Papers

The evolution of stimulated Brillouin scattering (SBS) driven by broadband lasers in high-Z plasmas is investigated using one-dimensional collisional particle-in-cell simulations. The temporal incoherence of broadband lasers modulates the pump intensity, generating stochastic intensity pulses that intermittently drive SBS. The shortened coherence time weakens the three-wave coupling and continuously reduces the temporal growth rate, while the saturated reflectivity remains nearly unchanged until the bandwidth exceeds a critical threshold. Simulations with varying laser intensities and bandwidths reveal a consistent scaling behavior, indicating that effective suppression occurs only when the laser bandwidth exceeds the temporal growth rate of SBS by several tens of times. Comparative simulations in Au and AuB plasmas exhibit similar suppression trends, with AuB showing reduced SBS growth rate and reflectivity, and the onset of suppression occurring at a lower bandwidth. These findings elucidate the coupled dependence of SBS mitigation on bandwidth and laser intensity in high-Z plasmas, offering useful guidance for optimizing broadband laser designs in inertial confinement fusion.

Phase field lattice Boltzmann (LB) models have undergone continuous development, resulting in multiple variants widely used for simulating multiphase flows. However, direct performance comparisons remain limited, especially for three-dimensional cases. In this study, we present a systematic comparative analysis of several recent and representative phase-field LB models, covering four major categories: conservative Allen-Cahn, nonlocal Allen-Cahn, hybrid Allen-Cahn, and Cahn-Hilliard models. Their accuracy, numerical stability and mass/volume conservation are assessed through a series of canonical two-phase flow problems. Beyond the commonly tested two-dimensional laminar cases, we extend the evaluation to three-dimensional droplet-laden turbulent flows, which expose more critical limitations of the existing models. The results show that while all models perform satisfactorily in two dimensions, they still suffer from substantial droplet volume loss in turbulence, particularly at high Weber numbers. Overall, conservative Allen-Cahn-based LB models exhibit the most favorable balance of numerical stability, accuracy and computational efficiency.

As a universal quantum mechanical approach to the dynamical many-body problem, the time-dependent density functional theory (TDDFT) might be inadequate to describe crucial observables that rely on two-body evolution behavior, like the double-excitation probability and two-body dynamic correlation. One promising remedy is to utilize the time-dependent 2-reduced density matrix (2-RDM) that directly represents two-body observables in an N-particle system, and resort to the extended TDDFT for multibody densities to break the confines of spatial local on one-body density [Phys. Rev. Lett. 26(6) (2024) 263001]. However, the usage of 2-RDM is prohibitive due to the augmented dimensionality, e.g., 4-D space for unidimensional 2-RDM. This work addresses the high-dimensional numerical challenges by using an equivalent Wigner phase-space formulation of 2-RDM and seeking efficient spectral approximations to nonlocal quantum potentials. For spatial periodic case, a pseudo-difference operator approach is derived for both the Hartree-exchange-correlation term and two-body collision operator, while the discretization via the Chebyshev spectral element method is provided for non-periodic case. A massively parallel numerical scheme, which integrates these spectral approximations with a distributed characteristics method, allows us to make the first attempt for real simulations of 2-RDM dynamics. Numerical experiments demonstrate the two-body correction to the quantum kinetic theory, and show the increase in the system's entropy induced by the two-bdoy interaction. Thus it may pave the way for an accurate description of 2-RDM dynamics and advance to a practical application of many-body TDDFT.

In fluids, vortices form at boundaries between flows of different velocities. Here, we show that pulsed electromagnetic waves form spatiotemporal vortices when light straddles media of different phase velocities. When the resulting relative time-delay is on the order of the pulse duration of light, spatiotemporal optical vortices (STOVs) are formed. This method allows generating arbitrary geometry spatiotemporal vortices, something impossible with previously demonstrated generation schemes. We provide experimental results showing THz frequency light can be encoded with arbitrary spatiotemporal vortex geometries using simple planar fused filament prints made from a commercial 3D printer. We provide several demonstrations of unique geometries including squares, triangles, ring arrays, and arbitrary curves that cannot be generated with existing techniques. We also show that multiple ring and line vortices can be formed simultaneously, which undergo complex reconnections with propagation. While demonstrated at THz frequencies, this concept is directly applicable to visible and infrared light and our theory offers a way for optimizing STOVs for the frequency range and pulse duration of a given light source. This technique provides a practical and straight-forward method to generate arbitrary electromagnetic spatiotemporal vortices and enables production of complex spatiotemporal phenomena like vortex reconnections.

We report on the development of transmission-line reflectionless filters operating with passbands at 100 GHz and 230 GHz, and stopband absorption up to 500 GHz, the highest operating frequencies yet recorded for such filters. The designs are based on a previously reported mathematical solution to the reflectionless condition, now successfully implemented for the first time, using an advanced thin-film fabrication process on Alumina substrates. Sub-millimeter wave wafer probe measurements show good agreement with theory.

The petabyte-scale data generated annually by High Energy Physics (HEP) experiments like those at the Large Hadron Collider present a significant data storage challenge. Whilst traditional algorithms like LZMA and ZLIB are widely used, they often fail to exploit the deep structure inherent in scientific data. We investigate the application of modern state space models (SSMs) to this problem, which have shown promise for capturing long-range dependencies in sequences. We present the Bytewise Online Autoregressive (BOA) Constrictor, a novel, streaming-capable lossless compressor built upon the Mamba architecture. BOA combines an autoregressive Mamba model for next-byte prediction with a parallelised streaming range coder. We evaluate our method on three distinct structured datasets in HEP, demonstrating state-of-the-art compression ratios, improving upon LZMA-9 across all datasets. These improvements range from 2.21$\times$ (vs. 1.69$\times$) on the ATLAS dataset to a substantial 44.14$\times$ (vs. 27.14$\times$) on the highly-structured CMS dataset, with a modest $\sim 4.5$MB model size. However, this gain in compression ratio comes with a trade-off in throughput; the Storage-Saving Rate ($\sigma_{SSR}$) of our prototype currently lags behind highly-optimised CPU-based algorithms like ZLIB. We conclude that while this Mamba-based approach is a highly promising proof-of-principle, significant future work on performance optimisation and hardware portability is required to develop it into a production-ready tool for the HEP community.

The Numerical Flow Iteration (NuFI) method has recently been proposed as a memory-slim while accurate in phase-space method for the electro-static Vlasov–Poisson system. It stores the temporal evolution of the electric field, instead of the distribution functions, and reconstructs the solution in each time step by following the characteristics backwards in time and reconstructing the solution from the initial distribution. NuFI has been shown to be more accurate than other state-of-the-art electro-static Vlasov solvers given the same amount of degrees of freedom. In this paper, we build on the Hamiltonian structure of the full Vlasov–Maxwell system to extend NuFI to handle electro-magnetic kinetic plasma dynamics. We show that the structure-preserving properties of the NuFI time-stepping are preserved when extending to the electro-magnetic case. Furthermore we discuss how NuFI can be incorporated into existing Semi-Lagrangian codes as an efficient while accurate subcycling technique.

Generative AI is rapidly reshaping how physicists teach, learn, and conduct research, yet little is known about how physics faculty are responding to these changes. We interviewed 12 physics professors at a major Scandinavian research university to explore their uses and perceptions of Generative AI (GenAI) in both teaching and research. Using the theoretical framework of epistemic framing, we conducted a thematic analysis that identified 19 overlapping practices, ranging from coding and literature review to assessment and feedback. From these practices, we derived six overlapping epistemic frames through which professors make sense of GenAI: as a threat to genuine learning and assessment, a source of knowledge, a discussion partner, a text-processing tool, a coding tool, and a labor-saving device. While the latter five position GenAI as a useful tool in the physicists' toolbox, the threat frame represented an overarching concern that colored all other frames. These findings reveal how GenAI is beginning to transform what it means to be a physicist, highlighting both opportunities for innovation and challenges for academic integrity and learning.

Segmentation of the aorta is crucial for various medical analyses, such as the diagnosis and treatment of cardiovascular diseases. This work presents mathematical models and methods yielding a semi-automatic segmentation of the aorta from non-enhanced CT data. Our framework consists of three steps. First, using the minimal path approach, we extract a path within the aorta from two user-supplied points. Then, using 3D Lagrangian curve evolution, we move the initial path to the approximate centerline of the aorta. The centered path is used in the last step to construct the initial condition for the generalized subjective surface method (GSUBSURF). Applying the GSUBSURF method with this initial condition yields an accurate segmentation of the aorta. The segmentation results and the manual segmentations overlap, with a worst-case mean Hausdorff distance of $2.175 \pm 0.605$ mm for a voxel spacing of $0.977$ mm. Using the aorta centerline and segmentation, we define precise regions of interest along the aorta to assess large-vessel vasculitis from patient FDG-PET/CT image data. The application shows promising results, as we demonstrated widespread inflammation throughout the aorta in a patient before treatment. After treatment, we observed a significant reduction in inflammation while accurately identifying the aorta regions where inflammation persisted. These findings also align with those of experienced medical doctors who have worked on the same cases.

Room temperature superconductivity remains elusive, and hydrogen-base compounds despite remarkable transition temperatures(Tc) typically require extreme pressures that hinder application. To accelerate discovery under moderate pressures, an interpretable framework based on symbolic regression is developed to predict Tc in hydrogen-based superconductors. A key descriptor is an integrated density of states (IDOS) within 1 eV of the Fermi level (EF), which exhibits greater robustness than conventional single-point DOS features. The resulting analytic model links electronic-structure characteristics to superconducting performance, achieves high accuracy (RMSEtrain = 20.15 K), and generalizes well to external datasets. By relying solely on electronic structure calculations, the approach greatly accelerates materials screening. Guided by this model, four hydrogen-based candidates are identified and validated via calculation: Na2GaCuH6 with Tc =42.04 K at ambient pressure (exceeding MgB2), and NaCaH12, NaSrH12, and KSrH12 with Tc up to 162.35 K, 86.32 K, and 55.13 K at 100 GPa, 25 GPa, and 25 GPa, respectively. Beyond rapid screening, the interpretable form clarifies how hydrogen-projected electronic weight near EF and related features govern Tc in hydrides, offering a mechanism-aware route to stabilize high-Tc phases at reduced pressures.

Deep learning has emerged as a key tool for designing nanophotonic structures that manipulate light at sub-wavelength scales. We investigate how to inversely design plasmonic nanostructures using conditional generative adversarial networks. Although a conventional approach of measuring the optical properties of a given nanostructure is conceptually straightforward, inverse design remains difficult because the existence and uniqueness of an acceptable design cannot be guaranteed. Furthermore, the dimensionality of the design space is often large, and simulation-based methods become quickly intractable. Deep learning methods are well-suited to tackle this problem because they can handle effectively high-dimensional input data. We train a conditional generative adversarial network model and use it for inverse design of plasmonic nanostructures based on their extinction cross section spectra. Our main result shows that adding label projection and a novel embedding network to the conditional generative adversarial network model, improves performance in terms of error estimates and convergence speed for the training algorithm. The mean absolute error is reduced by an order of magnitude in the best case, and the training algorithm converges more than three times faster on average. This is shown for two network architectures, a simpler one using a fully connected neural network architecture, and a more complex one using convolutional layers. We pre-train a convolutional neural network and use it as surrogate model to evaluate the performance of our inverse design model. The surrogate model evaluates the extinction cross sections of the design predictions, and we show that our modifications lead to equally good or better predictions of the original design compared to a baseline model. This provides an important step towards more efficient and precise inverse design methods for optical elements.

Polarization-sensitive optical coherence tomography (PS-OCT) extends OCT by analyzing the polarization states of backscattered light to quantify tissue birefringence. However, conventional implementations require polarization-diverse detection and are therefore incompatible with most commercial OCT systems. As a result, PS-OCT has largely remained restricted to specialized research groups, limiting its broader scientific and clinical use. Here, we present a modular PS-OCT framework that integrates with a standard spectral-domain OCT platform through a detachable rotating achromatic half-wave plate in the sample arm. This waveplate modulates both incident and reflected polarization states. Three or more repeated measurements at distinct waveplate orientations enable reconstruction of the sample's round-trip Jones matrix and the corresponding polarization properties. To mitigate random phase variations between repeated measurements, we introduce a phase optimization strategy. We validate the framework with imaging of birefringent phantoms and the human retina in vivo, demonstrating reliable reconstruction of retardance and optic axis orientation. In this study, we implemented a polarization-sensitive module on the SPECTRALIS platform with minimal hardware modification. The framework could potentially be extended to other OCT systems with appropriate mechanical integration and access to acquisition data. By reducing the hardware complexity typically associated with conventional PS-OCT, this framework may facilitate broader adoption of PS-OCT imaging in both research and clinical settings.

The dynamics of turbulent mixing induced by Rayleigh-Taylor instability are heavily dependent on the acceleration experienced by the fluids and the frequency content of the initial interface between them. Both are readily controllable in numerical simulations, but in experimental studies are difficult to influence and adequately diagnose. In this paper we present the CAMPI apparatus, an experimental facility for study of low Atwood number Rayleigh-Taylor instability with highly controllable, complex acceleration histories. The apparatus provides unique and novel capability for the experimental study of variable acceleration Rayleigh-Taylor instability with fully miscible fluids and at a scale suitable for high resolution optical diagnostics. We present experimental results of initially single mode instability evolution through two stepwise acceleration reversals, a case termed Accel-Decel-Accel, demonstrating the ability of the apparatus to accurately generate a prescribed acceleration history. We observe the behaviour predicted by previous numerical studies, with instability growth reaching a terminal velocity in the first episode of acceleration, followed by a shrinking and homogenisation of the mixing region throughout deceleration, and unstable growth from a multi-frequency initial condition during the second acceleration. We present the CAMPI apparatus to the field as a much needed. resource of ground truth data on the behaviour of Rayleigh-Taylor instability across a broad range of regimes.

Optical fields polarized along three dimensions are frequent in optical microscopy and nanophotonics, and yet retrieving their polarization distribution is challenging. We present the experimental implementation of three-dimensional (3D) Stokes polarimetric imaging of nonparaxial optical fields with nanoscale spatial resolution. This approach extends classical Stokes polarimetry (traditionally limited to paraxial fields) into the nonparaxial regime. We use an array of gold nanospheres, each acting as a localized electric dipolar scatterer, to probe 3D polarization states over a field of view of tens of micrometers. The scattered signal is collected by a high numerical aperture objective lens and separated into its circular polarization components, providing a very simple imaging system. We introduce a computational algorithm to efficiently extract the physical parameters from the generated dipole spread functions with a high throughput across the whole field of view. Finally, we show that this method can also be applied to single-molecule localization and orientation fluorescence microscopy.

The kinetic model with multiple integral terms based on the Enskog-Vlasov(EV) equation is widely employed to describe the inhomogeneous fluids at the nanoscale. However, previous studies have mainly focused on one-dimensional cases, partly due to the significant computational cost $O(NN_{\sigma})$ associated with direct computation of integrals, where $N$ is the number of cells in the flow field and $N_\sigma$ is the number of cells in a cube with a side length equal to the molecular diameter $\sigma$. In this study, we propose a discrete unified gas kinetic scheme (DUGKS) with efficient numerical strategies for integrals to overcome the inefficiency of the direct method, reducing the computational cost to $O(N)$. Both accuracy and efficiency of the proposed DUGKS are assessed through several test cases, including static fluid structures and force-driven flow dynamics in parallel plate channels. As example applications, pressure-driven flow between two flat plates and force-driven flow in a square duct are investigated to highlight distinctive phenomena at the nanoscale.

We present a Raman-based Distributed Temperature Sensor (RDTS) with centimetre-scale resolution for thermographic analysis of electronic circuits. Temperature is measured along a single-mode fiber routed across a custom printed circuit board (PCB) with 1 cm$^2$ heating elements, using optical time-domain reflectometry of Raman signals detected by superconducting nanowire single-photon detectors (SNSPDs). This approach enables two-dimensional thermal mapping of the PCB under heating configurations with multiple hotspots. A spatial resolution of 3 cm and a temperature accuracy of 2 {\deg}C are achieved with an integration time of 5 minutes. Thermography can be performed down to 77 K, revealing that the PCB thermal resistance decreases by nearly an order of magnitude compared to room temperature, due to enhanced convective cooling in liquid nitrogen. These results establish centimetre-scale RDTS as a robust technique for real-time, spatially resolved thermography of electronic circuits, particularly in regimes where infrared imaging is ineffective, such as at low temperatures or within volumetric electronic architectures.

This paper addresses the critical challenge of improving predictions of climate extreme events, specifically heat waves, using machine learning methods. Our work is framed as a classification problem in which we try to predict whether surface air temperature will exceed its q-th local quantile within a specified timeframe. Our key finding is that aggregating ensemble predictions using a power mean significantly enhances the classifier's performance. By making a machine-learning based weather forecasting model generative and applying this non-linear aggregation method, we achieve better accuracy in predicting extreme heat events than with the typical mean prediction from the same model. Our power aggregation method shows promise and adaptability, as its optimal performance varies with the quantile threshold chosen, demonstrating increased effectiveness for higher extremes prediction.

Electron accumulation in interatomic regions is a fundamental quantum phenomenon dictating chemical bonding and material properties, yet its origin remains elusive across disciplines. Here, we report a quantum accumulation effect – potential-barrier affinity (PBA) – revealed by solving the Schrödinger equation for a crystalline potential. PBA effect drives significant interatomic electron accumulation when electron energy exceeds the barrier maximum. This effect essentially enhances interatomic electron density, governing microstructures and properties of condensed matter. Our theory overturns the traditional wisdom that the interstitial electron localization in electride requires potential-well constraints or hybrid orbitals, and it serves as the fundamental mechanism underlying the formation of conventional solid bonding. This work delivers a paradigm shift in understanding electron distribution and establishes a theoretical foundation for the microscopic design of material properties.

The coagulation of blood after it is drawn from the body poses a significant challenge for hematological analysis, potentially leading to inaccurate test results and altered cellular characteristics, compromising diagnostic reliability. This paper presents a deep learning-enhanced framework for delineating anticoagulant efficacy ex vivo using Digital Holographic Microscopy (DHM). We demonstrate a label-free, non-invasive approach for analyzing human blood samples, capable of accurate cell counting and morphological estimation. A DHM with an automated image processing and deep learning pipeline is built for morphological analysis of the blood cells under two different anti-coagulation agents, e.g. conventional EDTA and novel potassium ferric oxalate nanoparticles (KFeOx-NPs). This enables automated high-throughput screening of cells and estimation of blood coagulation rates when samples are treated with different anticoagulants. Results indicated that KFeOx-NPs prevented human blood coagulation without altering the cellular morphology of red blood cells (RBCs), whereas EDTA incubation caused notable changes within 6 hours of incubation. The system allows for quantitative analysis of coagulation dynamics by assessing parameters like cell clustering and morphology over time in these prepared samples, offering insights into the comparative efficacy and effects of anticoagulants outside the body.

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements Uniform Manifold Approximation and Projection (UMAP) for non-linear dimensionality reduction to reveal correlations in highly dimensional data. We also discuss potential extensions to deep autoencoders for feature learning, and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric's robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT's capabilities, we analyze photo double ionization data measured using the COLTRIMS method for 3-body dissociation of the D2O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software's modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Accurate determination of the neutrino energy is central to precision oscillation measurements. In this work, we introduce the W$^2$-based estimator, a new neutrino energy estimator based on the measurement of the final-state hadronic invariant mass. This estimator is particularly designed to be employed in liquid-argon time-projection chambers exposed to broadband beams that span the challenging transition region between shallow inelastic scattering and deep inelastic scattering. The performance of the W$^2$-based estimator is compared to four other commonly used estimators. The impact of the estimator choice is evaluated by performing measurements of $\delta_{CP}$ and $\Delta m^2_{23}$ in a toy long-baseline oscillation analysis. We find that the W$^2$-based estimator shows the smallest bias as a function of true neutrino energy and it is particularly stable against the mismodelling of lepton scattering angle and momentum, missing energy, hadronic invariant mass and final state interactions. However, studies of the resolution of each estimator as a function of true neutrino energy show this is somewhat offset by worse energy resolution when perfect modeling of these quantities is assumed. This estimator is valid for events with at least one proton and any number of pions; an inclusive channel that complements the strength of more exclusive methods that optimize the energy resolution. By providing a detailed analysis of the strengths, weaknesses and domain of applicability of each estimator, this work informs the combined use of energy estimators in any future LArTPC-based oscillation analysis.

In this study, a novel method for simulating plasma dynamics using parallel programming has been developed. The equations based on Particle-in-Cell (PIC) method were utilized and adapted for this purpose. We utilized 35 processors from Sharif High Performance Computing (HPC) center and divided the plasma volume into 35 parts, with each part's PIC equation solved on a separate processor. Once the computations were completed, the results from all processors were combined to form a complete plasma volume. The simulations revealed that there is an optimal pressure for argon, at which the ion flux onto the electrode surface is maximized. Increasing the absolute value of the electrode potential also increases this flux. Therefore, for a given potential, selecting the optimal pressure is crucial for the most effective surface modification using argon plasma. In this work, for applied voltage of -500 V, the optimum pressure was 100 mTorr.

Metal-halide perovskite nanocrystals (PNCs) have emerged as leading candidates for next-generation quantum emitters, offering a unique combination of high photoluminescence quantum yield, tunable emission, short radiative lifetimes, and record-high single-photon purity under ambient conditions. These properties, together with low-cost and scalable solution-phase fabrication, position PNCs as attractive alternatives to traditional epitaxial and colloidal quantum dots. In this Review, we outline the physical parameters that define quantum emission in PNCs, compare their performance to other established and emerging quantum emitters, and assess the key figures of merit, including photostability, single-photon purity, and photon indistinguishability, required for practical quantum applications. We discuss underlying mechanisms affecting PNC emission behavior and highlight recent advances in improving their quantum emitting properties through synthetic and photonic engineering approaches. While challenges related to environmental stability and photon indistinguishability remain, emerging strategies, such as surface passivation, metal ion doping, and coupling with electromagnetic nano- and micro-cavities, are steadily closing the gap between PNCs and ideal quantum light sources.

Fibonacci time quasicrystals can be approximated by temporal supercells to reveal a fractal collection of $k$ gaps, in which wave energy is amplified exponentially. These estimates are validated by the observation of "super" $k$ gaps that are independent of the duration of the temporal supercell. This approach predicts the regions of parametric amplification and provides accurate estimates of the energy growth rate.

Physics-consistent optimization of reconfigurable intelligent surfaces (RISs) is thwarted in practice by the difficulty of experimentally estimating the mutual coupling (MC) between RIS elements. For large RISs, experimental MC estimation is fundamentally challenging because of the quadratic scaling of the number of unknowns with the number of RIS elements. In this Letter, we present a generic and flexible reduced-rank MC representation that allows wireless practitioners to choose a trade-off between model complexity and accuracy. We experimentally validate the direct reduced-rank MC estimation for a 100-element RIS in three radio environments (rich scattering, attenuated scattering, free space). We observe a strong environmental dependence of the influence of rank reduction on accuracy. Model-based performance evaluations highlight that the importance of MC awareness in optimization depends strongly on the radio environment and the performance indicator.

We study here the social network generated by the asynchronous visits, to a fixed set of sites, of mobile agents modelled as independent random walks on the plane lattice. The social network is constructed by assuming that a group of agents are associated if they have visited the same set of sites within a finite time interval. This construction is an instance of a random intersection graph, and has been used in the literature to study association networks in a number of animal species. We characterize the mathematical structure of these networks, which we view as one-mode projections of suitable bipartite graphs or, equivalently, as 2-sections of the corresponding hypergraphs. We determine analytically the probability distribution of the random bipartite graphs and hypergraphs associated to this construction, and suggest that association networks generated by the use of common resources are better described by hypergraphs rather than simple projected graphs, that miss important information regarding the actual associations among the agents.

Background & Aim: Transcranial temporal interference stimulation (TIS) is a novel transcranial electrical stimulation modality that enables focused targeting of deep brain structures. When targeting deep regions, current pathways traverse the highly anisotropic white matter, making anisotropy a potentially critical factor. This study aimed to clarify how anisotropy influences interferential currents in deep brain regions and to assess its impact on TIS montage optimization for the first time. Methods: Anatomical head conductor models with anisotropic and isotropic conductivities were compared to evaluate the role of anisotropy in the intracranial interferential currents distributions and montage optimization. For the anisotropic conductivity, conductivity tensors were derived from diffusion-weighted imaging data for gray matter, white matter, and deep brain structures. Montage optimization was conducted based on Pareto front optimization. Results: In literature-reported TIS montages, anisotropic conductivity significantly altered the interferential electric field intensity, with differences of up to 18% in the white matter, whereas discrepancies in deep brain structures remained below 12%. For TIS-optimized montages, these variations across conductivity models yielded only 50% montage disagreement. However, when constraining differences in focality and target field strength to within 10%, the agreement improved to nearly 90%. Conclusions: Incorporating anisotropic conductivities is important to determine individualized focality and target-field characteristics. Moreover, anisotropy can substantially affect the selection of the optimal montage, but under practical focality and target-field tolerances, montage choices largely converge.

Peri-implant inflammation in orthodontic mini-implant may lead to patient discomfort and treatment failure. This study aims to evaluate the effects of diode laser application on the health of mini-implant, preventing peri-implantitis and promoting healing. A randomized controlled trial was conducted involving 30 orthodontic patients (12 males and 18 females, aged 18-32) who had mini-implants implanted on both sides of the maxilla for anterior teeth retraction. One side of each patient was assigned to either an experimental group receiving diode laser irradiation (650 nm, 25 mW) at specific postoperative intervals or a control group receiving simulated radiation. Clinical assessments included plaque index, modified sulcus bleeding index, probing depth, and incidence of peri-implant mucositis and implant mobility, measured at 1, 4, and 12 weeks post-implantation. Additionally, interleukin-1 beta (IL-1e̱ta}) levels in peri-implant fluid were analyzed via enzyme-linked immunosorbent assay (ELISA). Results indicated that the experimental group exhibited significantly lower plaque indices, sulcus bleeding indices, and probing depths (p < 0.05) compared to the control group. Moreover, the experimental group had fewer cases of peri-implant mucositis (p < 0.05), while differences in implant stability were not statistically significant (p > 0.05). IL-1e̱ta} levels were consistently lower in the experimental group throughout the study duration (p < 0.05). In conclusion, adjunctive diode laser therapy appears to enhance peri-implant health and reduce complications associated with orthodontic mini-implants, suggesting a promising direction for improving patient outcomes in orthodontics. Future research should explore long-term effects and the mechanisms underlying these benefits.

The encounter of diffusing entities underlies a wide range of natural phenomena. The dynamics of these first-passage dynamics are strongly influenced by confining geometries. Confinement modifies microscopic diffusion through conservative and hydrodynamic interactions, making it essential for realistic modeling. In this Letter, we investigate how confinement affects the first-passage statistics of a diffusing particle. Using state-of-the-art holographic microscopy combined with advanced statistical inference, we probe this motion with nanometric precision. Our experimental and numerical results show that confinement can either hinder or enhance first-passage kinetics, depending on the spatial direction. In particular, wall-normal target finding is accelerated by confinement-induced non-Gaussian displacement statistics, which increases the probability of rare, large displacements, with implications for confined chemical reactions and biological winners-take-all processes near boundaries.

Strongly-confined perovskite nanoplatelets (PeNPLs) offer opportunities not found in conventional isotropic nanocubes, especially in producing linearly polarized light, as well as enhancing outcoupling through control over the transition dipole moment. But this requires ultrathin nanoplatelets with three or fewer monolayers of PbI6 octahedra across the thickness, which are challenging to synthesise uniformly, and their luminescence is strongly affected by surface defects. Together, these limit the performance of ultrathin PeNPLs in light-emitting diodes (LEDs). Here, we address these challenges with an ancillary ligand engineering strategy. We demonstrate that ligands with phosphoryl functional groups strongly bind to the perovskite surface, while having an organic backbone that is not sterically bulky ensures high ligand density. By modulating nucleation and growth, these ancillary ligands lead to monodisperse PeNPLs that stack more uniformly when self-assembled into superlattices, with suppressed agglomeration. As a result, from edge-up PeNPL superlattices, we achieve enhanced degree of polarization, while from face-down PeNPL superlattices, we achieve enhanced outcoupling that results in LEDs with 13.1% external quantum efficiency, the highest reported for ultrathin PeNPL LEDs. This work establishes ancillary ligand-induced synthesis as a decisive route to achieve uniform nanoplatelets with robust orientation control, enabling full utilization of the multifunctionality of anisotropic PeNPLs.