Papers

A generalized anisotropic-diffusion framework is developed for transport problem in media described by a tensorial scattering coefficient and a scalar Henyey–Greenstein asymmetry factor. In this regime the classical similarity relation between scattering and transport parameters fails, and each principal diffusion coefficient depends on all components of the microscopic scattering rate. Explicit expressions are derived for the direction-averaged mean free path, the diagonal elements of the diffusion tensor, and boundary condition lengths via rapidly convergent spherical-harmonics expansions, along with open-source implementations. The resulting predictions are validated against anisotropic Monte Carlo simulations, showing excellent agreement across broad ranges of structural anisotropy and phase-function asymmetry factors. The theory provides a compact, general route connecting microscopic anisotropic scattering to macroscopic diffusion coefficients and boundary conditions in bounded geometries.

Time series in energy systems, such as solar irradiance, wind speed, or electrical load, are characterized by strong diurnal and seasonal periodicities. Accurate forecasting requires accounting for time varying statistical properties that stationary or classical persistence models cannot capture. A family of analytical forecasting operators for cyclostationary processes is introduced, extending persistence through a closed form coefficient $\tilde{\lambda}(t,\tau)=\tfrac{1}{2}\bigl(1+\rho(t,\tau)\bigr)$, where $\rho(t,\tau)$ denotes the local correlation between the current observation and its phase aligned time lag ($\tau$). This formulation preserves periodic variance and covariance, achieving a symmetry induced reduction of effective degrees of freedom. The resulting operator defines a training free analytical limit of persistence under periodic non stationarity. Validation on synthetic cyclostationary signals and empirical renewable energy datasets demonstrates consistent accuracy gains over classical persistence, particularly at multi hour horizons. By embedding temporal symmetry into the prediction process, the framework provides a physically interpretable, reproducible, and computationally minimal baseline for forecasting periodic processes across energy and complex systems.

Theoretical ecology has traditionally equated persistence with the stability of a fixed equilibrium point. Here we argue that the primary threat to ecosystem persistence need not be the loss of stability, but instead the escape of the stable equilibrium to a negative orthant. In a realistic setting, fluctuations in interactions do not merely disturb abundances about an equilibrium but can displace the equilibrium point itself. We theoretically and empirically analyze such displacements of the equilibrium point in a complex community. Theoretically, we find that light-tailed fluctuations in species interactions, no matter how small, lead to a heavy-tailed power law $P(y)=1/y^\alpha$ for the equilibrium abundance $y$ of a species. Remarkably, the exponent $\alpha=2$ is a universal value independent of interaction structure, community size, and species. Empirically, our analysis of 34 species reveals a power law signal for most, with a median exponent $\alpha \sim2.56$. Next, we derive a formula for the critical noise, $\sigma_c$, beyond which the community experiences feasibility loss “with near certainty”. We find that $\sigma_c(N)\sim N^{-1}$, implying that larger communities are significantly more fragile to noise induced feasibility loss. Lastly, we define and calculate biologically measurable analytical metrics for both global and species-specific feasibility escape rates, and implement these metrics in dynamic simulations of 98 real world mutualistic and food web networks, to successfully predict their fragility.

We obtain bounds on the Kullback–Leibler divergence to equilibrium for mass-action chemical reaction networks (CRNs) with equilibrium. The associated trajectory-dependent bounds are characterized in terms of the singular values of the stoichiometric matrix, convexity parameters, and time-integrated activities via deformed-exponential-type functions. We further extend these bounds within a generalized gradient flow framework. We highlight the biological relevance of this framework: the resulting bounds apply to quasi-steady-state regimes, where long transients and plateau-like behavior are common and functionally important. We illustrate the framework using a catalytic CRN exhibiting plateaus, where the bounds provide an a posteriori characterization of slow relaxation induced by local convexity and provide a bound-based approach to quantifying relaxation in CRNs.

In this letter we explore how full frequency conversion can be performed in shorter optical devices by using a STIRAP-like protocol modified by the time rescaling shortcut to adiabaticity. We first show a detailed, step-by-step process of how the coupled equations for two simultaneous three-wave mixing processes can be written in terms of a STIRAP-like system. From the properties of the STIRAP protocol, we know that the ensuing process will result in robust conversion, but require long propagation distances inside the bulk crystal or waveguide. We then apply the time rescaling shortcut to adiabaticty in the conversion process to create a TR-STIRAP protocol, showing that full and robust conversion is also obtained, but at a fraction of the propagation distance. We also show how the original shaping of the coupling coefficients required by the TR-STIRAP can be approximated by Gaussian functions with high conversion fidelity, thus simplifying the experimental implementation. Finally, a possible application of the new protocol is discussed in the context of single-photon detection in the MIR.

Quantum computation with Trotter product formulae is straightforward and requires little overhead in terms of logical qubits. The choice of the orbital basis significantly affects circuit depth, with localised orbitals yielding lowest circuit depths. However, literature results point to large Trotter errors incurred by localised orbitals. Here, we therefore investigate the effect of orbital transformations on Trotter error. We consider three strategies to reduce Trotter error by orbital transformation: (i) The a priori selection of an orbital basis that produces low Trotter error. (ii) The derivation of an orbital basis that produces a ground state energy free of Trotter error (as we observed that the Trotter error is a continuous function in the Givens-rotation parameter, from which continuity of this error upon orbital transformation can be deduced). (iii) Application of propagators that change the computational basis between Trotter steps. Our numerical results show that reliably reducing Trotter error by orbital transformations is challenging. General recipes to produce low Trotter errors cannot be easily derived, despite analytical expressions which suggest ways to decrease Trotter error. Importantly, we found that localised orbital bases do not produce large Trotter errors in molecular calculations, which is an important result for efficient QPE set-ups.

Cells often divide under mechanical confinement, where surrounding structures restrict shape changes during cytokinesis. Although forces generated during confined division have been measured experimentally, it remains unclear how confinement geometry and mechanics determine the transmitted force. Here we develop a minimal mechanical theory of cell division under confinement. Modeling the cell as an incompressible volume bounded by an interface with effective isotropic tension, we show that confinement restricts the set of mechanically admissible furrow shapes. As the furrow radius decreases, it reaches it reaches a confinement-induced minimum. Beyond this point, further ingression does not alter the interface shape, and both pressure and axial force saturate. We analyze force and pressure in rigid, soft, and strong three-dimensional confinement and demonstrate that a single geometric mechanism underlies these distinct cases. After rescaling force and length by the appropriate geometric scale, cells of different size and surface tension collapse onto a single universal curve. The relevant length scale is the cell size for rigid and soft confinement, and the confinement size in fully enclosing three-dimensional confinement. In soft confinement, environmental stiffness and spindle-generated axial forces determine the operating force and pressure, while the geometric constraint fixes the maximal attainable levels. In summary, our results show that mitotic force transmission and mitotic pressure during cytokinesis are bounded by confinement geometry, with material properties and active forces selecting the operating point within these geometry-imposed limits.

Light-harvesting and excitation energy transfer in photosynthesis generally involve chlorophyll-molecules, maintained by their host proteins at short distances from each other, this resulting in excitonic coupling. The transfer of excitation energy to the reaction centers consists of exciton migration and relaxation within and between photosynthetic proteins. The dynamics of this process depends on the vibrational modes resonant with the energy gaps between the participating excited states. The precise structure and vibrational landscape of excitons is thus essential knowledge to understand the amazing efficiency of photosynthesis. In this work, we characterize the vibrational properties of excitons in light-harvesting proteins from purple photosynthetic bacteria, which remarkably unveil on how many bacteriochlorophylls they reside and in which proportions. Vibrational spectra obtained from bacteriochlorophylls in proteins generally contain additional vibronic contributions when compared to isolated pigments, opening additional pathways for vibrationally-assisted excitation energy transfer. In contrast, the absence of new vibronic contributions in the spectra of chlorophyll -containing photosynthetic proteins above 100 cm-1 suggests that in oxygenic photosynthesis, vibrationally-assisted excitation energy transfers occurs through vibrational modes of chlorophyll molecules in equilibrium configuration.

We investigate a growing network model that combines preferential and uniform attachment with two distinct mechanisms of edge deletion. In addition to the usual uniform random edge deletion, we introduce a novel node-based rule in which uniformly chosen non-isolated nodes lose one of their incident edges. This mechanism differs fundamentally from uniform edge deletion and leads to a nonlinear evolution for the stationary degree distribution due to the nonlinear dependence on the fraction of isolated nodes. We solve the general problem in the stationary regime and obtain closed-form expressions for the degree distribution in terms of hypergeometric and confluent hypergeometric functions. Depending on the balance between attachment and deletion rates, three asymptotic regimes for the degree distribution arise: power-law, exponential, and a critical regime characterized by a stretched exponential decay. We show that the node-based edge deletion mechanism is less likely to disrupt the scale-free (power-law) regime than the uniform edge deletion. Moreover, we also demonstrate that the precise balance between preferential attachment and node-based deletion can transform a scale-free network into a critical one with stretched exponential decay. Large-scale numerical simulations exhibit excellent agreement with the theoretical predictions.

A model for the tensorial dielectric permittivity of phospholipid membranes is presented here. The four-nanometer-thick membrane is treated as a composite made up of three dielectric slabs: one for each of the two phospholipid head-group regions, and one for the entire domain spanned by the lipid tails. Equal and opposite bound surface charge densities surround each head-group slab, and account for the membrane dipole potential. Three-slab model parameters are obtained from molecular dynamics simulations, and capture both the zero-field electric potential and the membrane response to applied electric fields. The tail region is well-approximated as having vacuum permittivity, while the head-group region is highly anisotropic due to the configurations of molecular dipoles. For the bilayers studied, the out-of-plane permittivity of the head-group region is 10–15 times that of the vacuum, while the in-plane permittivity is an order of magnitude larger. Membrane responses to applied electric fields up to 30 millivolts per nanometer are found to be in the linear regime. The model overcomes a fundamental limitation of microscopic theories—where the out-of-plane permittivity lacks a meaningful continuum interpretation in the head-group region due to large gradients in the local electric field—by averaging over slab widths, thereby introducing new length scales. Our approach can be extended to characterize general interfacial systems with similar microscopic permittivities.

On Mars, a relatively pure water ice layer lies beneath several centimeters of dry soil at mid-latitudes. Its widespread presence poleward of 60{\deg} latitude was detected by remote neutron spectroscopy and confirmed by the Phoenix lander at 68{\deg}N. Recent observations of exposed ice indicate that the near-surface ice layer extends to 35{\deg} latitude and exhibits pronounced spatial structure. However, previous models did not capture the observed spatial structure of the midlatitude ice layer. Here, on the basis of improved calculations using the Mars Planetary Climate Model, we show that mid-latitude buried ice could be the remnant of a ice layer deposited on the surface when the obliquity was higher than today. Assuming that the ice subsequently sublimated and became buried beneath a sublimation lag, we estimate that surface ice emplaced 630 kyr (4.18 Myr) ago at 35{\deg} obliquity (40{\deg}), at latitudes of 40-55{\deg}N, would today reside at depths of 25-150 (41-255) cm, depending on the regolith and ice properties. For ice emplaced 630 kyr ago, the modeled burial depths align with observations and capture the observed longitudinal depth variations, in contrast to ice emplaced 4.18 Myr ago. We therefore infer that the mid-latitude subsurface ice is younger than 4 Myr.

Light springs are space-time beams that have a helical wavepacket. Due to this special property, light springs result into a rotating pulse when intercepting a plane lying orthogonal to their propagation direction. Associated to this, we introduce here the orbital group velocity, an additional tunable property of light springs. The orbital group velocity quantifies the speed of the light spring intensity rotation, distinctly from the conventional longitudinal group velocity, which describes the motion of the wavepacket envelope along its propagation axis. We demonstrate experimentally by tunable Fourier synthesis that the orbital group velocity can assume sub- and superluminal values, thus becoming a new platform for synthetic motion studies and control of laser-matter interactions. Particularly, in the superluminal regime, when interacting with a thin overdense plasma, we reveal by particle-in-cell simulations that the light spring unlocks superradiant radiation, due to the coherent excitation of the electrons in the plasma acting as a quasiparticle. This superradiant source inherits the ultrafast temporal dynamics of the light springs while emitting in the terahertz region, thus creating a new source of terahertz radiation controlled by the properties of spatiotemporal coupling of the laser. Therefore, spatiotemporal tuning of light springs is at the frontier of controlling laser-matter interaction and generating new tunable sources of radiation.

Nuclear quantum effects (NQEs) remain a major challenge for molecular simulations, as rigorous treatment requires imaginary-time path-integral methods with heavy computational overhead. Neglecting NQEs leads to systematic errors in thermodynamic properties and failures in predicting isotope effects, quantum tunnelling, and anharmonic zero-point motion. Here, we introduce entropic path-integral coarse-graining (EPIGS), which enables rigorous quantum thermodynamics at the cost of classical simulations by training size- and temperature-transferable effective potentials utilising absolute centroid free energy and entropy. Central to EPIGS is an instanton-based free-energy perturbation scheme that enables efficient and accurate evaluation of the centroid free energy and entropy for large systems, making construction of the EPIGS training dataset practical. Benchmarks against full path-integral simulations on representative hydrogen-bonded systems, including liquid water, show that EPIGS reproduces quantum free energies and enthalpies within 0.2 meV/atom at near-classical computational cost. EPIGS provides a highly accurate, scalable and low-cost framework for quantum thermodynamic simulations of complex systems across temperatures.

Recently, it has been shown (Gavrilov et al., Nonlinear Dyn, 112, 2024) that in a linear solid discrete-continuous system with several slowly time-varying parameters, the amplitude of a strongly localized mode (a trapped wave) can be calculated as a function of current parameter values and does not depend on the history of the parameter change. This result allows us to introduce the adiabatic invariant for such a system according to the general definition as a quantity that remains approximately constant if the parameters vary slowly. In this paper, we show that, defined in this manner, the adiabatic invariant can be calculated as the ratio of the total energy of the trapped wave to its frequency. This yields a significantly simplified approach to solving a class of problems concerning localized oscillation of continuous systems with discrete inclusions, although the definition of the wave energy can be ambiguous. Thus, we can consider the newly introduced adiabatic invariant as a straightforward generalization of the concept known to Hamiltonian systems. Finally, we introduce an effective Hamiltonian system, which is characterized by the same adiabatic invariant as the trapped wave. This yields another highly straightforward approach to deriving the amplitude evolution law, although further investigation is required.

Physics-informed neural networks (PINNs) have shown remarkable prospects in solving partial differential equations (PDEs) involving fluid mechanics. However, the method has so far succeeded only in inviscid flows and incompressible viscous flows, while the solution of compressible viscous flows still faces significant challenges. In previous work, we proposed a feature-enhanced neural network (FENN), which enhances the ability of PINNs to approximate flows by introducing beneficial features into the network inputs, thereby improving the performance in solving PDEs. In this study, we extend FENN to compressible viscous flows, which are governed by the compressible Navier-Stokes equations including the continuity, momentum, and energy equations. By solving four forward problems under different flow conditions and geometries together with a parametric problem involving angle of attack, we validate the effectiveness of FENN. In contrast, existing advanced methods that are well established for inviscid flows and incompressible viscous flows fail in this scenario. To the best of our knowledge, this is the first time that a PINN-like method has successfully solved forward and parametric problems involving compressible viscous flows.

As artificial intelligence becomes increasingly prevalent, the demand for faster and more energy-efficient computing approaches grows. While optical computing offers intrinsic advantages in bandwidth and power consumption, existing implementations remain bulky, wavelength-specific, and dependent on complex training procedures, limiting scalability and parallel operation. In this work, we demonstrate a compact, training-free optical processor based on wavy diffractive features, known as Fourier surfaces, for parallel image classification. Our device achieves classification accuracies of up to 84% for digit datasets and 66% for fashion datasets within a 40$\times$40 $\mu$m$^2$ footprint. The diffractive layer inherently separates incident wavelengths into distinct output directions, enabling broadband operation and allowing multiple colors to function as independent computation channels. As a result, this passive system supports up to 20 simultaneous computations within a single optical pass. These results highlight the potential of nanoscale diffractive systems to achieve high compute densities, paving the way for scalable, low-power optical processors for machine learning and image-recognition applications.

Next-generation high-power radio-frequency (RF) devices increasingly demand transistors that operate efficiently with high gain at high frequencies. High-aluminum-content ultra-wide-bandgap (UWBG) AlGaN alloys have shown great potential for enabling such high-frequency RF technologies. However, the widespread adoption of AlGaN-based RF devices is limited by thermal-management challenges arising from the intrinsically low thermal conductivity of AlGaN, which leads to higher device thermal resistance for a given geometry compared to GaN RF devices. As a result, these next-generation devices are highly susceptible to self-heating. This study investigates the thermal behavior of UWBG AlGaN devices, focusing on the effects of AlGaN channel thickness, substrate technology, and high-k material integration on reducing device thermal resistance to enable high-power operation. Experimental results demonstrate a record-low thermal resistance of 3.96 mm$\cdot$K/W when an AlN substrate is employed and the AlGaN channel thickness is reduced to 5 nm. These findings provide valuable insights into mitigating thermal limitations in UWBG devices through device-level engineering and the strategic integration of high-k materials.

Interfaces that couple digital information with physical matter enable computation to be expressed through tangible motion and touch, yet typically rely on embedded actuators, rigid mechanisms, or enclosed environments. Consequently, contactless manipulation and interaction with centimeter-scale tangible elements in open settings remain difficult to achieve. Here, we present PolygonWave, a solid–fluid acoustic interface that enables transport and tangible interaction by coupling airborne ultrasound with liquid-mediated support. The system employs lightweight Janus icons with asymmetric wettability: a superhydrophobic upper surface permits dry touch interaction, while a hydrophilic lower surface couples to a water droplet resting on a superhydrophobic mesh. Focused acoustic fields generated by a 256-element phased array induce lateral forces, enabling programmable motion without mechanical contact. Systematic characterization demonstrates transport of payloads up to 525 mg across variations in icon size, droplet volume, and applied load. Beyond translation, the liquid layer functions as a reconfigurable mechanical element, enabling button-like input with self-recovery and resonance-driven vibro-visual feedback, exhibiting a peak response near 22 Hz for 200 \textmu L droplets. Liquid-mediated acoustic coupling provides a unified mechanism for mechanically expressive, touch-accessible tangible interfaces bridging acoustics, soft matter physics, and physical human–computer interaction.

Tearing instability, also known as plasmoid instability, is an effective mechanism to speed up magnetic reconnection process, working in a wide range of magnetized plasma systems with different spatial scales, ionization degrees, and collisionality. However, due to observational limitations, observations of {plasma sheet} tearing and the resulting plasmoids are rather scarce. This scarcity significantly hinders our understanding of the role of plasmoids in the reconnection process from an observational perspective. Using high-spatiotemporal multiwavelength observations from the Solar Dynamics Observatory, we traced the entire evolution of a coronal {plasma sheet}. Its formation was driven by the emergence of photospheric magnetic flux, followed by tearing, and eventual decay. The evolution of the {plasma sheet} exhibited two distinct stages. Initially, it rose rapidly, lengthened, and underwent tearing at a low frequency. Subsequently, its ascent slowed, it began to shorten, and the tearing occurred more frequently. Detailed analysis of the reconnecting {plasma sheet} focuses on heating, plasmoid dynamics (formation and ejection), and the resulting reconnection rate change. Two key heating processes are identified: {plasma sheet} tearing and coalescence involving plasmoids and magnetic cusps. More importantly, combining observations with analytical studies suggests that plasmoids are key carriers of magnetic flux fast transferring in the observed torn {plasma sheet}, and their formation and ejection significantly enhance the reconnection rate and facilitate the onset of fast reconnection.

Droplet microlasers, as promising tools for biophotonics and biomedical sciences, have witnessed rapid advances due to their flexible reconfigurability, high sensitivity to stimuli, and label-free biosensing ability. However, designing these biosensors with simultaneously critical properties of low lasing threshold, high spectral purity, and ultimate sensitivity remains challenging. Here, we propose a versatile strategy to build liquid photonic molecules (LPMs) that combine all these features in a single device. We find that through tailoring the spectral Vernier overlap in size-mismatched droplets, this device enables single-mode lasing with a low threshold of ~610 nJ mm-2. The LPM lasers are engineered for dynamic tunability using a molecular isomerization strategy, which induces spectral mode hopping and thus yields a nearly ten-fold enhancement in spectral sensitivity over single droplets. Moreover, by leveraging the self-referenced intensity response of the LPM lasing modes, we demonstrate a three-orders-of-magnitude enhancement in biomolecular sensing, with a detection limit of 30 aM and a dynamic range spanning nine orders of magnitude. Our work offers exciting prospects for bio-integrated liquid sensors in diverse applications.

We address the existence, stability, and dynamics of single-ring and multi-ring vorticity-carrying necklace solitons under the action of the Kerr nonlinearity and a Bessel-lattice potential modulated in the azimuthal direction. The model may be realized in the spatial domain for bulk optical waveguides, the spatiotemporal domain for optical cavities, and for effectively two-dimensional Bose-Einstein condensates. The setup supports single- and multi-ring necklace vortex patterns, including monopoles, dipoles, tripoles, quadrupoles, pentapoles, sextupoles, octupoles, and 12-poles. In contrast with the inherent instability of conventional vortex beams with high topological charges (winding numbers), vortex necklace-shaped solitons with large winding numbers are found to be stable in the present setup. In particular, octupoles exhibit stable breathing dynamics, and 12-pole necklaces with high winding numbers may be stable. These findings provide a new way for generating stable vortex necklaces, offering a vast potential for manipulations of complex spatiotemporal light fields.

Modeling the propagation of light through disordered media is central to understanding and controlling wave transport in diverse optical and mesoscopic applications. Here, we present a random matrix simulation framework for modeling the transport of polarized light through random media composed of arbitrary particulate scatterers. Our approach employs extended scattering channels applied to angular spectral decompositions of the underlying fields, enabling flexible representations of arbitrary illumination and detection profiles. In contrast to previous work, this framework provides a rigorous treatment of scattering matrix correlations and offers novel geometric insights into the optical memory effect. We provide a detailed exposition of the underlying theory and illustrate several key features through numerical simulations. Our work is supported by a free accompanying codebase.

One of the most common designs for magnetic control in tokamaks is to “linearize an equilibrium” to obtain a sensitivity mapping, then invert this mapping in order to determine the feedback control currents or voltages. For this work, we refer to this broad class of methods as inversion-based shape control (IBSC). In this work we describe the IBSC framework in a comprehensive manner and show how variations such as using dynamic voltage mappings and quadratic-program constrained control fit naturally into the framework. Despite the prevalence of IBSC, some of these extensions and the challenges associated with specific variations of IBSC are less widely known. We pay special attention to the challenge of decoupling the interaction between shape control and vertical control, which was a source of degraded vertical control performance on NSTX-U. This work is intended to provide a systematic overview of IBSC, and to that end, we have provided background material, proposed design procedures, and tutorials on the magnetic control design process within the appendices. Applying the systematic design procedure to NSTX-U, we find that the vertical control bobble on NSTX-U can be removed via decoupling, and that the vertical control phase margin can be improved by 6 degrees just by including PF1A and PF2 as vertical actuators.

Over the past two decades, photonic inverse design has emerged as a powerful approach to implement photonic devices with improved performance, or realize new functionalities. While the efforts over the first decade focused on proof of concept devices designed and fabricated in university labs, the focus over the past 5-10 years has shifted towards implementation of scalable photonic systems. This article reviews this recent progress, challenges and new directions in photonics inverse design, thus providing a complementary and updated review of the field. We focus on large scale three dimensional photonic inverse design, including metasurfaces, translation of inverse design to commercial foundries and practical silicon photonics, application of photonic inverse design to different materials systems, wavelengths, and optical effects, and finally new directions such as inverse design of quantum systems.

A substantial share of the Earth's land surface is managed by humans, with cities representing the most extreme form of anthropogenic land use. There are zillion ways in which settlements can be arranged across a given area, and their specific spatial configuration has important consequences for both urban systems and the natural environment. Here, we introduce a novel approach to characterizing settlement configuration by systematically quantifying it in terms of a transition resembling percolation – that is, by identifying the critical distance at which isolated settlements merge into a giant, overarching settlement cluster. We estimate this critical distance across multiple spatial scales and units, including national and subnational levels, non-overlapping tiles, and moving windows, covering the entire globe. The critical distance provides an independent measure of settlement connectivity and thus adds value to spatial analyses of settlement structure and its social, economic, and ecological impacts. Accordingly, our Global Settlement Percolation (GSP) dataset is relevant to a wide range of research communities, including those studying urban morphology, land-use patterns, and landscape ecology.

Reconstructing the topology of complex networks from observational data remains a central challenge in network science. Here we propose a framework that is based on the Dempster-Shafer evidence theory to infer network structures directly from time series. By integrating multi-source information within an evidential reasoning scheme, the method captures underlying interaction patterns with high fidelity. Tests on three representative network models Barabasi-Albert Network, Erdos-Renyi Network, and Watts-Strogatz Network-show that the reconstruction accuracy is consistently high and remains robust against increases in network size and density. The application of the framework to real-world datasets from diverse domains further confirms its stability and general applicability. These results suggest that evidential reasoning offers a powerful and scalable approach for uncovering the structural organization of complex systems, especially when dealing with uncertain or incomplete multi-source data.

Accurate evaluation of building energy performance remains challenging in regions where scalable Energy Performance Certificate (EPC) assessments are unavailable. This paper presents a cost-efficient framework that leverages Vision-Language models for automated EPC pre-assessment from limited visual information. The proposed Multimodal Modular Chain of Thoughts (MMCoT) architecture decomposes EPC estimation into intermediate reasoning stages and explicitly propagates inferred attributes across tasks using structured prompting. Experiments on a multimodal dataset of 81 residential properties in the United Kingdom show that MMCoT achieves statistically significant improvements over instruction-only prompting for EPC estimation. Analysis based on accuracy, recall, mean absolute error, and confusion matrices indicate that the proposed approach captures the ordinal structure of EPC ratings, with most errors occurring between adjacent classes. These results suggest that modular prompt-based reasoning offers a promising direction for low-cost EPC pre-assessment in data-scarce settings.

Coherent Elastic Neutrino-Nucleus Scattering (CEvNS) provides a sensitive probe of neutrino interactions at low momentum transfer, but its experimental observation is strongly constrained by detector-related effects such as energy threshold, resolution, noise, and event-selection criteria. In this work, we perform a detector-level assessment of CEvNS nuclear recoil observability under realistic experimental conditions, with particular emphasis on the role of detector response in shaping measurable recoil spectra. Using detailed Geant4-based simulations, CEvNS interactions are modeled for a set of alternative target nuclei spanning light to intermediate mass ranges. The true nuclear recoil energy distributions are propagated through a simplified yet realistic detector-response chain incorporating energy smearing, noise-induced fluctuations, threshold cuts, and veto-based event selection. We present a systematic analysis of recoil energy spectra before and after detector effects, response matrices linking true and reconstructed energies, and energy-dependent selection efficiencies. The results demonstrate that detector response effects significantly modify the observable CEvNS signal, particularly in the near-threshold region where most recoil events are concentrated. Differences in efficiency turn-on behavior and reconstructed energy distributions highlight the target-nucleus dependence of CEvNS observability under identical detector conditions. Rather than focusing on absolute event-rate predictions, this study emphasizes the relative impact of detector effects on signal accessibility and target performance. The presented framework provides a consistent methodology for evaluating and comparing prospective CEvNS target materials at the detector level, offering practical guidance for future low-threshold CEvNS experiments and detector design optimization.

An AI-based Limited-Area Model (LAM) is developed for dynamical downscaling over the Southern Great Plains and the southeastern United States, with strong generalization abilities under diverse boundary conditions. The model is trained using 0.25-degree, 3-hourly ERA5 as forcings and CONUS404 as targets in 1980–2019, producing 4-km, hourly dynamical downscaling outputs; it is also connected to a post-processing model to derive additional diagnostic variables. The model is evaluated across multiple forcing datasets, time periods, and climate regimes. For present-day downscaling in the 2021–2024 water years, the model produces stable multi-year simulations with no unrealistic drift; its deterministic verification scores are comparable to other weather-forecasting-oriented AI models. The model also generalizes robustly to a 1.0-degree, 6-hourly non-ERA5 forcing dataset, yielding only minor performance changes. Frontal cyclone and hurricane case studies further demonstrate that the model reconstructs realistic, interpretable weather-scale dynamical and thermodynamic structure from coarse boundary information. The AI-based LAM is further tested by downscaling 30-year global climate model runs in 1980–2010 and 2070–2100, and climate model ensembles in 2025-2027. In this application, the model remains stable at hourly downscaling frequencies for all 30 years and effectively captures future climate-change signals, indicating meaningful generalization across different climate regimes. When downscaling ensembles, the model produces well-posed ensemble distributions without collapsing the ensemble spread. Overall, the AI-based LAM of this study offers good downscaling performance and generalization abilities. It provides a practical and transferable example of adapting AI weather prediction models for regional climate applications.

In April 2024, the White House directed NASA to establish Coordinated Lunar Time (LTC) by December 2026. The programme assumes that a unified time standard can be constructed by deploying atomic clocks on the lunar surface, computing relativistic corrections, and distributing synchronized time via LunaNet. This paper argues that the entire enterprise rests on a category mistake in the sense introduced by Ryle and developed by Spekkens in quantum foundations: it treats "synchronized time" as an ontic entity – something that exists independently and can be transmitted from authoritative sources to dependent receivers – when it is in fact an epistemic construct: a model-dependent representation of observer-relative clock relationships. We analyze the cislunar time programme through the lens of Forward-In-Time-Only (FITO) assumptions, Spekkens' Leibnizian operationalism, the Wood-Spekkens fine-tuning argument, and the distinction between ontic and epistemic interpretations that has dissolved long-standing puzzles in quantum mechanics. We show that the same conceptual move that dissolves quantum "mysteries" – recognizing what is epistemic versus what is ontic – dissolves the apparent coherence of the cislunar time programme and reveals it as an engineering project built on a philosophical confusion. We sketch a transactional alternative grounded in bilateral atomic interactions rather than unidirectional time distribution.