Papers

The development of novel quantum many-body computational algorithms relies on robust benchmarking. However, generating such benchmarks is often hindered by the massive computational resources required for exact diagonalization or quantum Monte Carlo simulations, particularly at finite temperatures. In this work, we propose a new algorithm for obtaining thermal pure quantum states, which allows efficient computation of both mechanical and thermodynamic properties at finite temperatures. We implement this algorithm in our open-source C++ template library, Physica. Combining the improved algorithm with state-of-the-art software engineering, our implementation achieves high performance and numerical stability. As an example, we demonstrate that for the $4 \times 4$ Hubbard model, our method runs approximately $10^3$ times faster than $\mathcal{H}\Phi$ 3.5.2. Moreover, the accessible temperature range is extended down to $\beta = 32$ across arbitrary doping levels. These advances significantly push forward the frontiers of benchmarking for quantum many-body systems.

Understanding how transient dynamics unfold in response to localized inputs is central to predicting and controlling signal propagation in network systems, including neural processing, epidemic intervention, and power-grid resilience. Existing theoretical frameworks typically assume homogeneous network structures and constant or pulse-like inputs, overlooking how heterogeneity in structure and variety of input shape transient responses, often leading to discrepancies between theory and observation. Here, we develop a general theoretical framework that establishes quantitative relationships between the strength and timing of transient dynamics to various inputs in heterogeneous networks. Using a Neumann series expansion, we disentangle the distinct roles of self-dynamics and network structures beyond the scope of standard spectral theory, yielding intuitive and interpretable formulations. We show that node-to-node propagation can be represented as the cumulative effect of all directed walks, each weighted recursively by the self-dynamics of participating nodes. This framework further quantifies how heterogeneity, such as broad degree distributions or additional motifs, amplifies both response strength and time. Our results advance the understanding of transient dynamics across network structures and input types, extend the existing theory to more general settings, and provide practical guidance for optimizing transient responses.

While humans become more reliant on Earth's space environment, the potential for significant harm from severe space weather continues to grow. As structures from the sun reach Earth's magnetosphere and space environment, they deposit energy that fuels geomagnetic storms. Currently, space weather researchers work to predict the timing and intensity of space weather events, often providing warnings of several days prior to the initiation of a strong geomagnetic storm. Here a new paradigm is presented where, rather than prediction, active steps are taken to mitigate the impact of solar wind structures through temporarily modifying Earth's magnetosphere. Global magnetohydrodynamic simulations are used to demonstrate that artificial mass-loading Earth's dayside magnetosphere can fortify Earth's space environment against strong space weather events. The simulations and supporting analysis use realistic mass-loading from model spacecraft at geosynchronous orbit to show the validity of the enabling physics as well as technical feasibility with current technology. The results demonstrate that with modern technology, the intensity of a major geomagnetic storm could be actively reduced by 50 percent or more, protecting technology and human life.

Numerous astronomical and cosmological observations point to the existence of dark matter, which constitutes about 27% of the Universe. Despite extensive efforts, only the DAMA/LIBRA experiment, using NaI(Tl) detectors at Gran Sasso National Laboratory, has reported a positive dark matter signal. To independently verify this result, using the same NaI target is essential. This is the goal of ANAIS-112, operating with 112.5 kg of NaI(Tl) scintillators since 2017 at the Canfranc Underground Laboratory (LSC). This thesis presents work within ANAIS-112, focusing on data analysis and the development of Geant4-based simulations to reduce systematics and increase sensitivity. Efforts have focused on understanding the ANAIS-112 crystals' response to nuclear recoils. Systematic uncertainties related to the scintillation quenching factors (QFs) of sodium and iodine recoiling in NaI(Tl) must be addressed to ensure reliable comparisons with DAMA/LIBRA and other experiments. For that purpose, onsite neutron calibrations in ANAIS-112 have been performed since 2021 at LSC using 252Cf sources. This work has compared calibration data with Geant4-based neutron simulations, which has revealed Geant4 deficiencies in certain decay processes and cross sections in some versions. The simulations have proven highly sensitive to the QF used, favouring models in which the QF increases with energy and disfavouring DAMA values. In parallel, this thesis has contributed to improving the ANAIS-112 background model through a multiparametric fit of its background components. New physics searches have been conducted using ANAIS-112 data, including a reanalysis of the annual modulation signal and a search for solar axions using six years of exposure. Finally, this thesis also includes work within the COSINUS experiment, focused on background modelling and the impact of internal backgrounds on the experiment's sensitivity.

Obtaining truly representative pore-scale images that match bulk formation properties remains a fundamental challenge in subsurface characterization, as natural spatial heterogeneity causes extracted sub-images to deviate significantly from core-measured values. This challenge is compounded by data scarcity, where physical samples are only available at sparse well locations. This study presents a multi-conditional Generative Adversarial Network (cGAN) framework that generates representative pore-scale images with precisely controlled properties. The framework was trained on thin section samples from four depths (1879.50-1943.50 m) of a carbonate formation, simultaneously conditioning on porosity and depth within a single model. It processes RGB thin section images that preserve critical mineralogical information (anhydrite-dolomite differentiation, grain boundaries, porosity distinctions) lost in conventional grayscale representations, capturing characteristics from grainstone fabrics to crystalline textures with anhydrite inclusions. The model achieved strong porosity control (R^2 = 0.95) across all formations with mean absolute errors of 0.0099-0.0197. Morphological validation confirmed preservation of average pore radius, specific surface area, and tortuosity within acceptable tolerances. Two-point correlation (S2) analysis confirmed that generated images preserve the spatial continuity and characteristic length scales of natural pore networks, with results consistent across the imaging resolutions tested (1.8-3.0 micron/pixel). Validated against core sample properties, generated images showed higher property fidelity with dual-constraint errors of 1.9-12.4% compared to 37.5-713.6% for randomly extracted real sub-images. This capability provides practical tools for subsurface characterization, particularly valuable for carbon storage, geothermal energy, and groundwater management.

High-power laser technologies are essential in precision manufacturing, defense, and scientific research, where accurate control of the beam profile is paramount. Although several beam-shaping methods exist, they often face implementation and scalability challenges. To address these limitations, we introduce a comprehensive and versatile framework for on-demand beam engineering through coherent beam combining (CBC) systems to precisely craft far-field intensity distributions. The proposed approach integrates limitless key capabilities: (i) dynamic beam shaping through sequential steering, (ii) structured static beam shaping allowing the direct formation of target-defined profiles, and (iii) high-speed dynamic beam sequencing without mechanical movement. Thus, the proposed approach could be a potential one-stop solution to meet wide manufacturing requirements. Rapid reconfiguration is achieved through optimized phase control of the CBC channels, supported by a deep-learning-inspired optimization algorithm. This unified CBC framework significantly improves beam uniformity, power delivery efficiency, and scalability compared to conventional techniques, thus establishing a robust platform for next-generation laser systems in industrial manufacturing, materials processing, and directed-energy systems.

The Global Overturning Circulation (GOC) is a key component of the climate system, transporting heat, carbon, and salt throughout the global ocean. Previous reduced-dimensional models have sought to represent this three-dimensional circulation but often neglected three key observational features: (1) the meridional overturning circulation is in geostrophic balance below the Ekman layer, (2) diapycnal mixing is strongly enhanced near ocean boundaries, and (3) upwelling is partly driven by adiabatic dynamics in the Southern Ocean. Building on Callies and Marotzke (2012), we develop a reduced model that consistently incorporates all three by simulating temperature in latitude-depth space along the eastern and western boundaries of a semi-enclosed basin connected in the south to a zonally periodic re-entrant channel. The model clarifies how zonal temperature differences in the basin arise and are maintained through adiabatic and diffusive processes, giving rise to the geostrophic GOC. It also provides a transparent framework for understanding how geostrophic currents cross the equator to form the interhemispheric overturning, and how boundary-intensified mixing and Southern Ocean winds regulate polar downwelling rates. The reduced model shows good agreement with both a three-dimensional ocean model and theoretical scaling laws for stratification and overturning strength. Owing to its simplicity, it is well suited for long integrations exploring the GOC response under extreme forcing scenarios and offers a useful framework for testing eddy and mixing parameterizations.

The modeling of diffusion processes on graphs is the basis for many network science and machine learning approaches. Entropic measures of network-based diffusion have recently been employed to investigate the reversibility of these processes and the diversity of the modeled systems. While results about their steady state are well-known, very few exact results about their finite-time evolution exist. Here, we introduce the conditional entropy of heat diffusion in graphs, and outline a mathematical framework that contextualizes diffusion and conditional entropy within the theories of continuous-time Markov chains and information theory. In particular, we highlight that this entropic measure satisfies an information-theoretical version of the second law of thermodynamics, thereby providing a parallelism between diffusion dynamics on networks and their physical counterparts. Furthermore, we obtain explicit results for its evolution on complete, path, and circulant graphs, as well as a mean-field approximation for Erdös-Rényi graphs. We also obtain asymptotic results for general networks and provide bounds for the evolution of conditional entropy. Finally, we experimentally demonstrate several properties of conditional entropy for diffusion over random graphs, such as the Watts-Strogatz model.

Timbre allows us to distinguish between sounds even when they share the same pitch and loudness, playing an important role in music, instrument recognition, and speech. Traditional approaches, such as frequency analysis or machine learning, often overlook subtle characteristics of sound. Topological Data Analysis (TDA) can capture complex patterns, but its application to timbre has been limited, partly because it is unclear how to represent sound effectively for TDA. In this study, we investigate how different time delay embeddings affect TDA results. Using both synthetic and real audio signals, we identify time delays that enhance the detection of harmonic structures. Our findings show that specific delays, related to fractions of the fundamental period, allow TDA to reveal key harmonic features and distinguish between integer and non-integer harmonics. The method is effective for synthetic and real musical instrument sounds and opens the way for future works, which could extend it to more complex sounds using higher-dimensional embeddings and additional persistence statistics.

This study investigates the impact of wind velocity averaging on Dynamic Thermal Rating (DTR) calculations. It is based on a high-temporal-resolution (1 second) wind measurements obtained from a transmission line in Slovenia, Europe. Wind speed and direction variability are analysed, and two averaging methods, namely vector averaging, where velocity is averaged as vector, and hybrid averaging, where speed is averaged as scalar, are employed. DTR calculations are performed on both high-resolution data and averaged data (5 minute averaging window). It is demonstrated that averaging has a significant effect on both Nusselt number and ampacity, and the effect exhibits a strong angular dependency on the relative angle of the wind to the line. Therefore, two limit cases are studied: in the case of parallel wind, averaged data underestimates the ampacity, and there is a significant amount of cases where the underestimation is larger than 10 %. In the case of perpendicular wind, the two averaging methods affect the results in different ways, but both result in a substantial amount of cases where ampacity is overestimated, potentially leading to unsafe operation. The main takeaway of the study is that averaging wind velocity has a significant impact on DTR results, and special emphasis should be given to the averaging method, as different methods affect the results in different ways.

Tensor-network renormalization group (TNRG) is an efficient real-space renormalization group method for studying the criticality in both classical and quantum lattice systems. Exploiting symmetries of a system in a TNRG algorithm can simplify the implementation of the algorithm and can help produce correct tensor RG flows. Although a general framework for considering a global on-site symmetry has been established, it is still unclear how to incorporate a lattice symmetry in TNRG. As a first step for lattice symmetries, we propose a method to incorporate the lattice-reflection symmetry in the context of a TNRG with entanglement filtering in both two and three dimensions (2D and 3D). To achieve this, we write down a general definition of lattice-reflection symmetry in tensor-network language. Then, we introduce a transposition trick for exploiting and imposing the lattice-reflection symmetry in two basic TNRG operations: projective truncations and entanglement filtering. Using the transposition trick, the detailed algorithms of the TNRG map in both 2D and 3D are laid out, where the lattice-reflection symmetry is preserved and imposed. Finally, we demonstrate how to construct the linearization of the TNRG maps in a given lattice-reflection sector, with the help of which it becomes possible to extract scaling dimensions in each sector separately. Our work paves the way for understanding the lattice-rotation symmetry in TNRG.

In soft matter, the phase of nematic liquid crystals can be made from anisotropic molecules in single component materials, or as a suspension of mesoscopic nematogens. The later offers more versatility in the experimental design of complex shapes, in particular thin curved shells, and is often found in biological systems at multiple scales from cells to tissues. Here, we investigate theoretically the transition from three-dimensional nematics to a two-dimensional description restricted to a tangent plane, using a mean-field approach. We identify a transition from first to second order isotropic-nematic transition in presence of weak tangential anchoring. Then, we clarify the conditions under which a two-dimensional description of thin nematic shells is relevant. Nonetheless, using the example of active nematic stress, we identify physical differences between two- and three-dimensional descriptions in curved geometry. Finally, we construct a thin film approximation of nematohydrodynamics for a nematic shell in contact with a curved substrate. All together, those results show that a tangential restriction of nematic orientation must be used with care in presence of curvature, activity, or weak anchoring boundary conditions.

Decomposing arbitrary unitary transformations into physically realizable operations is fundamental to quantum information, quantum control, and linear optics. Layered programmable architectures implement such transformations by alternating tunable phase masks with fixed mode mixers. However, establishing their universality at fixed depth remains challenging: we show that neither a sufficient parameter count nor successful synthesis of sampled targets guarantees global coverage. We prove that an architecture is universal exactly when every reachable transformation can absorb a diagnostic input Fourier multiport by reprogramming its phase masks. The associated Fourier absorption residual (FAR) requires no external target ensemble, vanishes exactly for universal architectures, and bounds worst-target synthesis error. For four-mode Fourier architectures, a permutation obstruction rules out universality with five masks, even under small unitary mixer perturbations, whereas a constructive factorization establishes universality with seven. Beyond exact universality, comparison with a universal reference of the same depth yields certified uniform approximation bounds in terms of unitary mixer errors. Finally, numerical simulations using a geodesic phase optimizer illustrate the distinction between Fourier absorption and direct target synthesis.

Active control of the radiative properties of quantum emitters through engineered light-matter interactions is a key challenge in nanophotonics and quantum optics. In this work, we demonstrate dynamic modulation of dipole's decay rate by exploiting the tunable plexcitonic modes (graphene plasmons and QD-excitons) in the strong coupling regime. By integrating a quantum dot inside a graphene spherical shell and tuning the local optical response of hybrid modes via voltage-bias, we achieve continuous and reversible control over the decay rate, leading to significant enhancement or suppression of dipole emission from near- to far-infrared regime. Furthermore, the plexcitonic peaks shows much sharper linewidths in contrast to bare graphene plasmons even in the off-resonant coupling which indicates higher sensitivity of the systems at tuned wavelengths. We demonstrate the phenomenon with the numerical solution of 3D Maxwell's equations using MNPBEM tool. Our approach demonstrate a versatile platform for programmable emission control and offer a promising pathway for developing reconfigurable quantum photonic devices, such as tunable single-photon sources and ultrafast optical switches.

We investigate the interaction-induced resistivity of ultracold fermions in a three-dimensional optical lattice. In situ observations of transport dynamics enable the determination of real and imaginary resistivity. In the strongly interacting metallic regime, we observe a striking saturation of the current-dissipation rate towards a value that is independent of the interaction strength. This phenomenon is quantitatively captured by a dissipation model that uses a renormalized two-body scattering matrix. We further measure the temperature dependence of resistivity in the strongly interacting limit and discuss the predicted asymptotic high-temperature behavior. Our results provide a clear microscopic understanding of bounded resistivity of low-density metals, thus providing a useful benchmark for studies of strongly correlated atomic and electronic systems.

Adiabatic frequency conversion enables fast and efficient tuning of laser light by coupling it into an optical resonator whose eigenfrequency is varied on a timescale shorter than its photon lifetime. In this regime, the optical frequency follows the cavity resonance, allowing frequency shifts of several hundred gigahertz within sub-microsecond time - independent of optical power and without phase-matching constraints. While a linear dependence of the cavity resonance on a control parameter (e.g., applied voltage) suggests that arbitrary temporal signals could be linearly transferred to optical frequency changes, we show that this assumption fails near mechanical resonances of the resonator. Using a millimeter-sized lithium niobate whispering gallery resonator with a pronounced mechanical mode at 10.5 MHz, we observe strong deviations from linearity even when higher harmonics of the control signal coincide with this resonance. The experimental results are in excellent agreement with theoretical predictions. They demonstrate that mechanical resonances impose intrinsic limits on the linearity of adiabatic frequency conversion and other frequency control schemes based on the variation of the eigenfrequency of an optical cavity.

Multiferroic materials, particularly rare-earth orthochromates (RECrO$_3$), have garnered significant interest due to their unique magnetic and electric-polar properties, making them promising candidates for multifunctional devices. Although extensive research has been conducted on their antiferromagnetic (AFM) transition temperature (N$\acute{\textrm{e}}$el temperature, $T_\textrm{N}$), ferroelectricity, and piezoelectricity, the effects of doping and substitution of rare-earth (RE) elements on these properties remain insufficiently explored. In this study, convolutional neural networks (CNNs) were employed to predict and analyze the physical properties of RECrO$_3$ compounds under various doping scenarios. Experimental and literature data were integrated to train machine learning models, enabling accurate predictions of $T_\textrm{N}$, besides remanent polarization ($P_\textrm{r}$) and piezoelectric coefficients ($d_{33}$). The results indicate that doping with specific RE elements significantly impacts $T_\textrm{N}$, with optimal doping levels identified for enhanced performance. Furthermore, high-entropy RECrO$_3$ compounds were systematically analyzed, demonstrating how the inclusion of multiple RE elements influences magnetic properties. This work establishes a robust framework for predicting and optimizing the properties of RECrO$_3$ materials, offering valuable insights into their potential applications in energy storage and sensor technologies.

We develop a general theory of radiative heat exchange between dipoles with time-modulated optical properties. This framework extends fluctuational electrodynamics beyond equilibrium by incorporating nonstationary correlations and memory effects induced by temporal modulation. Closed-form expressions for the heat currents in modulated many-body systems are obtained, together with a generalized Landauer-like formulation of the pairwise exchanges, where the transmission coefficient accounts for all inelastic frequency-conversion channels. Near-resonant modulation redistributes and amplifies thermal fluctuations across Floquet sidebands, acting as a parametric amplifier of thermal radiation and enabling active, frequency-selective control of nanoscale heat transfer.

Elucidating the catalytic descriptor that accurately characterizes the structure-activity relationships of typical catalysts for various important heterogeneous catalytic reactions is pivotal for designing high-efficient catalytic systems. Here, an interpretable machine learning technique was employed to identify the key determinants governing the nitrate reduction reaction ($\rm NO_3RR$) performance across 286 single-atom catalysts (SACs) with the active sites anchored on double-vacancy $\rm BC_3$ monolayers. Through Shapley Additive Explanations (SHAP) analysis with reliable predictive accuracy, we quantitatively demonstrated that, favorable $\rm NO_3RR$ activity stems from a delicate balance among three critical factors: low $\rm N_V$, moderate $\rm D_N$, and specific doping patterns. Building upon these insights, we established a descriptor ($\psi$) that integrates the intrinsic catalytic properties and the intermediate O-N-H angle ($\theta$), effectively capturing the underlying structure-activity relationship. Guided by this, we further identified 16 promising catalysts with predicted low limiting potential ($U_{\rm L}$). Importantly, these catalysts are composed of cost-effective non-precious metal elements and are predicted to surpass most reported catalysts, with the best-performing Ti-V-1N1 is predicted to have an ultra-low $U_{\rm L}$ of $-0.10$ V.

As artificial intelligence-generated content (AIGC) reshapes knowledge acquisition, higher education faces growing inequities that demand systematic mapping and intervention. We map the AI divide in undergraduate education by combining network science with survey evidence from 301 students at Nanjing University, one of China's leading institutions in AI education. Drawing on course enrolment patterns to construct a disciplinary network, we identify four distinct student communities: science dominant, science peripheral, social sciences & science, and humanities and social sciences. Survey results reveal significant disparities in AIGC literacy and motivational efficacy, with science dominant students outperforming humanities and social sciences peers. Ordinary least squares (OLS) regression shows that motivational efficacy–particularly skill efficacy–partially mediates this gap, whereas usage efficacy does not mediate at the evaluation level, indicating a dissociation between perceived utility and critical engagement. Our findings demonstrate that curriculum structure and cross-disciplinary integration are key determinants of technological fluency. This work provides a scalable framework for diagnosing and addressing the AI divide through institutional design.

Wireless power transfer (WPT) with coupled resonators offers a promising solution for the seamless powering of electronic devices. Interactive design approaches that visualize the magnetic field and power transfer efficiency based on system geometry adjustments can facilitate the understanding and exploration of the behavior of these systems for dynamic applications. However, typical electromagnetic field simulation methods, such as the Method of Moments (MoM), require significant computational resources, limiting the rate at which computation can be performed for acceptable interactivity. Furthermore, the system's sensitivity to positional and geometrical changes necessitates a large number of simulations, and structures such as ferromagnetic shields further complicate these simulations. Here, we introduce a machine learning approach using Gaussian Process Regression (GPR), demonstrating for the first time the rapid estimation of the entire magnetic field and power transfer efficiency for near-field coupled systems. To achieve quick and accurate estimation, we develop 3D adaptive grid systems and an active learning strategy to effectively capture the nonlinear interactions between complex system geometries and magnetic fields. By training a regression model, our approach achieves magnetic field computation with sub-second latency and with an average error of less than 6% when validated against independent electromagnetic simulation results.

In our recent work (J. Phys. Chem. Lett. 2023, 14, 7680), we utilized the exact quantum dynamics results as references and proposed a general machine learning method to obtain the optimal decoherence time formula for surface hopping simulation. Here, we extend this strategy from one-dimensional systems to the much more intricate scenarios with multiple nuclear dimensions. Different from the one-dimensional situation, an effective nuclear kinetic energy is defined by extracting the component of nuclear momenta along the non-adiabatic coupling vector. Combined with the energy difference between adiabatic states, high-order descriptor space can be generated by binary operations. Then the optimal decoherence time formula can be obtained by machine learning procedures based on the full quantum dynamics reference data. Although we only use the final channel populations in 24 scattering samples as training data for machine learning, the obtained optimal decoherence time formula can well reproduce the time evolution of the reduced and spatial distribution of population. As benchmarked in a large number of 56840 one- and two-dimensional samples, the optimal decoherence time formula shows exceptionally high and uniform performance when compared with all other available formulas.

Quantum emitters coupled to traveling photons in waveguides, known as waveguide quantum electrodynamics (WQED), offer a powerful platform for understanding light-matter interactions and underpinning emergent quantum technologies. While WQED has been extensively studied in one dimension, two-dimensional (2D) WQED remains largely unexplored, where novel photonic scattering phenomena unique to higher dimensions are expected. Here, we present a comprehensive scattering theory for 2D WQED based on the Green function method. We show that the mean displacement between emitted and injected photons serves as a quantum analogue of the Goos-Hänchen shift. When a photon is injected into a single off-centered port, the quantum Goos-Hänchen (QGH) shift can be enhanced in backward scattering under resonant conditions with subradiant states. When a photon is injected into the center port, there is no QGH shift due to the mirror symmetry of structure. However, for multiple-port injection with transverse momentum, the QGH shift is recovered and proportional to the derivative of phase with respect to transverse momentum. Unlike the classical Goos-Hänchen shift, these effects can be flexibly tuned by the injected photon's frequency. Our work provides a general framework for exploring and manipulating photonic scattering in complex WQED networks.

Animal experiments play a vital role in drug discovery and development by providing essential data on a drug's efficacy, safety, and physiological effects before advancing to human clinical trials. In this study, we propose a stent-based flow sensor designed to measure airflow in the airways of laboratory animals. The stent was fabricated from biocompatible Ti using a combination of fiber laser digital processing and an origami-inspired folding technique. The sensing structure was developed through standard micro-electromechanical systems (MEMS) microfabrication technology. To integrate the sensing structure with the metallic stent, a facile insertion process was employed, where the sensor film was positioned at the stent's center using its natural buckling mechanism. Once fabricated, the stent implant was expanded and installed within an airway-mimicking tube to validate its functionality. A proof-of-concept trial using an artificial ventilator successfully demonstrated real-time respiration monitoring, confirming the feasibility of the proposed system for airflow measurement in preclinical studies. This stent-based sensor offers a promising approach for enhancing respiratory assessments in laboratory animals, potentially improving the accuracy of drug evaluations and respiratory disease research.

Polar amplification describes the predicted reduction in the latitudinal surface temperature gradient, which will have physical implications for mid-latitude dynamics. The precise nature of these dynamical consequences remains unclear. Here we explore aspects of polar amplification by way of 24 distinct idealised laboratory experiments. The apparatus employed can independently prescribe laboratory analogues for the latitudinal temperature gradient ($\Delta{T}$, which controls the stratification $N$), background zonal flow speed ($U_{b}$), and strength of the background gradient in potential vorticity ($\beta$). The ability to control these processes individually is beneficial as decoupling them from one another enables their influences can be examined separately. Reducing the sidewall temperature difference substantially reduces small-scale and high frequency dynamics, but does not affect the large scale features of the flow, including the north-south amplitude of standing meanders. Reducing the zonal flow speed does reduces the length-scales and amplitudes of the standing Rossby waves, while reducing the potential vorticity gradient has the opposite effect; these responses are well described by the canonical expression relating the standing Rossby wavelength to $\sqrt{U_{b}/\beta}$. Variability is partitioned into components that are standing and transient; the response of this variability partitioning depends on all 3 experimental parameters, and a non-dimensional term is developed ($U_{b}\beta/N^{2}$) which captures the behaviour of the variability. These findings suggest that the dynamical consequences of polar amplification is a tendency for mid-latitude weather to shift away from transient storms towards more persistent events, however the zonal wavelength and north-south extent of these persistent events will tend to decrease.

Laboratory experiments with rotating tanks remain the premier physical analogue for atmospheric dynamics. Often, the equipment involved is engineered to be sufficiently versatile and modular so as to be able to accommodate experiments that explore a wide range of atmospheric processes. The exercise of initially configuring the apparatus then involves running experiments that sweep through parameter space to identify the specific dynamical regimes of interest. This process is typically considered part of the development and testing phase of a given project, and these initial experiments are generally left unreported; while many of these test experiments may not be directly relevant to the project at hand, they may be useful for other applications or scientific communities. Here we report on a series of 93 different laboratory experiments run with the intention of identifying suitable experimental configurations that excite and sustain standing Rossby waves.

Purpose: Proton therapy provides superior dose conformity compared to photon therapy, but its treatment planning is challenged by sensitivity to anatomical changes, setup/range uncertainties, and computational complexity. This review evaluates the role of artificial intelligence (AI) in improving proton therapy treatment planning. Materials and methods: Recent studies on AI applications in image reconstruction, image registration, dose calculation, plan optimization, and quality assessment were reviewed and summarized by application domain and validation strategy. Results: AI has shown promise in automating contouring, enhancing imaging for dose calculation, predicting dose distributions, and accelerating robust optimization. These methods reduce manual workload, improve efficiency, and support more personalized planning and adaptive planning. Limitations include data scarcity, model generalizability, and clinical integration. Conclusion: AI is emerging as a key enabler of efficient, consistent, and patient-specific proton therapy treatment planning. Addressing challenges in validation and implementation will be essential for its translation into routine clinical practice.

A numerical investigation of flat fan sprays is conducted via Direct Numerical Simulations (DNS). Diverging liquid sheets are generated using tailored initial velocity profiles, where the opening angle serves as an explicit control parameter. The analysis reveals two distinct regimes: at low Weber numbers, the sheet features thick, retracting rims moving at the Taylor-Culick velocity, though rim-driven break-up is not observed without advanced techniques like Adaptive Mesh Refinement (AMR), At high Weber numbers, aerodynamic instabilities govern disintegration, with hole break-up absent in all cases. Representing the spray as a triangular sheet, a simplified model is proposed to predict the axial thickness evolution, showing good agreement with numerical measurements. The study also quantifies the influence of Weber number and opening angle on surface wave properties. An existing break-up length model is successfully applied, incorporating the present initial conditions, offering a predictive tool for future numerical and experimental studies.

Antiferromagnetic materials with spin splitting have attracted considerable attention for their symmetry-enabled anisotropic spin textures that sustain a zero net magnetization, thereby facilitating efficient spin-current generation. In this work, the highly efficient generation of nonrelativistic spin currents is demonstrated to be facilitated by the distinctive Fermi surface geometry of X-type collinear antiferromagnets. As a prototype conducting X-type antiferromagnet, the Fermi surface of $\beta-\mathrm{Fe_2PO_5}$ exhibits a distinct $d$-wave altermagnetic characteristic, which compresses into a nearly X-shaped configuration. This results in highly efficient spin currents, achieving a charge-spin conversion efficiency of up to 90%. Moreover, the spin current polarization is controlled by the orientation of the Néel vector. When the Néel vector tilts to the out-of-plane direction, an in-plane injected charge current can generate a special spin current component with both spin polarization and propagation along the out-of-plane direction, whose charge-spin conversion efficiency substantially exceeds that of known ferromagnets, altermagnets, noncollinear antiferromagnets, and low-symmetry materials. The highly efficient charge-spin conversion in X-type antiferromagnets provides a novel and highly effective spin source system for the development of low-power spintronic devices.

Machine learning enables powerful cosmological inference but typically requires many high-fidelity simulations covering many cosmological models. Transfer learning offers a way to reduce the simulation cost by reusing knowledge across models. We show that pre-training on the standard model of cosmology, $\Lambda$CDM, and fine-tuning on various beyond-$\Lambda$CDM scenarios – including massive neutrinos, modified gravity, and primordial non-Gaussianities – can enable inference with significantly fewer beyond-$\Lambda$CDM simulations. However, we also show that negative transfer can occur when strong physical degeneracies exist between $\Lambda$CDM and beyond-$\Lambda$CDM parameters. We consider various transfer architectures, finding that including bottleneck structures provides the best performance. Our findings illustrate the opportunities and pitfalls of foundation-model approaches in physics: pre-training can accelerate inference, but may also hinder learning new physics.