phantom, assessing angular and peak number errors across multiple signal-to-noise ratio (SNR) levels and CSD variants (single-/multi-shell, single-/multi-tissue); (ii) the Sherbrooke in vivo dataset, evaluating spatial coherence of FODs; (iii) a six-subject test-retest dataset acquired with both full (96 gradient directions) and subsampled (45 directions) protocols, assessing reproducibility; and (iv) the DiSCo phantom, evaluating tractography accuracy under varying SNR levels and multiple noise repetitions. Across all evaluations, SR$^2$-CSD consistently reduced angular and peak number errors, improved spatial coherence, enhanced test-retest reproducibility, and yielded connectivity matrices more strongly correlated with ground-truth. Most improvements were statistically significant under multiple-comparison correction. These results demonstrate that incorporating spatial priors into CSD is feasible, mitigates estimation instability, and improves FOD reconstruction accuracy.

We proposed a new approach, which is inspired by the method of super-resolution (SR) structured illumination microscopy (SIM) for overcoming the resolution limit in microscopy due to diffraction of light, for increasing the resolution of clinical positron emission tomography (PET) beyond its instrumentation limit. We implemented the key idea behind SR-SIM by using a rotating intensity modulator in front of a stationary PET detector ring. Its function is to modulate down high-frequency signals of the projection data that originally were above the system's bandwidth and unobservable to appear as aliased lower-frequency ones that are detectable. We formulated a model that relates an image whose resolution is above the instrumentation limit to several thus obtained limited-resolution measurements at various rotational positions of the modulator. We implemented an ordered-subsets expectation-maximization algorithm for inverting the model. Using noise-free data produced by an analytic projector, we showed this approach can resolve 0.9 mm sources when applied to a PET system that employs 4.2 mm width detectors. With noisy data, the SR performance remains promising. In particular, 1.5 mm sources were resolvable, and the visibility and quantification of small sources and fine structures were improved despite the sensitivity loss incurred by the modulator. These observations remain valid when using more realistic Monte-Carlo simulation data. More studies are needed to better understand the theoretical aspects of the proposed method and to optimize the design of the modulator and the reconstruction algorithm.

A comprehensive geoscientific downscaling model strategy is presented outlining an approach that has evolved over the last 20 years, together with an explanation for its development, its technical aspects, and evaluation scheme. This effort has resulted in an open-source and free R-based tool, 'esd', for the benefit of sharing and improving the reproducibility of the downscaling results. Furthermore, a set of new metrics was developed as an integral part of the downscaling approach which assesses model performance with an emphasis on regional information for society (RifS). These metrics involve novel ways of comparing model results with observational data and have been developed for downscaling large multi-model global climate model ensembles. This paper presents for the first time an overview of the comprehensive framework adopted by the Norwegian Meteorological Institute for downscaling aimed at supporting climate change adaptation. A literature search suggests that this comprehensive downscaling strategy and evaluation scheme are not widely used within the downscaling community. In addition, this strategy involves a new convention for storing large datasets of ensemble results that provides fast access to information and drastically saves data volume.

Hot electrons and holes generated from the decay of localized surface plasmons (LSPs) in aluminum nanostructures have significant potential for applications in photocatalysis, photodetection and other optoelectronic devices. Here, we present a theoretical study of hot-carrier generation in aluminum nanospheres using a recently developed modelling approach that combines a solution of the macroscopic Maxwell equation with large-scale atomistic tight-binding simulations. Different from standard plasmonic metals, such as gold or silver, we find that the energetic distribution of hot electrons and holes in aluminium nanoparticles is almost constant for all allowed energies. Only at relatively high photon energies, a reduction of the generation rate of highly energetic holes and electrons close to the Fermi level is observed which is attributed to band structure effects suppressing interband decay channels. We also investigate the dependence of hot-carrier properties on the nanoparticle diameter and the environment dielectric constant. The insights from our study can inform experimental efforts towards highly efficient aluminum-based hot-carrier devices.

Mass vaccination remains a long-lasting challenge for disease control and prevention with upticks in vaccine hesitancy worldwide. Here, we introduce an experience-based learning (Q-learning) dynamics model of vaccination behavior in social networks, where agents choose whether or not to vaccinate given environmental feedbacks from their local neighborhood. We focus on how bounded rationality of individuals impacts decision-making of irrational agents in networks. Additionally, we observe hysteresis behavior and bistability with respect to vaccination cost and the Q-learning hyperparameters such as discount rate. Our results offer insight into the complexities of Q-learning and particularly how foresightedness of individuals will help mitigate - or conversely deteriorate, therefore acting as a double-edged sword - collective action problems in important contexts like vaccination. We also find a diversification of uptake choices, with individuals evolving into complete opt-in vs. complete opt-out. Our results have real-world implications for targeting the persistence of vaccine hesitancy using an interdisciplinary computational social science approach integrating social networks, game theory, and learning dynamics.

Predicting particle transport in complex flows is traditionally achieved by solving the Navier-Stokes equations. While various numerical and experimental methods exist, they typically require deep physical insights and incur high computational costs. Machine learning offers an alternative by learning predictive patterns directly from data, avoiding explicit physical modeling. However, purely data-driven approaches often lack interpretability, physical consistency, and generalizability in sparse data regimes. To this end, we propose TrajectoryFlowNet, a Lagrangian-Eulerian physics-informed neural network architecture, for fluid flow velocimetry and imaging via learning to predict spatiotemporal flow fields and long-range particle trajectories. The salient features of our model include its ability to handle complex flow patterns with irregular boundaries, predict the full-field flows, image the long-range flow trajectory of any arbitrary particle, and ensure physical consistency in predictions based only on very scarce measurement of flow trajectories. We validate TrajectoryFlowNet via both numerical examples (e.g., lid-driven cavity flow and complex cylinder flow) and experimental test cases (e.g., aortic and ventricle blood flows) across diverse flow scenarios. The results demonstrate our model's effectiveness in capturing intricate particle-laden flow dynamics, enabling long-range tracking of particles and accurate construction of flow fields in real-world applications.

This study presents a numerical investigation of solid-core photonic crystal fibers with circular and hexagonal cladding geometries. The goal is to optimize optical parameters for nonlinear photonics and environmental sensing. Full-vectorial simulations using FDTD, PWE, and FDE are used to analyze the effects of core diameter, pitch, and air filling fraction on the zero-dispersion wavelength, nonlinear coefficient, effective mode area, and confinement loss. Reducing the core diameter from 2.4 to 1.4 microns tunes the zero-dispersion wavelength from 791 to 646 nanometers and increases the nonlinear coefficient by 72 percent, from 72 to 124 inverse watts per kilometer. The study also examines the effect of isopropyl alcohol infiltration, which causes a red-shift in dispersion and degrades confinement. These results offer a design framework that balances nonlinear efficiency and environmental robustness for supercontinuum generation and chemical sensing.

The performance of amorphous selenium (a-Se) as a cryogenic photodetector material is evaluated through a series of experiments using laterally structured devices operated in a custom optical test stand. These studies investigate the response of a-Se detectors to low-photon fluxes at high electric fields near avalanche conditions, the linearity of the photoconductive response over a wide dynamic range and the direct detection of narrowband 130 nm vacuum ultraviolet (VUV) illumination. At 87 K, matched-filter analysis shows reliable single-shot detection with efficiencies greater than or equal to 80 percent and area under the curve (AUC) greater than or equal to 0.85 using as few as approximately 6800 incident 401 nm photons, corresponding to approximately 3400 photons within field-active regions after accounting for geometric constraints. Measurements are performed at cryogenic temperatures using calibrated photon fluxes derived from a silicon photomultiplier reference and a characterized optical filter stack. Additional experiments using a tellurium-doped a-Se (a-SeTe) device explore the material's behavior under identical test conditions and demonstrate that avalanche is achievable in a-SeTe at cryogenic temperatures. The results demonstrate reproducible low-noise operation, VUV sensitivity and field-dependent gain behavior in a lateral a-Se architecture, representing the first reported observation of avalanche multiplication in laterally structured a-Se and a-SeTe devices at cryogenic temperatures. These findings support the potential integration of laterally structured a-Se devices into next-generation pixelated liquid-argon time projection chambers (TPCs) requiring scalable, high-field-compatible photon detection systems.

This paper introduces a new robust formulation for local correlation-based laminar-to-turbulent transition models. This mechanism is incorporated into Reynolds-Averaged Navier-Stokes (RANS) equations, coupled with the Spalart-Allmaras (SA) turbulence model, considering both $\gamma$ and $\gamma$-${\widetilde{\mathrm{Re}}_{\theta,t}}$ transition frameworks. In this context, special attention is placed on numerical stabilization of the $\gamma$ transport equation, which is identified as the root cause of instabilities observed in both $\gamma$ and $\gamma$-${\widetilde{\mathrm{Re}}_{\theta,t}}$ based models. To this end, the intermittency equation is reformulated in logarithmic form and further stabilized through an energy--based limiting to bound excessively high positive values. In order to suppress unphysical pressure oscillations in the transition region, a gradient-driven artificial viscosity is also introduced. Additionally, the SA equation is augmented with strain-rate modulated production and rotation correction terms. The presented approach has demonstrated consistent effectiveness and robustness in the simulation of flow fields around airfoils over a wide range of Reynolds numbers, making it suitable for practical aerodynamic design applications.

This study focuses on developing a predictive model for mean velocity profiles and total shear stress profiles in turbulent boundary layers subjected to adverse pressure gradients, especially with history effects. A new scaling using friction velocity modified by Clauser pressure gradient parameter is introduced to restore streamwise self-similarity. Furthermore, an estimation-correction model is developed, explicitly incorporating a streamwise derivative of pressure gradient, which effectively captures history effect beyond the reach of Reynolds-averaged Navier-Stokes equations. With the help of the model, the total shear stress is decomposed into four parts, representing respectively the Reynolds number effects, equilibrium pressure gradient effects, the coupling between free-stream velocity and pressure gradient, and local non-equilibrium pressure gradient effects. The latter two are considered first-order history effects, and can account for up to approximately half of the total stress. Validation against multiple DNS/LES datasets across a wide range of pressure gradients and Reynolds numbers demonstrates the model's accuracy in predicting both mean velocity profiles and total shear stress profiles.

The paper ``Magnetic moments in the Poynting theorem, Maxwell equations, Dirac equation, and QED", arXiv:2501.02022, by Peter J. Mohr, purports to show that Maxwell's equation, $\mathbf{\nabla}\cdot{\bf B}=0$, and Poynting's theorem require significant modications for the $\bf B$ field of a magnetic dipole. We show here that, because of critical errors in the paper, these claims are false, and that Maxwell's equation and the Poynting theorem are the same for a magnetic moment as for any other charge-current distribution.

We study flows generated within a two-dimensional corner by the chemical activity of the confining boundaries. Catalytic reactions at the surfaces induce diffusioosmotic motion of the viscous fluid throughout the domain. The presence of chemically active sectors can give rise to steady eddies reminiscent of classical Moffatt vortices, which are mechanically induced in similar confined geometries. In our approach, an exact analytical solution of the diffusion problem in a wedge geometry is derived and coupled to the diffusioosmotic slip-velocity formulation, yielding the stream function of associated Stokes flow. In selected limiting cases, simple closed-form expressions provide clear physical insight into the underlying mechanisms. Our results open new perspectives for the design of microscale mixing strategies in dead-end pores and cornered microfluidic channels, and offer benchmarks for numerical simulations of confined (diffusio)osmotic systems.

This paper reports an 11.7 GHz compact 50 ohm ladder filter based on single layer Scandium Aluminum Nitride (ScAlN) film bulk acoustic resonators (FBARs) with platinum (Pt) electrodes, and uses it as a quantitative case study of the limits encountered when directly scaling to higher frequencies. The measured filter achieves a 3 dB fractional bandwidth (FBW) of 4.0% and an out of band rejection greater than 23.1 dB, with a minimum insertion loss (IL) of 6.8 dB. We analyze the origin of this performance through a quantitative framework: (1) a loss decomposition study, (2) frequency shift sensitivity that explains the discrepancy between simulated and measured center frequency, (3) FBW sensitivity to series shunt separation and port impedance, and (4) stress limited aperture that constrains device size. The results establish a realistic, fabricable baseline for directly scaled single layer ScAlN FBAR filters and outline materials, electrode, and stress management directions toward lower loss mmWave acoustic filters.

This study presents the development and validation of a compact device for measuring the water attenuation length (WAL), utilizing photomultiplier tubes (PMTs), optical fibers, and light-emitting diodes (LEDs). An 8 m water tank and the device was constructed and validated in the laboratory. The device is capable of measuring WAL values up to 50 m. The stray light was blocked mainly by a custom-designed shutter. Toy Monte Carlo simulations were employed to evaluate the measurement uncertainty, which was found to be within reasonable limits. These simulations further indicate that the uncertainty can be reduced and more accurately predicted for a larger-scale device with a length of 30 m. Real-time monitoring was achieved by integrating the device into a water purification circulation system, providing a practical, scalable solution for WAL measurement in future large-scale water Cherenkov detectors.

For the dynamic analysis of floating offshore wind turbines (FOWTs) in realistic operating environment, this paper develops a coupled aero-hydro-mooring-servo model applicable to turbulent wind and irregular sea states with high computational efficiency. A modified rotor control strategy with platform motion feedback is proposed with a novel gain-scheduling technique to mitigate the negative damping effect on platform motions and decouple the rotor dynamics from the platform dynamics for better rotor operating performance. Firstly, the performance for the bottom-fixed wind turbine in steady uniform wind is validated and the turbulent wind tests show that using the rotor-disk-averaged wind speed for control is beneficial for reducing fluctuations of wind thrust and operational parameters compared with using the hub-height wind speed. For the FOWT, the negative damping phenomenon at above-rated wind speeds when using the baseline control is demonstrated through a range of wind and wave scenarios, and effects of control strategy and turbulent wind are investigated. The results indicate that the modified control strategy eliminates the negative damping effect on the platform pitch while maintaining small variations in rotor speed. Though the control including the blade pitch compensation from platform motion feedback with a constant gain can also eliminate the negative damping effect, it produces much larger fluctuations in rotor speed and causes larger overspeed exceeding the safety threshold of 20% at large wind speeds. Compared to steady wind, turbulent wind yields significantly larger low-frequency platform responses and increases the maximum rotor speed by 7.9% to 23.7% for the wind speeds considered.

The Super Tau-Charm Facility (STCF) is a new-generation $e^+e^-$ collider proposed in China, designed to operate in the center-of-mass (CoM) energy range of 2-7 GeV. To achieve the design luminosity exceeding 5*10^34 cm^-2s^-1 at the optimal CoM energy of 4 GeV, a large crossing angle combined with the crab-waist correction scheme is adopted. However, this scheme introduces strong nonlinearities in the interaction region (IR) due to the extremely low vertical beta function of beta_y* <=1 mm, which significantly limits dynamic and momentum apertures of the collider ring. This paper presents a comprehensive modular optics design that addresses these challenges through several key features: 1) local chromaticity correction up to third order to enhance momentum bandwidth; 2) exact -I transformation between chromatic sextupole pairs for nonlinear cancellation; 3) minimization of the dispersion invariant along the IR to improve local momentum acceptance; 4) optimized beta functions at crab sextupole locations to reduce strength requirements and associated nonlinearities. Resonance driving terms analysis confirms effective suppression of geometric aberrations while preserving the intended crab-waist effects. When integrated into the collider ring, the design achieves a Touschek lifetime exceeding 300 s at beam energy of 2 GeV, meeting STCF requirements. The impact of fringe fields from superconducting quadrupoles is mitigated using octupole correctors, and detector solenoid effects are fully suppressed via local anti-solenoid compensation. Furthermore, the defined machine-detector interface layout ensures minimal synchrotron radiation background at the IP beryllium chamber, while ultra-high vacuum conditions are required to suppress beam-gas background. This IR design represents the current optimal solution for STCF and has been incorporated into the project's conceptual design report.

Tungsten ($W$) is widely valued for its exceptional thermal stability, mechanical strength, and corrosion resistance, making it an ideal candidate for high-performance military and aerospace applications. However, its high melting point and inherent brittleness pose significant challenges for processing $W$ using additive manufacturing (AM). Cold spray (CS), a solid-state AM process that relies on high-velocity particle impact and plastic deformation, offers a promising alternative. In this study, we employ atomistic simulations to investigate the feasibility of CS for tungsten. We show that ultrasound perturbation can significantly enhance the self-diffusivity and plastic deformation of $W$ compared to the negligible diffusion and plastic deformation observed in non-ultrasound-assisted CS of $W$. For different impact velocities, particle sizes, and ultrasound parameters, we demonstrate that ultrasound-assisted viscoplasticity enhances self-diffusivity by inhibiting grain boundaries and incorporating softening in $W$. Moreover, we found that this enhanced diffusion in ultrasound-assisted $W$ can be exploited to promote interdiffusion at the particle-substrate interface, enabling in situ alloy formation. Through the formation of an equimolar $V$-$W$ alloy on a $W$ substrate using ultrasound-assisted CS simulations, we observed distinct mechanical properties and a reduced dislocation density in the deposited coating compared to a pure tungsten substrate. These results highlight the potential of ultrasound-assisted CS as a viable approach for manufacturing uniform coatings and engineered alloys, addressing key limitations in the AM of refractory metals.

Femtosecond laser, owing to their ultrafast time scales and broad frequency bandwidths, have substantially changed fundamental science over the past decades, from chemistry and bio-imaging to quantum physics. Critically, many emerging industrial-scale photonic technologies -- such as optical interconnects, AI accelerators, quantum computing, and LiDAR -- also stand to benefit from their massive frequency parallelism. However, achieving a femtosecond-scale laser on-chip, constrained by size and system power input, has remained a long-standing challenge. Here, we demonstrate the first on-chip femtosecond laser, enabled by a new mechanism -- photorefraction-assisted soliton (PAS) mode-locking. Operating from a simple, low-voltage electrical supply, the laser provides deterministic, turn-key generation of sub-90-fs solitons. Furthermore, it provides electronic reconfigurability of its pulse properties and features an exceptional optical coherence with a 53 Hz intrinsic comb linewidth. This demonstration removes a key barrier to the full integration of chip-scale photonic systems for next-generation sensing, communication, metrology, and computing.

In this paper we present an analytical and numerical study of a generalized model of two-actor cooperative-competitive conflict of the Continuous Opinions and Discrete Actions (CODA) type. Theoretically, we note that the in troduction of a new parameter allows generalizing feedback as strong and weak. Furthermore, we show that for positive-positive and negative-negative feedback there exists a supercritical pitchfork bifurcation, and that the model does not admit limit cycles in any case, and we study the effect of different parameter values and initial conditions by using a difference equation approximation of the model. Additionally, our model offers important insight on social phenomena such as false levels of support among cooperators, often observed in agreement negotiations; instances of ``non-strict consensus'' when two people support the same political position, albeit with different intensities; and competitive situations, such as in competitions with disproportionate profit and losses. Thus, this generalized model offers an enhanced descriptive power compared to previously proposed models.

By returning to the topological basics of fusion target design, Generative Artificial Intelligence (genAI) is used to specify how to initially configure and drive the optimally entangled topological state, and stabilize that topological state from disruption. This can be applied to all methods; including tokamaks, laser-driven schemes, and pulsed-power driven schemes. The result is practical, room temperature targets that can yield up to 10 GJ of energy, driven by as little as 3 MJ of absorbed energy. The genAI is based on the concept of Ubuntu that replaces the Deep Convolutional Neural Network approximation of a functional, with the formula for the generating functional of a canonical transformation from the domain of the canonical field momentums and fields, to the domain of the canonical momentums and coordinates, that is the Reduced Order Model. This formula is a logical process of renormalization, enabling Heisenberg's canonical approach to field theory, via calculation of the S-matrix, given observation of the fields. This can be viewed as topological characterization and control of collective, that is complex, systems.

Relaxing the postulates of an axiomatic theory is a natural way to find more general theories, and historically, the discovery of non-Euclidean geometry is a famous example of this procedure. Here, we use this way to extend quantum mechanics by ignoring the heart of Heisenberg's quantum mechanics -- We do not assume the existence of a position operator that satisfies the Heisenberg commutation relation, $[\hat x,\hat p]=i\hbar$. The remaining axioms of quantum theory, besides Galilean symmetry, lead to a more general quantum theory with a free parameter $l_0$ of length dimension, such that as $l_0 \to 0$ the theory reduces to standard quantum theory. Perhaps surprisingly, this non-Heisenberg quantum theory, without a priori assumption of the non-commutation relation, leads to a modified Heisenberg uncertainty relation, $\Delta x \Delta p\geq \sqrt{\hbar^2/4+l_0^2(\Delta p)^2}$, which ensures the existence of a minimal position uncertainty, $l_0$, as expected from various quantum gravity studies. By comparing the results of this framework with some observed data, which includes the first longitudinal normal modes of the bar gravitational wave detector AURIGA and the $1S-2S$ transition in the hydrogen atom, we obtain upper bounds on the $l_0$.

In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture, at once, dependencies among variables and across time points. The objective of this systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application domains of GNNs for time series classification and forecasting. A database search was conducted, and 366 papers were selected for a detailed examination of the current state-of-the-art in the field. This examination is intended to offer to the reader a comprehensive review of proposed models, links to related source code, available datasets, benchmark models, and fitting results. All this information is hoped to assist researchers in their studies. To the best of our knowledge, this is the first and broadest systematic literature review presenting a detailed comparison of results from current spatio-temporal GNN models applied to different domains. In its final part, this review discusses current limitations and challenges in the application of spatio-temporal GNNs, such as comparability, reproducibility, explainability, poor information capacity, and scalability. This paper is complemented by a GitHub repository at this https URL providing additional interactive tools to further explore the presented findings.

We propose a space-time reduced-order model (ROM) for nonlinear dynamical systems, building upon previous work on linear systems. Whereas most ROMs are space-only in that they reduce only the spatial dimension of the state, the proposed method leverages an efficient encoding of the entire trajectory of the state on the time interval $[0,T]$, enabling significant additional reduction. Trajectories are encoded using SPOD modes, a spatial basis at each temporal frequency tailored to the structures that appear at that frequency. These modes have a number of properties that make them an ideal choice for space-time model reduction, including separability and near-optimality for long trajectories. We derive a system of algebraic equations involving the SPOD coefficients, forcing, and initial condition by projecting an implicit solution of the governing equations onto the set of SPOD modes in a space-time inner product. We therefore refer to the method as spectral solution operator projection (SSOP). The online phase of SSOP comprises solving this system for the SPOD coefficients, given the initial condition and forcing. We find that SSOP gives two orders of magnitude lower error than POD-Galerkin projection at the same number of modes and CPU time across a suite of tests, including ones that use out-of-sample forcings and affine parameter variation. In fact, the method is substantially more accurate even than the projection of the solution onto the POD modes, which is a lower bound for the error of any method based on a linear space-only encoding of the state.

Impurity-bound excitons in II-VI direct-bandgap semiconductors are promising optically active solid-state spin qubits that combine exceptional optical quantum efficiency with an ultra-low spin noise environment. Previous studies on single impurities relied on incoherent optical excitation to generate photons. However, many quantum applications require resonant driving of quantum emitters to precisely control optical transitions and maintain coherence of the emission. Here, we demonstrate coherent optical emission of quantum light from a resonantly driven single impurity-bound exciton in ZnSe. The resonantly driven emitter exhibits bright quantum light emission that preserves the phase of the resonant drive, validated through polarization interferometry. Resonant excitation enables us to directly measure the Debye-Waller factor, determined to be 0.94, which indicates high efficiency emission to the zero-phonon line. Time-resolved resonance fluorescence measurements reveal a fast optically-driven ionization process that we attribute to Auger recombination, along with a slower spontaneous ionization process having a lifetime of 21 {\mu}s due to charge tunneling from the impurity. We show that incoherent, low-power laser pumping efficiently stabilizes the charge of the impurity-bound exciton on the timescale of 9.3 ns, recovering the resonance fluorescence emission from the bound exciton. These results pave the way for coherent optical and spin control of the single impurity states through resonant excitation of impurity-bound excitons in II-VI semiconductors.

In response to the capabilities presented by the High-Intensity Heavy Ion Accelerator Facility (HIAF) and the Accelerator-Driven Subcritical System (CiADS), as well as the proposed Chinese Advanced Nuclear Physics Research Facility (CNUF), we are assembling a consortium of experts in relevant discipline--both domestically and internationally--to delineate high-precision physics experiments that leverage the state-of-the-art research environment afforded by CNUF. Our focus encompasses six primary domains of inquiry: hadron physics--including endeavors such as the super eta factory and investigations into light hadron structures; muon physics; neutrino physics; neutron physics; the testing of fundamental symmetries; and the exploration of quantum effects within nuclear physics, along with the utilization of vortex accelerators. We aim to foster a well-rounded portfolio of large, medium, and small-scale projects, thus unlocking new scientific avenues and optimizing the potential of the Huizhou large scientific facility. The aspiration for international leadership in scientific research will be a guiding principle in our strategic planning. This initiative will serve as a foundational reference for the Institute of Modern Physics in its strategic planning and goal-setting, ensuring alignment with its developmental objectives while striving to secure a competitive edge in technological advancement. Our ambition is to engage in substantive research within these realms of high-precision physics, to pursue groundbreaking discoveries, and to stimulate progress in China's nuclear physics landscape, positioning Huizhou as a preeminent global hub for advanced nuclear physics research.

The one-dimensional Fröhlich model describing the motion of a single electron interacting with optical phonons is a paradigmatic model of quantum many-body physics. We predict the existence of an arbitrarily large number of bound excited states in the strong coupling limit and calculate their excitation energies. Numerical simulations of a discretized model demonstrate the complete amelioration of the projector Monte Carlo sign problem by walker annihilation in an infinite Hilbert space. They reveal the threshold for the occurrence of the first bound excited states at a value of $\alpha \approx 1.73$ for the dimensionless coupling constant. This puts the threshold into the regime of intermediate interaction strength. We find a significant spectral weight and increased phonon number of the bound excited state at threshold.

Superconducting qubits have achieved exceptional gate fidelities, exceeding the error-correction threshold in recent years. One key ingredient of such improvement is the introduction of tunable couplers to control the qubit-to-qubit coupling through frequency tuning. Moving toward fault-tolerant quantum computation, increasing the number of physical qubits is another step toward effective error correction codes. Under a multiqubit architecture, flux control (Z) lines are crucial in tuning the frequency of the qubits and couplers. However, dense flux lines result in magnetic flux crosstalk, wherein magnetic flux applied to one element inadvertently affects neighboring qubits or couplers. This crosstalk obscures the idle frequency of the qubit when flux bias is applied, which degrades gate performance and calibration accuracy. In this study, we characterize flux crosstalk and suppress it in a multiqubit-coupler chip with multi-Z lines without adding additional readout for couplers. By quantifying the mutual flux-induced frequency shifts of qubits and couplers, we construct a cancellation matrix that enables precise compensation of non-local flux, demonstrating a substantial reduction in Z-line crosstalk from 56.5$\,$permille$\,$to 0.13$\,$permille$\,$ which is close to statistical error. Flux compensation corrects the CZ SWAP measurement, leading to a symmetric map with respect to flux bias. Compared with a crosstalk-free calculated CZ SWAP map, the measured map indicates that our approach provides a near-zero crosstalk for the coupler-transmon system. These results highlight the effectiveness of our approach in enhancing flux crosstalk-free control and supporting its potential for scaling superconducting quantum processors.

The concept of causality is fundamental to numerous scientific explanations; however, its extension to the quantum regime has yet to be rigorously explored. This letter introduces the development of a quantum causal index, a novel extension of the classical causal inference framework, tailored to learn the causal relationships inherent in quantum systems. Our study focuses on the asymmetric quantum conditional mutual information (QCMI), incorporating the von Neumann entropy, as a directional metric of causal influence in quantum many-body systems. We analyze spin chains using the QCMI, implementing a projective measurement on one site as the intervention and monitoring its effect on a distant site conditioned on intermediate spins. Additionally, we study the effective causal propagation velocity, which is the speed at which QCMI becomes significant at distant sites. These findings indicate the presence of finite-speed propagation of causal influence, along with the emergence of coherent oscillations.

The optimization of neural wave functions in variational Monte Carlo crucially relies on a robust convergence criterion. While the energy variance is theoretically a definitive measure, its practical application as a primary convergence criterion has been underexplored. In this work, we develop a lightweight, general-purpose solver that utilizes the energy variance as a convergence criterion. We apply it to several systems-including the harmonic oscillator, hydrogen atom, and charmonium hadron-for validating the variance as a reliable diagnostic, and using a empirical threshold $10^{-3}$ as the energy variance convergence values for performing rapid parameter scans to enable preliminary physical verification. To clarify the scope of our approach, we derive an inequality that delineates the limitations of variance-based optimization in nodal systems. Despite these limitations, the energy variance proves to be a highly valuable tool, guiding our solver to efficient and reliable results across a range of quantum problems.

This paper lays out the principles of how Bose-Einstein condensates can modify radioactive decay. We highlight the challenges of many modes and short coherence times due to the $\approx$ MeV energies of the emitted radiation. Recent proposals for gamma ray and neutrino lasers claim that using a Bose-Einstein condensate as a source would solve these issues. We show that this is not the case, and the proposed experiments would have a gain of only $10^{-20}$ or smaller. We also analyze proposals for gamma ray lasers based on stimulated annihilation of positronium Bose-Einstein condensates.

Machine learning (ML) is transforming modeling and control in the physical, engineering, and biological sciences. However, rapid development has outpaced the creation of standardized, objective benchmarks - leading to weak baselines, reporting bias, and inconsistent evaluations across methods. This undermines reproducibility, misguides resource allocation, and obscures scientific progress. To address this, we propose a Common Task Framework (CTF) for scientific machine learning. The CTF features a curated set of datasets and task-specific metrics spanning forecasting, state reconstruction, and generalization under realistic constraints, including noise and limited data. Inspired by the success of CTFs in fields like natural language processing and computer vision, our framework provides a structured, rigorous foundation for head-to-head evaluation of diverse algorithms. As a first step, we benchmark methods on two canonical nonlinear systems: Kuramoto-Sivashinsky and Lorenz. These results illustrate the utility of the CTF in revealing method strengths, limitations, and suitability for specific classes of problems and diverse objectives. Next, we are launching a competition around a global real world sea surface temperature dataset with a true holdout dataset to foster community engagement. Our long-term vision is to replace ad hoc comparisons with standardized evaluations on hidden test sets that raise the bar for rigor and reproducibility in scientific ML.