Papers

The high-frequency sensitivity of laser-interferometric gravitational-wave detectors is limited by quantum shot noise. Increasing the circulating optical power reduces shot noise, but is constrained by thermal effects and optomechanical instabilities. We propose cyclic-quadrature intracavity signal amplification, in which an optical parametric amplifier (OPA) inside a detuned signal-recycling cavity is used as a phase-sensitive amplifier of the gravitational-wave signal quadrature. By detuning the signal-recycling cavity for $\pi$/4, the optical quadratures rotate by $\pi$/2 on each round trip, so a signal generated in the phase quadrature appears in the readout phase quadrature only after odd-numbered passes through the OPA. Successive contributions to the readout phase quadrature, which are separated by two round trips, have alternating signs, making this two-round-trip evolution anti-resonant. During each two-round-trip cycle, however, the same field experiences one amplification and one deamplification, so neither vacuum squeezing nor parametric gain accumulates. Despite the destructive interference between signal contributions separated by two round trips, the OPA increases the signal component extracted through the output coupler, thereby improving the signal-to-noise ratio. When the OPA gain is sufficiently large, the response approaches that of an interferometer with an effective power enhancement of $1/\tau^2$, where $\tau$ is the amplitude transmissivity of the quadrature-rotation mirror that forms the amplifier cavity. We apply the proposed scheme to a current gravitational-wave detector with a near-future upgrade and show that it improves the quantum-noise-limited sensitivity over a broad frequency range extending into the kilohertz band.

In previous work, we introduced a semi-analytical method for computing time-domain solutions of linearized Vlasov problems. Rather than representing the plasma response as a sum of residues associated with Landau poles, the method constructs a regularized frequency-domain response spectrum that is subsequently inverted numerically. Explicit applications have so far been limited to unmagnetized plasmas. In this work, we extend the construction to plasmas with a uniform background magnetic field and a Maxwellian equilibrium distribution. We apply the resulting formulation to the electrostatic ion-Bernstein density response of a plasma with kinetic ions and adiabatic electrons, and provide error estimates for truncating both the spectral integration domain and the cyclotron-harmonic expansion. The solution serves as a time-domain reference for damped finite-$k_\parallel$ regimes, in which dispersion-relation roots alone are insufficient for pointwise-in-time verification of simulation codes. Finally, we indicate how the framework can be extended to fully electromagnetic problems.

Satellite precipitation products such as IMERG exhibit biases that vary with terrain, season, and precipitation regime, leaving the applicability boundaries of machine learning correction unclear. This study proposes the Terrain-Moisture-Intensity (TMI) framework, centered on mechanism purity, extending the correction problem from purely algorithmic optimization to physical consistency diagnosis. A proof-of-concept study in Hunan Province employs IMERG V07, SRTM DEM, and ERA5 variables (tcwv, u10, v10). Ablation results indicate that, under the conditions of this study, terrain-moisture relationships are predominantly additive: RF-Full yields merely +0.001 R^2 gain over LR-Full, while bias rises to 1.282 mm d^-1; MAE decreases by approximately 14%, reflecting a trade-off between tail-fitting improvement and mean shift. SHAP diagnostics identify three categories of boundaries. Spatially, Central Hunan exhibits significant degradation (R^2=0.133) despite strong variable activation, consistent with mechanism fragmentation induced by mixed terrain. Temporally, u10 undergoes directional reversal between summer and spring (+0.096 to -0.156), presenting "silent failure." Extreme precipitation (>=50 mm d^-1) approximates a mechanism saturation frontier rather than isolated out-of-distribution samples, with DEM showing the largest relative amplification in SHAP disorder (+150%). The results demonstrate that machine learning correction performance is primarily constrained by mechanism purity. A pre-registered cross-regional test (Hunan, Guangxi, Guangdong) confirms this screening capability out of sample: a priori coherence proxies predict correction efficiency with a mean absolute error of 2.6 percentage points, while the transfer-versus-retraining contrast separates mechanism mismatch (coastal Guangdong) from portability (Guangxi), establishing the framework as a validated applicability screen.

Reduced-frame structured illumination microscopy (SIM) is attractive for live-cell imaging because it can improve temporal throughput and reduce photobleaching, but incomplete phase sampling makes reconstruction unstable and computationally demanding. Here we present URA-SIM, a unified reduced-acquisition framework that turns fixed reduced-frame measurements into pipeline- compatible raw stacks through model-consistent phase-domain completion. Instead of solving a large object-level inverse problem or replacing established SIM reconstruction, URA-SIM estimates the missing phase content on the low-dimensional phase-harmonic manifold required by the target modality and then delegates order separation and image formation to classical reconstruction pipeline. This design combines three practical advantages: fidelity from the SIM forward structure, lightweight online computation, and direct compatibility with existing reconstruction workflows. For 2D-SIM, URA-SIM uses the first-harmonic phase structure of three-phase SIM to estimate a shared zero-order field and complete missing phase samples by direction-wise harmonic fitting. On calibration and biological 2D-SIM data, reduced-frame reconstructions preserve resolvable structures and remain competitive on COS7 mitochondria comparison data. In live-cell COS7 mitochondria imaging, URA-SIM reconstructs each time point from five acquired raw frames and resolves mitochondrial cristae across different temporal sampling regimes. Experiments on 3D-SIM and nonlinear SIM further show that the same design principle can be transferred when the phase model and reconstruction-pipeline interface are adapted to the target modality. These results support URA-SIM as a transparent, model-consistent and computationally lightweight route from fixed reduced-frame acquisition to classical SIM reconstruction workflows.

Nuclear batteries powered by alpha decay have been deployed successfully for over 60 years, on a worldwide $^{238}$Pu supply of kilograms per year. We show that the 14 MeV neutrons of a single deuterium-tritium fusion plant can produce alpha emitter battery fuels up to tons per year, in three classes: fuels with completely new production pathways ($^{236}$Pu, $^{227}$Ac, $^{210}$Pb), fuels previously proposed whose scarce feedstock the same pathways now breed at scale ($^{232}$U, $^{228}$Th), and the established $^{238}$Pu. OpenMC simulations of actinide channels in a tokamak blanket give, per GW yr of fusion: 11 to 57 kg of $^{236}$Pu, whose chain releases 18 GJ per gram over a century, ending at stable $^{208}$Pb, plus up to 5.2 t of co-product $^{238}$Pu; up to 1.4 t of $^{231}$Pa from thorium, and, from channel with $^{231}$Pa feedstock, up to $\sim$15 t of $^{232}$U or $\sim$122 kg of $^{210}$Pb, with $^{227}$Ac produced at 21 g/yr per tonne of $^{231}$Pa. Neutron capture also upgrades $^{241}$Am to a $^{242}$Cm/$^{242m}$Am/$^{241}$Am/$^{238}$Pu blend with up to 10 times higher power density. The same $^{236}$Pu and $^{232}$U also serve as proliferation safeguards: the $^{237}$Np, $^{232}$Th, and $^{231}$Pa channel products are self-protecting, the plutonium by $^{236}$Pu and $^{238}$Pu decay heat and the 2.6 MeV gammas from $^{208}$Tl content, and similarly the uranium from its $^{232}$U. Many of these fuels ($^{236}$Pu, $^{232}$U, $^{228}$Th, $^{227}$Ac) have an order of magnitude higher power and energy density than current alpha emitters, and at human spaceflight-relevant doses the $^{227}$Ac and $^{210}$Pb chains need less shield mass than $^{238}$Pu or $^{241}$Am above a few hundred watts. Fusion neutrons could therefore enable nuclear batteries at the kilowatt to megawatt scale and unlock new possibilities for power sources requiring exceptionally high energy density.

Binarized Fourier single-pixel imaging (FSI) takes full advantage of the high modulation speed of digital micromirror devices by applying Floyd-Steinberg spatial dithering to binarize grayscale Fourier patterns. However, the use of the spatial dithering introduces substantial quantization errors, leading to decreasing imaging quality. Here, we propose a binarization method for grayscale Fourier patterns based on exponential multi-graylevel computational-weighted dithering, aimed at reducing quantization errors and then enhancing the imaging quality of binarized FSI. The proposed method quantizes Fourier patterns into $2^{R}$ values $\{0, 1/(R-1), 2/(R-1)... 1\}$ and then decomposes them into binarized patterns. Both simulation and experimental results demonstrate that the method significantly reduces quantization errors in Fourier coefficients acquisition and improves imaging quality. The mean absolute percentage error of Fourier coefficients decreases from $194\%$ to $28\%$ and the structural similarity of reconstructed images ($256\times256$ pixels) improves from 0.430 to 0.971, a $126\%$ enhancement compared to the conventional method. Lateral resolution of this proposed method almost approaches the theoretical lateral resolution limit calculated by Rayleigh Criterion.

Fast and wide-range wavelength switching is desirable for optical communications and photonic systems that are frequency-agile. However, thermo-optic (TO)-tuned integrated lasers often have limited switching times and tuning rates. This study demonstrates a hybrid-integrated III-V/Si3N4 external-cavity laser (ECL), combining a dual-microring Vernier filter with thermal pumping to give wide-range and fast wavelength control. The ECL provides single-mode static lasing wavelength tuning in the 1486-1614 nm range. Impulsive thermal pumping that is applied through microheaters with shorter duration and higher amplitude accelerates the switching time. A simple first-order thermal fit reproduces the measurements well, indicating that the TO-tuning dynamics are highly predictable. Consequently, two-step pulse thermal pumping is applied to the on-chip microheaters to exploit the initial quasi-linear heating transient and sustain the target wavelength at a subsequent equilibrium. The results show that 101 and 104 nm red- and blue-shift switches are achieved with quasi-linear tuning rates of 8.91 and 9.68 nm/us, respectively. This approach provides a practical route toward fast wavelength switching in TO-tuned ECLs, potentially extending their applicability within frequency-agile systems, such as wavelength-division multiplexed transceivers.

How much work can an agent extract from a temporal sequence of quantum states when it can only operate online under causal constraints—deciding which energy extraction method to use with knowledge of what it has observed before? Here, we study this problem in the context of quantum state sequences that are potentially non-Markovian—generated by some underlying hidden Markov machine that the agent cannot observe. Using techniques from dynamic programming and computational mechanics, we present a method to identify the provably optimal agent strategy, with time complexity that scales linearly with sequence length. This motivates us to introduce the maximum work such agents can extract—time-ordered free energy(TOFE)—as a fundamental measure of free energy available in a temporally correlated quantum system subject to causal considerations.

Particle resuspension models are commonly evaluated using the macroscopic resuspended fraction, although this integrated observable may conceal the temporal dynamics leading to detachment. Here, a second-order Markovian Lagrangian stochastic model is developed by augmenting the angular-velocity state with a finite-correlated tangential acceleration. The model is examined over turbulent channel flows with ($Re_\tau \in [60, 430]$) and compared with an established first-order formulation. The two models produce nearly overlapping resuspended fractions, revealing a macroscopic degeneracy between distinct stochastic descriptions. Multiscale trajectory statistics break this degeneracy. The acceleration-augmented formulation changes the short-time regularity from $S_2(\tau) \propto \tau$ to $S_2(\tau) \propto \tau^2$, sustains angular-velocity correlation, and produces stronger directional asymmetry and heavier increment tails. The survivor-conditioned mean square displacement exposes a Reynolds-number-dependent anomalous-transport window, in which the attached-particle ensemble grows more rapidly than the diffusive reference and locally approaches ballistic and super-ballistic scaling before crossing toward diffusion-like transport. This finite-time pathway records how particles approach detachment and is compressed out of the macroscopic resuspended fraction. At $Re_\tau \approx 60$, the trajectories enter a distinct low-Reynolds-number statistical state characterized by converging velocity and acceleration decorrelation and near-Gaussian increments. These results establish multiscale trajectory statistics as essential discriminants between stochastic resuspension models and identify finite acceleration correlation as a source of dynamical information beyond macroscopic detachment kinetics.

Time-resolved infrared spectroscopy probes protein dynamics over timescales spanning more than ten orders of magnitude, yet the molecular motions underlying the observed kinetic signatures have remained elusive. Here we combine transient infrared spectroscopy with nonequilibrium molecular dynamics simulations to establish a direct connection between experimental relaxation times and local structural motions. Studying single-domain allosteric proteins, we find that inter-residue contact distances provide the structural representation that most faithfully reproduces the experimental dynamics. Correlation analysis identifies localized networks of coordinated contacts that mediate communication between secondary-structure elements. The characteristic timescales of these contact networks quantitatively match the experimentally observed relaxation processes, enabling each kinetic step to be assigned to a specific molecular motion. Applied to allosteric signal propagation in PDZ3 and photoinduced ligand unbinding in PDZ2, this framework provides an atomistic picture of hierarchical protein relaxation and establishes a general framework for connecting transient infrared spectroscopy with the molecular mechanisms of protein dynamics.

We introduce a bioinspired microactuator fabricated via a one-step meniscus guided 3D nanoprinting technique. This technique directly produces a freestanding three dimensional composite architecture by integrating a rigid nanoparticle framework with a hygroscopic polymer matrix in a single, assembly free process. Inspired by the functional integration of rigid and compliant components in insect exoskeletal joint, our design synergistically combines load-bearing strength and humidity-driven swelling in a single microscale pillar. Comprehensive experiments, including comparative control structures and microscopic analysis, elucidated the actuation mechanism: the nanoparticle scaffold provides mechanical support while the polymeric phase provides volumetric expansion, yielding large reversible elongation under humidity with preserved structural integrity. The resulting AuNP PVP microactuator shows humidity responsive actuation at the microscale, with representative demonstrations of repeated humidity cycling and load lifting up to approximately 800 times its estimated own weight under the tested conditions. Variant geometries such as hinged pillars demonstrate how axial swelling can be converted into bending motion, and an optical configuration shows how actuation can modulate reflected light signals. This nanoprinting approach provides a simple strategy for constructing bioinspired soft microactuators with humidity-responsive deformation and potential applicability in microscale sensing, optical modulation, and adaptive microdevices.

Inverse design of thin-film lithium niobate (TFLN) photonic devices is computationally demanding because optical birefringence and fabrication-induced slanted sidewalls generally require three-dimensional electromagnetic models. We introduce a birefringent effective-index (BEI) method to reduce this problem to two dimensions while retaining polarization-dependent slab confinement and a representative cross section of the etched geometry. The method is integrated with adjoint topology optimization and fabrication constraints to design a 30 x 10 um demultiplexer that routes 1550 and 775 nm light to separate output ports. Quantitative comparisons with three-dimensional finite-difference time-domain simulations establish the accuracy and etch-depth dependence of the reduced model. The fabricated device provides mean signal-to-crosstalk ratios of 13.9 dB across 1540-1560 nm and 13.3 dB across 770-780 nm. A two-stage cascaded configuration increases the output extinction ratio to 26.8 dB in the telecom band and 20.3 dB in the near-visible band. This fabrication-aware reduced-dimensional approach enables optimizations of the multifunctional photonic devices for nonlinear optical and quantum applications on the TFLN platform.

As a non-contact control approach, electric field (EF) can be utilised to drive droplet dynamics on a lubricant-infused surface (LIS), with numerous potential applications ranging from drug manufacturing to 3D printing. However, the resulting droplet dynamics remain poorly understood, especially as there are several possible droplet lubrication states on LIS. Here, we develop a lattice Boltzmann scheme that fully captures the interplay between the interfacial flows and electrohydrodynamics and harness it to investigate EF driven droplets on micropillar LIS. Combining simulations and analytical calculations, we establish quantitative expressions for the drag force and the electric force acting on a moving droplet. We demonstrate that the models can accurately capture droplet dynamics during programmable manipulation, including periodic motion and long-distance transport. Such reliable theoretical models can potentially transform precision control of droplet dynamics by removing the reliance on trial and error tests.

Whether neutral-atom quantum optimization protocols exhibit genuine concentration toward low-energy solution structure remains an open question. Here, we introduce a shots-to-approximate-solution metric, STS(r), where r denotes the approximation ratio, and evaluate it using postprocessed outputs modeled by a degeneracy-weighted shell distribution governed by a single effective parameter, $\beta$, that quantifies concentration toward near-optimal independent sets. To extract the genuine concentration effect in the quantum data, we apply identical postprocessing to both experimental bitstrings and randomly generated bitstrings with matched excitation density, thereby constructing an excitation-matched random baseline. Experiments on programmable Rydberg-atom arrays with system sizes up to 125 sites show that quantum annealing consistently exceeds the random baseline, demonstrating enhanced concentration toward low-energy solution structure beyond what can be attributed solely to excitation density. The results further reveal two distinct target-dependent regimes. For near-exact targets with $r \approx 1$, the required shot count grows exponentially with system size and is reduced at the same exponential level by quantum annealing within the shell-model description. By contrast, for relaxed targets, the shot cost becomes effectively constant, and the corresponding quantum enhancement diminishes, with the classical postprocessing heuristic alone reaching the target in order-unity attempts. Together, these results establish an operational method for quantifying quantum optimization performance and clarify the regimes under which quantum approaches can yield practical benefits.

Progress in nuclear and many-body physics today is predicated on the ability to solve large-scale, strongly correlated quantum many-body problems. As the theoretical models become more sophisticated, they also become more computationally complex. Simultaneously, quantifying uncertainty in model predictions and fitting free parameters to experimental observations requires repeated evaluation of these expensive models. Surrogate models—known as emulators—provide the means of accomplishing these goals. This thesis provides an introduction into the current state of emulation in nuclear and many-body physics. The motivations, goals, and origins of currently popular emulation methods are discussed along with selected examples. We see how many methods are closely mathematically related and how trade-offs are made to optimize specific properties or applications. The central work in this thesis is the method of parametric matrix models (PMMs), an emulation and general machine learning framework which combines aspects of traditional reduced basis method with modern parametric machine learning. PMMs are able to retain as much or as little physical information about the underlying system as desired, yielding not only excellent performance but also nearly unparalleled adaptability, interpretability, and trustworthiness as an emulation method. A formal mathematical framework for PMMs is developed and accompanied by practical step-by-step procedures for the application of the method. As part of this thesis, the open-source pyPMM package was developed. This package enables any researcher to construct, train, share, and deploy PMM-based emulators with modular, extendable, and graphics processing unit (GPU)-optimized code. All PMM examples in this thesis were created using this package.

Dimples offer a promising route to reducing drag on aerodynamic surfaces. However, whether such shallow concavities yield a net benefit depends sensitively on their topography, which demands systematic mapping and exploration of their comprehensive design space. In this study, dimple design is examined as a mixed-variable optimization over four design variables: dimple type, depth, in-plane scale, and streamwise stretch. The design space is explored using MixMOBO, a Bayesian optimizer, coupled with immersed-boundary large eddy simulations of channel flows at a constant flow rate corresponding to a flat channel at a friction Reynolds number of 180. The optimal solution, a relatively deep, fully packed, streamwise-elongated diamond dimple, attains a 13.2% drag reduction, notably above previously reported values. A Gaussian process metamodel sensitivity analysis identifies dimple topology as the dominant factor, with coverage and elongation acting mainly through interactions, and depth itself carrying no universal sign. A near-wall flow analysis links the leading designs to fully attached, groove-like flow, whereas poorer designs tend to produce local flow separation that incurs adverse form drag. From these findings, key design insights for drag-reducing dimples are provided.

The stationary drift-diffusion model is widely used to model charge transport in semiconductor devices. Classical methods, such as the finite volume Scharfetter–Gummel (FVSG) method, perform well on high-quality Delaunay meshes but struggle on irregular or distorted meshes due to their reliance on Voronoi diagrams. To overcome this mesh limitation, this article introduces a new approach that integrates harmonic average stabilization into the discrete duality finite volume method (DDFV-HA). To validate our scheme, we compare DDFV-HA and FVSG for semiconductor simulations on both high- and low-quality meshes. Experiments show that DDFV-HA matches FVSG on high-quality meshes and is more reliable and accurate on low-quality meshes. Applying DDFV-HA to a real-world thyristor further confirms that it is well-suited for semiconductor simulations in complex, irregular domains where high-quality meshes are not easy to generate.

We propose and demonstrate the first vertical Gallium oxide device architecture without the use of planarization etch back processes or mid-gap acceptor regions. The Isolated Source Electrode Field Effect Transistor (ISEFET) incorporates a dielectric blocking layer to access an isolated source pad extending from the top fin metal. Scaled multi-fin channels were formed by electron beam lithography with a width of 200 nm along with the source pads and then etched to a trench depth of ~1.2 um. The fabricated devices showed enhancement mode operation with threshold voltage of 2 V and on-off ratio > 1e7 with excellent gate modulation characteristics. The resulting device proved to be comparable to existing vertical transistors and suitable for high-throughput prototyping and large-scale manufacturing of future Gallium oxide and other wide bandgap semiconductor devices.

The Daniel K. Inouye Solar Telescope (DKIST) has resolved Kelvin–Helmholtz (KH) vortices at photospheric magnetic-flux boundaries with a characteristic wavelength of 65 km. I estimate whether these vortices can supply the photospheric driver for cross-scale plasma heating through reconnection across different heights from photosphere to low-corona. Using the simulated MURaM shear, density contrast, and 500 km vertical extent, together with a representative photospheric density, gives a shear-energy density of $1.35\times10^{2}$ J m$^{-3}$ and $2.2\times10^{24}$ erg per characteristic vortex. Magnetic fields $1^\circ$–$7^\circ$ from the exact perpendicular orientation ($ {\bf{B}}\perp{\bf{k}}$) remain KH unstable in an idealized calculation and provide an in-plane component that can be wound or compressed into current layers. The limiting case, in which the center-of-momentum shear reservoir becomes new magnetic free energy, gives $b_{\rm cs}=184$ G, identical to the ideal marginal-stability field and equivalent to a $7.6^\circ$ effective twist. This stores at most 135 J m$^{-3}$ in the layers. Using empirical collisionless reconnection heating fractions of 0.28–0.44, the same twist mapped to weakly collisional heights gives ion heating from $\approx$20 eV at the photosphere to $\approx$1.4 keV in the low corona. For an illustrative, snapshot-based KH-active surface fraction of 0.03, quiet-Sun and coronal-hole losses require 5–8% and 14–21%, respectively, of the shear reservoir to become reconnecting magnetic free energy that reaches such heights. Active regions likely require a separate guide-field twist and helicity reservoir. The required upward transport has not been measured by DKIST, but it is directly testable.

Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains. We develop a generative surrogate, focusing on Normalizing Flows (NFs). Through transfer learning, we pre-train on the full imbalanced dataset and fine-tune specialized models for different particle types ($\gamma$, $n$, $\Lambda$, $K_S^0$, $\Sigma^+$) using two gradual-unfreezing schemes. As standard ZDC metrics like Wasserstein distance overlook conditional structure, we introduce refined metrics: conditional weighted MAE, dispersion ratio, and Jaccard co-activation error, that better capture physics-relevant input-output dependencies and response variability. Our ensemble of fine-tuned models achieves a Wasserstein distance of $1.61 \pm 0.02$, outperforming baselines across all metrics. This work provides a generalizable NF-based framework for LHC detector simulation, combining NFs, conditional fine-tuning, and physics-motivated evaluation.

Metal surfaces undergo structural, compositional, and morphological changes in response to their chemical environment. Tuning the surfaces' function and stability for a given application correspondingly necessitates an understanding of how this surface evolution couples to external conditions. Here, we demonstrate the feasibility of nested sampling simulations to obtain this coupling at first-principles predictive quality. By exploring the full configuration space, nested sampling estimates the partition function and gives direct access to desired thermodynamic ensemble averages at any temperature without prior knowledge. Computational feasibility is achieved through machine-learned interatomic potentials, an efficient GPU implementation of the sampling algorithm and bespoke sampling moves. Applied to the early oxidation of Cu(100), the approach successfully predicts the experimentally observed, complex $(2\sqrt{2}\times\sqrt{2})$R45$^\circ$-O missing-row reconstruction. The full access to the partition function enables a detailed characterization of the temperature-dependent surface evolution, mapping the emergence of defect states and the order-disorder transition of the reconstructed surface.

In-space servicing has been receiving great attention to extend the operation of spacecraft with defective components. This requires rendezvous and proximity operations for a chaser to provide service to a target. This work constructs a fuzzy inference system-based controller for the chaser to reach the cooperative target on a circular orbit in the final approach phase while minimizing the energy consumption of the chaser. The offline training process performed by a genetic algorithm deals with multiple initial relative positions of the chaser, and the trained controller is validated using a testing environment with disturbances, which differs from the training scenarios.

Precise measurements of the metastable helium (1s)(2s)$\,^3S_1$ ionization energy have revealed a 9$\sigma$ discrepancy with QED theory that persists in isotopic measurements, suggesting a leptophilic bosonic interaction as a possible explanation. A subsequent investigation of such interactions has concluded that only a scalar boson interaction is consistent with the He observations. Taking this as a starting point, we derive the response of positronium energy levels to the corresponding finite-range Yukawa potential, using exact hydrogenic matrix elements and a numerical helium calculation. Across the viable mediator-mass range of 0-800 eV, the He anomaly interpreted in this way implies a positronium 1$^3$S$_1 \rightarrow 2^3$S$_1$ shift ranging from 0.250-0.850 MHz, and a 2S-ionization shift of 0.14-0.21 MHz. We discuss the feasibility of observing these shifts experimentally.

This work numerically validates the application of bio-inspired concepts on CMOS derived from bacterial photosynthetic light harvesters. We investigate a modification of symmetric inverted pyramid array CMOS image sensors into an asymmetrically shaped texture to explore the structural boundary of passive non-reciprocity. Diverging from traditional macroscopic continuum assumptions, we analyze whether structural asymmetry at sub-wavelength scales can induce non-reciprocal scattering under passive, linear, and time-invariant conditions. A theoretical framework based on perturbation theory is developed, estimating a potential efficiency enhancement of 5% to 15%. Numerical simulations performed via the MEEP finite-difference time-domain (FDTD) platform reveal that the linear response is highly localized, showing a subtle 0.02% change. This suggests that macroscopic Lorentz reciprocity remains robust at the investigated scale due to apex field concentration, defining a clear geometric threshold for microscopic non-reciprocity.

We investigate predissociation in homonuclear ($^{87}$Rb$^*$$^{87}$Rb$^+$) and heteronuclear ($^{87}$Rb$^*$$^{7}$Li$^+$) long-range Rydberg atom-ion molecules. Owing to their micron-scale bond lengths, these dissociate on time scales far removed from those of more tightly bound diatomic molecules. We employ the eigenchannel $R$-matrix method to compute predissociation rates for a broad range of principal quantum numbers $n$. The rates depend strongly on the mass, but more remarkably display a rapid and periodic variation as a function of $n$ as well as within a single vibrational ladder. A semiclassical Landau-Zener-Stückelberg analysis reveals that Stückelberg interference governs the decay process and produces the observed variation in the molecular lifetime. Although the heavy mass of the homonuclear Rb molecule constrains its predissociation rates to a sub-kHz level, the lighter molecule $^{87}$Rb$^*$$^{7}$Li$^+$ dissociates on time scales competitive with radiative and collisional decay. This can enable in situ study of non-adiabatic decay via ion microscopy.

This paper is associated with a poster winner of a 2025 American Physical Society's Division of Fluid Dynamics (DFD) Milton van Dyke Award for work presented at the DFD Gallery of Fluid Motion. The original poster is available online at the Gallery of Fluid Motion, at https://doi.org/10.1103/APS.DFD.2025.GFM.P012

In this paper, 65 nm n-type Metal-Oxide-Semiconductor Field-Effect-Transistors (nMOSFETs) are taped out and measured at 292 K to 4.5 K. Technology Computer-Aided-Design (TCAD) model parameters are then calibrated to fit the simulation curves to experimental electrical curves. Transmission electron microscopy (TEM) is used to unveil the dimensions of the transistors to ensure accurate modeling. The experimental and simulation capacitance-voltage (CV) curves, drain current - gate voltage (IDVG) curves, and drain current - drain voltage (IDVD) curves are fitted well using the calibrated parameters. The major models calibrated are the Lucent model, which is comprised of an extended PhuMob model, Lombardi model for surface mobility, and the Hänsch high field mobility model. A band-tail model with adjusted band width is applied to fit the sub-threshold swings at all temperatures. The calibrated parameters are expected to be applicable to other technologies because TCAD models are not technology-specific. It is also found that, except for saturation velocity, no new temperature-dependent model is needed. The calibration process and fitting strategies are detailed and are expected to be applicable to calibrate other transistor types.

Accurate solvation free energies from molecular dynamics simulations require efficient sampling of coupled slow variables, including solvent coordinates, solute conformational modes, and the alchemical coordinate $\lambda$. Here, we develop a $\lambda$-dynamics framework that combines mass scaling, on-the-fly probability enhanced sampling (OPES), and driven adiabatic free energy dynamics (d-AFED) to address these sampling challenges within a unified protocol. For rigid organic solutes, Hamiltonian replica exchange with mass scaling is first used to quantify the effect of octanol solvent relaxation. Reducing all octanol atomic masses by a factor of ten accelerates convergence by more than fivefold while preserving equilibrium solvation free energies. These calculations then provide reference benchmarks for $\lambda$-OPES, a dual-bias $\lambda$-dynamics strategy that combines the "standard" and "explore" variants of OPES to promote transitions along the alchemical coordinate. This approach reaches convergence on timescales comparable to replica exchange, but without predefined $\lambda$ windows or multiple parallel simulations. For flexible $N$-acetyl amino-acid amide solutes, $\lambda$-OPES is coupled with d-AFED on selected backbone and side-chain dihedrals to enable simultaneous alchemical and conformational enhanced sampling. This combined strategy improves agreement with experimental octanol-water partition coefficients and reduces the mean absolute error from 0.75 log units with $\lambda$-OPES alone to 0.30 log units with $\lambda$-OPES-d-AFED. Overall, this work establishes an integrated enhanced sampling protocol for solvation free energy calculations across rigid organic solutes and flexible peptide-like solutes, and provides a foundation for the application of alchemical free energy methods to larger and more conformationally complex systems.

An “Attention Residual U-Net” method is described for probabilistic quantitative precipitation forecasting (PQPF) that predicts the hourly probability of no precipitation plus the distribution of positive precipitation from a weighted mixture of two Gamma distributions. The neural network is trained on patches of numerical weather prediction (NWP) hourly precipitation from The Weather Company's convection-permitting GRAF (Global high-Resolution Atmospheric Forecasting) model along with terrain information and column-average relative humidity from the National Oceanic and Atmospheric Administration's (NOAA's) Global Forecast System (GFS). The target data are NOAA's Multi-Radar, Multi-Sensor (MRMS) gauge-corrected, quality controlled radar data sampled to the same grid as the GRAF data. The network outputs distributional parameters for each model grid point. Training uses negative log-likelihood as a proper scoring rule, with climatological initialization for stable convergence. Inference is performed as a single forward pass over the contiguous United States (CONUS) domain, with edge-replication padding to satisfy the network's spatial-divisibility requirement. The subsequent forecasts are spatially detailed, highly reliable, and skillful with respect to climatology and a simpler reference forecast method. The method is particularly useful for estimating probabilities in regions with large terrain variation.

Low-dimensional reductions provide a useful framework for studying high-dimensional dynamics on complex networks, but most existing approaches are restricted to pairwise interactions. Here, we develop a one-dimensional reduction for dynamical systems on networks with purely higher-order interactions. The reduction is formulated through an effective higher-order interaction strength ($\beta_{\Delta}$), associated with the triangular interactions of the underlying network and the dynamical system's effective state. We present a theoretical framework for the dimension-reduction approach and validate it across three dynamical models with exclusively higher-order interactions. We find that the reduction accuracy is mainly determined by the homogeneity of node states, i.e., the deviations in state values become very small. Numerical results on synthetic and real networks show that the reduced model captures the effective steady states and transitions of the full system with good accuracy.