Papers

Empirical Green's functions (EGFs) extracted from seismic ambient noise have been widely used to image Earth's interior structures, and the resolution of EGF-based tomography depends on the spatial density of seismic stations. However, due to cost and logistical constraints, it is often difficult to deploy dense seismic networks suitable for high-resolution tomography. While reliable interpolation of EGFs at unsampled locations could enhance tomographic resolution, the task remains inherently challenging and underexplored due to the dispersive nature of EGFs. In this study, we introduce DIER (diffusion-assisted implicit EGF representation), a self-supervised learning framework that integrates implicit neural representation with denoising diffusion probabilistic models to achieve high-fidelity EGF interpolation. In DIER, the diffusion process is conditioned on station coordinates to guide the transformation from random noise into EGF waveforms, which allows flexible reconstruction of five-dimensional EGF fields without labeled data or synthetic waveforms. We demonstrate the effectiveness of DIER through continent-scale EGF interpolation across the United States. The results show that DIER significantly outperforms the conventional radial basis function-based interpolation approach by generating EGFs with markedly improved phase alignment and dispersion characteristics. Surface wave tomography using the phase velocities derived from the interpolated EGFs also closely matches a reference model constructed from data acquired by a much denser seismic network. Our findings suggest that DIER provides a promising and cost-effective approach toward high-resolution ambient noise tomography in regions with sparse station coverage.

Purpose: Pushing MRI speed further demands more spatially-encoded information captured per unit time, e.g., by superimposing additional field modulations during oversampled readout. However, this can introduce calibration errors and increase reconstruction time. Thus, we propose a continuous field calibration approach and an efficient k-space reconstruction technique. Theory and Methods: Our auto-calibration generalizes GRAPPA kernels to explicitly extract continuous B0 modulation kernels, solving interpolation relationships between two ACS regions differing only in the extra field modulation. The k-space locations sharing the same instantaneous image-space modulation are grouped, so that subsets of time-invariant kernels can be separately estimated, as a generalized solution for Wave-CAIPI/FRONSAC-type scans. This view further inspires a k-space subregion-wise reconstruction technique, as an efficient alternative to conventional hybrid-space reconstruction. At 9.4T, FLASH accelerated by a local B0 coil array and Wave-CAIPI were tested with retrospective undersampling. Results: Artifact-free images were reconstructed, under diverse rapid B0 modulation schemes, reaching maximum acceleration factors of 8-fold in 2D and 14.6-fold in 3D. Some nonlinear gradients modulation schemes reach similar sampling efficiency as linear gradients modulation. The proposed reconstruction shows potentials in reducing reconstruction time. Conclusion: Rapid B0 modulations and widely-adopted parallel imaging can share a common mathematical framework, and consequently, achieve similarly-robust reconstructions. Specifically, for scans using dynamic B0 and static RF kernels, not only signal encoding, but also auto-calibration and reconstruction can be performed in k-space. This paves the way to robustly remove eddy currents, and explore more complex B0 modulation strategies towards ultimate MRI speed.

Burst Intensification by Singularity Emitting Radiation (BISER) appears as a bright temporally and spatially coherent Extreme Ultraviolet (XUV) and x-ray source driven by compact multi-terawatt femtosecond lasers in gas targets. There BISER originates from relativistic plasma singularities, so that the emission source size has a nanometer scale. The BISER x-ray yield quadratically depends on the driving laser power. BISER spectra have hundreds of electronvolt (eV) bandwidth embracing the 'water window' region (284 - 543 eV). Simulations predict that BISER pulses have durations close to the transform limit, which promises pulses shorter than the atomic unit of time (24 attoseconds). Based on the BISER brightness at ~20 terawatt laser power and the quadratic scaling, the brightness of BISER driven by petawatt-class lasers is predicted to exceed XUV free electron lasers. The BISER concept creates a new framework for a wide range of media emitting travelling waves capable of constructive interference, including gravitational waves. Here we review the BISER experimental discovery, its explanation based on the relativistic laser plasma simulations and catastrophe theory, experimental validation of the theoretical BISER model, proposal for imaging fast moving singularities, driving laser requirements, and future prospects emphasizing the driving laser wavelength scalability and possibility of terawatt attosecond coherent x-ray pulse generation.

The Fabry-Pérot resonator is a cornerstone of photonics and wave physics, providing a universal mechanism for spectral confinement and resonant enhancement of wave-matter interactions. In this work, we establish an analytically tractable class of Fabry-Pérot metacavities in which the reflecting elements are realized by single-layer periodic arrays of circular dielectric cylinders acting as metamirrors. Both the reflection efficiency and reflection phase of such metamirrors are obtained in closed form and shown to be widely and independently tunable, encompassing ideal electric and magnetic mirror limits with unit reflectivity. Building on these results, we derive explicit analytical expressions that fully describe the optical responses of Fabry-Pérot cavities composed of two such parallel metamirrors. Our combined analytical and numerical investigations reveal that these metamirrors provide exceptional flexibility for tailoring Fabry-Pérot resonances across a broad spectral range, enabling precise control over resonance positions and quality factors. In particular, the framework naturally predicts the emergence of Fabry-Pérot bound states in the continuum with formally infinite Q-factors. These results establish dielectric-metamirror-based Fabry-Pérot cavities as a versatile and fundamentally transparent platform for engineering high-Q optical resonances.

Photoacoustic imaging (PAI) uniquely combines the advantages of optical contrast with deep tissue penetration capability of acoustic waves, enabling imaging at depths of several centimeters. Conventional photoacoustic imaging methods have relied on pulsed lasers to induce the photoacoustic effect, coupled with arrays of pressure transducers to detect the resulting ultrasound signals. In this work, we propose an alternative all-optical approach that leverages optical deflection to record photoacoustic waves by an array of detection beams. The measured signal is shown to be the Radon transform of the pressure gradients. An optimization-based inversion procedure is used to reconstruct the initial time pressure gradient field. Subsequently, a Galerkin method is used to reconstruct the pressure field from the pressure gradient field. The new modality offers the potential for enhanced sensitivity and reduced signal distortion, advancing the capabilities of photoacoustic imaging beyond traditional transducer-based systems.

At its lowest frequencies, LIGO is limited by noise in its many degrees of freedom of suspended optics, which, in turn, introduce noise in the interferometer through their feedback control systems. Nonlinear interactions are a dominant source of low-frequency noise, mixing noise from multiple degrees of freedom. The lowest-order form is bilinear noise, in which the noise from two feedback-controlled subsystems multiplies to mask gravitational waves. Bilinear couplings require control trade-offs that simultaneously balance high- and low-frequency noise. Currently, there is no known lower limit to bilinear control noise. Here, we develop benchmark cost functions for bilinear noise and associated figures of merit. Linear-quadratic-Gaussian control then establishes aggressive feedback that saturates the lower bounds on the cost functions. We then develop a mixed LQG and $H_\infty$ approach to directly compute stable, robust, and optimal feedback, using the LIGO's alignment control system as an example. Direct computations are fast while ensuring a global optimum. By calculating optimal robust control, it is possible to construct the lower bound on controls noise along the Pareto front of practical controllers for LIGO. This method can be used to drastically improve controls noise in existing observatories as well as to set subsystem control noise requirements for next-generation detectors with parameterized design.

Polymorphism has been observed in viral capsid assembly, demonstrating the ability of identical protein dimers to adopt multiple geometries under the same solution conditions. A well-studied example is the hepatitis B virus (HBV), which forms two stable capsid morphologies both in vivo and in vitro. These capsids differ in diameter, containing either 90 or 120 protein dimers. Experiments have shown that their relative prevalence depends on the ionic conditions of the solution during assembly. We developed a model that incorporates salt effects by altering the intermolecular binding free energy between capsid proteins, thereby shifting the relative thermodynamic stability of the two morphologies. This model reproduces experimental results on the prevalence ratios of the large and small HBV capsids. We also constructed a kinetic model that captures the time-dependent ratio of the two morphologies under subcritical capsid concentrations, consistent with experimental data.

Quartz tuning fork-based atomic force microscopy (QTF-AFM) has become a powerful tool for high-resolution imaging of both conductive and insulating samples, including semiconductor structures and metal-coated surfaces as well as soft matter under ambient conditions, while also enabling measurements in more demanding environments including ultrahigh vacuum and cryogenic conditions where conventional cantilever-based AFM often encounters limitations. However, the broader adoption of QTF-AFM has been constrained by the difficulty of attaching a cantilever tip to a quartz tuning fork (QTF) with the positional and angular precision required for repeatable and reproducible probe fabrication. For stable operation, the tip must be placed precisely at the midline of a single tine, aligned parallel to the prong axis, and rigidly secured. Even slight lateral offsets or angular deviations disrupt the intrinsic antisymmetric flexural mode, induce torsional coupling, and ultimately lead to systematic image distortions and reduced measurement integrity. In this work, we present a comprehensive, semi-automated QTF-tip fabrication system that integrates precision alignment, real-time frequency-sweep monitoring, and controlled Q-factor tuning within a single workflow. Experimental characterization demonstrates consistent probe preparation across multiple trials, preservation of sharp and well-defined resonance responses with deliberately adjustable damping, and high-fidelity, high-resolution imaging in practical scanning tests. This integrated approach provides a reproducible framework to QTF-based probe fabrication, lowering the technical barrier to QTF-AFM implementation and broadening its applicability across diverse sample types and operating environments.

The study aimed to evaluate the applicability of environmental indices in the monitoring of smouldering coal-waste dumps. A dump located in the Upper Silesian Coal Basin served as the research site for a multi-method analysis combining remote sensing and field-based data. Two UAV survey campaigns were conducted, capturing RGB, infrared, and multispectral imagery. These were supplemented with direct ground measurements of subsurface temperature and detailed vegetation mapping. Additionally, publicly available satellite data from the Landsat and Sentinel missions were analysed. A range of vegetation and fire-related indices (NDVI, SAVI, EVI, BAI, among others) were calculated to identify thermally active zones and assess vegetation conditions within these degraded areas. The results revealed strong seasonal variability in vegetation indices on thermally active sites, with evidence of disrupted vegetation cycles, including winter greening in moderately heated root zones - a pattern indicative of stress and degradation processes. While satellite data proved useful in reconstructing the fire history of the dump, their spatial resolution was insufficient for detailed monitoring of small-scale thermal anomalies. The study highlights the diagnostic potential of UAV-based remote sensing in post-industrial environments undergoing land degradation but emphasises the importance of field validation for accurate environmental assessment.

We show that the quantum computational speedup of quantum algorithms is due to their teleological character, their being evolutions toward a goal (the solution of the problem) with an attractor in the very goal they will produce in the future (the solution of the problem again). We also show that, under the quantum cosmological assumption and for the Fine-tuned Universe version of the Anthropic Principle, the physical basis of the teleological character of quantum algorithms applies as well to the evolutions of the living for which the teleological notion was originally conceived.

This study aims to improve the TauFinder reconstruction algorithm for the MAIA detector concept. Through this work, we seek to increase the reconstruction efficiency and identification of hadronically decaying tau leptons. Through our work, we introduce a dynamic signal cone, known as a shrinking cone, which adjusts its size based on the transverse momentum of the tau candidate. In addition to the already studied one charged hadron and three charged hadron decay modes we extend TauFinder to include decays that consist of one charged hadron and up to two neutral pions. Furthermore, we have developed a tagger that prevents electrons being misidentified as one-prong tau candidates. Applying this tagger results in near-perfect electron rejection with negligible decrease in one-prong tau reconstruction efficiency.

Reliability has long been treated as an engineering practice supported by testing, statistics and standards, yet its status as a scientific discipline remains unsettled. From a philosophical perspective, scientific truth is characterized by a dual-structure that links empirical truth and mathematical truth, which requires an axiomatic system that is symbolically expressible and verifiable by universally repeatable controlled experiments. Building on this criterion, this paper examines whether reliability satisfies the dual-structure of scientific truth. Firstly, we analyze the philosophical foundations of the reliability problem, tracing its transition from experiential confidence and engineering practice toward scientific inquiry. Then, reliability science principles are introduced as an axiomatic system consisting of margin, degradation and uncertainty, which define reliability as the repeatability of system performance across time and space. Next, we present reliability science experiments as the empirical aspect of the dual-structure, where controlled and repeatable interventions are designed to verify the causal relations implied by the axioms. Furthermore, we develop the mathematical framework of reliability as the symbolic aspect of the dual-structure, articulating reliability laws through distance, relation and change, and developing a time-dependent measure, Biandong Statistics, to represent varying uncertainty beyond static descriptions. Accordingly, we argue that reliability is indeed a scientific discipline. The applicability of reliability science is demonstrated across engineering, living and social systems, and a unified logic for guiding engineering activities across the entire product lifecycle is provided, linking reliability to the conceptual, development, procurement, production and operation phases within a model-based structure.

The second industrial revolution saw the development of management methods tailored to the challenges of the times: firstly, the need for mass production, and then, the pursuit of improved quality and customer satisfaction, followed by a push to improve operational performances in response to market globalization. If these approaches were initially inspired by rational mechanistic thinking, they have since gradually broadened to integrate other dimensions such as psychology, sociology and systemic analysis. Business enterprises underwent a profound rethink in the 1990s introducing increasingly refined modi operandi, only to find their environment disrupted by the appearance of two new parameters: complexity and uncertainty. Enterprises of the third industrial revolution were able to integrate these parameters at the outset, introducing new styles of management. However, these may well be deficient with regard to activities where an error may be fatal, or a failure intolerable. Caught between the precautionary principle and the principle of experimentation, the third industrial revolution falters to find the right approach, whereas the fourth industrial revolution is almost already upon us, bringing its lot of upheavals. In this regard, faced with increasing complexities and uncertainties, Research and Development is of particular interest since its vocation consists precisely in confronting the complex and the uncertain. This article examines the fundamental principles of the R&D process, and analyses how these may act as a benchmark for contemporary management by providing sources of inspiration.

The rates of exciton transfer within dyads of perylene diimide and terrylene diimide connected by oligophenylene bridge units have been shown to deviate significantly from those of Förster's resonance energy transfer theory, according to single molecule spectroscopy experiments. The present work provides a detailed computational and theoretical study investigating the source of such discrepancy. Electronic spectroscopy data are calculated by time-dependent density function theory and then compared with experimental results. Electronic couplings between exciton donor and acceptor are estimated based on both transition density cube method and transition dipole approximation. These results confirm that the delocalization of exciton to the bridge parts contribute to significant enhancement of donor-acceptor electronic coupling. Mechanistic details of exciton transfer are examined by estimating the contributions of the bridge electronic states, vibrational modes of the dyads commonly coupled to both donor and acceptor, inelastic resonance energy transfer mechanism, and dark exciton states. These analyses suggest that the contribution of common vibrational modes serves as the main source of deviation from Förster's spectral overlap expression.

Analogue Hamiltonian simulation (AHS) in photonic systems can be an enticing alternative to direct experimental study of complex Hamiltonian systems as a result of the low cost and high degree of control one can have over the system's properties. Notably, the field of topological photonics has emerged in the last decade primarily by simulating tight-binding models of electrons within topologically nontrivial condensed-matter systems. Optical simulation of topologically nontrivial Hamiltonians requires optical resonators with minimal loss and well-matched frequencies whose intersite coupling can also be precisely controlled. The Surface Nanoscale Axial Photonics (SNAP) platform satisfies all these requirements, exhibiting ultra-low loss operation and sub-angstrom fabrication precision, making it an excellent platform for AHS. In this work, we experimentally demonstrate the first topologically nontrivial photonic SNAP devices by coupling together axial modes of adjacent SNAP microresonators to form a variety of Su-Schrieffer-Heeger (SSH) lattices. The devices manifest numerous distinct topological band structures corresponding to each axial mode of the microresonators, enabling us to observe behavior both close to and far from the topological-trivial phase transition. We further expand the scope of topological SNAP systems to contain not just higher-order generalizations of SSH lattices, but junctions between multiband lattices with dissimilar topological phases created by coupling up to 21 uniform and well-matched SNAP microresonators. Analyzing such "heterojunctions" necessitated our development of generalized topological polarization methods. We thus demonstrate the exceptional promise of the SNAP platform for AHS of 1D topological insulators, and also open the door to the potential for simulating >2 dimensional systems by utilizing nonlinear interactions.

This study, conducted among more than 250 physics and chemistry teachers in Morocco, analyzes the impact of experimentation on student learning and attention in middle and high school. The results show that the majority of teachers favor digital simulations, except for simple experiments such as electrical circuits. This choice is linked to material constraints, class size, and safety requirements. Simulations are perceived as practical and flexible, allowing experiments to be repeated or slowed down to facilitate understanding. However, teachers emphasize the need for specific ICT training in order to better integrate these tools into their practices. The most effective strategy identified is based on a hybrid approach: using simulations to explain abstract phenomena and real experiments to develop experimental skills, methodological rigor, and critical thinking. This complementary approach appears to be a promising solution for enriching science education and overcoming the constraints encountered in schools.

The knowledge of local spatial gradients (curl, divergence etc.) is crucial to examine the three-dimensional variation of flow fields including velocity and magnetic fields in space plasmas like the solar wind. Here we propose a simple method to calculate the same using the in-situ data of multi-spacecraft systems. Unlike the popular Curlometer method which depends on the vector integration theorems, our integration-free method is based on the construction of a local orthonormal coordinate system and the associated finite difference approximations. The Curlometer is applicable to a four spacecraft system arranged in a tetrahedron and yields a single volume-averaged estimate of the curl. Using our proposed method over 107 intervals of MMS (NASA) data, on the other hand, we successfully calculate the spatial derivatives at the position of each spacecraft of the tetrahedron and a three-spacecraft (non-collinear) subset of the same. The average value of all the curls calculated for a given tetrahedron shows an excellent agreement (correlation coeffcient ~ 0:99) with the curls calculated using Curlometer formula. The quality of the calculated curl (using our method) is found to improve if the spacecraft configuration approaches a regular tetrahedron. The current framework facilitates investigation of turbulent heating rates and the exploration of local flow features like Beltramization, existence of current sheets, etc., in present and future multi-spacecraft mission, including those involving more than four spacecraft.

Dynamical reweighting of path measures is a powerful approach for accurately evaluating slow molecular processes using modified potential energy surfaces used in enhanced sampling methods. Integrating this reweighting framework into the CP2K electronic-structure and molecular-dynamics (MD) software package delivers a robust and widely applicable tool for metadynamics, uncertainty quantification, and force-field optimisation based on both ab initio and classical MD simulations. Based on the Girsanov theorem for stochastic dynamical systems, the method is adapted to the Bussi-Donadio-Parrinello velocity-rescaling scheme. This scheme is accessible through the CSVR thermostat and can be interpreted as a Langevin OVRVO/OBABO update scheme requiring two random numbers per integration step. Comprehensive implementation details are provided, including a complete overview of the modified CP2K modules involved in the MD cycle and the computation of reweighting factors. The framework supports multiple sources of bias potentials, enabling reweighting via the PLUMED interface or CP2K-native external potentials and restraints. The feasibility of the implementation is demonstrated through rerun benchmarks, dynamical reweighting of Markov state models (MSMs) and computation of transport properties based on CP2K trajectories, showing excellent agreement with reference simulations.

Purpose: To accelerate MRI further, rapid B0 field modulations can be applied during oversampled readout to capture additional physical information, as in Wave-CAIPI/FRONSAC/local B0 coils modulation techniques. These methods, however, turn the Fourier readout into a non-Fourier-encoded dimension that cannot be reconstructed by FFT, posing significant reconstruction challenges especially in compressed-sensing or neural-network frameworks. Theory and methods: Because the rapid B0 modulations still vary slowly relative to the oversampled ADC dwell time, we exploit this encoding redundancy by compressing k-space patch-by-patch across subregions, each of which is jointly encoded by a distinct subset of B0 and RF (receive) spatial encoding functions. For each subset, a compression matrix is computed once and reused to compress all patches encoded by the same B0-RF spatial modulations. This can be implemented by feeding subsets of B0 and RF spatial encoding maps into an adapted conventional RF array compression algorithm, mimicking an expanded set of virtual receiver channels. This approach was evaluated on human brain scans at 9.4 T/3 T. Results: The proposed group-patch joint compression achieves substantially higher compression factors than conventional RF-only compression, while minimally compromising encoding efficiency. Typically, joint compression factors of 11x-20x led to negligible encoding loss, dramatically reducing reconstruction time and peak memory usage. For example, compressed-sensing reconstruction took 1.4-5.1 s/2D slice, 177 s-10.1 min/3D volume on a high-memory CPU node. Conclusion: Given joint encoding of dynamic B0 and static RF fields, compressing multidimensional k-space patches in separate groups outperforms compressing RF receivers alone. This substantially mitigates a fundamental computational bottleneck when combining rapid B0 and RF-receiver modulations.

FE simulations emulating TPV system deployment in patient-specific right ventricular outflow tracts (RVOT) assume material properties for the RVOT and adjacent tissues. Sensitivity of the deployment to variation in RVOT material properties is unknown. Moreover, the effect of a transannular patch stiffness and location on simulated TPV deployment has not been explored. A sensitivity analysis on the material properties of a patient-specific RVOT during TPV deployment, modeled as an uncoupled HGO material, was conducted using FEBioUncertainSCI. Further, the effects of a transannular patch during TPV deployment were analyzed by considering two patch locations and four patch stiffnesses. Visualization of results and quantification were performed using custom metrics implemented in SlicerHeart and FEBio. Sensitivity analysis revealed that the shear modulus of the ground matrix (c), fiber modulus (k1), fiber dispersion (kappa), and fiber mean orientation angle (gamma) had the greatest effect on 95th%ile stress, whereas only c had the greatest effect on 95th%ile Lagrangian strain. First-order sensitivity indices contributed the greatest to the total-order sensitivity indices. Simulations using a transannular patch revealed that peak stress and strain were dependent on patch location and the underlying vessel geometry, with increasing patch stiffness modulating the magnitude of local stress concentrations. As stiffness of the patch increased, greater stress was observed at the interface connecting the patch to the RVOT, and stress in the patch itself increased while strain decreased. The total enclosed volume by the TPV device remained unchanged across all simulated patch cases. This study highlights that while uncertainties in tissue material properties and patch locations may influence functional outcomes, FE simulations provide a reliable framework for evaluating these outcomes in TPVR.

We investigate novel strategies to extend the sensitivity of dark matter direct detection experiments to energy deposits well below the thresholds of conventional detectors. In liquid-argon time-projection chambers equipped with silicon photomultipliers (SiPMs), we show that improved optical readout, combined with a nuclear dielectric constant (NDC) correction to the WIMP nucleus interaction, enhances the response to low-momentum-transfer nuclear recoils. The NDC effectively amplifies the interaction strength at small recoil energies, increasing the expected ionization and scintillation yields without modifying the high-energy behavior constrained by calibration data. When coupled to SiPM based light collection, this mechanism lowers the effective detection threshold to the subKeV regime, significantly improving sensitivity to low-mass WIMPs and other weakly interacting particles. Complementarily, we present the design and projected performance of a qubit-based detector optimized for ultra-low-energy depositions. A novel two-chip architecture is employed to minimize signal dissipation, while quantum parity measurements enable enhanced single-phonon sensitivity. Full simulations of phonon propagation and quasiparticle dynamics demonstrate that energy deposits at the level of $\gtrsim 30 meV$ can be detected with nearly unit efficiency and high energy resolution. This capability is expected to advance sensitivity to dark-matter scattering for masses $m_\chi \gtrsim 0.01 MeV$ by several orders of magnitude for both light and heavy mediators, and to enable competitive searches for axion and dark-photon absorption in the $0.04 - 0.2 eV$ mass range.

The inner tracking system of the ATLAS experiment will be upgraded to a full silicon detector in 2030 for HL-LHC. The new tracking system is called ITk, the Inner Tracker. It is required to be operable with efficiency higher than 99% and noise hit occupancy smaller than 0.1%. During the pre-production phase of the ITk project, many short-strip modules were observed to exhibit so-called "Cold Noise (CN)", wherein clusters of strips displayed very high noise when the modules were operated at temperatures below~$-35\degree$C. To investigate the CN impact and ensure the quality of module production, huge amount of effort have been put in by the collaboration. This paper focuses on the impact of CN on the tracking performance by examining two short strip modules that exhibit CN: one is non-irradiated, while the other one has been irradiated to the maximum expected end-of-lifetime fluence. For each module, the global and single strip tracking performance are evaluated.

Verdazyl radicals are a versatile class of air-stable organic radicals used in various applications, especially for their magnetic properties. Despite the development of a wide range of verdazyl derivatives, however, they are all non-emissive. To investigate the reasons behind this and to understand the excited-state dynamics of verdazyls, we combine steady-state and femtosecond pump-probe spectroscopy with quantum chemical calculations. In the carbazole-substituted 2,4,6-triphenylverdazyl (TPV-Cz) , we observe ultrafast internal conversion of the first excited state on a timescale of 0.5 $\pm$ 0.1 ps, followed by vibrational relaxation with a lifetime of 3.7 $\pm$ 0.4 ps. Spin-flip time-dependent density functional theory calculations reveal that the sub-picosecond non-radiative decay comes from a low-energy conical intersection between the D1 and D0 states, driven by an out-of-plane distortion of the verdazyl ring. This distortion is observed and remains energetically accessible in the isolated verdazyl ring in 2,4,6-triphenylverdazyl and in TPV-Cz. This shows that the conical intersection geometry is a recurring feature across different types of verdazyl derivatives and explains why all verdazyls are non-emissive despite different functionalization. Our results provide a mechanistic understanding of the photophysical properties of verdazyl radicals and offers a pathway for the future design of emissive verdazyl derivatives.

We examine the differences between the driven turbulence described by the Kuramoto-Sivashinsky (KS) equation and the second law of thermodynamics. A general velocity and entropy density system is analyzed with the unified thermodynamic algorithm of metriplectic dynamics, and we show that the positive spectra of the KS equation due to an external energy source prevent its metriplectic description. A variant of the KS equation is produced that monotonically generates an entropy, but the only equilibria of this variant system are spatially constant. Numerical experiments are performed comparing the evolution of the KS equation and its thermodynamic variant. The entropy of this thermodynamic system is increased further by the driving effects of the KS equation, reconciling the generation of entropy with the energy source of the KS equation. Further numerical experiments restrict the positive spectra in the KS equation to determine the effect on the system time evolution. While rescaling the growth rates of instabilities reproduces similar behavior on a slower time scale, introduction of individual positive spectra reproduces the formation of equilibria, relative equilibria, and a transition to chaos.

Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of the computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrinsically disordered proteins. Developing a transferable, data-driven ISM that overcomes the limitations of traditional analytical formulas remains a central challenge in computational chemistry. Here we address this challenge by introducing a novel strategy that distills the evolutionary information learned by a protein language model, ESM3, into a computationally efficient graph neural network (GNN). We show that this GNN potential, trained on effective energies from ESM3, is robust enough to drive stable, long-timescale molecular dynamics simulations. When combined with a standard electrostatics term, our hybrid model accurately reproduces protein folding free-energy landscapes and predicts the structural ensembles of intrinsically disordered proteins. This approach yields a single, unified model that is transferable across both folded and disordered protein states, resolving a long-standing limitation of conventional ISMs. By successfully distilling evolutionary knowledge into a physical potential, our work delivers a foundational implicit solvent model poised to accelerate the development of predictive, large-scale simulation tools.

As ancient stellar systems, globular clusters (GCs) offer valuable insights into the dynamical histories of large galaxies. Previous studies of GC populations in the inner and outer regions of the Andromeda Galaxy (M31) have revealed intriguing subpopulations with distinct kinematic properties. Here, we build upon earlier studies by employing Bayesian modelling to investigate the kinematics of the combined inner and outer GC populations of M31. Given the heterogeneous nature of the data, we examine subpopulations defined by GCs' metallicity and by associations with substructure, in order to characterise possible relationships between the inner and outer GC populations. We find that lower-metallicity GCs and those linked to substructures exhibit a common, more rapid rotation, whose alignment is distinct from that of higher-metallicity and non-substructure GCs. Furthermore, the higher-metallicity GCs rotate in alignment with Andromeda's stellar disk. These pronounced kinematic differences reinforce the idea that different subgroups of GCs were accreted to M31 at distinct epochs, shedding light on the complex assembly history of the galaxy.

High-reflectivity mirrors in the mid-infrared (MIR) range are essential for next-generation optoelectronic devices but are still constrained by strain accumulation, poor thermal conductivity, and growth instability of thick multi-alloy stacks in conventional distributed Bragg reflectors (DBRs). We introduce plasmon-enabled DBRs (PE DBRs) based on modulation-doped monolithic InP, where plasmonic dispersion in highly doped layers provides a strong refractive-index contrast. Using inverse-design optimization targeting reduced free-carrier absorption and maximized reflectivity, we demonstrate that PE DBRs can achieve reflectivities approaching 100%. Experimentally grown 14 {\mu}m thick InP PE DBRs exhibit up to 99% reflectance with bandwidths reaching 18% of the design wavelength. The monolithic, junction-free configuration ensures low resistivity and enhanced thermal performance, offering a scalable platform for efficient plasmonic mirrors in MIR photonics, with potential applications in photodetectors, light-emitting diodes and lasers.

In this work, we quantify the effects of solar wind expansion on the dispersive properties of the three normal modes of ideal MHD using the Expanding Box Model, under a background magnetic field that follows the Parker spiral geometry. From the linearized MHD-EBM equations, we construct the dispersion tensor and derive analytical expressions for the eigenfrequencies $\omega(k,R)$, magnetic compressibility $C_B$, and the ratio of the parallel electric field to the perpendicular magnetic field $|\delta E_\parallel|/|\delta B_\perp|$ of the magnetosonic modes to quantify how radial solar wind expansion reshapes the character of compressive fluctuations in the solar wind. Magnetic compressibility increases with heliocentric distance, and this trend shows a better alignment with in-situ observations when expansion is included from the MHD-EBM framework. $C_B$ shows a well-defined minimum at small radii and then increases linearly with distance, which naturally reproduces the observed transition from Alfvénic to compressive fluctuations between $\sim$0.3-1 AU. The ratio $|\delta E_\parallel|/|\delta B_\perp|$ reveals opposite behaviors for the fast and slow modes: while the fast mode becomes more electrostatic with increasing distance, the slow mode evolves to a more magnetically dominated character. Expansion reduces the growth of their electromagnetic/compressive balance at large radii. Our results demonstrate that solar wind expansion actively redistributes energy between magnetically compressive modes and purely transverse fluctuations with respect to the background magnetic field, playing a major role in shaping the radial evolution of wave dynamics throughout the inner heliosphere.

Conventional radiotherapy dose calculation algorithms are often computationally slow and non-differentiable, creating bottlenecks for online adaptive radiotherapy (ART) and limiting end-to-end automatic planning. Deep learning provides consistent inference performance and a differentiable framework essential for rapid optimization. In this study, we developed a generalized, site-independent deep learning dose engine using a beamlet-based input strategy. This establishes a computationally consistent and differentiable module that enables end-to-end training for autoplanning while maintaining accuracy across diverse geometries. A dataset of 3,600 plans from 120 patients across six anatomical sites was used to train two 3D convolutional neural networks, a standard U-Net and a Cascade U-Net, to predict 3D dose distributions from CT images and divergent MLC/jaw projections. Performance was validated via 3D gamma analysis on an independent cohort of 60 VMAT plans. The optimal model (U-Net with MAE loss) achieved a mean gamma passing rate of $98.9 \pm 1.6\%$ (3%/2mm, 10% threshold). Performance remained robust across all sites (passing rates $>98\%$), demonstrating that the beamlet-based strategy generalizes effectively to complex geometries without site-specific training. These results indicate that a single, site-independent model can calculate radiotherapy dose distributions with clinical accuracy. This differentiable engine is highly suitable for integration into end-to-end automatic planning, online ART, and secondary dose verification workflows.

The Oak Ridge National Laboratory STS Project will enhance the Spallation Neutron Source by adding a new neutron source. The upgrade includes a 30% increase in beam energy and a 50% boost in beam current, doubling the accelerator's power capability to 2.8 MW. The Ring-to-Second-Target Beam Transport (RTST) system is vital in directing high-energy proton beams to the new second target station. New magnets have to fit into a restricted space within the existing beamline to add the new extraction line. New, fast kicker magnets (Pulsed Dipole), a focusing quad with an aperture for both beamlines (Large Quad) and the replacement of existing 21Q40 Quads with 'Narrow Quads' having identical fields as the existing design are Fermilab's responsibility. The design of these magnets poses unique challenges, as in addition to the high requirements on the quality of magnetic fields, they are subject to major restrictions related to their dimensions.