Papers

Frequency locking between coupled laser systems provides a powerful mechanism for stabilizing and controlling coherent emission, yet its implementation and applicability down to the nanoscale remains unknown and unexplored. Here, we demonstrate optical coupling and frequency locking in closely spaced ZnO nanowire lasers operating in the extreme near field (gap < 10 nm). We observe both full and partial frequency locking, manifested as the alignment of all or a subset of the lasing modes, by spatially controlling the optical excitation. We also observe single-mode lasing in a coupled nanowire pair where the multi-mode lasing of individual nanowires is suppressed. In contrast to previously reported coupled-cavity nanowire lasers, where spectral control arises from static filtering mechanisms such as the Vernier effect, our results indicate a dynamically established relationship between actively lasing nanowires. These findings establish frequency locking as a robust and tunable mechanism in nanowire lasers, opening new routes toward stabilized and controllable nanoscale light sources for integrated nanophotonic systems.

Cation-based Ag/Cu filaments formed in an insulating $\alpha$-Si matrix are widely used as memristors in crossbar arrays for efficient in-memory computing. However, the stochastic nature of filament formation and rupture gives rise to device-to-device and cycle-to-cycle variation. Despite successful implementation of large-scale memristor arrays, systematic studies of filament parameters and their spatial distribution in the memristors are scarce. In this work, we use conductive atomic force microscopy (c-AFM) to probe the spatial distribution of conductive filaments in $\alpha$-Si memristors. The charge transport is dominated by a limited number of discrete filaments rather than by uniform conduction across the device area. The systematic analysis of the experiment gives the mean surface density of the conductive filaments $\sim$3200 per $\mu\text{m}^2$. Both volatile and non-volatile filaments can be found within one memristor. The experimental data and the nature of volatile and non-volatile filaments may be explained within the model of multiple trap assisted tunnelling. The model yields reasonable estimates for physical properties for both types of filaments.

In this chapter, I will enter into the roots of entropically-driven transport with a focus on diffusio-osmotic transport in nanochannels. Diffusio-osmosis is a subtle surface transport, originating in entropic driving forces occuring within the diffuse layers at solid boundaries. Specifying diffusio-osmosis to nanochannels may first look like a marginal refinement, yet it reveals that osmotic drivings can arise in channels and membranes without the prerequisite of semi-permeability, so that diffusio-osmosis extends the domain of existence of entropically driven transport. Osmosis and diffusio-osmosis are two faces of the same phenomenon, naturally embedded in an Onsager framework and quantified by local and global force balances. This perspective clarifies why nanochannels are privileged arenas where diffusio-osmosis and its consequence do flourish. Throughout the chapter, I discuss a set of conceptually relevant examples to show how diffusio-osmosis "pops up" in various situations: as enhanced diffusion, mechano-sensitivity, rectified osmotic flows and, ultimately, as a lever for osmotic energy conversion from single nanopores to membrane modules approaching industrial reality.

Optical clocks require an ultra-stable laser to probe and precisely measure the frequency of the narrow-linewidth clock transition. We introduce a portable ultraviolet (UV) laser system for use in an aluminum quantum logic clock, demonstrating a fractional frequency instability of approximately $\mathrm{mod}\,\sigma_\mathrm{y} = 2 \times 10^{-16}$. The system is based on an ultra-stable cavity with crystalline AlGaAs/GaAs mirror coatings, alongside with a frequency quadrupling system employing two single-pass second harmonic generation (SHG) stages. Its acceleration sensitivity, measured in all three axes, does not exceed $4(2) \times 10^{-12}$/(ms$^{-2}$) and is among the lowest recorded for transportable systems to date. Additionally, partial cancellation between photo-thermal noise and photo-birefringence noise is used to effectively mitigate noise induced by intra-cavity optical power fluctuation at lower Fourier frequencies.

Radiomap prediction has found extensive applications in network planning and optimization. The radiomap is implicitly determined by electromagnetic (EM) wave propagation. The evolution of wireless communication toward higher frequency spectra has highlighted the effect of mesoscopic (i.e. wavelength-comparable scale) scatterers, which is negligible in the sub-6 GHz spectrum. To address this challenge, we develop a linear forward channel model to capture the propagation behavior in the multi-scatterer environment. The proposed model utilizes spherical-wave mode expansion to track the source radiation pattern, including scattering from a single scatterer and interactions among multiple scatterers. Both phenomena are represented as a superposition of spherical-wave modes, effectively capturing the multipath effect from a wave-based perspective. This forward model is then employed to formulate an inverse optimization problem, where the scattering responses and scatterer locations are jointly learned from sparse field measurements. Simulation results demonstrate that the proposed model accurately reconstructs and extrapolates the radiomap in both the spatial and the beam domain, further enables frequency-domain channel interpolation.

Universal scaling near phase transitions is one of the central ideas of physics, linking the growth of spatial correlations to the slowing down of dynamics. So far, direct experimental access to this critical behavior has remained largely confined to equilibrium many-body systems, and especially to static critical behavior. Here we probe how universality emerges in a driven quantum fluid of light formed by exciton–polaritons in a semiconductor microcavity. By probing the fluctuation-dominated disordered phase below the condensation threshold, we directly measure both the static growth of the correlation length $\xi$ and the dynamical slowing down of the relaxation time $\tau$. We find that these quantities obey the universal relation $\tau \propto \xi^{z}$ with dynamical exponent $z \approx 2$, revealing diffusive dynamics of a non-conserved order parameter. Our results extend the physics of critical dynamics from equilibrium matter to driven optical systems, bridging quantum condensates and lasers.

An 11-species thermochemical model for H$_2$/ He mixtures with state-specific kinetics for atomic H is developed and used to simulate 1-D shocks at conditions relevant for ice and gas giant entry flows. To implement this kinetic model, a literature review of the state-specific excitation and ionization rate constants of atomic H is first performed. While electron-impact rate constants from various sources are found to be in good agreement, large discrepancies are found in the limited data available on heavy-particle-impact rate constants. To validate the kinetic model, 1-D steady shocks are simulated using a space-marching code that explicitly accounts for shock tube boundary layer effects. The resulting radiance profiles are compared to experimental data from the NASA Ames Electric Arc Shock Tube (EAST) facility, and are found to reproduce the measured values reasonably accurately while capturing the distinct induction zone behavior observed in the experiments. A sensitivity analysis of the kinetic rates and boundary layer treatment reveals avenues for further improvement of the model. Finally, a comparison to alternate models from the literature underscores the improved accuracy of the present model in predicting ionization and radiation profiles.

Deterministic and bright quantum light sources based on scalable semiconductor technologies are a crucial building block for future quantum communication networks. While circular Bragg gratings (CBGs) are highly effective for extracting light from solid-state quantum emitters, conventional architectures rely on complex multi-layer processing or flip-chip bonding, which introduce detrimental strain and limit scalability. Here, we present a fabrication-minimal approach to realize monolithic, free-standing CBG cavities with deterministically positioned single GaAs quantum dots (QDs). By utilizing aspect-ratio-dependent etching (ARDE) in a single-step top-down process, we achieve the necessary vertical structural asymmetry for directional emission without requiring bottom reflectors. Finite-difference time-domain (FDTD) simulations validate this geometry, predicting free-space extraction efficiencies up to $68 \, \%$ and coupling efficiencies of $40 \, \%$ into a lensed single-mode fiber ($\text{NA} = 0.6$). Experimentally, the deterministically coupled QD-CBG devices yield a photoluminescence intensity enhancement of up to $\times 700$ compared to unprocessed planar QDs, reaching integrated count rates of $45 \, MHz$. Furthermore, the suspended membrane architecture effectively relaxes residual strain, significantly reducing the average exciton fine-structure splitting from $7.3 \, \mu eV$ in planar QDs to $1.3 \, \mu eV$ in the CBGs. Interferometric measurements confirm that the fabrication process preserves the optical quality of the emitters, with average coherence times of $70 \, ps$. By bridging optimized FDTD design with precise nanofabrication and robust optical performance, these results establish free-standing GaAs CBGs as a highly scalable platform for bright and coherent entangled photon pair sources.

Compositional inference - the decomposition of observations into an unknown number of latent components - is central to perception and scientific data analysis. Attention-based models perform well when components are approximately separable, as in object-centric vision. Under additive superposition, however - where multiple components contribute to every observation - we identify a structural failure mode we term slot collapse: multiple slots converge to the same dominant component while weaker ones remain unrepresented. We trace this to a general limitation: attention is memoryless with respect to explained evidence. All slots repeatedly operate on the same input without accounting for what has already been explained, so gradients are dominated by the strongest component, inducing shared fixed points across slots. As a result, attention fails to enforce non-redundant allocation under additive superposition. We address this by introducing residual evidence modeling, instantiated via evidence depletion - a minimal modification combining multiplicative depletion with an attention bias. Controlled ablations show that parallel attention, sequential processing alone, and loss-based regularization fail to resolve collapse; evidence depletion, which adds residual state to sequential attention, consistently succeeds. Across synthetic benchmarks and real-world audio mixtures (FUSS), evidence depletion reduces slot collapse by up to an order of magnitude, generalizing beyond synthetic settings. On gravitational-wave source inference for the ESA/NASA LISA mission, under identical architectures, data, and losses, standard attention fails while evidence depletion prevents collapse and enables multi-source posterior estimation. These results show that under additive superposition, residual evidence tracking is the operative ingredient for preventing collapse and enabling compositional inference.

We develop a nonequilibrium Green's function theory for dispersion interactions between two nanostructures, each an open quantum system driven into a nonequilibrium steady state by an applied bias voltage. Starting from the two-particle nonequilibrium Green's function, we derive a general expression for the interaction energy in terms of the polarisation propagators of the individual systems. The interaction energy admits a physically transparent decomposition into charge noise and charge dissipation contributions, providing a fluctuation-dissipation interpretation that generalises the equilibrium London picture. Model calculations for coupled molecular junctions demonstrate that the applied voltage can enhance the attractive dispersion interaction by nearly an order of magnitude relative to equilibrium. In thermal equilibrium, the dispersion interaction is universally attractive, irrespective of the specific form of the nanostructure Hamiltonians or their coupling to reservoirs. Out of equilibrium, we introduce a generalised Kubo-Martin-Schwinger ratio that parametrises the departure from detailed balance. We show that, in contrast to equilibrium, nonequilibrium conditions can lead to a repulsive dispersion interaction. Finally, we discuss the conditions under which population inversion in the electronic leads can drive a sign reversal of the dispersion interaction.

The quasiparticle effective mass $m^*$ of the three-dimensional uniform electron gas (UEG) is a fundamental Fermi-liquid parameter whose value and density dependence have remained controversial for decades. Using renormalized perturbation theory with explicit counterterms, we determine $m^*$ in the metallic regime ($r_s \le 6$) from first principles by two complementary routes – the self-energy and the forward-scattering four-point vertex via the $p$-wave spin-symmetric Landau parameter $F_1^s$ – that agree within uncertainties at each density through sixth renormalized order. The resulting $m^*/m$ remains close to unity throughout the metallic regime, with a shallow non-monotonic density dependence – a minimum near $r_s\approx 1$ followed by a gentle upturn – reflecting the interplay of exchange and dynamical screening in the self-energy, and disfavoring strong monotonic suppression. This finding supports a physical picture for the metallic UEG in which dominant charge correlations are concentrated in nearly forward scattering and generate only a weak $F_1^s$ component.

We introduce a composition-weighted symbolic regression framework for interpretable prediction of materials properties directly from chemical composition. The method jointly learns analytical functional forms and task-dependent elemental weightings without predefined descriptors. By incorporating max/min operators, it naturally enforces constraints such as non-negative band gaps and bounded classification probabilities, unifying regression and classification tasks. Efficient search is achieved through a hybrid Monte Carlo tree search–genetic programming algorithm with gradient-based refinement and parallel computation. Benchmarks on MatBench tasks show competitive accuracy relative to state-of-the-art black-box models while yielding explicit analytical expressions. Applied to III–V semiconductor alloys, the model produces smooth composition-dependent trends and learned elemental weights with chemically meaningful periodic behavior. This framework provides a scalable and interpretable route for materials discovery and property screening.

In far-field thermal radiation, electromagnetic waves emitted by an object propagate to infinity, requiring the far region to be modeled as an effective thermal bath. This bath was proposed as infinitesimal environmental "dust", but explicit calculations with such distributed dust involve integrals over infinite space that are difficult to evaluate. In this work, we map this environmental dust to a photonic bath at infinity within the nonequilibrium photonic Green's function formalism. By explicitly evaluating the spatial integral over the dust, we show that its contribution reduces to a simple local self-energy, for which we derive analytical expressions for both three-dimensional objects and planar systems. We further demonstrate that the bath behaves as a black body and clarify its role in far-field thermal radiation. An alternative derivation based on the surface Green's function framework is also provided, demonstrating the theoretical consistency of the results without invoking the dust model. The photonic bath at infinity provides a convenient framework for both analytical and numerical calculations in far-field thermal radiation.

Electromagnetic whistler-mode waves are a natural emission in the outer radiation belt and the Earth's magnetotail. The resonant interaction of these waves and energetic electrons are responsible for electron acceleration and losses, thus coupling the magnetosphere and ionosphere. Near-equatorial spacecraft use search-coil magnetometers for whistler-mode wave measurements, and one of the largest (covering the longest period of time) dataset of such waves has been collected by the THEMIS mission operating in the near-Earth magnetosphere within 2008-2025. However, after 2017, the search-coil magnetometers on two THEMIS spacecraft, THEMIS E and D, experienced problems with their signal along the spacecraft spin axis and were only able to detect the spin plane components of the wave vector. This significantly reduces our ability to detect the total wave amplitude wave magnitudes and limits our ability to incorporate the THEMIS E, D datasets into investigation of whistler-mode waves. In this technical report, we propose and validate a technique for reconstruction of magnetic field spectral density for Fast Fourier transform data product collected during Fast-Survey mode hereafter referred to as the fff dataset collected by THEMIS E and D. We use measurements of the electric field instrument and cold plasma dispersion relation to evaluate the whistler-mode magnetic field spectral density. Verification of this technique by comparison with THEMIS A measurements (which retained their 3D measurement capability intact) confirms that restored magnetic field spectral density is within a factor of ~1.5 of the actually measured magnitudes.

We compute the mean interval between successive returns of the apparent geocentric solar longitude $\lambda$ to a fixed value $L \in \{0^\circ, 45^\circ, 90^\circ, \ldots, 315^\circ\}$, averaged over a multi-millennium window; this gives eight “mean years” against which calendar leap rules can be tuned: four cardinal-point years (equinoxes and solstices); four cross-quarter years. The construction is built on Meeus's low-precision solar theory (Astronomical Algorithms, 2nd ed., 1998), itself a low-order truncation of Newcomb's Tables of the Sun re-expanded around J2000.0. Where Meeus presents polynomial coefficients without justification, we draw on Smart's Textbook on Spherical Astronomy (6th ed., revised by Green, 1977) for the underlying derivations. Numerical accuracy is validated against the cardinal-point intervals tabulated in Meeus, More Mathematical Morsels, 2002. We close with a derivation of the secular drift equation, showing that, regardless of how well a leap rule is tuned, the slow shrinkage of the tropical year produces a quadratic cumulative error that reaches one day in $\sim$5{,}700 years for any fixed intercalation rule.

For a society to remain healthy and prosperous, people must collectively behave and act to contribute to the common good, even if there is often a tradeoff against their individual benefit. Paradigmatic examples include the adoption of sustainable behaviors and technologies to combat the climate crisis, and the mobilization for collective action to promote the rights and freedoms of repressed minorities. In this tutorial, we illustrate how game theory and network systems theory can be powerful tools to model and study this collective decision-making problem. We provide examples of how awareness of this tradeoff can impact collective change toward the societal good, exploring different problem contexts such as sustainable behavior and collective action. Finally, we review recent developments using systems and control-theoretic approaches to generate awareness and guide the emergent population dynamics towards a desired outcome, and conclude by highlighting new research and application frontiers.

This work presents a model for generating nonthermal power-law tails of particles' energy probability density functions in turbulent collisionless plasmas, applicable to both non-relativistic and relativistic scenarios. We propose that strong Alfvénic turbulence energizes plasma particles through curvature acceleration, particularly for particles with Larmor radii comparable to the scales of turbulence. When the energy density of the energized particles increases, the efficiency of the energy exchange process diminishes. As a result, the acceleration process saturates, leading to power-law distributions of particle momentum and energy. In the non-relativistic case, the momentum probability density function scales as $f(p) dp \propto p^{-3} dp $, while in the ultrarelativistic case, the energy probability density function scales as $ f(\gamma) d\gamma \propto \gamma^{-3} d\gamma $, where $\gamma$ is the Lorentz factor. This model provides a unified framework for understanding particle acceleration in both energy regimes, complementing existing analytical approaches. The predicted scalings are consistent with available observations of energetic ion distributions in the heliosphere and with the findings from numerical simulations of ultrarelativistic particle acceleration in magnetically dominated plasma turbulence.

Kolmogorov-Arnold networks (KANs) offer a potent combination of accuracy and interpretability, thanks to their compositions of learnable univariate activation functions. However, the activations of well-fitting KANs tend to exhibit pathologically high-curvature oscillations, making them difficult to interpret, and standard regularization penalties do not prevent this. Here we derive a basis-agnostic curvature penalty and show that penalized models can maintain accuracy while achieving substantially smoother activations. Accounting for how function composition shapes curvature, we prove an upper bound on the full model's curvature relative to the curvature penalty, and use this to motivate richer forms of penalties. Scientific machine learning is increasingly bottlenecked by the trade-off between accuracy and interpretability. Results such as ours that improve interpretability without sacrificing accuracy will further strengthen KANs as a practical tool for both prediction and insight.

Numerical analysis of unconfined flow over an obstacle has always been challenging in computational fluid dynamics due to the truncation of the computational domain while replicating the real-life flows and the application of the boundary conditions. Confined flows studies have been well established and documented while unconfined flow studies are relatively challenging. Present work demonstrates the implementation of lattice Boltzmann method for unconfined flow over a circular cylinder for Re 100. The cylinder was placed at 10D upstream and 30D downstream and 10D from both the top and bottom walls. Different boundary conditions were implemented at the top and bottom walls to ensure unconfined flow. Drag and lift coefficients are also presented and were computed using the momentum exchange algorithm. Results are in complete agreement with the existing literature which demonstrate the capability of the solver.

Inspired by the design for the simple Columbia Non-neutral Torus (CNT) proposed by Pedersen and Boozer (Phys. Rev. Lett. 88 205002(2002)), and later revisited by Yu et al. (J. Plasma Phys. 88 905880306 (2022)), this work explores axi-symmetric equilibria using two planar vertical field coils and two non-planar intertwined coils. Neoclassical optimization studies are performed in a single stage approach using the DESC stellarator equilibrium solver (Dudt and Koleman, Phys. Plasmas 27 (2020) 102513). The physical parameters used as input were the toroidal magnetic flux function $\psi$, the rotational transform $\iota(\rho)$ as a function of the normalized flux function $\rho$ as the radial coordinate, the number of field periods $N_{FP}$, and an initial equilibrium assumption, given the major radius $R_0$ and the minor radius $a$. Once the equilibrium given by the coil configuration is defined, an optimization is made for quasi-symmetry. In this study, a triple product metric as defined by Dudt et al., (J. Plasma Phys. 89 (2023) 95589020) is used as a local error indicator, which is evaluated without resorting to Boozer coordinates. The goal is to optimize the effective ripple modulation amplitude ${\epsilon}_{eff}^{3/2}$, thus decreasing the neoclassical transport in the low collisionality $1/\nu$ regime. We show a sample of quasi-axisymmetric configurations obtained, both for the vacuum field and with finite pressure, which have reasonably good neoclassical transport in the sense of Boozer coordinates. In the best case scenarios the final rotational transfrom is inverted after optimization.

This study introduces an innovative Isotropic Elastic Lattice Spring Model (IELSM) that addresses the fundamental limitation of classical lattice spring models: the constraint of fixed Poisson's ratio. By amending the total strain energy within the Lattice Spring Model (LSM), IELSM provides a self-consistent formulation for simulating isotropic elastic materials with arbitrary Poisson's ratios. The model's core innovation lies in augmenting classical axial spring frameworks with additional volumetric constraints, establishing a direct and exact mapping between IELSM's parameters and macroscopic elastic constants. This enables simulation across the full admissible Poisson's ratio: -1 < {\nu} < 1 under plane stress and -1 < {\nu}< 0.5 under plane strain conditions. Eigenvalue analysis indicates that the IELSM has better numerical stability compared to the standard bilinear quadrilateral element and the constant strain triangular element. The characteristic of the numerical implementation lies in directly decomposing the additional volumetric constraints into an equivalent combination of standard mechanical components (axial, shear and rotational springs), laying the foundation for the realization of fracture simulation based on discrete methods. Comprehensive validation through uniaxial tension, pure shear, stress concentration around a circular hole, and stress singularity analyses for central and crucifix-shaped cracks demonstrates IELSM's exceptional accuracy, convergence and computational robustness. The model exhibits excellent performance in stress intensity factor calculations at crack tips, validating its effectiveness for singular stress field analysis. This work bridges the gap between LSM and continuum mechanics, establishing an analog framework that maintains theoretical consistency while offering computational accuracy for the solution of elastic boundary value problems.

A one-positron quantum cyclotron is realized with a single positron suspended indefinitely in the magnetic field of a Penning trap. This opens the possibility of quantum measurements of the positron magnetic moment at a precision much higher than attained with classical cyclotron motion. Comparing the magnetic moments measured using positron and electron quantum cyclotrons should provide the most stringent test of the fundamental CPT invariance of the Standard Model of particle physics in the lepton sector.

It is shown that the energy density formulas of Newtonian gravity by Maxwell, Bondi and Ohanian cannot be distinguished by boundary conditions, and also the corresponding energy balances are identical. However, they are not equivalent. From a thermodynamic point of view, the Ohanian energy density is distinguished.

Standard saturating response functions, such as the Hill function, are monotone and therefore cannot represent recruitment-induced overshoot or adaptive transients with a single block. Reproducing such non-monotone responses from saturating primitives requires at least a difference of two blocks with opposing amplitudes, doubling the static-block parameter count. Here, building on a recent mathematical result that a single binary operator, EML, generates all standard elementary functions, we use EML as a structured grammar for reduced nonlinear ODEs. This yields an activation-suppression module that captures overshoot directly. We validate the framework in three settings. First, on PKA-R relocalization data, the EML grammar discovers a reduced surrogate consistent with established mechanistic biology. Second, on Rho-GTPase recruitment data, an exhaustive search over EML expression trees selects the same compositional form across all four perturbation-response traces. Third, a 50-state simulated network is compressed by an EML cascade acting as a fixed temporal basis. Thus we demonstrate the power and potential of EML for reduced models of biological dynamics.

Explosive events from magnetars are likely due to the catastrophic release of stress in their crusts, but the behavior of crustal matter beyond linear elasticity is poorly understood. We argue here that seminal results from molecular dynamics informing crust breaking calculations are non-converged, and must be revisited. We estimate the criteria for quasi-static, rate-independent flow by comparing imposed deformation timescales to grain boundary diffusion in polycrystals. We argue that convergence in this regime should be observed at strain rates slower than $10^{-5}\,\omega_p$ (plasma frequency $\omega_p$) in simulations of $N\approx10^5$ particles across order 10 grains at a quarter of the melting temperature. Though computationally expensive, this is tractable with modern methods and GPU supercomputers.

In this study we analyze the topological invariants and edge states of transverse magnetic wave propagation in continuum photonic systems at a finite-width interface between two gyrotropic materials with different magnetic biases. Where previous studies have almost exclusively considered sharp transitions between two different electromagnetic media, we consider the more general geometry where the magnetic field bias is allowed to vary arbitrarily in a finite-width interface between two bulk regions. We find that when the magnetic field bias varies continuously between the two bulk regions, the Bulk Edge Correspondence (BEC) holds robustly with respect to well-defined Chern invariants. However, discontinuities in the magnetic field bias introduce new edge modes and anomalous high-wavenumber spectral effects which alter the BEC. We analyze these spectral alterations and define a new anomalous BEC in continuum photonic systems which includes contributions from topological invariants and discontinuities in magnetic field bias.

Sapphire optical fiber shows great promise for remote sensing in extreme environments approaching 2000 degC, by using laser-processing to form a single-mode waveguide within it. However, for practical application, longer devices with high manufacturability and reliability are required. We report the design, modeling, fabrication, and optimization of an index-guiding sapphire photonic crystal fiber Bragg grating temperature sensor. The device is fabricated using femtosecond laser direct writing to inscribe both the photonic crystal waveguide and the Bragg grating. A spatial light modulator was used to compensate for the mismatch between the immersion objective and the high-index oil used. This improved the aspect ratio and suppressed cracking during fabrication, for higher reliability. The design results in a 6-fold reduction in fabrication time over an equivalent depressed cladding waveguide, significantly reducing the cost of manufacture. Devices up to 7 cm long were fabricated and spliced to standard single-mode fiber. The propagation loss was estimated to be 0.7 dB/cm and the Bragg gratings had a bandwidth of approximately 0.12 nm. Devices were tested in a furnace showing a temperature sensitivity of between 19.0-32.3 pm/degC over a range 25-1200 degC. These longer devices have the potential to enable practical high precision extreme temperature monitoring in many applications, with lower manufacturing cost and higher reliability.

Variational quantum algorithms are promising for near-term quantum computing, but are severely limited by hardware noise and the substantial circuit overhead required for error mitigation methods such as Zero-Noise Extrapolation (ZNE). We propose a Physics-Informed Denoising Network (PIDN) that reduces the cost of ZNE by learning a surrogate model of its optimization dynamics. By viewing the variational update as a trajectory in the parameter space, PIDN is trained to reproduce ZNE-mitigated expectation values and gradient directions while incorporating a physics-informed loss that preserves the gradient descent dynamics. Once trained, PIDN replaces repeated multi-noise evaluations with denoised expectation and gradient estimation directly from the current noisy observation and the historical trajectory, significantly reducing circuit executions. We benchmark the approach on the quantum approximate optimization algorithm for 3-regular graphs, Sherrington-Kirkpatrick, and transverse-field Ising models, as well as the variational quantum eigensolver for LiH, BeH$_2$ and H$_2$O. Across all tasks, PIDN attains performance comparable to ZNE, while reducing the number of circuit executions by a factor of approximately 4 to 6. Gradient cosine similarity with ZNE remains above 0.95 throughout training. Robustness analysis shows that PIDN fails only when ZNE itself becomes unreliable, and ablation studies confirm the necessity of the physics-informed loss for maintaining directional consistency. We further find that PIDN tracks optimization dynamics most accurately when the effective loss landscape retains strong low-frequency structure. These results establish PIDN as a scalable, resource-efficient strategy for noise-resilient variational optimization in the noisy intermediate-scale quantum regime.

Cobalt ferrite (CoFe$_2$O$_4$) is a prototypical ferrimagnetic spinel oxide whose exceptional magnetic anisotropy, magnetoelastic coupling, and thermal stability underpin applications in spintronics, magnetic hyperthermia, energy harvesting, and catalysis. This chapter presents a comprehensive computational framework that integrates electronic$-$structure calculations with atomistic spin modeling, statistical mechanics, and continuum micromagnetics to predict magnetic functionality across length and time scales. Starting from density functional theory with Hubbard corrections (DFT$+$U), we derive exchange constants J$_{ij}$, magnetocrystalline anisotropy K$_1$, and magnetoelastic coefficients B$_1$, accounting for cation inversion, strain, and correlation effects. These parameters feed into generalized Heisenberg Hamiltonians, enabling Monte Carlo and Landau-Lifshitz-Gilbert simulations of finite-size effects, hysteresis, coercivity, and hyperthermia response in nanoparticles and thin films. Coarse-graining strategies bridge to micromagnetic modeling, ensuring consistent parameter flow without empirical fitting. Computational case studies demonstrate size-dependent anisotropy enhancement, surface spin disorder, strain-tunable switching, and doping trends, revealing design principles inaccessible to experiment alone. Validation against benchmarks, e.g. Curie temperature, anisotropy constants, coercivity, magnetostriction, confirms predictive accuracy. Current challenges, e.g., U$-$parameter sensitivity, realistic surface chemistry, spin-lattice coupling, and large-scale integration are discussed alongside emerging directions including DFT$+$DMFT, coupled dynamics, and machine-learned potentials.

An analytical expression for the diffusion wall time of a toroidally segmented conducting shell (the Armadillo configuration) is derived by extending the continuous-shell formulation to include the non-axisymmetric current pattern imposed by the presence of toroidal gaps. The segmentation constrains the toroidal current to follow a standing-wave structure that vanishes at the gap locations, introducing a correction to the effective resistivity that grows quadratically with the number of gaps and competes with the intrinsic toroidal scale of the mode. As a result, the wall time decreases rapidly for low toroidal-number modes, more gradually for intermediate ones, and only for sufficiently large segmentation in the high-n regime. The analytical formula shows agreement within 10% against 3D electromagnetic numerical calculations. The resulting expression provides a compact tool for estimating the wall time of segmented conducting structures surrounding the plasma, with direct applications to MHD stability and control in both RFPs and tokamaks.