Papers

Understanding the kinetics of drug-protein interactions is paramount for drug design, yet the field lacks large-scale, dynamic data to move beyond static structural analysis. Here, we present DD-03B, a massively scalable database providing dynamic, all-atom dissociation trajectories for a broad set of ligand-protein complexes. Utilising and extending a validated computational pipeline, we generated dissociation trajectories for 19,037 ligand-protein complexes sourced from PDBbind+v2020R1, resulting in a repository of approximately 0.3 billion simulation frames totalling 40 TB in size. For these systems-which possess experimental binding affinities (kd) but typically lack measured koff rates-we computed and assigned dissociation rate constants through trajectory reweighting. Our analysis reveals that protein-ligand complexes can be categorised into three mechanistic types (pathway-dominant, open-pocket, and entropy-pocket systems), each requiring distinct strategies for accurate kinetic characterisation. Together with our previously released DD-13M, DD-03B forms the core of the expandable Dissociation Dynamic Database (DDD) project, which will be continuously augmented with new trajectories. This large-scale, publicly available resource establishes a critical foundation for training and benchmarking next-generation generative AI models to predict and optimise drug-protein dissociation kinetics.

To study how geometrical shape affect the light scattering properties for an ensemble of randomly orientated particles, the single scattering matrices including complete polarization information are calculated statistically for a group of crystals with random geometrical shape and a group of hexagonal prisms with various aspect ratios in geometrical optics approximation method. To compare, the single scattering matrices for individual random irregular crystal and individual hexagonal prism are also presented. It should be noted that all statistical simulation experiments in this study are restricted to the following conditions: diffraction and absorption effects are neglected, calculations are performed at a single fixed wavelength, particles are assumed to be randomly oriented, and the simulations are limited to the regime where the geometric optics approximation is applicable. Using a unified computational framework for scattering matrices of convex polyhedra, we carried out a series of statistical numerical simulations. The flexibility of this framework in modifying particle geometry enables a systematic investigation of shape-dependent scattering characteristics. The results demonstrate that regular and irregular particles exhibit noticeably different scattering matrix signatures, and ensembles of irregular particles yield smooth and featureless non-zero matrix elements. In contrast, ensembles of regular hexagonal particles with varying aspect ratios retain common geometric scattering features.

Scanning Tunneling Microscopy (STM) has revolutionized our atomic scale understanding of surfaces and accelerated progress in nanotechnology. This technique, however, is restricted to metal or semiconducting samples, as it requires a tiny current to stabilize the tip-sample distance with atomic scale precision. We developed a new imaging and feedback method that relies on true alternating current (AC) without any direct current (DC) component. This technique does not only enable the imaging on non-conducting surfaces with atomic step resolution, like (thin) glass and oxides, it provides also access to high-frequency electronic signal coming from the sample. We demonstrate that it is possible to measure on 25nm thick silicon oxide with 10 MHz tunneling current.

While collisionless plasmas are ubiquitously present near astrophysical compact objects, the impact that their composition has on the high-energy emission is presently unknown. We present the first investigation of particle-acceleration mechanisms in kinetic, special-relativistic turbulence, modeling electrons, positrons, and protons with realistic mass ratios. Under global charge neutrality, we introduce a positron fraction and cover regimes ranging from an electron-proton plasma over to pair-dominated plasmas. Using a novel generalized Ohm's law for multispecies relativistic plasmas, we analyze particle acceleration due to electric fields in reconnection events that self-consistently emerge from turbulence. We demonstrate, for the first time, that energization occurs at reconnection current sheets driven by the divergence of the relativistic pressure tensor, which locally aligns with the particle velocity and leads to an efficient energy transfer. The imbalance between electrons and positrons systematically favors electron acceleration, highlighting the necessity of realistic multispecies modeling to capture the nonthermal contributions in accretion flows and relativistic jets from black holes.

Nonlinear phononics provides a route to control crystal structures through light-induced phonon excitation. In this study, we apply nonlinear phononics to an iron-based superconductor, LaFeAsO, with the aim of tuning its crystal structure toward the ideal one to enhance superconductivity. We simulate light-induced phonon dynamics on the anharmonic lattice potential determined by first-principles calculations. We find that the anion height $h$, a key structural parameter in iron-based superconductors, approaches its ideal value when an appropriate infrared-active phonon mode is selectively excited. This result suggests the possibility of controlling crystal structures and enhancing superconductivity in iron-based superconductors based on the concept of nonlinear phononics.

Protein phosphorylation provides a dynamic readout of cellular signaling yet remains difficult to detect at low abundance and stoichiometry. Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) using particle-in-pore plasmonic nanopores offers label-free molecular detection with submolecular sensitivity. However, reliable identification of subtle post-translational modifications (PTMs) is hindered by the stochastic nature of SM-SERS signals, partial excitation of peptide residues within the plasmonic hotspot, and background interference. Here, we introduce a physics-informed deep learning framework to decode complex SM-SERS dynamics and identify single-peptide PTMs. The model integrates multiple-instance learning with a temporal encoder combining temporal convolutional networks and bidirectional gated recurrent units to capture both local spectral variability and long-range blinking dynamics. To address diffusion-driven spectral heterogeneity, long spectral trajectories are segmented using Pearson-correlation, enabling weakly supervised training under label ambiguity. This framework robustly distinguishes single peptide phosphorylation despite strong background interference and stochastic signal fluctuations. By coupling nanoplasmonic confinement with spatiotemporal deep learning, our approach enables high-fidelity detection of single-molecule phosphorylation events and advances ultrasensitive phosphoproteomic analysis.

The $\mathbb{Z}_N$ cluster-state wavefunction, a paradigmatic example of symmetry-protected topological (SPT) order with $\mathbb{Z}_N \times \mathbb{Z}_N$ symmetry, is expressed in various equivalent ways. We identify the projector-based scheme called the $P$-representation as the efficient way to express cluster and dipolar cluster state's wavefunctions. Employing the restricted Boltzmann machine scheme to re-write the interaction matrix in the $P$-representation in terms of neural weight matrices allows us to develop the neural quantum state (NQS) and the matrix product state (MPS) representations of the same state. The NQS and MPS representations differ only in the way the weight matrices are split and grouped together in a matrix product. For both $\mathbb{Z}_N$ cluster and dipolar cluster states, we derive in closed form the weight function $W(s,h)$ that couples physical spins $s$ to hidden variables $h$, generalizing the previous construction for $Z_2$ cluster states to $\mathbb{Z}_N$. For the dipolar cluster state protected by two charge and two dipole symmetries, the procedure we have developed leads to the tensor product state (TPS) representation of the wavefunction where each local tensor carries three virtual indices connecting a given site to two nearest neighbors and one further neighbor. We benchmark the resulting TPS construction against conventional MPS representation using density-matrix renormalization group simulations and argue that the TPS could offer a more efficient representation for some modulated SPT states. As a by-product of the investigation, we generalize the previous $Z_2$ matrix product operator construction of the Kramers-Wannier (KW) operator to $\mathbb{Z}_N$ and interprets it as the dipolar generalization of the discrete Fourier transform on $\mathbb{Z}_N$ variables. The new interpretation naturally explains why the KW map is non-invertible.

Bright squeezed light from parametric down-conversion in the infrared (IR) frequency range has triggered the emergence of attosecond quantum optics – a new research field at the interface of quantum optics, strong-field physics, and attosecond technology. Two challenges arise at this interface: transferring quantum features of the IR light sources to the ultraviolet (UV) and extreme ultraviolet (XUV) frequency range via strong-field nonlinearities, and exploiting quantum optical properties of the nonlinear optical response as a new probe in ultrafast dynamics. Here, we address both by driving high-harmonic generation (HHG) in solids with entangled photon pairs either in degenerate or non-degenerate frequency modes. In the degenerate mode, single-shot measurements of harmonics up to the 10th order reveal strong photon bunching whose $g^{(2)}$ first grows and then decreases with the harmonic order. We show that this behavior tracks different microscopic mechanisms responsible for harmonic emission, demonstrating the potential of attosecond quantum optical spectroscopy. In the non-degenerate case, the harmonics retain quantum-induced correlations, verified by wavelength-resolved second-order cross-correlation maps. Our findings demonstrate transfer of quantum photon correlations into the XUV domain and open a pathway toward quantum-enhanced attosecond spectroscopy and control of ultrafast dynamics in solids.

Total solar eclipses are not only astronomical spectacles but also great astrophysical laboratories. Their historical records are particularly helpful for assessing the past variability of the Earth's rotation speed. Chinese records played a key role for such analyses. However, Chinese eclipse records from the Míng period have not been used for {\Delta}T reconstructions, partially because most of the contemporaneous eclipse reports are found not in official histories but in local treatises. This study examines eclipse records in the (quasi-)contemporaneous local treatises, concentrating on what explicitly mentioned eclipse totality on the day of a total solar eclipse and what were compiled during the Míng Dynasty. On their basis, our study revised the {\Delta}T constraint in 1361 to -408 s =< {\Delta}T =< 601 s and set new {\Delta}T constraints of 277 s =< {\Delta}T =< 890 s in 1514, -328 s =< {\Delta}T =< 332 s in 1542, and -1762 s =< {\Delta}T =< 1091 s in 1575, respectively. We also revised most of the existing {\Delta}T constraints in the 14th to 16th centuries, using the ephemeris data of the NASA JPL DE 441. Overall, our {\Delta}T constraints generally tighten the {\Delta}T variations more than what M+21 fit for their {\Delta}T spline curve, requiring downward modification and upward modifications for the {\Delta}T reconstructions around 1361 and 1542, respectively. Our results suggest that the {\Delta}T decrease between 1514 and 1567 was slightly steeper than previously considered.

The underground rates of cosmic-ray muons exhibit seasonal variations correlated with effective atmospheric temperature, quantified via a single coefficient. We compare two analysis methods for studying the correlation: the standard Unbinned Method, where all rate-temperature data points are fit simultaneously via linear regression, and the Binned Method, where data points with similar temperatures are first grouped into bins before fitting. We find that while both methods are unbiased in the limit of negligible temperature uncertainties, the Binned Method develops significant bias when temperature uncertainties are present, due to binning-induced distortions. In contrast, the Unbinned Method remains robust if the uncertainties are accurately known. To address the widely encountered issue of imprecise uncertainty estimation, we propose a novel procedure that assesses correlation stability by varying the time intervals and their assigned uncertainties. This approach resolves methodological tensions in studies of seasonal modulation of the muon rate and provides a practical framework for robust correlation estimation under real-world conditions.

Strong-field ionization is the cornerstone of attosecond physics, which has been extensively studied under coherent-state driving. Recently, the interface between attosecond physics and quantum optics has emerged as a new frontier. Yet, owing to experimental limitations, the role of the quantum nature of light in atomic strong-field ionization has remained unexplored. Here, we demonstrate strong-field ionization of xenon atoms driven by bright squeezed vacuum (BSV) with average pulse energy up to 10 \textmu J. We show that, as a nonclassical state with zero mean field and strong intensity fluctuations, BSV selectively enhances the spider-like holographic structures in the photoelectron momentum distributions. Using a quantum-light-corrected quantum-trajectory Monte Carlo (q-QTMC) model, we attribute this effect to the intrinsic coherence of trajectory pairs emitted within the same subcycle field fluctuation. These dynamically correlated paths exhibit enhanced phase stability and remain robust against dephasing, whereas asynchronous paths are filtered out by field noise. Our results reveal a quantum-fluctuation-induced mechanism for coherence protection in strong-field processes, positioning BSV as an effective coherence filter and establishing a new regime of quantum-enabled noise-resilient ultrafast dynamics.

This study investigates a simplified stellarator configuration employing circular coils, in which rotational transform is generated by tilting the toroidal field (TF) coils. A pair of axisymmetric poloidal field (PF) coils is introduced to compensate for the vertical magnetic field component produced by the tilted TF coils, together forming the three-dimensional magnetic configuration. The existence of clear, nested magnetic flux surfaces is confirmed through magnetic field-line tracing, and the corresponding vacuum free-boundary equilibrium is computed using the DESC solver. The coil set is partially optimized by varying the TF coil radius and tilt angle to reduce neoclassical transport and enhance alpha-particle confinement. The optimized configuration is compared with fully optimized stellarators such as W7-X and LHD in terms of alpha-particle confinement and the Gamma_C proxy. The neoclassical transport coefficient D11 is evaluated and found to be low. Collisionless guiding-center orbit calculations for 100 eV protons and 3.5 MeV alpha particles further demonstrate favorable confinement properties.

Laser frequency stabilization is conventionally analyzed using continuous-time control theory, which accurately models analog feedback but is insufficient for digital implementations where quantization, sampling, and stochastic noise shape the dynamics. In modern digital laser systems, such as Photonic Integrated Circuit (PIC)-based lasers, finite discriminator and actuator resolution, sampling delays, and measurement noise introduce stochastic behavior that deterministic models do not capture. We present a discrete-time Markov-state framework that models the evolution of the quantized actuator in a digital laser frequency lock, with state-transition probabilities determined by the frequency discriminator response, noise statistics, and implemented digital control logic. The steady-state actuator and locked-laser frequency distributions are obtained directly from the unit-eigenvalue solution of the transition matrix, providing immediate access to key stability metrics without long time-domain simulations. For white frequency noise, we show that the Markov formulation is exact under decorrelated sampling and update schemes, while correlated discriminator sampling introduces a predictable inflation of actuator variance without shifting the operating point. In the presence of colored noise, long-range temporal correlations induce sampling-dependent deviations in both actuator mean and variance, defining the regime of validity of the memoryless Markov description. This framework provides a compact and physically transparent tool for analyzing and optimizing digitally stabilized lasers in integrated photonic systems.

Developing compact, broadband mid-infrared coherent sources for applications in spectroscopy and sensing remains a pressing challenge in photonics. However, material limitations and integration constraints have restricted the accessible wavelengths and operation bandwidths of current mid-infrared lasers. Here, we address these challenges by developing a nonlinear integrated photonic device that converts a fixed-wavelength near-infrared pump laser into broadly tunable mid-infrared light. Our device, an optical parametric oscillator (OPO) integrated on thin-film lithium niobate, generates 22 THz of multi-milliwatt, voltage-tunable radiation from 2.7-3.4 microns, a region typically difficult to access but vital for environmental, chemical, and biological sensing. By introducing an on-chip-tunable OPO architecture taking advantage of the Vernier effect, we obtain electrical control of the emission wavelengths from coarse, multi-THz scales down to continuous, sub-100-GHz mode-hop-free tuning ranges. This work establishes a robust platform for a new class of compact, widely tunable mid-infrared sources with potential for future scaling.

Introduction: Mechanical thrombectomy can cause vessel deformation and procedure-related injury. Benchtop models are widely used for device testing, but time-resolved, full-field 3D vessel-motion measurements remain limited. Methods: We developed a nine-camera, low-cost multi-view workflow for benchtop thrombectomy in silicone middle cerebral artery phantoms (2160p, 20 fps). Multi-view videos were calibrated, segmented, and reconstructed with 4D Gaussian Splatting. Reconstructed point clouds were converted to fixed-connectivity edge graphs for region-of-interest (ROI) displacement tracking and a relative surface-based stress proxy. Stress-proxy values were derived from edge stretch using a Neo-Hookean mapping and reported as comparative surface metrics. A synthetic Blender pipeline with known deformation provided geometric and temporal validation. Results: In synthetic bulk translation, the stress proxy remained near zero for most edges (median $\approx$ 0 MPa; 90th percentile 0.028 MPa), with sparse outliers. In synthetic pulling (1-5 mm), reconstruction showed close geometric and temporal agreement with ground truth, with symmetric Chamfer distance of 1.714-1.815 mm and precision of 0.964-0.972 at $\tau = 1$ mm. In preliminary benchtop comparative trials (one trial per condition), cervical aspiration catheter placement showed higher max-median ROI displacement and stress-proxy values than internal carotid artery terminus placement. Conclusion: The proposed protocol provides standardized, time-resolved surface kinematics and comparative relative displacement and stress proxy measurements for thrombectomy benchtop studies. The framework supports condition-to-condition comparisons and methods validation, while remaining distinct from absolute wall-stress estimation. Implementation code and example data are available at https://ethanuser.github.io/vessel4D

The power sector is a major source of fossil fuel use and air pollutant emissions, making high-spatiotemporal-resolution emission accounting essential for effective mitigation policy and air quality management. Yet existing public inventories are often limited by low timeliness and coarse resolution. Here, we develop a global, plant-level, daily, multi-pollutant emission database for the power sector by integrating nearly 3 million hourly-to-daily near-real-time power generation records from 57 countries, representing about 81% of global fossil-fuel-based electricity generation, with fundamental information for more than 10,000 power plants worldwide, including location and installed capacity. The dataset substantially improves the timeliness and granularity of global power-sector emission estimates. From 2019 to 2025, emissions of most pollutants increased, with 2025 daily mean emissions reaching 0.274 kt/d for BC, 45.1 kt/d for CO, 0.418 kt/d for NH3, 52.2 kt/d for NOx, 3.01 kt/d for NMVOC, 0.418 kt/d for OC, 6.76 kt/d for PM10, 5.11 kt/d for PM2.5, and 78.5 kt/d for SO2. Compared with 2019, NMVOC showed the largest increase, whereas SO2 was the only pollutant to decline overall. Coal remained the dominant source of sulfur-, nitrogen-, and particulate-related emissions, while gas and biomass contributed more to carbonaceous species and reduced nitrogen. The dataset also captures pronounced seasonal, regional, and short-term variability. Against EDGAR for 2019-2022, our estimates agree well, with Pearson correlations of 0.92-0.99 and mean relative deviations of 8.8%-28.1%. This near-real-time, high-resolution dataset provides a strong foundation for air pollution control, carbon mitigation, emission monitoring, and satellite-based inversion.

The extraordinary one-dimensional properties of carbon nanotubes have captivated scientists and engineers since their discovery in the early 1990s. In particular, semiconducting single-wall carbon nanotubes (SWCNTs) are highly promising for optoelectronic applications because of their diameter-dependent direct band gaps and strong, tunable light-matter interactions. However, the prevalence of structural disorder, misalignment, and chirality heterogeneity in macroscopic assemblies has hindered their practical applications. Recently, advanced assembly methods, combined with post-growth chirality separation techniques, have enabled the fabrication of wafer-scale, nearly crystalline films of highly aligned and densely packed SWCNTs with tailored properties. In this Opinion, we discuss how these films provide a transformative platform for engineering "Single$^3$" heterostructures-assemblies that are simultaneously single-crystal, single-chirality, and single-wall. Stacking these layers with nanometer-scale precision and tunable thicknesses allows for the realization of artificial bilayer junctions, quantum wells, and superlattices. We posit that these architectures will enable a new generation of high-performance devices, including lasers, photodiodes, solar cells, and single-photon emitters.

Clinical decisions for unruptured intracranial aneurysms depend on detecting growth on follow-up magnetic resonance angiography (MRA). Growth is typically judged from manual 2D diameters on few slices, which vary across clinicians and frequently miss subtle 3D change. Even with 3D segmentations, apparent differences can reflect resolution, segmentation, surface processing, or registration mismatch rather than true growth; most criteria remain heuristic and binary. We show that a Bayesian displacement-based model using the surrounding vessel as an internal reference achieves strong discrimination of aneurysm growth (AUC 0.86-0.87) and improves agreement with expert labels (Cohen's kappa up to 0.66 vs. 0.35 for volumetric criteria), while providing calibrated posterior probabilities with uncertainty bounds. The method registers baseline and follow-up surfaces, computes normal-directed displacements, and summarizes change as the difference between mean aneurysm displacement and mean displacement on the surrounding non-aneurysmal vessel segment. The vessel segment serves as an internal control for imaging and processing variability, assuming negligible structural change over the surveillance interval. We evaluate two cohorts spanning time-of-flight and contrast-enhanced longitudinal MRA studies: a public dataset labeled from neuroradiologist-provided measurements and an institutional dataset labeled by senior and junior raters. Performance is preserved when training on lower-expertise labels, indicating robustness to label variability. Calibrated probabilities may aid clinical decision-making in borderline cases, where high uncertainty can motivate repeat imaging. This framework provides interpretable probabilistic growth assessment from longitudinal MRA, reduces dependence on clinician expertise, and supports cross-center surveillance across scanners and angiography sequences.

Deep-UV ~ 250 nm (4.96 eV) tilted in-situ co-illumination of the sample under imaging by scanning electron microscope (SEM) is developed at a robust and practical instrument level. Precise mechanical control of the lateral position and tilt angle (within 6.5$^\circ$ from a 42$^\circ$ baseline) of the UV-C LED source is achieved using mechanisms external to the vacuum chamber. The incorporated linear polariser (for s-pol. mode illumination) with external polarisation plane adjustment allows for modulation and tuning of tangential $E^{(t)}$ and normal $E^{(n)}$ electric field components and their enhancement for controlled directional electron emission from the surface of the sample. Numerical modelling of E-field enhancement corroborates the expected enhancement in the production of secondary electrons. This modality of SEM imaging does not require metal coatings, preserving sample integrity for subsequent analysis. The feasibility of having linearly polarised incident UV-C light with azimuthal orientation control in $(s,p)$-plane is modeled and discussed.

Partially miscible ganglia trapped in a porous medium evolve through Ostwald ripening, driven by differences in interfacial curvature. In practice, ganglia can span multiple pores and undergo discrete capillary events - invasion, snap-off, retraction, fragmentation, coalescence, and dislocation - that alter their topology and induce local flow. Existing pore-network models (PNMs) for ripening are limited to single-pore ganglia, assume idealized pore shapes, and operate under quasi-static conditions that preclude flow. We present an image-based pore-network model (iPNM) that removes these limitations. Unlike existing PNMs, iPNM requires no idealization of pore shapes, as the effect on capillarity is encoded locally in curvature-saturation curves computed via the pore-morphology method. iPNM couples two-phase flow, solute transport, and Ostwald ripening within a unified framework. We first verify iPNM against a prior quasi-static PNM, then validate it against recent high-resolution microfluidic experiments of hydrogen ripening in a sandstone-patterned micromodel over 15-24 days at 40C and 80C. Good agreement is obtained without adjustable parameters. Comparison with a continuum model shows that while macroscopic saturation is captured by both approaches, iPNM uniquely resolves population statistics, individual ganglion curvatures, and pre-equilibrium ripening dynamics within a representative elementary volume. Its computational efficiency over direct numerical simulation makes it suitable for guiding the development of improved theories of ripening in confined geometries.

In recent years, Deep-Learning Earth System Models (DL-ESMs) have emerged as promising, computationally efficient complements to traditional Earth system models. Here, we present an evaluation framework for testing DL-ESMs from an Earth system model-development perspective using standardized diagnostics from the PCMDI Metrics Package (PMP). This framework allows DL-ESMs, including Ai2's ACE2 and Google's NeuralGCM, to be assessed with metrics that quantify their ability to reproduce climatology, major modes of variability, monsoons, and precipitation variability relative to observational reference datasets and CMIP-class benchmarks. By evaluating DL-ESMs with tools commonly used for traditional models, we extend their assessment beyond short-range forecast skill and toward longer Earth System-relevant applications. The results identify encouraging strengths in several large-scale fields and modes of variability, while also highlighting persistent challenges in precipitation, tropical variability, and long-run stability for some model versions. This evaluation is a critical step toward building trust in DL-ESMs, guiding future model development, and clarifying their fit-for-purpose for Earth system science applications.

In this paper, we establish the global well-posedness of the one-phase Muskat problem with surface tension for small initial data. This problem describes the motion of the interface separating a wet region from a dry region within a porous medium, a process governed by Darcy's law. Although physically essential, the inclusion of surface tension introduces an additional challenge. We prove that if the initial free boundary is sufficiently small in $H^s$, $s>d/2+1$, then the problem admits a unique global strong solution. Moreover, the solution converges to zero in Lipschitz norm as $t\rightarrow\infty$. To the best of our knowledge, this work constitutes the first global well-posedness result for the one-phase Muskat problem with surface tension.

Accurate predictions of exfoliation energies and lattice constants of layered materials hinge on a correct description of London dispersion physics. Modern a posteriori dispersion corrections in density-functional theory (DFT), such as the exchange-hole dipole moment (XDM) model, capture the proper asymptotic behaviour at long range while making use of damping functions to prevent unphysical divergence at short range. In the united-atom limit, the dispersion energy is damped to a finite, non-zero value by both the canonical Becke–Johnson (BJ) damping function and the new Z-damping function. XDM(BJ) has previously demonstrated exceptional accuracy for modelling layered materials, such as in the LM26 benchmark, which includes graphite, hexagonal boron nitride, lead(II) oxide, and transition-metal dichalcogenides. This work presents the first assessment of XDM(Z) on the same benchmark. We also show that inclusion of three-body interactions via the Axilrod–Teller–Muto (ATM) term further improves the computed exfoliation energies for both XDM(BJ) and XDM(Z), yielding the best performance achieved on LM26 using semi-local functionals to date, relative to reference data from the random-phase approximation.

In a series of papers, we analyzed the mechanisms of capital allocation and accumulation among firms, investors, and banks within the framework of Field Economics. At the macro level, we found that multiple collective states may emerge, each organized in groups of agents characterized by distinct levels of disposable capital, returns, and stakes across sectors. This multiplicity of collective states reflects the inherent instability and default-prone nature of financial markets. It also underscores the necessity of transitions among these states. These transitions were not fully accounted for in our previous description of collective states, where we only considered marginal fluctuations around equilibrium levels. To fill this gap, the present paper aims to characterize the possible dynamic mechanisms underlying transitions between these collective states. It reviews and extends our previous analysis by examining their probability and stability. By introducing the notion of dynamic sub-collective states, it explores potential patterns of transition-such as morphing, merging, or splitting-among collective states. This study thus provides the foundational tools and analytical framework that will be employed to develop a field-theoretic description of dynamic collective states.

The aim of this work is to develop dual-functional scaffolds for bone tissue regeneration and local antibiotic delivery applications. In this respect, bioresorbable bredigite (Ca7MgSi4O16) porous scaffolds were fabricated by a foam replica method, loaded with vancomycin hydrochloride and encapsulated in poly lactic-co-glycolic acid (PLGA) coatings. Field emission scanning electron microscopy, Archimedes porosimetry and Fourier-transform infrared spectroscopy were used to characterize the structure of the scaffolds. The drug delivery kinetics and cytocompatibility of the prepared scaffolds were also studied in vitro. The bare sample exhibited a burst release of vancomycin and low biocompatibility with respect to dental pulp stem cells based on the MTT assay due to the fast bioresorption of bredigite. While keeping the desirable characteristics of pores for tissue engineering, the biodegradable PLGA coatings modified the drug release kinetics, buffered physiological pH and hence improved the cell viability of the vancomycin-loaded scaffolds considerably.

Bioceramic porous microspheres are promising substances for dental and orthopedic bone void filling, tissue engineering and drug delivery applications. In this research, the structure and cytocompatibility of bioactive magnesium-calcium silicate macroporous microspheres loaded with vancomycin hydrochloride, an antibiotic drug, were studied. In this regard, bredigite (Ca7MgSi4O16), akermanite (Ca2MgSi2O7) and diopside (CaMgSi2O6) carriers were fabricated through a sequence of sol-gel, calcination, droplet extrusion and sintering processes, followed by impregnated with vancomycin. X-ray diffraction (XRD) and scanning electron microscopy verified the formation of the desired ceramic crystalline phases and macroporous characteristics of the carriers, respectively. Based on the MTT assay, the antibiotic-loaded bredigite, akermanite and diopside devices comparatively exhibited the lowest, intermediate and highest levels of human bone marrow mesenchymal stem cells (hBM-MSCs) viability and proliferation, respectively. It was concluded that the role of the carrier bioresorption kinetics prevails over the drug delivery kinetics in determining the cell biocompatibility of the devices.

Recent advances in visual-language models (VLMs) have led to significant improvements in a plethora of complex multimodal tasks like image captioning, report generation, and visual perception. However, generating text from meteorological data is highly challenging because the atmosphere is a chaotic system that is rapidly changing at various spatial and temporal scales. Given the complexity of atmospheric phenomena, it is critical to verifiably quantify the effectiveness of existing VLMs on weather forecasting data. In this work, we present SynopticBench, a high-quality dataset consisting of 1,367,041 text samples of Area Forecast Discussions created by the National Weather Service over the continental United States paired to images of 500mb geopotential height, 2 meter temperature, and 850mb wind velocity in weather forecasts. We also present Synoptic Phenomena Alignment and Coverage Evaluation (SPACE), a novel evaluation framework that can be used to effectively estimate the quality of text descriptions of synoptic weather phenomena. Extensive experiments on generating forecast discussions using state-of-the-art VLMs show the sensitivity of existing evaluation metrics in this domain and enable further exploration into synoptic weather and climate text generation.

Representing turbulent flow fields in a compact yet physically faithful form remains a central challenge in computational fluid dynamics. We propose a continuous parametric representation based on localized Gaussian primitives, in which the velocity field is modeled as a superposition of kernels with learnable positions, amplitudes, and scales. This formulation yields a compact, grid-independent encoding while enabling evaluation of derived quantities such as vorticity and enstrophy. The approach is assessed on three-dimensional Taylor-Green vortex fields spanning stages from smooth flow to fully developed turbulence. We quantify the compression-accuracy trade-off using both primary variables and derivative-sensitive diagnostics. The baseline isotropic formulation achieves high velocity accuracy at compression ratios exceeding 1e3-1e4, but exhibits substantial enstrophy degradation due to loss of small-scale structure. To address this limitation, we investigate structure-aware extensions including adaptive placement, multi-resolution kernels, and anisotropic Gaussians. The anisotropic formulation provides the most consistent improvement, better aligning with elongated vortical structures and recovering intermediate- and high-wavenumber content, while other strategies yield modest gains. A compact-support Beta basis improves enstrophy in some cases but introduces localized artifacts. Overall, the results indicate that the main limitation of baseline Gaussian representations lies in geometric expressiveness rather than parameter count. The proposed framework provides a compact, interpretable, and continuous representation of turbulent flows, and establishes a foundation for structure-aware and physics-informed flow compression.

In this Comment, we discuss recent approaches to electromagnetic interference (EMI) mitigation in low-field Magnetic Resonance Imaging (LF-MRI), as presented in arXiv preprints 2509.05955v1, 2406.17804v3, or 2210.06730v2. These and other works explore noise cancellation strategies based on external sensing coils for post-elimination of EMI. We argue that, under realistic conditions, such approaches lead to residual signal contamination that necessarily exceed that obtained with optimal hardware-based pre-elimination.

When asked what causes the magnetic curvature drift of a charged-particle moving in a curving magnetic field, people respond that there is an `F-cross-B' motion of the `guiding center' due to the centrifugal force on the particle as it follows the magnetic field line. This and similar explanations `beg the question' by assuming that the particle follows the field line. In a curving magnetic field, however, a particle moving parallel to the field direction soon won't be. The convective rotation of the field along the particle trajectory ensures that the Lorentz force switches on, and the resulting acceleration rotates the velocity vector back into alignment periodically. The gyration is not symmetric about the field vector, and the resulting velocity offset is the curvature drift. This explanation is guided by Newton's second law of motion in vector notation. It provides a common framework for explaining the three guiding-center motions of a charged particle in a static nonuniform magnetic field: curvature drift, mirror reflection in a magnetic bottle, and gradient-B drift. The discussion aims to provide insight to instructors of electricity and magnetism or plasma physics at the intermediate- to advanced-undergraduate level.