Papers

We demonstrate a compact piezoelectric-driven micropipette launcher for localized in-vacuum delivery of nano- and microparticles into optical traps. The launcher has been integrated into multiple optical trapping setups, including a single-beam trap, a non-interfering dual beam trap, and a standing-wave dual beam trap, showcasing the versatility and ease of integration of the setup. Using the micropipette launcher, we have successfully trapped silica spheres of $170\text{ nm}$, $300\text{ nm}$, 3 $\mu\text{m}$ diameter, as well as 6 $\mu\text{m}\times$ 0.2 $\mu\text{m}$ $\beta$-NaYF hexagonal prisms and $\sim 100$ nm diameter high-purity nanodiamonds. We characterize the performance of the device including the peak acceleration, angular distribution of emitted particles, and the dependence on vertical displacement between the pipette tip and optical trap. Trapping efficiency as high as 93% is achieved.

Iridium-based photosensitizers have attracted significant attention in photodynamic therapy (PDT) due to their exceptional photophysical properties and chemical stability, as well as tunable phosphorescence emission spectrum and high triplet state production yields. Photosensitizers with large two-photon absorption (TPA) and mitochondrial targeting capabilities are particularly promising for clinical PDT, as they enable deeper tissue penetration and reduced damage to normal cells. In this study, we theoretically studied photophysical, photodynamic properties and photosensitization reaction mechanism of a series of iridium-based photosensitizers with modified C^N and N^N ligands (a2-a6, b1/b1-r and b2/b2-r) by TDDFT/DFT methods. The photophysical properties, including one- and two-photon absorption spectra, frontier molecular orbitals, and singlet and triplet excitation energies, were calculated. Additionally, rate constants for intersystem crossing, fluorescence, and phosphorescence, along with water solubility and lipophilicity metrics (logP), were determined to assess both efficacy and biocompatibility. The results elucidate the modulation roles of the chelated ligands and ancillary ligands in TP-PDT efficiency, indicating that the asymmetric iso-fused-benzene ring modification to the N^N ligand is a robust design strategy for comprehensively enhancing photosensitization performance. Complexes a2, b2 and b1-r show greater promise as candidates for two-photon PDT photosensitizers, owing to their large TPA cross-sections, extended triplet state lifetimes, and balanced water solubility and lipophilicity. Notably, the b1-r complex can undergo both Type I and Type II PDT photosensitization mechanisms, which will help address the issue of drug resistance arising from the hypoxic environment in deep-seated tumors.

Infrared breast thermography measures skin-surface temperature, but heat diffusion and thermal screening can obscure intratumoral physiology. We introduce a tolerance-aware, full-profile framework to determine when distinct intratumoral perfusion patterns become observationally equivalent at the breast surface. We solve a steady-state modified Pennes model for a two-dimensional multilayer breast containing a finite tumor with uniform, rim-enhanced, necrotic-core, or anisotropic perfusion. A conservative finite-difference scheme with a BiCGSTAB solver and an incomplete-LU (IncompleteLUT) preconditioner is verified by mesh refinement and an analytical benchmark. For a representative configuration, equivalent-pair fractions are $2/6$, $5/6$, and $6/6$ at tolerances of $0.020$, $0.050$, and $0.100~^\circ\mathrm{C}$, respectively. Noise tests show that classification depends on how profile-level uncertainty is modeled. Matching tumor-averaged perfusion reduces rim-enhanced–uniform and necrotic-core–uniform profile distances by $97.7\%$ and $99.0\%$, respectively, whereas anisotropic perfusion remains more distinguishable. Greater tumor depth increases thermal-signature equivalence, while larger diameter enhances distinguishability. Among four parameter groups, heat generation has the strongest positive influence on mean profile separation. For fixed representative geometry and perfusion-pattern shapes, a 300-point Latin-hypercube analysis with independent uniform $\pm20\%$ perturbations of these parameter groups finds that about $79\%$ of sampled configurations retain at least one equivalent pair at $\varepsilon_T=0.020~^\circ\mathrm{C}$. Our results show that a detectable static surface anomaly does not uniquely determine its internal perfusion structure.

Photonic flat bands offer significant potential for strong light-matter interactions, nonlinear optics, and sensing thanks to their localization of light and high density of states. However, realizing these flat bands typically requires intricate fabrication, perfect alignment and/or specialized geometries, and a general design strategy is missing. In this work, we demonstrate a simple yet versatile strategy to engineer radiative flat bands above the light line, using only a single-layer honeycomb photonic crystal slab. By applying a density wave like geometric perturbation-a spatially periodic displacement of the lattice air holes-we couple intrinsic flat band states from below the light cone into the radiative continuum. This structural modulation creates a highly anisotropic band structure that exhibits linear, Dirac-like dispersion in one direction and nearly flat dispersion in the orthogonal direction, forming an extended van Hove singularity at band extrema. Furthermore, by tuning the Fourier components of the modulation, we can manipulate the Dirac mass term to realize band inversion and switch between two topologically distinct phases. As an application, we demonstrate a Jackiw-Rebbi interface state positioned at the junction of two domains with opposite Dirac mass, that also shows flat band dispersion along the interface. This density-wave perturbation approach provides a conceptually clear and fabrication friendly platform for programming complex photonic band dispersions, opening new avenues for both topological photonics and practical flat-band optoelectronic devices.

Previous work showed that ultralight-dark-matter solitons can provide dynamical friction for supermassive black-hole binaries, suppressing low-frequency power in the pulsar-timing-array gravitational-wave background and constraining the particle mass and effective ultralight-dark-matter fraction. Here we extend that analysis by comparing the predictive performance of four models: simplified and realistic ultralight-dark-matter implementations, a phenomenological environmental-hardening model, and a gravitational-wave-only model. We use Bayesian leave-one-out cross-validation on the five lowest pulsar-timing-array frequency bins. The phenomenological model gives the largest expected log predictive density, but its advantage over the other models is not large compared with the estimated standard errors. The current data therefore do not decisively prefer one model overall. The clearest pairwise result is within the ultralight-dark-matter framework: the simplified model outperforms the realistic implementation in all five frequency bins. Current pulsar-timing-array data are therefore compatible with ultralight-dark-matter-induced low-frequency suppression, but do not yet distinguish ultralight-dark-matter significantly from more generic environmental descriptions of supermassive-black-hole-binary evolution.

We introduce a scalable, translationally invariant variational theory for ab initio polarons that remains applicable across coupling regimes without resorting to supercells. Our approach combines a momentum-projected Toyozawa-type wavefunction with a low-rank factorization of the electron-phonon kernel, enabling near-linear scaling with the number of $\mathbf{k}$-points while capturing both delocalized and self-trapped carriers. Benchmarks for the Fröhlich model, LiF, and anatase and rutile TiO$_2$ yield accurate polaron binding energies, thermodynamic-limit band structures, and transparent real-space measures of polaron extent. For LiF, comparison with first-principles diagrammatic Monte Carlo (DiagMC) reveals close agreement for the weak-coupling electron-polaron ground state and band structure. However, in the hole-polaron of LiF, which is in the strong-coupling regime, we found a significant bias in DiagMC results. These results establish momentum-projected variational wavefunctions as a systematically improvable route to thermodynamic limit studies of polarons in real materials.

Stacking multiple SiSiGe channels in advanced logic devices faces severe thermal budget accumulation, which degrades interfaces via Ge-Si interdiffusion and strain relaxation.This strategy lowers the Ge diffusion coefficient to 5.6-7% of its value at 650C (Arrhenius estimate), suppressing interdiffusion and preserving pseudomorphic strain. The 4 + 4 channel stack exhibits clear XRD satellite peaks, fully coherent strain state (reciprocal space mapping), sharp interfaces (1.5-2.6 nm transition width) and low RMS roughness (0.08 nm). Quantitative analysis from bottom to top reveals that prolonged high-temperature exposure broadens bottom interfaces and dilutes Ge concentration (from 20% to 18.5%), while the top stack maintains design targets. This work provides a process-physics understanding of thermal budget effects in multi-channel superlattices and establishes a high-quality material foundation for advanced logic devices beyond 2 nm node.

Balmer-dominated shocks in Type Ia supernova remnants offer powerful probes into collisionless shock physics and hints towards supernova progenitor environments. Prior studies focused on the hydrogen Balmer lines, which manifest as a superposition of broad and narrow emission lines. Using integral-field spectroscopy with MUSE, we discovered broad and narrow helium emission lines from Balmer-dominated filaments of three Type Ia supernovae remnants in the Large Magellanic Cloud: SNR 0509-67.5, SNR 0519-69.0 and N103B. We detect broad and narrow He~i 5876~Å~,7065~Å\ emission in SNR 0519 and N103B and He ii 8236~Å\ in SNR 0519. In SNR 0509 we detect narrow He~i 5015~Å, 6678~Å, 7065~Å\ and 7281~Å, with only 7065~Å~ exhibiting a broad component. The detection of narrow He\,ii challenges existing shock models, where such emission is not expected, and may indicate either incomplete ion-ion equilibration behind the shock or an origin in shock precursors. For SNR 0509 and N103B, the neutral He/H line ratios indicate enhanced helium abundances, whereas SNR 0519 is consistent with the primordial He/H value. We therefore propose helium emission in Balmer-dominated shocks as a new diagnostic of shock physics and Type Ia supernova circumstellar environments. Although our modeling is primarily a proof of concept, it demonstrates the possibility to infer the total He-to-H abundance ratio, with dominant uncertainties arising from the assumed initial ionization fractions. Despite the uncertainties, we demonstrate that narrow helium lines can serve as effective probes of circumstellar conditions and progenitor evolution when analysed alongside reliable constraints on the preshock neutral H/He abundance ratio.

We propose a mixed quantum-classical hydrodynamic framework to model short-time inertial effects in the non-adiabatic evolution of a quantum solute coupled to a classical polar solvent. Drawing upon the work of Burghardt and Bagchi [Chem. Phys. 329 (2006), 343], we employ the Hamiltonian approach to incorporate consistent backreaction and preserve quantum decoherence beyond standard Ehrenfest dynamics. The solvent is treated as an ideal polar fluid and the quantum solute state is coupled to both the position and molecular orientation coordinates of the liquid. This approach retains essential solute-solvent correlations while reducing the computational complexity of previous approaches. We further incorporate dissipative terms to capture both inertial effects and polarization relaxation. After establishing the general setting for non-local dielectric continua, the Marcus local approximation is integrated into the model thereby extending traditional solvation theory to account for collective fluid sloshing on fast timescales.

A photonic time crystal usually writes a clock into a medium. Here one clock does more than program the medium: it seeds a quasiperiodic temporal order that the nonlinear medium selects for itself. In a guided-wave lattice of nonlinear dipoles, a single-tone pump modulates the polarization sector, while Maxwell–polarization back-action selects two response frequencies whose only resolved low-order relation is the pump-locked sum condition. Their sum phase locks to the pump and the complementary phase winds, producing a photonic discrete time quasicrystal with torus-like phase dynamics and a discrete combination spectrum. Site-resolved measurements show locked-phase coherence across the measured lattice sites over a finite control-parameter window. These results establish a route from externally programmed time-varying media to self-organized temporal order in nonlinear photonic systems.

Phase-coherent optical frequency transfer is essential for optical clock networking, relativistic geodesy, and distributed precision metrology. However, realizing coherent optical networks spanning thousands of kilometers in standard single-mode fiber (SMF) generally requires densely distributed amplifiers or repeater stations together with complex operational control, while long-term instability remains limited by thermally driven residual phase fluctuations. Here we show that hollow-core fiber (HCF) can simultaneously improve transfer instability and relax the reach limitation of long-span optical frequency transfer. Compared with SMF, HCF exhibits lower fiber-induced phase noise and shorter propagation delay, supporting improved short-term instability, while its much lower thermal sensitivity supports nearly one-order-of-magnitude better long-term instability. In addition, for long-haul HCF links, no observable stimulated Brillouin scattering induced saturation is found up to the maximum available injected power of 34 dBm, whereas the threshold of an equal-length SMF link remains only a few dBm. Together with the lower attenuation achievable in modern HCF, this enables ultra-long single-span optical frequency transfer. Using a 152 km HCF link with an average attenuation of 0.18 dB/km, we demonstrate single-span optical frequency transfer, achieving a fractional frequency instability of 7.3 x 10^-21 at 10,000 s and a fractional uncertainty of 1.8 x 10^-20. These results establish HCF as a transmission medium that simultaneously improves instability and extends single-span reach, opening a practical route toward future intercontinental optical frequency networks with ultrahigh precision.

Ga$_2$O$_3$/SiC heterointegration is attractive for ultra-wide-bandgap power electronics, but interfacial thermal boundary conductance (TBC) remains a major heat-removal bottleneck. Direct experimental access to intrinsic atomistic interfacial transport remains limited, particularly for ideally synthesized materials with defect-free interfacial contact. First-principles simulations are too expensive at relevant length and time scales, while empirical Molecular Dynamics (MD) potentials often lack transferability across oxide and carbide bonding environments. We develop a unified feedforward neural network potential and validate it against density-functional data, bulk phonon dispersions, and anisotropic thermal-conductivity trends in both $\beta$-Ga$_2$O$_3$ and SiC. Nonequilibrium simulations show that TBC decreases with transport length, increases with temperature, and is consistently higher for Ga$_2$O$_3$$(\bar{2}01)$/SiC(0001) than for Ga$_2$O$_3$(100)/SiC(0001). These trends are explained by attenuation of long-mean-free-path carriers, enhanced incoherent and anharmonic interfacial exchange within broadly unchanged spectral channels, and stronger bonding and vibrational coupling at the $(\bar{2}01)$ interface. The results show how a single transferable feedforward neural network potential can enable large-scale transport prediction and physics-grounded mechanistic understanding of thermal boundary conductance. Code for NEP training and simulation workflows is available at the project repository https://github.com/knowhow07/TBC_Ga2O3_SiC.git

Tunneling ionization in static or slowly varying electric fields is a cornerstone of strong-field physics and provides the entry point for semiclassical descriptions of above-threshold ionization and high-harmonic generation. In conventional quantum mechanics, the Perelomov–Popov–Terent'ev (PPT) theory and its Ammosov–Delone–Krainov (ADK) form yield an ionization rate whose defining feature is an exponential dependence governed by an under-barrier (imaginary-time) action. Here we develop an analytical ADK-like tunneling model within space-fractional quantum mechanics, where the quadratic kinetic energy is replaced by the Riesz fractional Laplacian of order $1<\alpha\le2$. Working in a static electric field in the length gauge, we derive a closed-form tunneling exponent for a triangular exit barrier. The fractional kinetic operator deforms the conventional $I_p^{3/2}$ scaling to $I_p^{1+1/\alpha}$ and introduces a characteristic $\sin(\pi/\alpha)$ factor encoding the complex-phase structure associated with nonlocal dispersion. We position this benchmark relative to prior tunneling studies in fractional quantum mechanics (primarily scattering through model barriers and fractal potentials) and provide a validation protocol for testing the exponent in time-dependent simulations of the fractional Schrödinger equation under a constant field. The result establishes a transparent reference for static-field ionization in nonlocal quantum dynamics and a baseline for strong-field approaches extensions.

Proton radiography of laser direct-drive spherical implosions has shown anomalous structures that correspond to strong electric or magnetic fields extending throughout the corona. These fields have the ability to affect laser-target interactions and act as an energy sink. To better understand the these fields, simplified experiments were conducted in planar geometry on the OMEGA EP laser at the Laboratory for Laser Energetics. Varying target material, target size, pulse shape, and intensity, and measured the field structure using dual-axis proton radiography and a 4w probe. Proton radiographs were analyzed and quantitatively demonstrate that the growth of these features is dominated by laser energy and target Z. The data strongly supports that a secondary instability as a consequence of the expansion driven Weibel instability in these interactions is the primary driver for these fields.

The concept of "thermal inductance" expands the options of thermal circuit design. However, the inductive component is the only missing components in thermal circuits, unlike their electromagnetic counterparts. Herein, we report an electrically controllable reverse heat flow, in which heat flows from a low-temperature side to a high-temperature side locally and temporarily in a single material by imposing thermal inertia and an ac current. This effect can be regarded as an equivalent of the "thermoinductive" effect induced by the Peltier effect. We derive an exact solution indicating that this reverse heat flow occurs universally in solid-state systems and that it is considerably enhanced by thermoelectric properties. A local cooling of 25 mK is demonstrated in (Bi,Sb)2Te3, which is explained by our exact solution. This effect can be directly applied to the potential fabrication of a "thermoinductor" in thermal circuits.

The numerical modeling of hydraulic jumps remains challenging due to complex interactions among free-surface deformation, air entrainment and detrainment, and turbulent bubble transport. Whereas accurate prediction of these flows is essential for the design of hydraulic structures, existing high-fidelity tools require prohibitive computational resources for engineering applications. This study implements a three-phase mixture model based on an Unsteady Reynolds-Averaged Navier Stokes (URANS) framework, to numerically simulate flow and air entrainment across twelve hydraulic jumps with Froude numbers ranging from $1.98$ to $8.48$, representing the first systematic analysis for such a comprehensive range of Froude numbers. The model accurately represents time-averaged velocity fields and air concentration profiles, as well as dynamic features including jump toe oscillation and free-surface deformation, showing good agreement with experimental data from seven facilities. Compared to Improved Delayed Detached Eddy Simulations (IDDES), the proposed approach achieves similar accuracy with approximately 400-fold fewer cells and a 300-fold reduction in computational cost. The investigation shows that the selection of turbulence closure affects the accuracy of the prediction of air entrainment. These findings establish the three-phase mixture approach as a practical engineering tool for hydraulic jump simulation, offering an effective balance of accuracy and computational cost.

Floquet engineering of cavity magnon-polaritons by periodically modulating the magnon frequency has recently attracted much interest as a way to manipulate the energy spectrum of magnon-photon hybrid systems. However, modulating the frequency of magnons by a time-varying bias magnetic field can be challenging. We demonstrate cavity magnon-polariton Floquet engineering by modulating the microwave cavity frequency, allowing large modulation depth and bandwidth. We apply commensurate two-frequency Floquet modulations with the higher frequency at twice and three times the lower frequency, and demonstrate that the resulting spectrum depends on the relative amplitude and phase of the two drive tones. In comparison with single-frequency Floquet modulations, the spectrum has qualitatively different features; in particular, new anticrossings appear between previously uncoupled sidebands. Our platform offers an alternative way to manipulate Floquet quasi-energy levels in hybrid systems.

Rapidly rotating Rayleigh-Bénard convection admits a class of exact steady single-mode solutions describing high-amplitude convection cells. Using a matched asymptotic analysis in the high-Rayleigh-number limit, we obtain a rigorous characterization of their bulk and boundary-layer structure, yielding explicit scaling laws for the Nusselt and Reynolds numbers, including their dependence on the horizontal wavenumber. We show that, for suitable wavenumbers, these solutions attain the diffusivity-free ultimate scalings frequently assumed for geophysical and astrophysical convection, with additional enhancing logarithmic corrections. This reveals a specific mechanism through which rapidly rotating convection can approach ultimate heat transport via coherent columnar structures with well-defined horizontal scales.

Particle-mesh methods, such as the particle-in-cell (PIC) method, cannot retain exact pairwise interaction at sub-cell scales. For dense nonneutral relativistic electron bunches, this makes it difficult to accurately capture the inter-particle electromagnetic interaction and the associated bunch divergence. In this work, the previously developed electromagnetic particle-particle (EM-PP) model for relativistic two-particle interaction is extended to many-particle electron bunch transport in the Earth's magnetosphere. The method combines the Liénard–Wiechert fields, an improved retarded-time evaluation procedure, and a relativistic particle pusher, and adopts a two-stage strategy to couple the dense early self-field-dominated evolution to the later long-range geomagnetic-field-controlled transport. The method provides a practical mesh-free approach for accurately simulating long-range transport of relativistic electron bunches when short-range electromagnetic interaction is important.

Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories. Neural operators provide inexpensive autoregressive forecasts but can drift in turbulent regimes, whereas rolling diffusion and latent generative surrogates can represent stochastic transitions at the cost of multi-step denoising, noise-schedule design, or auxiliary compression models. We propose MeanFlow Long-term Invariant Spatiotemporal Consistency Autoregressive Models (MeLISA), a latent-free autoregressive generative surrogate built on pixel-space MeanFlow. MeLISA defines a blockwise stochastic transition kernel that generates each forecast block with a single model evaluation, avoiding latent encoders and iterative diffusion solvers at inference time. To stabilize long-horizon rollouts, MeLISA combines a Window-Consistency MeanFlow objective that learns conditional spatiotemporal generation from partially observed temporal windows with a Time Increment Consistency loss that constrains multi-lag finite increments and targets temporal-correlation structure. We evaluate MeLISA with compact UNet and scalable DiT backbones on two high-resolution benchmarks, extended 2D Kolmogorov flow at $256 \times 256$ and turbulent channel-flow slice at $192 \times 192$. MeLISA outperforms neural-operator baselines on short-term forecasting accuracy and long-horizon statistical metrics, including energy spectra, turbulent kinetic energy, and mixing-rate-related dynamics, while achieving inference speeds comparable to, and in some cases faster than, neural operators. To our knowledge, this is the first method for high-resolution one-step generation for physical dynamical systems with performance comparable to state-of-the-art deterministic surrogates.

We present a multi-source traffic dataset derived from Automatic Vehicle Identification (AVI) recordings in Padua, Italy, spanning from February 2026 to August 2026. The dataset combines traffic volume time series, aggregated at 10-minute intervals, with time-varying trajectory-based flow statistics including transition probability matrices, average travel times, and flow residuals. To enrich the traffic measurements with urban contextual information, we integrate Points Of Interest (POIs), demographic data, meteorological variables, and road infrastructure data. All components are accessible through a Python class that loads temporal and contextual data exploiting a spatio-temporal graph representation. Validation analyses confirm that the dataset captures expected traffic patterns, such as morning and evening rush hours, as well as weekdays vs. weekend days traffic routines.

Folk meteorological proverbs encode centuries of empirical observation by agricultural communities. Two Hebrew proverbs link lunar calendar anchor days to monthly winter rainfall: (i) "If Rosh Chodesh is rainy, the whole month is rainy" and (ii) "If it rains on Shabbat Mevarechim, the whole month is rainy." Shabbat Mevarechim is the last Saturday before each new Hebrew month, preceding Rosh Chodesh by one to seven days. The first proverb is widely known; the second circulates in Hasidic oral tradition with no identified written source. Both have never been formally tested. We analyse 75 years (1950-2024) of daily precipitation data from seven Israeli cities across three climatic regions, comprising 191,758 station-days and 2,422 Hebrew-month observations during the winter rainy season (Marcheshvan-Adar). A rainy Rosh Chodesh increases the probability of a rainy month from 22.2% to 38.6% (lift +16.4 percentage points; chi-square = 57.8, p = 2.9e-14; Bayes factor 1.81). A rainy Shabbat Mevarechim produces a similar effect (lift +16.5 percentage points, p = 8.0e-13), despite preceding Rosh Chodesh by up to seven days. The effect decays with lag and mirrors daily rainfall autocorrelation (r = 0.35-0.44 at lag 1; ~0 at lag 7), consistent with Mediterranean cyclone persistence. A bootstrap permutation test (p < 1e-4) and a 15-year rolling analysis show declining predictive power (-0.20 percentage points per year, p < 0.001), consistent with shortening precipitation events under warming climate conditions. Both proverbs encode real but probabilistic meteorological signals whose reliability is decreasing over time.

High-fidelity experimental characterization of turbulent premixed flames remains limited by the cost and complexity of advanced diagnostics, particularly under elevated pressures and intense turbulence where measurements of coupled flame morphology and burning dynamics are sparse. Here, we develop a hierarchical multi-fidelity neural network framework (MuFiNNs) to address this challenge by integrating sparse high-fidelity experimental data with structured low-fidelity representations encoding dominant physical trends. The framework combines hierarchical low-fidelity construction with nonlinear multi-fidelity correction to learn coupled geometric and reactive flame behavior while recovering discrepancies that simplified models alone cannot capture. The methodology is applied to expanding turbulent premixed flames to predict three-dimensional flame wrinkling dynamics and turbulent mass burning velocity across varying fuels, pressures, and turbulence intensities. Using experimentally informed low-fidelity trend models with sparse high-fidelity measurements, MuFiNNs accurately reconstruct observed flame behavior, enable interpolation across unseen operating conditions, and demonstrate robust extrapolation beyond the training domain. Importantly, the framework remains effective in noisy, weakly structured, or experimentally inaccessible regimes where conventional data-driven approaches often fail. These results show that hierarchical multi-fidelity learning provides a scalable and physically grounded strategy for predictive combustion modeling in data-limited regimes. More broadly, this work establishes multi-fidelity scientific machine learning as a practical framework for extracting physically meaningful predictive models from sparse experiments, particularly for instability-dominated and turbulence-sensitive reactive flows where high-fidelity data acquisition is demanding.

This study presents a classroom-friendly method for measuring the coefficient of viscosity of a liquid using a smartphone s accelerometer sensor. A metallic ball tied with a spring-mass system and submerged in mustard oil undergoes damped oscillations due to viscous forces. The Phyphox app is used to record the temporal variation of acceleration, from which the damping constant is calculated to determine the coefficient of viscosity of the oil. The experimentally obtained value is further validated using the Tracker app, and this value is shown to be in close agreement with the standard literature. This method provides an accurate, low-cost experiment ideal for educational settings, utilizing smartphone sensors for viscosity measurement.

In this paper, we have proposed a simple method of measuring the density of a solid material. We have utilized the pressure sensor of a smartphone as a pressure-measuring device. By measuring the values of pressure when a solid object is in air and also in the fully immersed condition in a non-reactive liquid, we have determined the density of the object.

In this paper, we present a hands-on activity designed to verify the dependence of the magnetic force between two identical N35 neodymium disc magnets on their separation distance. Utilizing a weight-measuring device incorporating a smartphone pressure sensor placed inside an inflated Ziplock bag, with a glass plate ensuring perfect contact, we measured the magnetic force with high precision. Our results confirm the established inverse fourth power relationship between magnetic force and distance. The linear plot of magnetic force versus the inverse fourth power of distance corroborates the corresponding theoretical model. From the slope of this linear plot, we have calculated the magnetic dipole moment of each magnet, providing a practical validation of theoretical predictions. This methodology also offers an effective approach for educational and experimental verification of magnetic interactions.

Bulk niobium has long been the material of choice for superconducting radio-frequency applications. An alternative approach is the superconductor-insulator-superconductor multilayer structure, which enables the use of brittle high-$T_c$ materials such as NbTiN. At present, SIS coatings are limited to flat samples, with the single-cell TESLA cavity representing a key milestone. Extending coating processes to non-flat geometries is expected to introduce macroscopic inhomogeneities in coating thickness. We model these variations using Gaussian random fields parametrized by a length scale, and generated by solving a stochastic partial differential equation. The resulting field is incorporated into the boundary condition of the cavity eigenvalue problem, from which quantities of interest – such as resonant frequency and quality factor – are computed. This procedure is repeated for eight length scales, with \num{2048} samples per length scale, where the resulting quality factors are recorded. Our results show that the quality factors follow a normal distribution. The standard deviation increases with the length scale and can be statistically distinguished. In contrast, the mean values remain largely unchanged, with only a few significant differences. In extreme cases, depending on the length scale, the quality factor may differ from the uniform case by \SIrange{2}{6}{\percent}.

Commercial treatment planning systems for electron FLASH radiotherapy are unavailable, and the dosimetric precision required for ultra-high dose rate delivery makes Monte Carlo (MC) simulation the gold standard approach. This work establishes a methodology for generating pulse-width-specific phase space (PHSP) files for the Mobetron UHDR system (9 MeV), accounting for systematic beam quality shifts caused by RF waveguide loading across pulse widths of 1.2-4.0 microsecond. Using GAMOS 6.2.0, source parameters were iteratively refined against experimental targets: mean energy was optimized by matching phantom-measured R50 in the fall-off region, while energy spread was refined using surface dose and build-up gradients. Relationships derived from a mid-range 6 cm aperture were applied across all clinical configurations (2.5-10 cm) to test the aperture-independence of beam loading effects. Mean energy decreased exponentially from 9.58 to 9.04 MeV (R^2=0.99) with increasing pulse width, while energy spread increased quadratically (R^2=0.99), with a strong negative correlation (r=-0.98). Cross-aperture validation confirmed that energy shifts are independent of downstream collimation. The geometric mean pulse width (2.28 microsecond) was evaluated as a universal clinical reference, yielding 9.32 MeV mean energy. Across experimental extremes, R50 deviations were within 1.3 mm and critical depth-dose parameters remained within 2.0 mm, meeting AAPM TG-106 tolerances. Validated regression models enable beam parameter prediction at arbitrary pulse widths, and the universal reference reduces computational burden by 75% while maintaining clinical accuracy.

We demonstrate that the conversion gain of a superconducting hot-electron bolometer (HEB) mixer can be increased by biasing the device within the negative differential resistance (NDR) region of its current-voltage characteristic. Although NDR biasing has historically been avoided due to MHz-range resistive oscillations, we show that these oscillations arise from an LC resonance formed by the bias-T inductance and the effective thermal capacitance of the HEB. By applying stability criteria analogous to those developed for tunnel diodes, we redesigned the embedding circuit to suppress this resonance and achieve stable NDR operation. Direct measurements using two monochromatic 2.5-THz sources confirm the predicted gain enhancement. These results establish NDR biasing as a viable method for improving HEB mixer performance and motivate further studies of noise behavior and circuit optimization.

We compare different Poisson solvers within the context of an electrostatic Vlasov-Poisson system. These schemes are implemented as part of the IPPL (Independent Parallel Particle Layer) library (Frey et al., 2024), which provides performance portable and dimension independent building blocks for scientific simulations requiring particle-mesh methods, with Eulerian (mesh-based) and Lagrangian (particle-based) approaches. The simulation used to compare the performance and portability of the schemes is Landau damping, part of a set of mini-applications implemented to benchmark and showcase the capabilities of the IPPL library (Muralikrishnan et al., 2024). We use grid-sizes of $512^3$ and $1024^3$ with 8 particles per cell, running with different algorithms in the solve phase of the Particle-in-Cell (PIC) loop: a Fast Fourier Transform (FFT) pseudo-spectral solver, a matrix-free finite difference Preconditioned Conjugate Gradient (PCG) solver, and a matrix-free Finite Element (FEM) solver. We also compare these PIC schemes to the novel Particle-in-Fourier (PIF) scheme, which performs interpolations using non-uniform FFTs thereby avoiding a grid in the real space. We obtain results on different computing architectures, such as AMD GPUs (LUMI at CSC), and Nvidia GPUs (Alps at CSCS and JUWELS Booster at Jülich Supercomputing Center), showcasing portability. In terms of absolute time the FFT solver is advantageous, but is limited in its applicability. All other field solvers in the PIC scheme are an order-of-magnitude more expensive in terms of time, but scale similarly to the FFT case in the electrostatic PIC context. The PIF scheme serves as a high fidelity alternative to standard PIC, and while it is costlier than the FFT-based PIC scheme, it shows excellent scalability on all the architectures.