Papers

Atomically thin materials exhibit electronic and optical properties distinct from their three-dimensional counterparts. For metals, particularly gold, monolayer studies remain largely unexplored due to fabrication and characterisation challenges. Here we report the first optical study of a stable quasi-freestanding gold monolayer formed by Au intercalation between graphene and SiC. Mid-infrared nanoimaging reveals plasmon-polaritons with wavelengths nearly an order of magnitude shorter than free-space light. Analysis of their dispersion using a Drude model yields a relaxation time of $\tau = 18\,$fs, comparable to bulk gold, and a Drude weight of $D = 1.3\,$mS$\cdot$eV, nearly twice the bulk expectation. These results establish monolayer gold as a two-dimensional metal, opening opportunities for nanoscale photonics, plasmonics and ultra-thin electronics.

Turbulent transport near the X-point of a large tokamak is examined using local, gradient-driven simulations that determine the saturated plasma profiles. The distribution of a representative set of particle tracers evolving within these profiles is then analyzed. The study demonstrates that the resulting transport is diffusive, characterized by a coefficient that depends on the spectral properties of the turbulent energy and attains anomalous high values under broad conditions. These findings suggest that anomalous transport is an inherent outcome of the fundamental non-linear drift dynamics of plasmas. The scaling of transport with turbulent energy is also addressed, with implications for future progress toward a mean-field framework for turbulent transport.

The spatial public goods game has been used to examine factors that promote cooperation. Owing to the complexity of the dynamics of this game, previous studies on this model neglected analytical approaches and relied entirely on numerical calculations using the Monte Carlo (MC) simulations. In this paper, we present the approximate master equations (AMEs) for this model. We report that the results obtained by the AMEs are mostly qualitatively consistent with those obtained by the MC simulations. Furthermore, we show that it is possible to obtain phase boundaries analytically in certain parameter regions. In the region where the noise in strategy decisions is very large, the phase boundary can be obtained analytically by considering perturbations from the steady state of the voter model. In the noiseless region, discontinuous phase transitions occur because of the characteristics of the function that represents strategy updating. Our approach is useful for clarifying the details of the mechanisms that promote cooperation and can be easily applied to other group interaction models.

Perovskite based nanocomposites provide a versatile platform for tailoring light matter coupling and carrier injection in low dimensional optoelectronics. In particular hybrid structures incorporating transition metal dichalcogenides (TMDCs) enable tunable band alignment and excitonic dynamics. However, the central challenge lies in the scalable synthesis of defect controlled layered TMDCs suitable for coherent interfacial coupling. Here we demonstrate geometry dependent femtosecond (fs) laser ablation in liquid as a scalable strategy for defect controlled exfoliation of WS2 and in situ formation of WS2/CsPbBr3 nanocomposite. Using 50 fs pulses at identical fluence, we directly compare Gaussian and Bessel beam profiles to elucidate how spatial energy distribution governs nonlinear carrier generation, Coulomb stress and electron-lattice energy transfer. Multiphysics modeling combining nonlinear absorption, Coulomb instability criteria and two temperature dynamics reveals that the axially extended, weakly diffracting Bessel profile excites carrier densities above the electronic destabilization threshold while reducing the peak lattice heating. In contrast, Gaussian excitation concentrates energy within the focal volume, promoting rapid thermalization and defect formation. Spectroscopic analysis reveals reduced trap assisted recombination and enhanced excitonic stability in Bessel processed samples. Transient absorption spectroscopy (TAS) further demonstrates prolonged carrier lifetime. Extending this approach, single step ablation yields WS2/CsPbBr3 hybrids exhibiting Type-I band alignment and geometry dependent interfacial carrier injection, evidenced by photoluminescence quenching and modified ultrafast decay dynamics.

A multiband hybrid metasurface supporting multiple gap-surface plasmon (GSP) and localized surface plasmon (LSP) modes is presented. The structure adopts a metal-dielectric-metal configuration consisting of an aluminum bottom layer, a silicon dioxide spacer, and a bar-disc hybrid resonator patterned in the top aluminum layer. Optimized geometrical parameters yield four distinct resonances across the near-infrared and telecommunication bands, arising from the interplay between GSP modes and LSP excitations. The reflectance spectra are systematically analyzed as functions of geometric parameters and polarization, demonstrating tunable multiband operation. Experimental measurements of the fabricated metasurface show good agreement with numerical predictions. Furthermore, the second-harmonic generation (SHG) response is numerically investigated, revealing enhanced SH emission at the resonance wavelengths due to strong electromagnetic field confinement within the metal-dielectric-metal cavity. The proposed metasurface provides a compact platform for multiband and multifunctional nanophotonic applications.

In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes. We consider three families of sequence models based on LSTM, GRU, and Transformer architectures, with uncertainty quantification enabled by either Monte Carlo dropout or through evidential output layers compatible with Deep Evidential Regression. Using the HUPA-UCM diabetes dataset for validation, we find that Transformer-based models equipped with evidential output heads provide the most effective uncertainty-aware framework, achieving consistently higher predictive accuracies and better-calibrated uncertainty estimates whose magnitudes significantly correlate with prediction errors. We further evaluate the clinical risk of each model using the recently proposed Diabetes Technology Society error grid, with risk categories defined by international expert consensus. Our results demonstrate the value of integrating principled uncertainty quantification into real-time machine-learning-based blood glucose prediction systems.

With the development of high repetition rate laser sources and advanced multi-particle correlation analyses such as covariance mapping, particle detection techniques such as velocity map imaging (VMI) are poised to offer unprecedented views into molecular phenomena. Taking full advantage of the high count rates in these experiments requires the development of detectors with sufficient spatial and temporal resolution that can process data in real time. The TimePix3 camera (TPX3CAM) is an event-based pixel detector capable of spatio-temporally localizing many simultaneous particle hits in an efficient manner. While the sparse nature of the data stream allows for compact representation of particle hits, it also presents algorithmic and computational challenges for clustering individual pixels into hits. Here we present the theory and application of a rapid data processing and centroiding algorithm for ion and electron hits collected in a VMI instrument. The array-based computations that comprise the algorithm take full advantage of the data sparsity of the TimePix3 data stream and localize particle hits on the microchannel plate (MCP) to better than a single pixel on the pixel detector. Centroiding can be parallelized on a commercially available graphics processing unit (GPU) for additional speed. Using these innovations, data processing occurs about 25 times faster than data acquisition, for a 1 kHz repetition rate instrument and tens of particles per shot. In addition to its speed, the TPX3CAM detector outperforms state-of-the-art delay line anode detectors at discriminating multiple simultaneous hits, enabling high-fidelity coincidence and covariance studies in the near future.

Understanding how opinions evolve through social interactions is crucial for mitigating polarization. Existing opinion-dynamics models incorporate both attractive and repulsive interactions but typically assume that all issues are equally important. We develop and analyze a stochastic agent-based model where issues carry heterogeneous weights that influence both social affinity and the likelihood of opinion change. Surprisingly, introducing even a single issue with arbitrarily small weight can destabilize otherwise stable states, increasing convergence times by orders of magnitude. To explain these dynamics, we derive a mean-field approach and characterize the equilibrium symmetries governing consensus, polarization, and persistent pluralism. A complete classification of these symmetries for up to five issues reveals that polarization increases when importance is concentrated on a small number of issues. Conversely, distributing importance more broadly across issues promotes diversity of opinions and reduces polarization. Our symmetry-based framework highlights how issue salience and social tolerance jointly shape collective opinion evolution.

Compact models of memristors are essential for simulating large-scale neuromorphic systems, yet they often do not include description of complex dynamics like volatile relaxation and synaptic plasticity. We introduce a modular, computationally efficient memristor model that bridges this gap by integrating principles from physics and computational neuroscience. The model defines a framework consisting of a standard formulation of memristive device dynamics, a functional rule mapping state variables to cumulative conductance, a volatility module inspired by the theory of linear viscoelasticity and a saturation module implementing a linear-nonlinear technique. Additionally, we develop a formulation of synaptic-like plasticity inspired by a biological spike-timing-dependent plasticity (STDP) rule, which is compatible with the general framework for memristive devices. Finally, we propose a Laplace transform-based technique to derive the precise form of the mapping from state variables to cumulative conductance, replacing ad hoc voltage-current relationships with principled construction. We quantitatively validate the complete model against a rich set of experimental data from polymeric memristors exhibiting potentiation, synaptic-like plasticity and volatile decay. Our work presents a new paradigm for memristor modeling that is both practical for large-scale simulation and rich in explanatory power, providing a principled tool for the design of next-generation neuromorphic hardware.

Nonlinear Raman-Nath diffraction (NRND) and nonlinear Cherenkov radiation (NCR) are significant nonlinear diffraction phenomena in optics. Previous studies have primarily focused on NRND and NCR in uniaxial crystals, particularly in periodically poled lithium niobate (PPLN) crystals. However, research on these phenomena in biaxial crystals, such as periodically poled potassium titanyl phosphate (PPKTP), has been limited, and the study of NCR in PPKTP has not yet been undertaken.In this work, we experimentally investigated NRND and NCR phenomena in PPKTP crystals under varying incident angles, pump polarizations, poling periods, and crystal temperatures. Our findings indicate that PPKTP exhibits over ten times greater thermal stability compared to PPLN. This high thermal stability is promising for applications in parallel optical computing, as it helps reduce optical mode deviations and minimize bit error rates.

In-line digital holography (DIH) is a widely used lensless imaging technique, valued for its simplicity and capability to image samples at high throughput. However, capturing only intensity of the interference pattern during the recording process gives rise to some unwanted terms such as cross-term and twin-image. The cross-term can be suppressed by adjusting the intensity of reference wave, but the twin-image problem remains. The twin-image is a spectral artifact that superimposes a defocused conjugate wave onto the reconstructed object, severely degrading image quality. While deep learning has recently emerged as a powerful tool for phase retrieval, traditional Convolutional Neural Networks (CNNs) are limited by their local receptive fields, making them less effective at capturing the global diffraction patterns inherent in holography. In this study, we introduce HoloPASWIN, a physics-aware deep learning framework based on the Swin Transformer architecture. By leveraging hierarchical shifted-window attention, our model efficiently captures both local details and long-range dependencies essential for accurate holographic reconstruction. We propose a comprehensive loss function that integrates frequency-domain constraints with physical consistency via a differentiable angular spectrum propagator, ensuring high spectral fidelity. Validated on a large-scale synthetic dataset of 25,000 samples with diverse noise configurations (speckle, shot, read, and dark noise), HoloPASWIN demonstrates effective twin-image suppression and robust reconstruction quality.

Physical reservoir computing (PRC) is a promising brain-inspired computing architecture for overcoming the von Neumann bottleneck by utilizing the intrinsic dynamics of physical systems. However, a major obstacle to its real-world implementation lies in the tension between extracting sufficient information for high computational performance and maintaining a hardware-feasible, high-speed architecture. Here, we report spectral dynamics reservoir computing (SDRC), a broadly applicable framework based on analogue filtering and envelope detection that bridges this gap. SDRC effectively exploits the fast spectral dynamics embedded in short-time, coarse spectra of material responses to attain strong computational capability while maintaining high-speed processing and minimal hardware overhead. This approach circumvents the need for implementation-intensive, precision-sensitive integrated circuits required in high-speed time-multiplexing measurements, while enabling real-time use of the material's spectral manifold as a high-dimensional computational resource. We implement and experimentally demonstrate SDRC applied to spin waves that achieves state-of-the-art-level performance with only 56 nodes on benchmark tasks of parity-check and second-order nonlinear autoregressive moving average, as well as high accuracy of 98.0% on a real-world problem of speech recognition.

Internal–external field separation is crucial for many aspects of geomagnetism, aiming at distinguishing contributions of the magnetic field generated within a given observation surface from those generated in the exterior. When data are available on a full spherical observation surface, this separation is a standard, stable, and widely used procedure dating back to Gauss. However, when data are only available in a subdomain of the observation surface (as is the case for aeromagnetic and ground-based surveys), the situation drastically changes. Here we show that, without prior assumptions, an internal–external field separation is not uniquely possible. Given the geophysically reasonable assumption that the exterior sources, e.g., magnetospheric and ionospheric current systems, are located above a source-free spherical shell, we show that a unique separation becomes possible but that it is highly unstable. The results are based on the spherical Hardy–Hodge decomposition and explain the intrinsic difficulties of regional data-based internal–external potential field separation.

Ultrafast laser writing of single lattice defects in wide-bandgap semiconductors is shown to present a new physical setting in which deeply subwavelength laser-writing positioning precision is attainable, but where the whole notion of positioning can only be understood in a statistical sense. We outline a framework for the analysis of this class of laser - matter interactions, grounding the concepts of optical super-resolution and subdiffraction positioning in statistical optics. Working along these lines, we derive closed-form solutions for physically meaningful quantifiers of laser-matter interactions on a subwavelength scale, suggesting a physically clear view of how deeply subdiffraction resolution can emerge from the interplay between determinism and stochasticity. We show that subdiffraction positioning precision in single-lattice-defect laser writing is achieved at the cost of a lower throughput, setting physical bounds on the scalability of integrated quantum photonic systems fabricated by means of super-resolving laser writing.

Targeted hub removal is known to weaken connectivity in heterogeneous networks. We show that in Barabási–Albert networks the same intervention can also shift Watts threshold dynamics across the cascade critical point. For BA networks with $N=2{,}000$ and $m=2$, removing the top 10% of nodes by degree raises the bond-percolation threshold from $p_c=0.174$ to $0.776$ and, at $\varphi=0.22$, increases mean cascade size from $0.86\%$ (95% CI 0.43–1.30) to $23.1\%$ (21.3–24.9). A controlled hub-vulnerability experiment on fixed topology shows that most of this cascade effect is dynamical: lowering hub activation thresholds produces much larger cascades even without deleting nodes, while deletion partly offsets the increase by removing edges. Using a configuration-model approximation, we derive the post-removal branching factor $z_1$ and identify a window in which the original network is subcritical but the hub-removed network is supercritical. The effect persists across system sizes and is not seen in matched ER or WS controls. These results identify a regime in which hub removal simultaneously worsens connectivity and cascade exposure in BA networks.

Neighborhood algorithms may take a considerable percentage of computer time in discrete element methods (DEM). While the sort-and-sweep algorithm is ideal in some ways, as it only deal with particles whose relative positions change in one coordinate direction, the other directions must be processed too, for all particles. In contrast, tree-codes deal only with adjacent particles. We compare sort-and-sweep and tree-code neighborhood algorithms for two-dimensional DEM simulations of polygonal particles in a rotating drum with up to 12000 particles. We discuss the effects of system size and inlining on the performance with respect to the cache memory. For the tree code, the performance is slightly better, at the cost of significantly increased cyclomatic complexity. In particular, one benefit is improved possibilities for shared memory parallelization.

We use a series of global models to investigate the linear and nonlinear interactions between shock cells, Kelvin-Helmholtz waves, guided jet modes, and other fluctuations in a screeching jet. First, we identify a set of lightly damped global eigenmodes of the Navier-Stokes operator linearized about the mean flow and show that they result from interactions with different shock-cell wavenumbers. Second, we use resolvent analysis to study the linear input-output behavior of the jet and obtain a time-periodic representation of the screech mode, which compares favorably with experimental data. Third, we use harmonic resolvent analysis to study triadic interactions, including inter-frequency energy transfer, between the screech mode determined from resolvent analysis and other fluctuations in the jet. The components of the optimal harmonic resolvent mode at harmonics of the screech frequency are consistent with experimental observations that have not been previously predicted by global models. Fourth, we leverage a novel bilinear formulation of harmonic resolvent analysis to study the impact of the screech mode's nonlinear self-interaction on other fluctuations in the jet. We show that the forcing provided by this nonlinear self-interaction of the screech mode, along with its triadic interactions with other frequencies embedded within the harmonic resolvent operator, is sufficient to explain the redistribution of energy to other frequencies and the associated experimental observations. In aggregate, these findings underscore the critical role of triadic and nonlinear interactions in shaping screech dynamics and offer a promising workflow for studying similar interactions in other flows dominated by periodic motions.

Structured light fields exploit spin and orbital angular momentum for precision manipulation, advanced imaging, and high-capacity communication. Orbital angular momentum coherent state beams interpolate between Hermite- and Laguerre-Gaussian beams, enabling continuous spatial control. We introduce a symmetry-based framework for joint control of polarization and spatial structure under the shared \$su(2)\$ Lie algebra of spin and orbital angular momentum. Within this structure, we construct total angular momentum fields as superpositions of circular polarization and Laguerre-Gaussian beams, and define their \$su(2)\$ coherent states within fixed-angular-momentum subspaces. A single complex parameter controls both polarization and spatial degrees of freedom, enabling continuous, symmetry-preserving tuning.

Orbital angular momentum (OAM), a topological degree of freedom of light, is theoretically invariant under continuous deformations; yet, its physical observability degrades precipitously in complex media, creating a fundamental "topology-observability gap." Here, we propose a novel paradigm for topological measurement based on the non-separable coupling between polarization and topological features in vectorial vortex beam. By constructing a topological non-separability measure derived from global Stokes fields, and integrating it with a physics-guided machine learning calibration framework that combines Bayesian Gaussian process regression with XGBoost-driven adaptive model selection, we achieve high-fidelity identification of topological features up to 200. Crucially, this robustness persists even when beam intensity and phase structures are completely distorted by extreme complex media, including strong atmospheric turbulence, oceanic turbulence, and high-temperature jet exhausts. This approach overcomes the dual bottlenecks of limited accessible OAM modes and susceptibility to perturbations that constrain conventional methods. Our work not only bridges the fundamental divide between topological theory and physical observability, but also establishes a robust framework for the reliable deployment of high-dimensional OAM in real-world complex environments, promising exciting advancements for wireless optical communications, remote topological sensing, and classical analogs of quantum information protocols.

Smartphones equipped with sensors such as accelerometers, gyroscopes, and magnetometers offer valuable opportunities for physics education, allowing students to measure motion using their own devices. However, commonly used applications provide acceleration only in the device-fixed coordinate system, which makes it difficult to analyze two- or three-dimensional motion when the device rotates. To address this limitation, we developed a web-based accelerometer application that can provide acceleration in a stationary global coordinate system. This is achieved by simultaneously recording acceleration in the device-fixed coordinate system and Euler angles, and converting them to rotation-compensated acceleration in real time. We also built a companion web application for numerical integration, noise reduction, and visualization of the measured data. Both applications are installation-free and can be accessed directly through a smartphone browser. We demonstrate the capabilities of the newly developed system through several representative types of motion, including sliding motion, projectile motion, and circular motion, by showing that rotation-compensated acceleration enables accurate reconstruction of velocity, displacement, and trajectories even when the smartphone changes its orientation. The applications were implemented in undergraduate mechanics classes, where students used them in group-based experiments. Classroom observations suggested that the use of these tools facilitated a deeper understanding of the relationships among acceleration, velocity, and position. These results suggest that rotation-compensated smartphone measurements provide a practical and effective tool for physics education.

This work investigates toroidal momentum transport in type-I ELMy H-mode plasmas in the ASDEX Upgrade tokamak, focusing on the formation of hollow rotation profiles under strong electron cyclotron resonance heating (ECRH). Applying the established momentum transport analysis framework to a neutral beam injection (NBI) modulation experiment, momentum transport coefficients were inferred self-consistently. This was done for phases with dominant NBI heating and with additional strong ECRH, during which the rotation profile severely collapsed without significant changes in the externally applied torque. The experimental rotation profiles were accurately reproduced, confirming the robustness of the inferred diffusive, convective, and residual-stress contributions. While the Prandtl number and inward Coriolis pinch remained comparable between phases, the NBI+ECRH phase exhibited a strong counter-current intrinsic torque. Linear gyrokinetic simulations indicate a transition from ion-temperature-gradient (ITG) turbulence to an ITG-trapped-electron-mode (TEM) mixed regime under strong ECRH, consistent with the observed counter-current intrinsic torque and particle pinch behavior. Additional high-ECRH discharges with modified density demonstrated that hollow rotation profiles emerge from a balance between counter-current intrinsic torque and inward convective momentum transport, strongly influenced by the pedestal-top rotation level, which is dominantly set by variations in the pedestal-top density. These findings highlight the importance of intrinsic torque and inward convection for maintaining favorable rotation profiles in future low-torque tokamak scenarios and motivate further exploration of edge torque generation mechanisms.

We study the effects of hydrodynamic interactions between a wall and the Purcell three-link swimmer in the two-dimensional case. After deriving the equations of motion in a low Reynolds number regime using Resistive Force Theory with suitably modified drag coefficients, we show, by means of criteria from Geometric Control Theory, that the system is controllable at configurations that are nearly parallel to the wall. Furthermore, we study configurations that are tilted, and we show net displacement with respect to the initial orientation. Some numerical experiments illustrate the analytical results.

On April 27, 1900, William Thomson, better known as Lord Kelvin, delivered a visionary speech before the Royal Institution of Great Britain. In it, he presented two unresolved problems which, to him, appeared fundamental and unavoidable at the turn of the 20th century. He compared them to two clouds obscuring our understanding of physics. Dissipating these two clouds would eventually require the development of special relativity and quantum mechanics. This article revisits the second cloud which, contrary to what is often claimed in the literature, did not concern black-body radiation, but rather the specific heat of polyatomic molecules. To clarify this, the article aims to place Kelvin's speech within the historical context of the time and to situate it within the sequence of developments, from Kirchhoff to the first Solvay Conference in 1911, that marked the path of the extraordinary intellectual adventure that led to the birth of quantum mechanics. It will also be shown that Max Planck's initial motivation was not to solve the problem of the so-called "ultraviolet catastrophe."

Artificial intelligence (AI) systems are rapidly becoming more capable, autonomous, and deeply embedded in social life. As humans increasingly interact, cooperate, and compete with AI, we move from purely human societies to hybrid human-AI societies whose collective dynamics cannot be captured by existing behavioural models alone. Drawing on evolutionary game theory, cultural evolution, and Large Language Models (LLMs) powered simulations, we argue that these developments open a new research agenda for social physics centred on the co-evolution of humans and machines. We outline six key research directions. First, modelling the evolutionary dynamics of social behaviours (e.g. cooperation, fairness, trust) in hybrid human-AI populations. Second, understanding machine culture: how AI systems generate, mediate, and select cultural traits. Third, analysing the co-evolution of language and behaviour when LLMs frame and participate in decisions. Fourth, studying the evolution of AI delegation: how responsibilities and control are negotiated between humans and machines. Fifth, formalising and comparing the distinct epistemic pipelines that generate human and AI behaviour. Sixth, modelling the co-evolution of AI development and regulation in a strategic ecosystem of firms, users, and institutions. Together, these directions define a programme for using social physics to anticipate and steer the societal impact of advanced AI.

This study investigates the steady-state kinematics of single-winged spinning samaras and re-evaluates the simplifying assumptions commonly used in existing theoretical models. High-speed imaging was employed to quantify key parameters, including descent velocity, rotational speed, coning angle, pitching motion, and the precessional trajectory of the center of mass. The results show that all measured parameters exhibit significant temporal variation, contradicting the long-standing assumption that these quantities remain constant in steady-state flight. This variability reveals that commonly used steady-state simplifications in previous studies may overlook essential aerodynamic mechanisms governing natural samara descent. Despite this complexity, the observed sinusoidal variations in pitch, cone angle, and translational velocity, together with the nearly linear rotation rate, provide a physically grounded basis for reformulating the governing nonlinear differential equations into a simplified algebraic form. Such experimentally validated harmonic representations offer a more realistic alternative to traditional steady-state assumptions. The visualized trajectories of the center of mass, root tip, wingtip quarter-chord point, and wingtip trailing edge reveal the inherently coupled nature of samara motion and highlight the need for future experiments that capture full three-dimensional kinematics. Overall, this work advances the understanding of samara aerodynamics by emphasizing the importance of natural variability while identifying a tractable path toward more accurate modelling frameworks.

Observational constraints are considered on a $w_{log}$CDM model of the dark energy equation of state, $w_{d}(z) = w_{0} + w_{a}\left( \frac{\ln(2+z)}{1+z} - \ln 2 \right)$, using the most recent cosmological datasets including DESI Baryon Acoustic Oscillation (BAO) measurements, Big Bang Nucleosynthesis (BBN) priors, Cosmic Chronometer (CC) observations, and Pantheon Plus (PPS) Type Ia supernovae. From the combined DESI BAO+BBN+CC+PPS dataset, we obtain $H_0 = 71.02 \pm 0.66~\text{kms}^{-1}\text{Mpc}^{-1}$, $\Omega_m = 0.2863 \pm 0.0080,$ $w_0 = -0.875 \pm 0.066,$ $w_a = -0.69^{+0.37}_{-0.32},$ at the 68% and 95% confidence levels, indicating a preference for phantom dark energy with mild evidence for temporal evolution. The Hubble constant obtained from our model is closer to the local SH0ES measurement than the standard $\Lambda$CDM prediction, partially easing the Hubble tension. We perform extensive parameter-space exploration revealing correlations between $w_0$, $w_a$, and $H_0$, showing that dynamical dark energy models can fit higher values of the Hubble constant. The reconstructed deceleration parameter $q(z)$ shows the transition from deceleration to acceleration at $z \sim 0.6$–$0.7$, while the equation-of-state reconstruction remains consistent with a cosmological constant across the observed redshift range. A model comparison using information criteria indicates that the $w_{log}$CDM model remains statistically competitive with $\Lambda$CDM.

The acoustic signature of thermal spray processes is known to vary with changes in operating conditions, which also influence particle in-flight velocity and distribution. Building on this idea, the present work first develops an analytical model that links chamber and nozzle parameters to far-field acoustic levels using gas-dynamics relations and simplified acoustic power propagation. The model is then calibrated to reduce systematic error associated with neglected turbulence effects and to improve agreement across operating conditions. In addition, a numerical framework is implemented to complement the analytical model and to resolve supersonic jet flow and in-flight particle transport. The second part of the study uses unsteady compressible simulations with hybrid turbulence modeling such as Unsteady Reynolds-Averaged Navier-Stokes (URANS) and Delayed Detached Eddy Simulation (DDES) to capture the development of the shock-containing jet and the associated near-field pressure fluctuations. Far-field sound is predicted using the Ffowcs Williams-Hawkings acoustic analogy, while a Lagrangian approach tracks particles injected at the nozzle exit to quantify velocity evolution, radial spreading, and downstream flux distributions. The influence of operating conditions (e.g., chamber pressure and temperature) is assessed, and predictions are evaluated against published microphone spectra and particle flux measurements. Overall, the combined analytical and numerical approach captures how changes in nozzle operating conditions affect jet unsteadiness and mixing, leading to measurable shifts in acoustic level and spectral content. These results suggest that aeroacoustic signatures could be used as a non-intrusive pathway to monitor and potentially control thermal spray operating conditions.

Data-driven surrogate models can significantly accelerate the simulation of continuous dynamical systems, yet the step-wise accumulation of errors during autoregressive time-stepping often leads to spectral blow-up and unphysical divergence. Existing global regularization techniques can enforce contractive dynamics but uniformly damp high-frequency features, causing over-smoothing; meanwhile, long-horizon trajectory optimization methods are severely constrained by memory bottlenecks. This paper proposes Jacobian-Adaptive Weighting for Stability (JAWS), which reformulates operator learning as a Maximum A Posteriori (MAP) estimation problem with spatially heteroscedastic uncertainty, enabling the regularization strength to adapt automatically based on local physical complexity: enforcing contraction in smooth regions to suppress noise while relaxing constraints near singular features such as shocks to preserve gradient information. Experiments demonstrate that JAWS serves as an effective spectral pre-conditioner for trajectory optimization, allowing short-horizon, memory-efficient training to match the accuracy of long-horizon baselines. Validations on the 1D viscous Burgers' equation and 2D flow past a cylinder ($\text{Re}=400$ out-of-distribution generalization) confirm the method's advantages in long-term stability, preservation of physical conservation properties, and computational efficiency. This significant reduction in memory usage makes the method particularly well-suited for stable and efficient long-term simulation of large-scale flow fields in practical engineering applications.

Intracellular delivery of biomolecules remains a critical challenge in both basic cell biology and translational therapeutics. We introduce Programmable Acoustic Standing-wave Transfection (PAST), a microfluidic tool that leverages dynamically programmable ultrasonic fields to transiently permeabilize cell membranes and enhance biomolecular transport within cell clusters. By generating programmable acoustic potential landscapes, PAST drives cells through cycles of hydrodynamic and acoustic stresses that induce reversible pore formation, enabling diffusion-based delivery without chemical carriers or contrast agents. Experimental studies demonstrate controlled influx and efflux dynamics across multiple biomolecular species, with transport rates tunable via acoustic power, frequency modulation, and duty cycles. Theoretical scaling and numerical simulations reveal that membrane tension, pore energetics, and acoustic field distributions collectively govern transmembrane transport of biomolecules. Post-treatment assays confirm high cellular viability and sustained proliferation, underscoring the biocompatibility of the method. Remarkably, effective diffusivity estimates derived from model predictions closely match experimental transport timescales. Together, these findings establish PAST as a programmable, high-throughput, and non-invasive intracellular delivery platform, offering new opportunities for precision drug screening, gene editing, and mechanistic exploration of cellular membrane biophysics.

Deep learning (DL) methods are currently being explored to restore images from sparse-view-, limited-data-, and undersampled-based acquisitions in medical applications. Although outputs from DL may appear visually appealing based on likability/subjective criteria (such as less noise, smooth features), they may also suffer from hallucinations. This issue is further exacerbated by a lack of easy-to-use techniques and robust metrics for the identification of hallucinations in DL outputs. In this work, we propose performing Fourier Ring Correlation (FRC) analysis over small patches and concomitantly (s)canning across DL outputs and their reference counterparts to detect hallucinations (termed as sFRC). We describe the rationale behind sFRC and provide its mathematical formulation. The parameters essential to sFRC may be set using predefined hallucinated features annotated by subject matter experts or using imaging theory-based hallucination maps. We use sFRC to detect hallucinations for three undersampled medical imaging problems: CT super-resolution, CT sparse view, and MRI subsampled restoration. In the testing phase, we demonstrate sFRC's effectiveness in detecting hallucinated features for the CT problem and sFRC's agreement with imaging theory-based outputs on hallucinated feature maps for the MR problem. Finally, we quantify the hallucination rates of DL methods on in-distribution versus out-of-distribution data and under increasing subsampling rates to characterize the robustness of DL methods. Beyond DL-based methods, sFRC's effectiveness in detecting hallucinations for a conventional regularization-based restoration method and a state-of-the-art unrolled method is also shown.