Papers

In this work we present a pedagogically motivated analysis of spin-correlation calculations in a quantum system composed of two spin-$1/2$ particles. Rather than aiming at new physical results, our purpose is to clarify and bring attention to different strategies for evaluating expectation values of the form $\langle \psi | S^{(1)}_{\hat{\boldsymbol{u}}} S^{(2)}_{\hat{\boldsymbol{v}}} | \psi\rangle$, which play an important role in discussions of entanglement and Bell-type correlations. We compare three complementary approaches. The first follows a direct algebraic evaluation in the product basis, closely related to standard textbook methods. The second uses a matrix representation of bipartite states, in which the tensor-product structure is expressed in terms of $2\times2$ complex matrices. This representation keeps the calculation close to the familiar Pauli-matrix algebra and makes the independent action of operators on each subsystem more transparent. The third explores a symmetry-based argument, highlighting both its usefulness and its limitations when applied beyond the singlet state. We show explicitly that the singlet state is rotationally invariant, which explains why the symmetry argument successfully reproduces its correlation function, while a naive extension fails for triplet states. The discussion illustrates how entanglement, tensor-product structure, and rotational symmetry interplay in spin correlations.

Nonlinear optical (NLO) responses in two-dimensional quantum-confined systems are typically described within bulk-based frameworks as macroscopic spatial averages. In finite-thickness van der Waals multilayers directly relevant to nanoscale devices, this picture substantially breaks down. Here, we establish a general symmetry-based framework for classifying second-order NLO responses in multilayers. We reveal a layer-resolved organization into skin, weak-skin, and hidden effects governed by local symmetry and stacking order. First-principles calculations for both nonmagnetic and spin-polarized systems confirm our predictions, demonstrating that stacking alone suffices to dramatically reshape both the spatial pattern and magnitude of the NLO response, a phenomenon not explainable within standard bulk theory. Our results establish stacking geometry as an effective knob for engineering surface-selective NLO responses in layered materials.

Resistive wall mode is one of the leading causes for tokamak disruptions above the no-wall $\beta_N$ limit. This paper presents nonlinear three-dimensional resistive MHD simulations of an RWM-induced disruption in a CFETR baseline steady-state equilibrium using the NIMROD code. Linear calculations confirm the dominant presence of the $n=1$ RWM instability, whose growth rate is strongly sensitive to the wall response and becomes weakly dependent on plasma resistivity in the high-$S$ limit, along with a global external-kink-like structure. In the nonlinear phase, the RWM drives rapid flux surface stochastization and a thermal quench, followed by a current quench that is intensified by the post quench increase of Spitzer resistivity. The transient current spike before the current quench is shown to be the outcome of the conservation of poloidal flux and a rapid reduction of internal inductance. During the late current quench stage, closed flux surfaces partially reform from the core region to the edge, relaxing toward the force-free state. Toroidal mode coupling, parallel heat transport, plasma resistivity, and wall conductivity strongly modulate the disruption onset and the quench dynamics. Within the MHD model, these results provide a complete view on the RWM-driven disruption process in advanced tokamak configurations.

The Neural Spectral Element Method (NSEM) evaluates each network only at fixed Legendre-Gauss-Lobatto quadrature nodes and replaces all derivative calls with precomputed spectral differentiation matrices. The resulting deterministic loss enables limited-memory BFGS (L-BFGS) to reach residuals of 10^-9 to 10^-10. A Kosloff-Tal-Ezer coordinate map resolves electrochemical boundary layers, while a mesh-free neural mortar framework couples multi-element domains. On the four-example Poisson-Nernst-Planck (PNP) benchmark of Huang and co-workers, NSEM attains 10^-4 to 10^-7 relative pointwise error with two orders of magnitude fewer collocation points than the adaptive-resampling PINN baseline. Both a tanh multilayer perceptron (MLP) and a basis-aligned Legendre Kolmogorov-Arnold Network (KAN) backbone attain spectral accuracy within the same NSEM infrastructure, with the KAN requiring roughly half the Adam steps to enter the L-BFGS basin of attraction on the 1D PNP benchmark.

Reduced-order models (ROMs) are widely employed to describe complex system dynamics when simulations with full-order models (FOMs) are computationally prohibitive. This study presents POD-AS-PRS, a novel model-reduction framework based on the active subspaces (AS) technique, which performs dimensionality reduction in both the state and parameter spaces, enabling efficient and high-fidelity approximations of quantities of interest (QoI). The approach employs proper orthogonal decomposition (POD) to extract low-dimensional coefficients from CFD snapshots, which are inputs to a residual neural network (ResNet) with linear layers to learn their nonlinear mapping to QoI. Reverse-mode automatic differentiation (AD) is utilized to compute gradients with respect to the coefficients, enabling AS analysis to identify influential modes by shifting the analysis to the POD coefficient space, thereby achieving a dual-stage dimensionality reduction driven by QoI sensitivity rather than modal energy. A surrogate model is subsequently constructed using a polynomial response surface (PRS) based on AS-derived active variables, retaining only the highly influential POD coefficients to ensure accurate and efficient QoI reconstruction. The framework is validated on periodic and chaotic bluff-body flows, demonstrating high accuracy with few influential parameters, while AD-based gradients achieve a two-order-of-magnitude speed-up over finite-difference approximations. Sensitivity analysis further reveals that the influential coefficients are not necessarily proportional to modal energy, highlighting the critical flow structures. Consequently, POD-AS-PRS identifies a low-dimensional manifold of sensitivity-dominant parameters that govern the QoI, elucidating the essential flow structures and their coupling with control parameters, thereby enabling efficient and accurate QoI reconstruction.

The deterministic incompressible Navier-Stokes equations are physically incomplete: any viscous fluid at finite temperature must exhibit thermal fluctuations whose form is dictated by the fluctuation-dissipation relation. We formulate the stochastic Navier-Stokes equations with the kinematically selected deformation Laplacian on compact Riemannian manifolds with strictly negative Ricci curvature. The fluctuation-dissipation relation, derived from a topological (Poincaré lemma) argument, uniquely determines the noise from the viscous operator. For the spectrally truncated system, we prove that the unique stationary distribution is the Gibbs measure (Gaussian in the mode amplitudes, because the nonlinear convective terms preserve energy), and that convergence to equilibrium is exponentially fast with rate at least $2\nu\lambda_\Def$, where $\nu$ is the kinematic viscosity and $\lambda_\Def$ is the spectral gap of the deformation Laplacian. The spectral gap satisfies $\lambda_\Def \geq \kappa^2$ when $\Ric \leq -\kappa^2 g$, and is independent of the volume of the domain. On flat space, the analogous thermalisation rate vanishes in the infinite-volume limit. The equilibrium velocity-velocity correlation function decays exponentially in geodesic distance, in contrast to the algebraic decay on flat space. These results provide a rigorous statistical-mechanical foundation for viscous fluids on negatively curved manifolds and illustrate how the geometry of the domain controls not only the deterministic dynamics but also the approach to thermal equilibrium.

Biomolecular condensates must assemble at specific locations and times inside living cells to perform their biological functions. However, it remains unclear whether condensate formation can achieve sufficient spatiotemporal precision, responding sensitively to local chemical modifications while remaining robust to fluctuations in protein concentration. Here we study chromatin-associated BRD4 assemblies to determine whether co-condensation with chromatin can provide the degree of discrimination required for selective targeting of highly acetylated chromatin regions. Using an ultra-coarse-grained molecular-dynamics model, we find that heterogeneous nucleation alone does not provide robust spatial discrimination, whereas co-condensation leads to spatial selectivity and rapid formation of BRD4 assemblies across a broad range of subsaturated conditions. Multivalent binding between BRD4 and acetylated histone tails sharpens the dependence of co-condensation on acetylation density through combinatorial effects, increasing discrimination between highly acetylated regions and weakly acetylated background chromatin. Together, these results show how co-condensation and multivalent binding jointly enable the sensitive, concentration-robust spatiotemporal discrimination required for selective chromatin-associated assembly.

Topological textures of light offer powerful routes for structuring optical fields, controlling wave transport, and manipulating matter. Skyrmions, long studied as topological solitons in vector fields, have recently been extended to scalar wave systems, including acoustics, hydrodynamics, and plasmonics. However, their realization in two-dimensional scalar wave propagation with nonlinearities and in quantum fluids remains uncharted. Here, we establish such a Skyrmion framework for scalar fields in optical microcavities. With focus on exciton-polaritons, we show that nonresonant excitation without imposed phase can spontaneously generate isolated Skyrmions and self-organized Skyrmion lattices in a polariton condensate. We trace this mechanism to gain- and loss-induced phase curvature together with outward polariton flow. We further demonstrate that polariton nonlinearities provide all-optical control of these textures, enabling switching of the Skyrmion number and reconfiguration of Skyrmion moiré lattices through resonant and nonresonant excitation schemes. These results establish nonlinear non-Hermitian resonators as a versatile platform for the spontaneous generation and active control of scalar topological light fields.

String theory is a minefield for philosophers in terms of conceptual difficulty and mathematical technicality. Presentations aimed at philosophers generally focus on providing precise and technical mathematical descriptions of toy models and inter-theoretical toy derivations to give an idea of what is at play in the field. This presentation takes the opposite approach, filtering out most mathematical considerations to provide an overview of the field, eyes on the ontology. The aim is to provide a compass for metaphysicians to find their way around string theory and the philosophy of string theory. The development of metaphysical arguments based on string theory against the fundamentality of spacetime is deferred to the next chapter.

Electronic absorption spectra of multichromophoric systems are often governed by complex excited-state structures arising from excitonic and charge-transfer (CT) interactions between chromophores, while direct identification of the underlying electronic transitions is frequently hindered by strong vibronic and solvent-induced broadening. In this paper, we develop a theoretical framework for the analysis and decomposition of absorption spectra of molecular dimers with coupled Frenkel exciton (FE) and charge-transfer states, including solvent-induced stabilization of zwitterionic configurations within a unified adiabatic-state formalism. The analysis reveals that exciton-CT mixing strongly reorganizes the electronic absorption profile and produces pronounced spectral broadening, while leaving the first spectral moment essentially unaffected. Numerical calculations show that the dominant mechanism of CT-induced broadening originates primarily from additional energetic splitting between spectral components rather than from broadening of the individual bands themselves. The electronic model is further extended to include coupling to high-frequency intramolecular vibrations and low-frequency environmental degrees of freedom, providing a practical framework for interpretation of realistic experimental spectra. The developed formalism is applied to the absorption spectrum of a covalently linked 9,10-bis(phenylethynyl)anthracene dimer in dichloromethane, where spectral decomposition reveals a predominantly excitonic low-energy doublet and higher-energy states with substantial CT character. The proposed approach offers a physically transparent framework for the analysis of complex absorption spectra in molecular aggregates and organic electronic materials with coupled excitonic and CT states.

In quantum mechanics, a wavepacket acquires a geometric phase, known as the Berry phase, as it evolves along a closed trajectory in parameter space. In condensed matter systems, the Berry phase underlies a broad range of phenomena, including the anomalous Hall effect, orbital magnetism, and electric polarization. However, in centrosymmetric materials possessing time-reversal (TR) symmetry, its manifestation is suppressed and effectively vanishes. When a system is driven by a strong terahertz (THz) field, it can be coherently driven far from equilibrium, transiently reshaping its symmetry on sub-picosecond timescales. This capability opens new avenues for quantum control with potential applications in information processing and sensing. Here, we experimentally demonstrate that a strong THz field can transiently break inversion symmetry in MgO, inducing a dynamical complex Berry phase, thereby manipulating the topological properties of the material. Applying high-harmonic generation (HHG) spectroscopy, we directly resolve the Berry phase, accessing both its real and imaginary components. The first is associated with coherent intraband dynamics while the second with quantum tunneling through a potential barrier. This observation enables the reconstruction of the time-dependent evolution of the Berry phase within the cycle of the THz field. The coherent manipulation of solids with strong fields, combined with attosecond-resolved HHG spectroscopy, represents a fundamental step toward unveiling and controlling geometric quantum phenomena in condensed matter systems.

This paper explores the design of multibeam metasurface (MTS) antennas excited by multi-directional plane-wave launchers. First, we solve the fundamental yet open problem of a plane surface wave (SW) that propagates obliquely to the modulation direction of a sinusoidally modulated MTS. Closed-form expressions are provided to accurately predict the beam pointing angles for any propagation direction of the illuminating plane SW and for any Floquet harmonic. Then, the proposed formulation is used to design a highly directive multibeam MTS antenna at K-band with linear polarization. The designed antenna combines an MTS and a pillbox quasi-optical beamformer arranged in a very compact space. A pillbox is a double-layer structure that embeds a reflector coupled to multiple primary feeds in the lower layer. The beams launched by the primary feeds are thus transformed into plane waves with different directions in the upper layer. Printing a modulated MTS in the upper layer results in a low-profile, multibeam antenna suitable for PCB fabrication. Experimental results validate the proposed formulation. The fabricated MTS antenna exhibits a maximum directivity of 30.5 dB and a 17.5% fractional -3 dB directivity bandwidth over the 19.7-21.7 GHz band. By switching feeds and modifying the operating frequency, the scanning range is [$3^o$,$35^o$] in elevation and [$-76^o$,$+76^o$] in azimuth. Additionally, different antenna designs are included that demonstrate the validity and generality of the derived formulation. The proposed multibeam concept can be exploited in satellite communications and 5G/6G networks.

Gyrotrons are essential for electron cyclotron resonance heating (ECRH) in fusion reactors, making their efficient operation crucial for fusion energy. Past experiments revealed instability issues due to trapped electrons in the magnetron injection gun (MIG) region, causing undesired currents and operational failures. To address this, tight manufacturing tolerances are required for the MIG geometry~\cite{pago2}. We present findings of the TRapped Electrons eXperiment (T-REX) at the Swiss Plasma Center, designed to understand electron cloud physics in gyrotron MIGs. T-REX replicates MIG geometries, electric and magnetic fields, and is supported by the 3D FENNECS code. The setup includes two coaxial electrodes in a vacuum chamber atop a superconducting magnet; a central electrode is biased to negative DC voltages and an outer one is grounded, creating a radial electric field up to 2 MV/m and an axial magnetic field B < 0.31 T. Initial discrepancies between experiments and simulations were linked to the diocotron instability, leading to FENNECS being upgraded to 3D and a dedicated set of diagnostics for T-REX. This instability causes the electron cloud to collapse and reform at a frequency depending on plasma conditions. Within this article, time-resolved current measurements on the outer electrode and top flange are presented. Further, a fast current probe array installed at the top flange is detailed. Measurements highlight rotating structures in the electron cloud resulting from the diocotron instability. Simulations show remarkable agreement with experiments, especially regarding the cloud's build-up/collapse frequency, and the rotation frequency and direction of the modes. These results improve our understanding of non-neutral plasmas in environments mimicking a real gyrotron MIG, paving the way for better gyrotron reliability.

Lithium niobate photonic crystal nanobeam cavity (PCNBC) represents a premier platform for integrated electro-optics, offering deep sub-wavelength mode confinement, enhanced light-matter interactions, and ultralow power consumption. However, accurate characterization of the electro-optic (EO) tuning efficiency in such high-Q devices is fundamentally impeded by DC drift, a time-dependent spectral instability arising from charge redistribution, surface screening, or buffer layer relaxation under sustained electric fields. Here, we report the systematic analysis of DC drift dynamics in lithium niobate nanocavities and demonstrate that conventional quasi-static DC voltage scanning yields highly unreliable characterization data. To circumvent this limitation, we introduce a drift-free, dynamic measurement methodology that employs high-frequency triangular-wave voltage sweeps to effectively decouple the instantaneous electronic Pockels response from slow charge-relaxation processes. Validated across 35 devices with varying electrode geometries, our method delivers reproducible tuning efficiency of 4.3-4.5 pm/V with a low coefficient of variation of 1.1%, showing excellent quantitative agreement with three-dimensional finite-element simulations. This robust, drift-free measurement technique establishes a rigorous standard for the characterization and optimization of resonant cavity electro-optics, accelerating the development of high-performance thin-film lithium niobate photonic integrated circuits.

In structured electromagnetic fields, polarization textures are often closely linked to the spatial variation of the energy flow. However, this familiar picture has been established mainly for lossless and isotropic settings, and concrete examples showing how it is modified in media with gain and loss remain limited. Here, we demonstrate that optical skin modes associated with the non-Hermitian skin effect (NHSE) carry a finite transverse circular-polarization texture and further show that the accompanying in-plane electric-field spin texture deviates from the familiar lossless spin-flow picture. Using exact TE mode solutions, we separate the common exponential skin envelope from the oscillatory component. This decomposition shows that the circular-polarization texture is not generated by the skin envelope itself but by the oscillatory interference component modified by non-Hermiticity. It also reveals a handedness bias and a reshaped spatial relation between circularity and intensity. Finite-element calculations confirm that these features remain robust in loss-biased anisotropic media. These results show that gain and loss provide additional freedom for engineering electric-field spin textures beyond conventional lossless photonic settings.

We present an algorithm that computes the action of a generic spin Hamiltonian on a state vector on the fly, entirely avoiding explicit matrix assembly. This is achieved through mixed-radix indexing of the full tensor-product basis, which translates local spin operations into simple integer offsets. The result is an explicit framework for evaluating single- and two-site terms across arbitrary spin lattices, including mixed-spin systems. Our construction bridges the basis-indexing logic familiar from exact diagonalization with the matrix-free state-update philosophy of address-based frameworks. By writing the indexing logic in closed form, a single uniform loop applies to every site regardless of its local Hilbert-space dimension. The method is parallelizable and memory-conserving, and can be extended to restricted basis or truncated bosonic levels.

We measure picosecond dynamics of labyrinthine stripe domains with chiral Néel-type domain walls using time-resolved x-ray resonant magnetic scattering (XRMS). At the stripe-domain wavevector, the helicity-summed signal shows ultrafast demagnetization, recovery, and a weak oscillatory contribution that we identify as a signature of laser-launched coherent surface phonons. In contrast, the dichroic signal, which is sensitive to the in-plane magnetization inside the Néel walls, shows a strong oscillatory response whose frequency decreases with increasing pump fluence. We attribute this softening to pump-induced changes in the effective anisotropy and saturation magnetization, which modify the restoring field of an internal domain-wall mode. Time-resolved XRMS thus isolates wall-specific dynamics and provides access to internal domain-wall motion in disordered stripe textures on picosecond time scales.

We report on the development, construction, and integration of a remote automation controller NA64-DTC for the so-called “DESY Table” motorised platform widely used at CERN East-Area and North-Area experimental installations. The device is based on an ESP32-C3 System-on-Module that interfaces with the table manual control panel via a signal duplication connector. Button presses are emulated through opto-isolated switches, to allow for simultaneous operation with the manual system without modifying the original hardware. A HTTP server running on the controller allows interfacing with the device, enabling experiment-specific integration solutions. \\ The device was successfully commissioned at CERN, exploiting the experimental installations at PS-T9 and SPS-H4 beamlines. This work describes in detail the device technical design and operation, as well as the performance obtained during the commissioning operations. Although initially conceived for the NA64 experiment, the proposed solution can be of interest to all CERN experiments making use of the DESY Table platform, enabling remote and automated operation while reducing manual intervention during beam activities.

Identifying minimum-energy structures of lattice proteins is a challenging discrete optimization problem. Quantum approaches such as analog quantum annealing and the gate-based quantum approximate optimization algorithm (QAOA) can address this problem after mapping it to a binary representation, which typically involves introducing penalty terms to enforce valid chain configurations. However, in this and many related problems, the use of quadratic penalty terms can be avoided by restricting the search space to independent sets in a conflict graph and using a QAOA mixer designed for the maximum independent set problem. In this work, we implement and explore this QAOA variant for lattice protein folding. Here, the objective function consists solely of the protein energy together with a simple linear bias term, without quadratic penalties. We validate this approach through classical simulations of the quantum circuits for lattice proteins of lengths $N=4$ and $N=6$. To explore larger systems, we further introduce a heuristic iterative local-search scheme, with which we successfully fold lattice proteins with lengths up to $N=14$ using local subgraphs with at most 26 qubits.

Correct estimation of the heating power of magnetic nanoparticles is important for magnetic hyperthermia treatment. This work investigates the impact of sample insulation in AC calorimetry. We show that temperature increase in the insulation can lead to systematic errors when estimating the heating power by the corrected slope method. The errors arise if the temperature of the sample environment is kept fixed at its initial temperature in the data analysis. To correct for this, we propose the use of a local temperature difference between the sample and the sample environment.

Three-dimensional inelastic computational homogenization of complex engineering components requires a multitude of nonlinear microstructural simulations, making it computationally expensive. This work investigates a projection-based model order reduction (pMOR) method with 'Sequential Subspace Mode Adaptation', which can be easily integrated into existing codes using linear subspaces. Starting with a 'conventional' linear subspace strain approximation, the dynamic online construction of a second – lower dimensional – affine subspace embedded in the linear subspace determined offline leads to a further reduction of the dimensionality. A second novelty is the outline of the E3C hyper-reduction method for non-crystalline dissipative materials with internal variables, introducing a viscous regularization of non-differentiable stress-strain relations. In addition, a theoretical discussion is provided, illustrating that the E3C method aims at satisfaction of a projected and hyper-reduced variant of the classical Hill-Mandel macro-homogeneity condition. The latter theoretically implies equivalence with the high-dimensional model and satisfaction of both the hyper-reduced weak equilibrium and compatibility conditions. The influence of training batch size, material nonlinearity, and microstructure on the performance are evaluated through parameter studies. Three-dimensional elastoplastic two-scale simulations with hundreds of thousands of macroscopic degrees of freedom illustrate the efficiency and accuracy, with computational times approaching those of single scale simulations.

In magnetic confinement fusion reactors, hydrogen plasma irradiation causes material saturation and recycling, where hydrogen released from the tungsten wall significantly impacts the peripheral plasma. Kinetic Monte Carlo (kMC) simulations are essential for investigating the dynamic balance between incident and emitted fluxes at the atomic scale. However, standard kMC frameworks are inadequate for handling realistic material complexities, such as polycrystalline structures and dynamic evolution under irradiation, being computationally bottlenecked by continuous transition parameter updates. Conventionally, evaluating migration barriers in disordered systems (e.g., grain boundaries) relies on computationally prohibitive on-the-fly atomistic calculations like the Nudged Elastic Band (NEB) method. Here, we present a deep learning-accelerated Dynamic kMC framework that eliminates this reliance. Our approach integrates a three-stage deep learning pipeline: a pix2pix model for predicting local 3D potential energy distributions, a U-Net for extracting hydrogen trapping sites, and a 3D-CNN for directly evaluating migration barriers. To achieve macroscopic timescales, we implemented a hierarchical spatial index combined with a differential local-update algorithm operating in O(1) complexity. This architecture restricts recalculations to the immediate vicinity of moving atoms, accelerating updates. Demonstrated on a large-scale realistic polycrystalline tungsten model, the framework successfully reproduces preferential hydrogen trapping along grain boundaries, bridging the gap between atomic-scale accuracy and macroscopic timescales for full-scale plasma-wall interaction simulations.

We propose a novel computational toolbox that integrates Topological Data Analysis (TDA), Differential Box Counting (DBC), Multifractal Partition (MFP), and Local Binary Patterns (LBP), applied to time-lapse super-resolution STED microscopy images of sodium caseinate gelation induced by glucono-delta-lactone (GDL) at 30 {\deg}C and 40 {\deg}C and two GDL concentrations (1.8% and 3.5% w/v). TDA tracked topological loops, closed ring-like structures reflecting protein network interconnectivity, via max-Betti-1 curves, which revealed a lag phase of dispersed aggregates, a sharp decay coinciding with network percolation and the rheologically observed sol-gel transition, and a post-gelation increase corresponding to network rearrangements. These topological transitions were corroborated by DBC and MFP as these methods were able to resolve changes in structural complexity and spatial heterogeneity. The toolbox was validated on simulated fractal images prior to experimental application. Together, these descriptors provided sensitivity to subtle microstructural transitions that bulk rheology captured as averaged bulk mechanical responses. This integrated approach provides a robust quantitative tool for characterizing complex microstructure in food and material science with evolving microstructural dynamics. Code is available at https://github.com/Zahratabatabaei/Delifood_CV_paper.git

Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects. In supervised machine learning frameworks trained on simulated data, discrepancies between the noise characteristics of simulated and acquired signals introduce a form of covariate shift, whereby the input signal distribution differs between training and inference. We investigated the impact of this mismatch on microstructure parameter estimation and propose a realistic noise synthesis (RNS) framework to mitigate it. RNS incorporates both the Rician expectation and the effective post-processing noise variance into simulated training signals. The Rician expectation was modelled using a noise standard deviation estimated with MPPCA, while the effective standard deviation was derived from spherical harmonic residuals of preprocessed data. The method was evaluated using the cylinder-zeppelin and the SANDI models on simulated datasets across multiple SNR levels and on in vivo diffusion data with repeated acquisitions. Sensitivity to noise misestimation was also assessed. Ignoring magnitude-induced noise effects during training produced systematic, SNR-dependent parameter bias, particularly at low SNR. Incorporating the Rician expectation substantially reduced bias to the level of noise-aware nonlinear least-squares fitting. Modelling the effective standard deviation further improved precision. Performance was largely independent of regression architecture but sensitive to accurate noise estimation. These findings demonstrate that realistic noise modelling in simulated training data mitigates signal-domain covariate shift and is essential for unbiased supervised microstructure estimation, particularly in low-SNR regimes associated with high b-values or high spatial resolution.

Reconstructing spatially continuous daily temperature fields from sparse observations is important for urban climate monitoring and heat-risk analysis, but practical deployments are limited by sensor budgets and spacing constraints. This study proposes an uncertainty-aware graph neural network (GNN) framework for reconstructing daily maximum temperature fields from sparse sensors while supporting distance-constrained sensor placement and probabilistic exceedance mapping. The model predicts both the temperature field and a spatially varying predictive uncertainty field using a graph-attention-based mean-residual architecture trained with a Gaussian negative log-likelihood. Sensor placement is addressed using a Proper Orthogonal Decomposition with QR factorization (POD-QR) strategy with a 4 km minimum inter-sensor distance constraint and is compared with random feasible placement and farthest-point sampling. The framework is evaluated over a Montreal-area polygon using Daymet v4.1 daily temperature data (1 km resolution) under a strict temporal hold-out protocol (training: 2020-2023; testing: 2024). Across sensor budgets (10-40 sensors), the proposed GNN consistently outperforms inverse distance weighting and ordinary kriging in RMSE and MAE on unobserved nodes. Sensor-placement effects are most pronounced at low budgets and diminish at higher budgets, with a practical saturation regime emerging around 30 sensors under the imposed spacing constraint. Probabilistic evaluation further shows improved uncertainty calibration with increasing sensor density and a better sharpness-calibration trade-off than kriging. These results support the proposed framework as an effective tool for uncertainty-aware temperature field reconstruction and decision-oriented heat-risk mapping.

Multipolar decomposition is a powerful tool for analyzing and designing metasurfaces, but its practical application is often limited by the mathematical complexity that arises when a large number of multipole moments must be taken in to account. To minimize this modeling complexity without sacrificing accuracy, we present an efficient method that exploits the coordinates origin dependence of spherical multipole moments. We show that the optimal origins for minimizing higher-order contributions, such as quadrupoles and octupoles, depend strictly on the spatial parity of the electromagnetic response. This is achieved by modeling a metasurface response using multipolar generalized sheet transition conditions (GSTCs). By separating the GSTCs into independent even and odd parity components, we can evaluate the electric and magnetic discontinuities at distinct physical positions. This parity-splitting framework allows us to systematically suppress unwanted higher-order terms and reconstruct the complete scattering parameters using only the dipole moments. We validate our analytical approach using two numerical examples: vertically asymmetric dielectric cones on a substrate, and a horizontally symmetry-broken metasurface supporting a double quasi-bound state in the continuum resonance. In both cases, the retrieved scattering parameters show excellent agreement with full-wave simulations. This method provides a simple, physically intuitive framework that simplifies the modeling of geometrically complex and non-local metasurfaces down to a purely dipolar level.

Multiple formulations of the Energy Return on Investment (EROI) coexist in the literature, differing mainly in their treatment of self-consumption and external direct energy inputs. This article shows that these differences are not merely conventional: they determine whether EROI measures the net energy surplus available to society or the internal conversion efficiency of the production process. By benchmarking three established formulations against theoretical limit cases, we demonstrate that only the external variant (EXT), which excludes self-consumed energy from the investment term, correctly measures the net energy surplus available to society. The internal (INT) and standard (STD) variants instead converge toward measures of process efficiency. We further show that, in multi-source systems exchanging direct energy flows, consistency requires converting these flows into their upstream embodied energy equivalents before including them in the energy investment term. A generic reallocation formula is derived and shown to uniquely preserve aggregation consistency. Applied to U.S. oil and gas production (1919–2007) and China's fossil fuel sectors (1995–2010), the revised framework yields systematically higher EROI values than standard formulations, potentially placing the same physical system on opposite sides of the net energy cliff, with direct implications for energy transition assessment and policy.

Generation and detection of coherent phonons in semiconductors by femtosecond optical pulses is a powerful tool for high-frequency acoustic control of their electronic properties. Here, we demonstrate a propagating one-dimensional acoustic grating in bulk semiconductors using above-band-gap excitation by a train of laser pulses with high repetition rate of 1 GHz. This approach enables shaping of the coherent acoustic phonon spectrum and leads to a significant enhancement of Brillouin light scattering at selected probe wavelengths in pump-probe configuration. We demonstrate this effect at a cryogenic temperature of 5 K in prototypical semiconductor systems, namely bulk crystalline GaAs and (Cd,Zn)Te, which serve as benchmark materials for the proposed method. The spectral dependence of the time-domain Brillouin light scattering amplitude exhibits resonant peaks at discrete probe wavelengths arising from Bragg reflection of the probe light by the propagating acoustic grating. A 10-30-fold resonant enhancement of the signal amplitude is observed for GaAs and (Cd,Zn)Te, determined by the finite spectral width of the probe pulse. With further spectral narrowing, enhancements of the order ~ 100-150 are expected, set by the number N of strain pulses in the acoustic grating within the sample and ultimately limited by the material parameters and sample thickness.

Climate change is expected to increase heatwave risk, but exceedance frequency alone cannot explain why some regions show stronger amplification in event persistence. This study develops an integrated event-dynamical workflow to diagnose changes in warm-season heatwaves and link them to coherent, multiscale structures of temperature variability. Heatwaves are identified over southern Canada using a fixed historical 90th percentile threshold (2001-2010 reference, 15-day moving window) and a minimum-duration criterion. Events are summarized using frequency (HWF, HWN), persistence (HWMD, HWD), and intensity (HWI, HWM) metrics. The daily mean temperature field is analyzed using multiresolution dynamic mode decomposition (mrDMD). Event and dynamical perspectives are connected through heatwave-conditioned mode participation ratios and spatial alignment analyses between mode-energy footprints and gridded heatwave metrics using hotspot overlap and Spearman rank association. The workflow is applied to CORDEX-NAM12 regional simulations (CRCM5 downscaling of CanESM5) under SSP5-8.5 for 2016-2025, 2051-2060, and 2091-2100. Results show a clear shift toward persistence-dominated heatwave regimes in the continental interior. By the late century, increases in seasonal heatwave days are accompanied by much longer events, with regional HWMD reaching about 26.66 days/event and HWD about 69 days, together with stronger above-threshold intensity, with HWI reaching about 6.88 K. Dynamical diagnostics indicate a redistribution of dominant activity toward lower-frequency levels and weaker effective damping in interior regions, while coastal and maritime regions show smaller changes. Heatwave-relevant low-frequency modes remain active during long events and align with persistence and intensity hotspots, supporting a process-informed interpretation of regional heatwave amplification under climate change.

Thermalization of light, where photons acquire a temperature and chemical potential analogous to a material gas, remains a striking yet experimentally elusive manifestation of quantum statistical physics. To date, it has been realized only in carefully engineered photonic environments that enforce repeated absorption-emission cycles. Here we show that such thermalization can emerge in a radically simpler setting: an open scattering medium. Using a pumped fluorescent dye solution doped with colloidal particles, we demonstrate that multiple scattering alone suffices to trap photons long enough to drive them to thermal equilibrium. The emitted radiation follows a Bose-Einstein distribution with a finite chemical potential, independently tunable via optical pumping, while its temperature is set by the host medium. A clear spectroscopic signature of thermalization is observed as a plateau at the sample temperature over a finite spectral range. Our results establish disordered scattering media as a generic platform for photon thermalization, extending this fundamental phenomenon beyond resonant cavities and opening new routes towards cavity-free photonic thermodynamics and thermal light sources operating under ambient conditions.