Papers

Antimicrobial photodynamic therapy (aPDT) is a promising modality for inactivation of antibiotic resistant bacteria, relying on the activation of a photosensitizer (PS) by light of a specific wavelength. This process results in the formation of reactive oxygen species, which ultimately induce cell death. However, aPDT in its conventional form, is limited by the shallow penetration of visible light, restricting its effectiveness for treatment of soft tissue and orthopaedic tissues. To overcome this limitation, near-infrared (NIR) absorbing PS can be used. However, poor stability in vivo after injection, ineffective microbial targeting due to hydrophilic nature and off-site tissue damage are the issues with use of NIR absorbing bare PSs. This issue can be mitigated by combining NIR light with a upconverting nanoparticles (UCNPs), which mediate in conversion of NIR into visible light for effective PS activation. In this study, LaF$_3$:Er$^{3+}$,Yb$^{3+}$ nanoparticles were synthesized using a hydrothermal method and coated with Rose Bengal (RB), a promising hydrophilic PS for aPDT, to evaluate the potential for NIR-triggered aPDT. Characterization of the synthesized UCNPs confirmed the crystalline structure, size distribution and successful RB functionalization. Photophysical studies demonstrated efficient energy transfer between UCNPs and RB, leading to singlet oxygen ($^1$O$_2$) generation in vitro. Antibacterial studies against Methicilin resistant Staphylococcus aureus (MRSA), a superbug of implicated soft tissue and orthopaedic infections, revealed significant photo-bactericidal efficacy upon NIR irradiation, indicating the potential of RB-coated UCNPs for aPDT applications.

As transistor scaling approaches its fundamental physical limits in the Angstrom era, two-dimensional (2D) semiconductors have emerged as the promising channel material candidates for future computing. While the device physics of 2D semiconductors have been rigorously explored, translating these nanodevices into fully functional integrated circuits remains a largely uncharted frontier. This review bridges the gap between material- and device-centric breakthroughs and circuit-level chip design in 2D semiconductors, a valley of death that has so far prevented translation of high-performance individual transistors into functional chips. We track the evolution of 2D semi-conductor field-effect transistors from basic Boolean logic families and standard cells to complex chip architectures, including recent milestones in RISC-V and monolithic CMOS microprocessors. Critically, we highlight the indispensable role of multiscale compact modeling, spanning semiclassical, quantum-hybrid and data-driven approaches, as the necessary link between device physics and the electronic design automation workflows for scalable chip development. By summarizing recent breakthroughs and identifying the bottlenecks in both fab and fabless trajectories of 2D semiconductors, this review shall provide insights that motivates the translation of proof-of-concept 2D transistors into fully functional computing chips, paving a way towards future Angstrom era computing technology empowered by 2D semiconductors.

Randomly connected neural networks have long served as a theoretical tool for studying collective dynamics in neural populations, yet quantitative comparisons to experiments remain limited. Recent technological advances have made it possible to resolve population-wide correlations across neurons, and minimal models such as random neural networks predict their generic structure. Whether the two agree quantitatively remains untested. In this work, we examine whether a minimally structured random neural network can account for the low dimensionality of activity in neural population recordings by building on recent developments in Dynamical Mean-Field Theory and incorporating two additional experimentally relevant features into the model: finite measurement time and variability across behavioral contexts. We show that, when these factors are included, the dimensionality measured from large-scale recordings is consistent with the values predicted by random models. However, current recording durations make it difficult to use dimensionality to discriminate among connectivity structures. We further show that analytically predicted dimensionality varies non-monotonically with external input strength, and that the orientation similarity between neural manifolds recorded under different behavioral contexts can be more sensitive to network structure than dimensionality is. Together, these results provide quantitative guidance for experimental design to infer the connectivity structure underlying population activity.

Energetic materials power mining, demolition, propulsion and airbags, yet today's compounds were designed decades ago. A successor must combine high energy release, low sensitivity to accidental initiation and a practical synthesis route, found within an astronomically large molecular space. Generative models are the natural search tool, but their training data are mostly untrustworthy: of approximately 66,000 molecules with recorded properties, only approximately 3,000 were measured or computed from first principles. Models trained on all of them imitate the rough estimates and propose molecules that collapse under real physics. We introduce Domain-Gated Latent Diffusion (DGLD), a diffusion model that treats data reliability as an explicit design parameter: labels are sorted into four trust tiers, and only trustworthy ones steer generation, while the unreliable majority still teaches the model what a plausible molecule looks like. Learned controls tune performance, safety and viability independently, and every proposal passes a four-stage screen ending in a quantum-chemical DFT audit. DGLD proposes 10 molecules unknown to PubChem that survive this screen. The best, 3,4,5-trinitro-1,2-isoxazole, matches the benchmark explosives HMX and PETN in calculated detonation performance, is unlike molecules in its training set, and has a four-step synthesis route. Trust gating is chemistry-independent and can be applied wherever abundant weak data surround a reliable core.

While negative capacitance has been demonstrated in ferroelectric-dielectric heterostructures in the form of capacitance enhancement, all experimental evidence, to date, suggests the existence of domains therein. Here, we address the question: what are the conditions to achieve ideal, domain-free negative capacitance in ferroelectric-dielectric heterostructures? Our main claim is that for given thicknesses of the ferroelectric and the dielectric layers, there is a critical value of domain wall energy parameter – above which the system would be stabilized in an ideal and robust domain-free negative capacitance state and would be robust against domain formation. Our analyses suggest that to achieve ideal negative capacitance, efforts should lie in understanding the means to control the domain wall energy on all fronts, both theory and experiments via high throughput design, discovery, and engineering of ferroelectrics.

The inverse problem of multilayer thin-film optical coatings design represents a complex combinatorial-continuous optimization challenge. We present PRISM (Position-encoded Regressive Inverse Spectral Model), a unified decoder-only autoregressive transformer that streamlines this process by jointly predicting discrete material selection and continuous thickness regression within a single backbone. PRISM introduces two primary architectural innovations: (1) spectrum prefix conditioning, which utilizes standard prefix tokens for in-context target injection, and (2) cumulative-depth Rotary Position Embeddings, which encode continuous thickness directly into the positional representation to preserve the physical spatial relationships of the stack. Our benchmarks demonstrate that a PRISM-13M model reduces MAE by over 50% compared to other transformer baselines while utilizing only one-fifth of the parameters. Furthermore, a 44M-parameter variant achieves state-of-the-art performance (MAE = 0.010) on our in-distribution validation benchmark and operates significantly faster than simulated annealing, offering a highly efficient alternative to classical optimization methods.

During conventional confinement of a linear plasma using ponderomotive end plugs, a repulsive potential is created at the axial ends by employing a perpendicularly-polarized wave with a wave frequency greater than the ion gyrofrequency. This potential is then partially cancelled out by an ambipolar potential that arises to equilibrate electron and ion densities along the field line. However, recent work on centrifugally-confined plasmas has shown that the appropriate choice of a dopant in the end region can dramatically change the structure of the ambipolar potential. For the ponderomotive potential, this ambipolar shaping can be even more powerful, since a lightweight dopant can see a different sign of ponderomotive potential relative to the confined species. As a result, the dopant can reverse the sign of the ambipolar potential, causing it to dramatically enhance the end plug ponderomotive confinement.

Synchronously pumped optical parametric oscillators (OPOs) provide uniquely versatile platforms to generate ultrafast mid-infrared pulses within a spectral range beyond the access of conventional mode-locked lasers. However, conventional OPO sources based on bulk crystals have been plagued by complex optical alignment and large physical footprint. Here, we devise and implement two OPO variants based on a polarization-maintaining fiber-feedback cavity, which allow to robustly deliver sub-picosecond MIR pulses without the need of active stabilization. The first one integrates an erbium-doped fiber into the OPO cavity as the additional gain medium, which significantly reduces the pump threshold and allows stable optical pulse formation within a spectral range of 1553-1586 nm. The second one adopts a chirped poling nonlinear crystal in a passive-fiber cavity to further extend the operation spectral coverage, which facilitates broad tuning ranges of 1350-1768 nm and 2450-4450 nm for the signal and idler bands, respectively. Therefore, the presented mid-infrared OPO source is featured with high compactness, robust operation, and wide tunability, which would be attractive for subsequent applications such as infrared photonics, biomedical examination, and molecular spectroscopy.

Quantum light provides opportunities for controlling multiphoton absorption beyond classical limits. Here, we investigate biexciton generation in nanocrystal quantum dots driven by energy-time-entangled photon pairs generated via spontaneous parametric down-conversion. We show that frequency-time correlations between pairs of photons increase the population of biexcitons over excitons, thereby lending specificity to the excitation of many-body states. By employing a three-level model, we demonstrate that biexciton generation depends nontrivially on the photon entanglement time and the pump bandwidth. We find that maximizing efficiency requires an optimally shaped entangled photon field rather than simply scaling parameters for a monotonic improvement. Extending to a realistic CdSe/CdS core-shell quantum dot containing many excitonic states coupled to the quantum field, we demonstrate that increasing the bi-photon arrival-time entanglement (closer arrival time) enhances constructive pathway interference and expands accessible excitation channels while preserving better energy-conservation excitation than classical light when generating biexcitons. Furthermore, tuning the time correlation properties enables selective excitation of closely spaced biexciton states. These results establish entangled photons as a tool for selective excitation and control of higher-order excited states in quantum-confined systems.

Frequency upconversion technique offers an appealing approach for sensitive mid-infrared (MIR) imaging at room temperature. However, the spatial resolution of the upconversion imager has been notoriously restricted by the limited transverse section of the involved nonlinear crystal at the Fourier plane. Here, we implement a wide-field and high-resolution MIR upconversion imaging system based on elliptical pumping and Fourier ptychography. Specifically, an elliptical pump beam is engineered to accommodate the narrow aperture of chirped-poling crystals, thus facilitating the acquisition of high spatial frequency components in the lateral direction. Such an elliptical passband in the Fourier space is then discretely rotated to generate a sequence of upconversion images, which allows computational recovery of a high-resolution object image through a combination of synthetic aperture and phase retrieval operations. Consequently, an enhanced spatial resolution of 39 $\mu$m is achieved within a field of view about 25 mm, which corresponds to a space-bandwidth product of 3.2$\times$10$^5$, over tenfold larger than previously demonstrated values. Moreover, the MIR upconversion imager can operate under a low-light illumination of 1 photon/pulse/pixel. Therefore, the presented paradigm of nonlinear Fourier ptychography paves the way toward high-throughput infrared imaging with massive resolvable elements and single-photon sensitivity, which would stimulate a variety of applications such as industry inspection and biomedical diagnosis.

Single-photon laser ranging has widespread applications in remote sensing and target recognition. However, highly-sensitive light detection and ranging (LiDAR) has long been restricted in visible or near-infrared bands. An appealing quest is to extend the operation wavelength into the mid-infrared (MIR) region, which calls for an infrared photon counting system at high detection sensitivity and precise temporal resolution. Here, we devise and demonstrate a MIR upconversion LiDAR based on nonlinear asynchronous optical sampling. Specifically, the infrared probe is interrogated in a nonlinear crystal by a train of pump pulses at a slightly different repetition rate, which favors for a temporal optical scanning at a picosecond timing resolution and a kilohertz refreshing rate over $\sim$50 ns. Moreover, the cross-correlation upconversion trace is temporally stretched by a factor of 2$\times$10$^4$, which can thus be recorded by a low-bandwidth silicon detector. In combination with time-correlated photon-counting technique, the achieved effective resolution is about two orders of magnitude better than the timing jitter of the detector itself, which facilitates a ranging precision of 4 $\mu$m under a low detected flux of 8$\times$10$^{-5}$ photons per pulse. The presented MIR time-of-flight range finder is featured with single-photon sensitivity and high positioning resolution, which would be particularly useful in infrared sensing and imaging in photon-starved scenarios.

Defect modes localize vibrations within phononic-crystal bandgaps. Prescribing their frequencies alone does not ensure separation from competing modes. We use frequency-dependent selection weights to combine attraction of target-adjacent modes with repulsion of competing in-gap modes. Only the central defect cell is optimized within a previously designed, fixed host bandgap. Cell-level Craig–Bampton reduction retains physical interface coordinates, allowing the host contribution to be assembled once while the defect basis and matrices are updated. Across three numerical cases, the reduced-order method accelerates defect optimization by approximately 13–20 times. A comparison with attraction-only optimization shows that the combined objective moves competing modes away from the target and increases the minimum target-to-non-target frequency separation. Full-order reanalysis shows that the optimized modes remain close to the prescribed frequencies and localized around the defect. A finite-array calculation shows enhanced response near the target relative to the defect-free host under the prescribed loading.

Muon-catalyzed nuclear fusion (\mucf) replaces atomic electrons with negative muons, compressing atomic orbitals by about two orders of magnitude and enabling deuterium–tritium (D–T) fusion under near-room-temperature conditions. This paper reviews the physical principles of \mucf{} and formulates its essential dynamics as a four-step cycle: muonic-atom formation, muon transfer, resonant \dtmu{} molecular formation, and D–T fusion with muon release and recycling. A kinetic model is used to quantify the number of catalysis cycles per muon and the corresponding energy gain. We focus on the central limitation of catalytic efficiency, namely the alpha-sticking effect, and discuss possible breakthrough routes including nuclear-spin and muon dual polarization, in-flight muon-catalyzed fusion, and heavy-ion-driven magneto-inertial fusion. Within the idealized assumptions of the present model, a four-dimensional synergistic scheme combining dual polarization, high-density confinement, electric-field-assisted muon recovery, and resonant enhancement may increase the number of catalysis cycles per muon from the present experimental record of about 150 to more than 500, potentially enabling an energy gain \(Q>2\). On this basis, we propose a conceptual fusion–fission fuel-breeding hybrid reactor, denoted as \mucf-FBR, which exploits the 14.1-MeV neutron yield of \mucf{} to breed \({}^{239}\mathrm{Pu}\) from a \({}^{238}\mathrm{U}\) blanket in a decoupled fusion–fission operating mode. This concept may offer advantages in engineering robustness, radiation-damage tolerance, and natural-uranium utilization.

Narrow-linewidth lasers with absolute frequency anchoring are essential for precision metrology, coherent sensing, and emerging quantum technologies beyond laboratory environments. Optical cavities and interferometers provide exceptional short-term spectral purity but lack intrinsic absolute frequency references. Atomic transitions, in contrast, provide stable frequency anchors but offer limited discrimination sensitivity. Recent hybrid approaches have demonstrated the combination of compact optical resonators with atomic references, yet achieving the Hz-level regime remains challenging. Here, we present a hybrid architecture that enables simultaneous realization of Hz-level linewidth and atomic-referenced frequency stability. An external-cavity diode laser is first stabilized to a fiber interferometer to achieve Hz-level spectral purity, while the interferometer is subsequently anchored to an 87Rb D2 transition via modulation transfer spectroscopy to suppress long-term drift and define the laser frequency relative to the atomic transition. This dual-stabilization scheme realizes a compact atomic-referenced laser with a 3.4-Hz linewidth (1-rad integrated-phase method), a minimum fractional frequency stability of 3.4x10-14 at 0.56 s, and 9x10-13 at 100 s. This architecture establishes a practical and scalable route toward compact and field-deployable atomic-referenced narrow-linewidth lasers for precision metrology and quantum technologies.

Quantifying structural stress in transaction networks requires metrics that capture structural organization beyond transaction volume alone. In this work, we introduce the Inefficiency Metric, a deterministic indicator designed to characterize the routing structure of capital flows in decentralized systems. Using Principal Component Analysis and Pearson correlation matrices computed from a six-year Hedera transaction dataset, we identify two dominant and largely independent structural dimensions: the effective diameter, related to the spatial extension of transaction propagation, and the closeness centrality, associated with the efficiency of network-level flow processing. The proposed metric reveals significant topological fluctuations associated with major macroeconomic and ecosystem-level events. Increased inefficiency is observed during periods marked by intermediary fragmentation or rapid smart-contract expansion, whereas lower inefficiency corresponds to phases of network compaction during market stress or institutional concentration. Comparison with a seven-dimensional Isolation Forest approach shows that the metric effectively captures severe multidimensional anomalies while preserving a clear structural interpretation. Overall, these results provide a physics-inspired framework for relating the large-scale organization of decentralized transaction networks to observable economic dynamics.

Classical Proper Orthogonal Decomposition (POD)-based Galerkin projection models of chaotic flows typically require a large number of modes as well as stabilization or closure terms to achieve adequate accuracy and long-term stability. We present a novel differentiable programming framework that stabilizes low-rank POD-Galerkin models without increasing the number of modes or introducing additional closure terms, thereby delivering both high efficiency and high accuracy. Model stabilization is achieved by tuning the linear and quadratic tensors in the POD-Galerkin using differentiable programming, trained on short-term trajectory data. A key finding of this study is that a purely point-wise trajectory-based loss function yields poor long-term accuracy for chaotic systems. In contrast, a hybrid loss function that combines trajectory error with a physics-based conservation-of-energy term provides superior long-term performance. We demonstrate the approach on a chaotic lid-driven cavity flow at Re = 30,000. The stabilized ROM achieves an order-of-magnitude reduction in computational cost compared with the classical POD-Galerkin method: it remains accurate and stable with only 20 modes, whereas the classical ROM requires 80 POD modes.

Coronal mass ejections (CMEs) are explosive and energetic events consisting of strong magnetic structures erupting from the solar corona. We use superposed epoch analysis to investigate the general properties of CMEs as measured in situ from 0.2 to 2.2 au based on over 1600 events obtained from the HELIO4CAST catalog. We examine the dependence of the CME global properties on solar cycle phase, and compare the CME parameters derived in the active phase (AP) with the quiet phase (QP). Our findings show that during the AP of the solar cycle, the occurring CMEs are faster and have stronger magnetic field strength than during the QP, which has denser but weaker magnetic strength. These differences in magnetic field strength and density remain even when controlling for the speed. This may indicate that the enhanced profiles observed during the AP are not only a consequence of the CME propagation speed but may also reflect intrinsic differences in the eruption mechanism during different solar cycle phases. We also study how the magnetic field strength and components of the CME magnetic ejecta (ME) structure evolve with heliocentric distance. We find that the toroidal and poloidal ME magnetic field components have a similar power law decrease with distance, indicating a comparable expansion behavior of CMEs in these dimensions. We further quantify the CME magnetic field asymmetry %(often associated with CME aging) using the front-to-rear ratio of the toroidal component across heliocentric distance and find evidence of an increase of this ratio with heliocentric distance.

This report explores detector design considerations for the Future Circular Collider in its electron-positron mode (FCC-ee) through an extended dialogue between a physicist and an AI assistant. Starting from initial "prejudice" detector concepts proposed by the AI assistant without explicit physicist input, each subsystem is examined in detail, with the AI's assumptions challenged and revised through the exchange. The discussion covers the full detector from beam pipe to luminosity monitor, with particular attention to the interplay between subsystem choices and the practical considerations - calibration, stability, and operational simplicity - that are essential for a fifteen-year precision physics program. The narrative documents how the integrated detector design evolved substantially from the starting point to revised "prejudice" detector concepts of the AI assistant. The focus of this report is on the process to illustrate both the potential and the limitations of human-AI collaboration in experimental physics design, and the physics capabilities of any of the "prejudice" detector concepts remain to be explored.

The internal carotid artery (ICA), especially its cervical segment (ICA-C1), holds significant importance in the diagnosis of cerebrovascular diseases. Traditional polynomial fitting methods often encounter problems such as order selection, overfitting, and oscillation. To address these issues, this paper proposes a fractional polynomial fitting model based on the effective order and Bayesian Information Criterion (BIC). Through statistical analysis, the high-frequency effective orders $\{1.1, 1.5, 2.0, 2.7, 3.4\}$ are determined to improve computational efficiency. The experimental results of 379 clinical cases show that the proposed method outperforms the traditional methods in terms of fitting accuracy, noise resistance and computational efficiency. It can achieve low-error fitting and accurately depict the complex spatial morphology of the ICA-C1 segment. Moreover, by optimizing the solution strategy and sequence selection mechanism, the running time of the algorithm has been reduced from 153.145 seconds to 23.054 seconds. Furthermore, the proposed model shows good application potential in predicting missing vascular segments in imaging tasks. The prediction results are overall stable, and in most cases, the error is small. The normalized mean square error (NMSE) is below 1.68% in 90% of the cases. This study presents a stable, efficient and clinically interpretable model approach for analyzing the morphology of cerebral vessels, opening up a new technological frontier for intelligent diagnosis of cerebral vascular diseases.

The discrimination of neutron and gamma pulse shapes is a key technology in fields such as nuclear safety monitoring and radiation assessment. An enhanced recursive gated cyclic residual-sparse linear attention network is developed on the CLYC detector experimental platform to overcome weak noise resistance, limited feature extraction and inferior real-time performance of conventional algorithms. The experimental dataset comprises 19,971 samples, which were pre-processed and stratified for model training and testing. Results indicate that the proposed algorithm achieves a quality factor of 2.2, with a classification accuracy of 98.7% and a recall rate of 99.4%. It achieves an accuracy of 95.1% under the 20 dB low signal-to-noise ratio condition, exhibiting excellent anti-noise ability.With around 2.8 million parameters, the model takes merely 0.05 ms to process a single pulse on GPU, satisfying real-time monitoring and embedded deployment demands.

Whether a rubric-based LLM judge can replace human annotation is decided by its measured agreement with human labels. Yet the same verdicts can support wildly varying agreement numbers, depending on seemingly minor choices: the judgment scale, the retained cases, the handling of abstentions and invalid outputs, and the pooling of verdicts across items and rubric criteria. The statistics that settle these choices are established, but in psychometrics, econometrics, and corpus annotation rather than in the evaluation practice that needs them. We treat the choices as a measurement protocol that fixes what the reported number estimates before any metric is computed, assemble the relevant results into a single source-attributed analysis, and apply it to three published LLM-judge evaluations. For non-degenerate binary verdicts, Pearson's $r$, Spearman's $\rho$, Kendall's $\tau_b$, the phi coefficient, and the Matthews correlation coefficient are exactly the same statistic, so reporting several repeats one number under different names. Cohen's $\kappa$ differs from them only through a marginal-mismatch factor in $(0,1]$ and shares their asymptotic variance when judge and human assign the positive verdict equally often. Under exclusion, accuracy over all cases is pinned down only to a worst-case interval as wide as the uncovered fraction. On a rubric benchmark carrying per-criterion human labels, protocol choice alone moves reported accuracy from $0.551$ to $0.899$ and carries $\kappa$ across zero, without altering a single verdict. We distill the analysis into a reporting checklist that makes agreement claims reconstructible and comparable.

We present MARUT, a scalable multi-GPU computational fluid dynamics (CFD) framework designed for high-fidelity simulations of compressible flows spanning subsonic to hypersonic regimes, including chemically reacting nonequilibrium flows with finite-rate chemistry and adaptive mesh refinement (AMR). The framework addresses a central challenge in contemporary scientific computing: the development of numerically accurate and computationally scalable algorithms capable of resolving strongly nonlinear, multiscale flow physics on emerging heterogeneous supercomputing architectures. Built around a distributed-memory MPI-parallel infrastructure and implemented natively on NVIDIA GPUs, MARUT combines high-order spectral discontinuous Galerkin discretisations with strong-stability-preserving Runge–Kutta time integration to achieve low-dissipation and high-resolution representation of shocks, vortical structures and reactive interfaces. Dynamic AMR further enables efficient concentration of computational resources in localized regions of physical complexity, thereby substantially reducing computational cost while preserving solution fidelity. MARUT is designed to maintain strong parallel efficiency through GPU-resident computations and scalable MPI communication strategies, achieving near-linear strong scaling across multiple GPUs. The solver is validated against a broad suite of canonical benchmark problems involving inviscid, viscous, and reactive compressible flows, including subsonic, transonic, supersonic, and hypersonic configurations with multi-species nonequilibrium chemistry. The numerical predictions show close agreement with established reference solutions. Beyond its immediate performance characteristics, the framework reflects the broader transition of computational science towards modular, adaptive and AI-compatible simulation ecosystems.

Electron and ion-cyclotron waves are well known to exist in solar system plasmas but their existence and importance in galaxy clusters is an open question. Guided by numerical simulations, (Ley et al. (2024)) argued that whistlers (electron-cyclotron) and ion-cyclotron (IC) waves are generated by trapped particles in mirror modes in the nonlinear stages of the mirror instability under ICM conditions. Building on this work, we construct a novel particle propagation simulation of the ICM plasma based on the static electromagnetic field configuration from the fully kinetic particle-in-cell (PIC) simulation of the nonlinear mirror instability by (Ley et al. (2024)). We study how the trapping rate of particles is related to the secondary waves driven by mirror modes. We observe that secondary whistlers and IC waves enhance trapped particle escape from mirror modes. We measure the particle-wave scattering rate by whistlers and IC waves, demonstrate that the scattering rates and wave amplitudes follow the proportionality relation expected from quasilinear theory, and show the existence of a significant correlation between scattering rates and the excitation of secondary instabilities.

We calculate parity non-conservation (PNC) amplitudes for all magnetic-dipole (M1) transitions within the ground $5p^2$ configuration of Sn, including the standard model interaction and contribution of a hypothetical additional $Z'$-boson. Among the transitions considered, the $^1$S$_0$-$^3$P$_1$ transition has the largest PNC amplitude and appears to be the most promising candidate for an experiment. We also discuss a measurement method capable of achieving unprecedentedly high precision in a measurement of PNC in this transition. We argue that the most robust test should be based on ratios of PNC amplitudes for different isotopes, since the atomic-structure factor largely cancels in such ratios. We study the effect of the neutron skin on these isotope ratios using available nuclear data for Sn and show that the uncertainty associated with the neutron skin can be reduced to the $10^{-4}$ level relative to the isotopic variation of the PNC effect. Our results indicate that PNC measurements along a chain of Sn isotopes offer a realistic and sensitive probe of new physics.

Intermittency, manifested through anomalous scaling, remains one of the central unresolved problems in turbulence theory, with few analytical approaches extending beyond idealized linear transport models. We introduce a perturbative framework for anomalous scaling in turbulent transport based on multiplier statistics, rather than zero-mode calculations. We demonstrate the approach using a shell model combining deterministic and Kraichnan-like stochastic components. We reduce the problem to the analysis of a stationary Fokker-Planck equation for Kolmogorov multipliers, defined as ratios of successive scalar amplitudes. Its solution yields the invariant measure through a perturbative expansion around a Gaussian distribution. Using the resulting multiplier statistics, we compute explicit anomalous scaling exponents for structure functions of arbitrary order, including odd, even, and non-integer moments. Although demonstrated here for a shell model, the framework suggests a systematic perturbative route toward analytical theories of intermittency in turbulence.

Turbulence is ubiquitous in engineering and science, yet direct simulation is prohibitively expensive. The Reynolds-averaged Navier-Stokes (RANS) equations provide savings exceeding ten orders of magnitude but introduce unclosed terms (the closure problem). Offline-trained machine-learning (ML) closures suffer distribution shift in predictive simulations, while ML methods that bypass the governing equations struggle to generalise from scarce high-fidelity data. We develop a physics-derived deep learning closure model for RANS, the Deep Algebraic Reynolds Stress Model (DARSM), which can be trained on small datasets and accurately generalise across Reynolds numbers, to unseen geometries, and to different flow regimes. A neural network maps flow invariants to empirical parameters in an implicit algebraic Reynolds stress equation, derived from the Reynolds stress transport equations under the weak-equilibrium assumption, imposing physics-based structure on the ML closure. End-to-end optimisation through the governing PDEs and the coupled implicit closure eliminates distribution shift, but both unrolled and implicit automatic differentiation fail on the stiff coupled solver. We derive adjoint equations that exploit the solver's implicit-explicit structure for efficient optimisation. On canonical square-duct and periodic-hill benchmarks, DARSM reduces average test velocity error over baseline RANS by $2$-$4\times$ across Reynolds number, geometries, and flow regimes, with peak case-level reductions of $12\times$. The model trained on attached, anisotropy-dominated flows (square duct) accurately generalises without retraining to separated flows (periodic hills), a regime change in the underlying physics. DARSM also outperforms five established ML methods: offline training, tensor-basis neural networks, field-inversion machine learning, DeepONets, and physics-informed neural networks.

Looking at physical systems as computers allows us to regard physical properties, such as thermal noise, symmetry or topology, as unconventional resources for computation. However, harnessing these resources requires programming computational functionality through strong, controllable nonlinearities in the system. Here, we show that cavity optomechanical interactions allow laser-controlled computation with nanomechanical degrees of freedom. We demonstrate a set of basic digital logic gates with level restoration and controlled mechanical couplings as essential ingredients for arbitrary computing networks. Owing to the strong optomechanical nonlinearity and precise readout, the system operates close to thermal amplitudes, in the regime where thermodynamic stochasticity governs its behavior. This opens a new path for the realization of physical computing with controlled nonlinear resonators.

The demand for fast mid-wave infrared photodetectors is fueled by high-rate free-space optical communication and optical frequency comb spectroscopy. To date, only a few multi-GHz photodetectors have shown sensitive room-temperature operation in the 3.0-3.7 {\mu}m band, yet their commercial availability remains scarce. In this work, we present the remarkable response speed of an InAs/InAsSbP nBp barrier detector - a type typically not associated with high-frequency operation. A weakly reverse-biased photodiode with a diameter of 121 {\mu}m achieves a -3 dB electrical bandwidth of 2.4 GHz and -20 dB bandwidth of 8.0 GHz. This is the best result in this class of mid-infrared photodetectors confirmed optically. High signal-to-noise photodetection is also demonstrated at frequencies exceeding 19 GHz. The relatively simple device structure (devoid of cascaded structure or type-II superlattice) was realized on the mature InAs material platform, which opens new perspectives for accessible, sensitive, multi-GHz photodetectors for the 3.0-3.7 {\mu}m spectral region.

Identifying possible clusters in datasets and estimating their hierarchical modularity are central tasks in pattern recognition. In the present work, concepts and methodologies are described for performing these tasks while considering only the density of mergings obtained from hierarchical representations (dendrograms) of data inter-relationship along a scale variable. More specifically, the mergings of subclusters along the scale variable are obtained, yielding a respective merging density function. After this function is equalized along the scale variable, peak detection is applied in order to estimate, within a specified resolution, the main hierarchical levels and their clusters. After quantifying infinitesimal modularity of the dendrogram at a fixed scale value, taking into account the uniformity of the size of the identified clusters and their average size, the overall, average, and group hierarchical modularities are obtained. The potential of the reported approach is illustrated for some types of data and dendrograms and additive dendrograms obtained from DLA patterns, and the possibility of recursive cluster detection is also considered.

As CMOS technology scales down, thermal fluctuations increasingly impact circuit behavior, posing challenges to conventional circuit design. However, the inherent stochasticity introduced by thermal noise is now being explored as a potential resource in the emerging field of probabilistic computing. This work presents a fully CMOS experimental platform that enables direct control over its intrinsic thermal fluctuations. These devices function as programmable multivariate Gaussian samplers, offering a hardware primitive for energy-efficient stochastic computing and serving as an experimental platform for studies in electronic noise and stochastic thermodynamics.