Papers

In photonics, twisted bi-layer systems have demonstrated unprecedented control over light-matter interactions, primarily through the modulation of photonic band structures and the formation of Moiré patterns. Meanwhile, magnetic photonic crystals have served as cornerstone platforms for manipulating light propagation, facilitating key applications such as Faraday rotation-based isolators and non-reciprocal devices. Nevertheless, the synergistic integration of twist engineering and magneto-optical effects in bi-layer architectures remains unexplored. This work introduces twisted magnetic bi-layer photonic crystal slabs as a novel platform to unify these degrees of freedom. By continuously tuning the twist angle between two magneto-active photonic layers, the giant circular dichroism is observed, and the transmitted waves can be perfectly linearly polarized and rotated. These effects arise from the interplay between resonant properties of the Moiré cell and magnetization-dependent coupling of circularly polarized states. This work establishes a foundation for magnetic topological photonics, bridging twistronics and magneto-optics to unlock new mechanisms for dynamic light control in compact and reconfigurable devices.

We investigate the thermoelectric response of single-molecule junctions composed of acyclic cross-conjugated molecules, including dendralene analogues and related iso-poly(diacetylene) (iso-PDA) motifs, in which node-possessing repeat units are connected in series. Using many-body quantum transport theory, we show that increasing the number of repeat units leaves the fundamental gap essentially unchanged while giving rise to a split-node spectrum whose cumulative broadening dramatically enhances the thermopower. This form of quantum enhancement can exceed other interference-based mechanisms, such as the coalescence of nodes into a supernode, suggesting new opportunities for scalable quantum-interference-based materials. Although illustrated here with cross-conjugated systems, the underlying principles apply broadly to series-connected architectures hosting multiple interference nodes. Finally, we evaluate the scaling of the electronic figure of merit ZT and the maximum thermodynamic efficiency. Together, these results highlight the potential for split-node-based materials to realize quantum-enhanced thermoelectric response.

High Bandwidth Memory (HBM), as a key development trend in future memory chip technology, significantly enhances computer performance. At the same time, the thermal challenges arising from its stacked architecture have drawn considerable attention. Most existing studies on HBM thermal management are based on Fourier's law, neglecting the non-Fourier effects introduced by the micro/nanoscale structures within HBM. In this study, the Monte Carlo method (MC) is employed to solve the phonon Boltzmann transport equation (BTE) and investigate the impact of non-Fourier heat transport on the thermal behavior of HBM structures. The results reveal that non-Fourier heat transport leads to a junction temperature that is 59.8 K higher than that predicted by Fourier's law. Furthermore, it is found that the phonon transmittance at the chip interlayers has a severe impact on heat dissipation, with the temperature variation reaching up to 56.6 K. These findings provide more accurate thermal insights, which are critical for the optimized design of HBM systems.

Arrays of hundreds or thousands of low temperature detectors have been deployed for many experiments, both bolometers for long wavelength applications and calorimeters for shorter wavelength applications. One challenge that is common to many of these arrays is the efficient use of focal plane area to achieve a large fill fraction of absorbers coupled to detectors. We are developing an integrated fabrication of soft X-ray transition edge sensors (TES) and microwave SQUID multiplexers ($\mu$MUX) with the goal of maximizing the fill fraction of the focal plane area on a scale of many thousand pixel detectors. We will utilize lithographically defined high density interconnects to circumvent limitations in existing solutions that use wirebonds or flip-chip bonds. Here we report the first demonstration of combining TES and $\mu$MUX processes into a single TES-System-on-a-Chip (TES-SoC) fabrication on a silicon wafer. The $\mu$MUX SQUIDs and TES electrothermal feedback circuits are microfabricated first and protected with passivating SiO$_2$, then the TES devices and TES-to-SQUID interconnects are fabricated, and finally the protective layer is removed before the fabrication of the microwave resonators. We show that the microwave SQUIDs are functional and have reasonable yield, and that we are able to read out the transition temperature of the connected TESs using those SQUIDs.

High-quality factor microresonators are an attractive platform for the study of nonlinear photonics, with diverse applications in communications, sensing, and quantum metrology. The characterization of loss mechanisms and nonlinear properties in a microresonator is a necessity for the development of photonic integrated circuits. Here, we demonstrate a high-quality chalcogenide ($Ge_{23}Sb_{7}S_{70}$) micro-racetrack resonator utilizing Euler curves. The racetrack geometry is studied to minimize loss at both the straight-curved waveguide junction and through the waveguide curve. The material absorption, intrinsic quality factor, and nonlinear index are extracted by a comprehensive model fit to laser wavelength resonance scans. The micro-racetrack resonator possesses an absorption loss of $0.43 dB/m$, an intrinsic quality factor of $4.5 \times 10^6$, and nonlinear index of $1.28 \times 10^{-18} m^2/W$, in a waveguide cross-section less than $1 {\mu}m^2$. Our results yield state-of-the-art nonlinear microresonators and establish $Ge_{23}Sb_{7}S_{70}$ as a low-loss PIC platform.

This work introduces a calibration framework for material parameter identification in isotropic hyperelastic constitutive models. The framework synergizes the Virtual Fields Method (VFM) to define an objective function with a Genetic Algorithm (GA) as the optimization method to facilitate automated calibration. The formulation of the objective function uses experimental displacement fields measured from Digital Image Correlation (DIC) synchronized with load cell data and can accommodate data from experiments involving homogeneous or inhomogeneous deformation fields. The framework places no restrictions on the target isotropic hyperelastic constitutive model, accommodating models with coupled dependencies on deformation invariants and specialized functional forms with a number of material parameters, and assesses material stability, eliminating sets of material parameters that potentially lead to non-physical behavior for the target hyperelastic constitutive model. To minimize the objective function, a GA is deployed as the optimization tool due to its ability to navigate the intricate landscape of material parameter space. The VFM-GA framework is evaluated by applying it to a hyperelastic constitutive model for compressible elastomeric foams. The evaluation process entails a number of tests that employ both homogeneous and inhomogeneous displacement fields collected from DIC experiments on open-cell foam specimens. The results outperform manual fitting, demonstrating the framework's robust and efficient capability to handle material parameter identification for complex hyperelastic constitutive models.

We propose cascaded spontaneous four-wave mixing (SFWM) in microring resonators as a scalable and efficient approach for directly generating non-Gaussian states of light. Focusing on the well-understood "low-gain" regime, we demonstrate that triplet generation through cascaded SFWM can be achieved with high efficiency and favorable spectral characteristics using realistic microring sources in AlGaAs. The ability to achieve the generation of light in a single set of supermodes – and the predicted accessibility of the "high-gain" regime at realistic pump powers – makes this source a promising candidate as a direct and deterministic source of non-Gaussian light for photonic quantum information processing.

Acousto-optic deflectors (AODs) are widely used across physics, microscopy, neuroscience, and laser engineering, providing fast, precise, and non-mechanical control of light. While conventional AODs naturally support multiplexing in one and two dimensions, no analogous device has existed for three-dimensional control, leaving a critical gap in rapid focus tuning and 3D beam shaping. Here we demonstrate a three-dimensional AOD system capable of multiplexed axial and lateral beam control with high speed and large dynamic range. We achieve this by combining a double-pass AOD with a diffraction grating in the Littrow configuration to realize a compact frequency-tunable lens with multiplexing capability. Our device enables axial scanning over more than twenty Rayleigh ranges with switching rates up to 100 kHz, while simultaneous multi-tone driving produces arbitrary multi-focal beam profiles. By integrating the axial module with lateral deflection, we generate reconfigurable 3D optical patterns. This approach establishes a broadly applicable platform for multiplexed 3D beam control, with potential applications from high-resolution microscopy and laser processing to scalable neutral-atom quantum technologies.

Speculation about the existence of advanced forms of life in the Universe and in our galaxy, has been since ever a subject of fascination and discussion in fiction, as well as in astrophysics, biology and philosophy. The well-known Fermi's 1950s challenge, "Where are the aliens?" has acquired more substance with the realisation of the potentialities of radioastronomy, which led to the paradigmatic Drake's equation. The emergence of astrobiology, together with the discovery up to now of more than seven thousand exoplanets, has brought increasing support to the discussion about putative life cradles. However, after more than six decades, the only quantitative tool available to estimate how widespread is life and, in particular, advanced forms of life, is, besides direct searches, which so far provided no evidence, still Drake's equation. In the present work we review the current knowledge about this equation and present new arguments of multiple origin in order to evaluate one of its most critical terms, namely the one associated to the time span that a technological civilisation must search for detectable signs of the existence and for how long a search must be extended to bear fruits. We propose that this term should be replaced by a more specific one which involves critical parameters in the enterprise of gathering information, such as energy expenditure, searching area and entropy generation. These terms can be regarded as the capability that any cosmic civilisation must show in order to face the challenge of going beyond the climate and other crises that its development inevitably ensues. Our considerations suggest that a typical time span is about a couple of decades, meaning that a successful and systematic searching programme around about hundred stars might take around a few thousand years.

We present an anisotropic mesh adaptation procedure based on Riemannian metrics for the simulation of two-phase incompressible flows with non-matching densities. The system dynamics are governed by the Cahn-Hilliard Navier-Stokes (CHNS) equations, discretized with mixed finite elements and implicit time-stepping. Spatial accuracy is controlled throughout the simulation by the global transient fixed-point method from Alauzet et al., in which the simulation time is divided into sub-intervals, each associated with an adapted anisotropic mesh. The simulation is run in a fixed-point loop until convergence of each mesh–solution pair. Each iteration takes advantage of the previously computed solution and accurately predicts the flow variations. This ensures that the mesh always captures the fluid-fluid interface, and allows for a dynamic control of the interface thickness at a fraction of the computational cost compared to uniform or isotropic grids. Moreover, using a modest number of time sub-intervals reduces the transfer error from one mesh to another, which would otherwise eventually spoil the numerical solution. The overall adaptive procedure is verified with manufactured solutions and the well-known rising bubble benchmark.

This paper discusses the history of Fraunhofer's puzzling discovery of the fixed lines in various spectra (most notably of the sun) and the implications of these spectral "imperfections". Moreover, the developments in spectroscopy by Kirchhoff, Bunsen, et al. in the 19th century and its effects on our understanding of the atomic structure are discussed.

Fresnel derived the drag coefficient of moving transparent matter from a mechanical argument in 1818, and a century of interferometry confirmed it exactly as he gave it. We show that the mechanical underpinning he sought was workable in principle and wrong in one word: density, where the mechanics says compliance. The complete first-order coefficient follows from a single constitutive calculation, each component responding along its own path at the frequency it samples: $$f(\omega) = C_{\rm soft}/C_{\rm total} + (\omega/2)\,C_{\rm soft}'(\omega)/C_{\rm total}.$$ The coefficient contains two laws. The first is the law Fresnel wrote: the non-dispersive part, $1-1/n^2$, equals the modified medium's share of the wave-accessible compliance, $\chi/(1+\chi)$: his excess hypothesis made exact. The second is a law of resonance, the term Lorentz derived and Zeeman confirmed. We prove the resonance law is universal mechanics: sound in a bubbly liquid obeys it in the same form and prefactor, eleven orders of magnitude below, invariantly across the analyzed two-fluid closures. The same analysis shows the share law is the carried-response term: present when the compliant channel stores and delivers in its moving frame, absent from every closure that severs response from carrier. Closure-bound cavities (geometry fixed by phase closure of the medium's waves) provide that custody by construction. One transport structure thus appears three times: light in moving glass carries both terms, the drifting bubbly liquid isolates the resonance term by slipping, and Lorentzian velocity composition reproduces the share. The direction of derivation is established: the mechanics generates what the kinematics reproduces; the composition is itself derived as the cavities' closure condition in a companion paper (arXiv:2604.27525). A documentary history traces how interpretation fused the two laws, Fresnel to Zeeman.

We present a detailed study of an 8-channel $1\times1$ mm$^{2}$ WSi superconducting microwire single photon detector (SMSPD) array exposed to 120 GeV hadron beam and 120 GeV muon beam at the CERN Super Proton Synchrotron H6 beamline. Following up on our first detailed characterization of the efficiency and response of an SMSPD fabricated on a 3 nm WSi film, we report measurements of enhanced particle detection efficiency using a sensor fabricated from a thicker 4.7nm-thick WSi film. We also report the first SMSPD detection efficiency measurement made for muons. Measurements are enabled by a silicon tracking telescope providing 10 $\mu$m in-situ spatial resolution. The results show a fill factor-normalized detection efficiency of 75% and a time resolution of about 130 ps across pixels. These findings represent a significant advancement toward developing high-efficiency SMSPD charged particle tracking systems with simultaneous precision timing, with potential applications in future accelerator-based experiments such as the FCC-ee and Muon Collider.

Chronic diseases frequently co-occur in patterns that are unlikely to arise by chance, a phenomenon known as multimorbidity. This growing challenge for patients and healthcare systems is amplified by demographic aging and the rising burden of chronic conditions. However, our understanding of how individuals transition from a disease-free-state to accumulating diseases as they age is limited. Recently, data-driven methods have been developed to characterize morbidity trajectories using electronic health records; however, their generalizability across healthcare settings remains largely unexplored. In this paper, we conduct a cross-country validation of a data-driven multimorbidity trajectory model using population-wide health data from Denmark and Austria. Despite considerable differences in healthcare organization, we observe a high degree of similarity in disease cluster structures. The Adjusted Rand Index (0.998) and the Normalized Mutual Information (0.88) both indicate strong alignment between the two clusterings. These findings suggest that multimorbidity trajectories are shaped by robust, shared biological and epidemiological mechanisms that transcend national healthcare contexts.

Zoonotic pathogens represent a growing global risk, yet the speed of adaptive immune activation across mammalian species remains poorly understood. Despite orders-of-magnitude differences in size and metabolic rate, we show that the time to initiate adaptive immunity is remarkably consistent across species. To understand this invariance, we analyse empirical data showing how the numbers and sizes of lymph nodes scale with body mass, finding that larger animals have both more and larger lymph nodes. Using scaling theory and our mathematical model, we show that larger lymph nodes enable faster search times, conferring an advantage to larger animals that otherwise face slower biological times. This enables mammals to maintain, or even accelerate, the time to initiate the adaptive immune response as body size increases. We validate our analysis in simulations and compare it to empirical data.

Modeling shock-to-detonation phenomena in energetic materials (EMs) requires capturing complex physical processes such as strong shocks, rapid changes in microstructural morphology, and nonlinear dynamics of chemical reaction fronts. These processes participate in energy localization at hotspots, which initiate chemical energy release leading to detonation. This study addresses the formation of hotspots in crystalline EMs subjected to weak-to-moderate shock loading, which, despite its critical relevance to the safe storage and handling of EMs, remains underexplored compared to the well-studied strong shock conditions. To overcome the computational challenges associated with direct numerical simulations, we advance the Physics-Aware Recurrent Convolutional Neural Network (PARCv2), which has been shown to be capable of predicting strong shock responses in EMs. We improved the architecture of PARCv2 to rapidly predict shear localizations and plastic heating, which play important roles in the weak-to-moderate shock regime. PARCv2 is benchmarked against two widely used physics-informed models, namely, Fourier neural operator and neural ordinary differential equation; we demonstrate its superior performance in capturing the spatiotemporal dynamics of shear band formation. While all models exhibit certain failure modes, our findings underscore the importance of domain-specific considerations in developing robust AI-accelerated simulation tools for reactive materials.

It is increasingly important to generate synthetic populations with explicit coordinates rather than coarse geographic areas, yet no established methods exist to achieve this. One reason is that latitude and longitude differ from other continuous variables, exhibiting large empty spaces and highly uneven densities. To address this, we propose a population synthesis algorithm that first maps spatial coordinates into a more regular latent space using Normalizing Flows (NF), and then combines them with other features in a Variational Autoencoder (VAE) to generate synthetic populations. This approach also learns the joint distribution between spatial and non-spatial features, exploiting spatial autocorrelations. We demonstrate the method by generating synthetic homes with the same statistical properties of real homes in 121 datasets, corresponding to diverse geographies. We further propose an evaluation framework that measures both spatial accuracy and practical utility, while ensuring privacy preservation. Our results show that the NF+VAE architecture outperforms popular benchmarks, including copula-based methods and uniform allocation within geographic areas. The ability to generate geolocated synthetic populations at fine spatial resolution opens the door to applications requiring detailed geography, from household responses to floods, to epidemic spread, evacuation planning, and transport modeling.

In a multiplex network, a set of nodes is connected by different types of interactions, each represented as a separate layer within the network. Multiplexes have emerged as a key instrument for modeling large-scale complex systems, due to the widespread coexistence of diverse interactions in social, industrial, and biological domains. This motivates the development of a rigorous and readily applicable framework for studying properties of large multiplex networks. In this article, we provide a self-contained introduction to the limit theory of dense multiplex networks, analogous to the theory of graphons (limit theory of dense graphs). As applications, we derive limiting analogues of commonly used multiplex features, such as degree distributions and clustering coefficients. We also present a range of illustrative examples, including correlated versions of Erdős-Rényi and inhomogeneous random graph models and dynamic networks. Finally, we discuss how multiplex networks fit within the broader framework of decorated graphs, and how the convergence results can be recovered from the limit theory of decorated graphs. Several future directions are outlined for further developing the multiplex limit theory.

High-frequency oscillations (HFOs) are a new biomarker for identifying the epileptogenic zone. Mapping HFO-generating regions can improve the precision of resection sites in patients with refractory epilepsy. However, detecting HFOs remains challenging, and their clinical features are not yet fully defined. Visual identification of HFOs is time-consuming, labor-intensive, and subjective. As a result, developing automated methods to detect HFOs is critical for research and clinical use. In this study, we developed a novel method for detecting HFOs in the ripple and fast ripple frequency bands (80-500 Hz). We validated it using both controlled datasets and data from epilepsy patients. Our method employs an unsupervised clustering technique to categorize events extracted from the time-frequency domain using the S-transform. The proposed detector differentiates HFOs events from spikes, background activity, and artifacts. Compared to existing detectors, our method achieved a sensitivity of 97.67%, a precision of 98.57%, and an F-score of 97.78% on the controlled dataset. In epilepsy patients, our results showed a stronger correlation with surgical outcomes, with a ratio of 0.73 between HFOs rates in resected versus non-resected contacts. The study confirmed previous findings that HFOs are promising biomarkers of epileptogenicity in epileptic patients. Removing HFOs, especially fast ripple, leads to seizure freedom, while remaining HFOs lead to seizure recurrence.

Artificial Intelligence and especially Large Language Models (LLM), such as ChatGPT has revolutionized the way educators work. The results we get from LLMs depend on how we ask them to help us. The process and the technique behind an effective input is called prompt engineering. The aim of this study is to investigate whether science educators in secondary education improve their attitude toward ChatGPT as a learning assistant after appropriate training in prompt engineering. The results of the pilot study presented in this paper show an improvement in the previously mentioned teachers perceptions.

An attractive approach for stabilizing entangled many-body spin states is to employ engineered dissipation. Most existing proposals either target relatively simple collective spin states, or require numerous independent and complex dissipative processes. Here, we show a surprisingly versatile scheme for many-body reservoir engineering that relies solely on fully collective single-excitation decay, augmented with local Hamiltonian terms. Crucially, all these ingredients are readily available in cavity QED setups. Our method is based on splitting the spin system into groups of sub-ensembles, and provides an easily tunable setup for stabilizing a broad family of pure, highly entangled states with closed-form analytic descriptions. Our results have immediate application to multi-ensemble quantum metrology, enabling Heisenberg-limited sensing of field gradients and curvatures. Notably, our approach solves an important challenge in differential quantum sensing by providing the first example of Heisenberg-limited differential sensing immune to common-mode noise and accessible with only simple one-body measurements. The same setup also allows the stabilization of an entire family of entangled states in a 1D chain of spin ensembles with symmetry-protected topological (SPT) order, and have a direct connection to the outputs of sequential unitary circuits. A special case of our protocol efficiently stabilizes the celebrated Affleck-Kennedy-Lieb-Tasaki (AKLT) state.

In plasmonics, nonlocal effects arise when the material response to optical excitations is strongly dependent on the spatial correlations of the excitation. It is well known that a classical free electron gas system supports local Drude volume plasmon waves. Whereas a compressible quantum electron gas system sustains hydrodynamic volume plasmons with nonlocal dispersion isotropic across all high-symmetry directions. Here, distinct from Drude and Hydrodynamic plasmon waves, we present the first observation of crystalline nonlocal volume plasmon waves. We use transmission-based momentum-resolved electron energy loss spectroscopy to measure the volume plasmon dispersion of silicon along all the fundamental symmetry axes, up to high momentum values ($q \sim 0.7$ reciprocal lattice units). We show that crystalline nonlocal plasmon waves have a prominent anisotropic dispersion with higher curvature along the light-mass ($\Gamma K$ & $\Gamma L$) axes, compared to the heavy-mass ($\Gamma X$) axis. We unveil the origin of this phenomenon by experimentally extracting the anisotropic Fermi velocities of silicon. Our work highlights an exquisite nonlocality-induced anisotropy of volume plasmon waves, providing pathways for probing many-body quantum effects at extreme momenta.

This volume contains contributions submitted to the 12th Low-Level RF Workshop which will be held in Newport News, Virginia, USA on October 12-16, 2025. This workshop continues the series of successful international workshops held in Newport News, USA (2001), Geneva, Switzerland (2005), Knoxville, USA (2007), Tsukuba, Japan (2009), Hamburg, Germany (2011), Tahoe City, USA (2013), Shanghai, China (2015), Barcelona, Spain (2017), Chicago, USA (2019), Brugg-Windisch, Switzerland (2022), and Gyeongju, South Korea (2023).

Thin-film lithium niobate (TFLN) has played a pivotal role in the advancement of integrated photonics, by supporting a diverse range of applications including nonlinear optics, electro-optics, and piezo-optomechanics. The effective realization and enhancement of these interactions rely heavily on the implementation of high quality photonic microresonators. The pursuit of novel resonator architectures with optimized properties thus represents a central research area in TFLN photonics. In this work, we design and fabricate TFLN Fabry-Perot microresonators, by placing a straight section of waveguide between a pair of tapered photonic crystal mirrors. The resonator features a high quality factor of 600k at 1530 nm and a compact length of 100 um. The functionality of the device is further demonstrated by integrating on-chip electrodes for high-frequency piezo-optomechanical modulation. Our device can serve as an appealing candidate for developing high-performance photonic components on the TFLN platform.

Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conventional molecular dynamics simulations provide atomistic resolution, but their femtosecond time steps limit access to the slow conformational changes and relaxation processes that govern chemical function. Here, we introduce a deep generative modeling framework that accelerates sampling of molecular dynamics by four orders of magnitude while retaining physical realism. Applied to small organic molecules and peptides, the approach enables quantitative characterization of equilibrium ensembles and dynamical relaxation processes that were previously only accessible by costly brute-force simulation. Importantly, the method generalizes across chemical composition and system size, extrapolating to peptides larger than those used for training, and captures chemically meaningful transitions on extended time scales. By expanding the accessible range of molecular motions without sacrificing atomistic detail, this approach opens new opportunities for probing conformational landscapes, thermodynamics, and kinetics in systems central to chemistry and biophysics.

Accurate wellbore trajectory prediction is a paramount challenge in subsurface engineering, governed by complex interactions between the drilling assembly and heterogeneous geological formations. This research establishes a comprehensive, mathematically rigorous framework for trajectory prediction that moves beyond empirical modeling to a geomechanically-informed, data-driven surrogate approach.The study leverages Log ASCII Standard (LAS) and wellbore deviation (DEV) data from 14 wells in the Gulfaks oil field, treating petrophysical logs not merely as input features, but as proxies for the mechanical properties of the rock that fundamentally govern drilling dynamics. A key contribution of this work is the formal derivation of wellbore kinematic models, including the Average Angle method and Dogleg Severity, from the first principles of vector calculus and differential geometry, contextualizing them as robust numerical integration schemes. The core of the predictive model is a Gated Recurrent Unit (GRU) network, for which we provide a complete, step-by-step derivation of the forward propagation dynamics and the Backpropagation Through Time (BPTT) training algorithm. This detailed theoretical exposition, often omitted in applied studies, clarifies the mechanisms by which the network learns temporal dependencies. The methodology encompasses a theoretically justified data preprocessing pipeline, including feature normalization, uniform depth resampling, and sequence generation. Trajectory post-processing and error analysis are conducted using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2).

Proteinaceous shells useful for various biomedical applications exhibit a wide range of anomalous structures that are fundamentally different from icosahedral viral capsids described by the Caspar-Klug paradigmatic model. Exploring the Protein Data Bank, we have identified nine different types of anomalous shells structurally close to flat octagonal quasicrystals. As we show, these numerous shells have cubic nets cut from short-period approximants of an octagonal tiling composed of square and rhombic tiles. The approximants and parent tiling are easily obtained within the Landau density wave approach, while the nonequilibrium assembly of them can be simulated using the pair potentials derived from critical density waves. Gluing a polyhedron net and mapping it onto a spherical surface induces tile distortions, and to reduce them, we introduce and minimize the effective elastic energy of the system. Thus, we return quasi-equivalence to previously equivalent tiles. Possible cubic faceting of the octagonal spherical tilings is discussed in terms of the topological charge distribution over the tiling vertices. The proposed structural models describe numerous proteinaceous shells including about half of the known symmetrical enzymes. Our results constitute a fundamental basis for further applications of identified octagonal assemblies and can help to discover and study similar systems in the future.

Magnetic particles underpin a broad range of technologies, from water purification and mineral processing to bioseparations and targeted drug delivery. The dynamics of magnetic particles in high-gradient magnetic fields-encompassing both their transport and eventual capture-arise from the coupled interplay of field-driven drift, fluid advection, and particle-field feedback. These processes remain poorly captured by existing models relying on empirical closures or discrete particle tracking. Here, we present a thermodynamically consistent continuum theory for collective magnetic particle transport and capture in high-gradient fields. The framework derives from a free-energy functional that couples magnetic energy, entropic mixing, and steric interactions, yielding a concentration-dependent susceptibility via homogenization theory. The resulting equations unify magnetism, mass transport, and momentum balances without ad hoc shut-off criteria, allowing field shielding, anisotropic deposition, and boundary-layer confinement to emerge naturally. Simulations predict canonical capture morphologies-axially aligned plumes, crescent-shaped deposits, and nonlinear shielding-across field strengths and flow regimes, consistent with trends reported in prior experimental and modeling studies. By organizing captured particle mass data into a dimensionless phase diagram based on the Mason number, we reveal three distinct regimes-thermodynamically controlled, transitional, and dynamically controlled. This perspective provides a predictive platform for in silico optimization and extension to three-dimensional geometries, and informing digital twin development for industrial-scale high-gradient magnetic separation processes.

We present a numerical framework for constructing a targeted digital twin (tDT) that directly models the dynamics of quantities of interest (QoIs) in a full digital twin (DT). The proposed approach employs memory-based flow map learning (FML) to develop a data-driven model of the QoIs using short bursts of trajectory data generated through repeated executions of the full DT. This renders the construction of the FML-based tDT an entirely offline computational process. During online simulation, the learned tDT can efficiently predict and analyze the long-term dynamics of the QoIs without requiring simulations of the full DT system, thereby achieving substantial computational savings. After introducing the general numerical procedure, we demonstrate the construction and predictive capability of the tDT in a computational fluid dynamics (CFD) example: two-dimensional incompressible flow past a cylinder. The QoIs in this problem are the hydrodynamic forces exerted on the cylinder. The resulting tDTs are compact dynamical systems that evolve these forces without explicit knowledge of the underlying flow field. Numerical results show that the tDTs yield accurate long-term predictions of the forces while entirely bypassing full flow simulations.

This study presents a computational and theoretical framework inspired by thermodynamic principles to analyze the dynamics of economic inflation within adiabatic and non-adiabatic systems. In a framework referred to as developmental symmetry, inflation is formulated as a scalar field evolving through continuity equations, drawing an analogy with the Raychaudhuri equation in gravitational dynamics. The results show that adiabatic systems fail to reach equilibrium, while non-adiabatic systems can evolve toward stable states over time. The model successfully reproduces observed inflationary regimes-from hyperinflation to stable low-inflation phases-with characteristic transition periods of about a decade. These results indicate that production continuity and controlled monetary flow are crucial for achieving stability in complex economic systems, linking thermodynamic balance to macroeconomic equilibrium.