Papers

In the study of alternative or extended theories of gravity, Dirac's Hamiltonian constraint algorithm is invaluable for enumerating the propagating modes and gauge symmetries. For gravity, this canonical approach is frequently applied as a means for finding pathologies such as strongly coupled modes; more generally it facilitates the reconstruction of gauge symmetries and the quantization of gauge theories. For gravity, however, the algorithm can become notoriously arduous to implement. We present a simple computer algebra package for efficiently computing Poisson brackets and reconstructing constraint algebras. The tools are stress-tested against pure general relativity and modified gravity, including the order reduction of general relativity at two loops.

Quantum imaging with undetected light has recently emerged as a technique in which quantum correlations and nonlinear interferometry are combined to decouple illumination and detection paths. This approach has been more recently extended and combined with digital phase-shifting holography and off-axis holography to extract both the amplitude and phase information of a sample relying on single-photon interference. Despite these advantages, implementing the technique in real-world scenarios where the observed system is subject to environmental noise and dynamic variations remains challenging. The primary limitation lies in the inability of quantum imaging systems to retrieve object information in real time under high-noise conditions. Here, we experimentally demonstrate real-time amplitude and phase imaging in noisy environments, building upon our previous implementation of quantum off-axis holography. Our results demonstrate real-time imaging at acquisition rates up to 4~Hz, even when the noise level exceeds the signal by an order of magnitude.

Optical computing could reduce the energy cost of artificial intelligence by leveraging the parallelism and propagation speed of light. However, implementing nonlinear activation, essential for machine learning, remains challenging in low-power optical systems dominated by linear wave physics. Here, we introduce an optical spiking neural network that uses optical rogue-wave statistics as a programmable firing mechanism. By establishing a homomorphism between free-space diffraction and neuronal integration, we demonstrate that phase-engineered caustics enable robust, passive thresholding: sparse spatial spikes emerge when the local intensity exceeds a significant-intensity rogue-wave criterion. Using a physics-informed digital twin, we optimize granular phase masks to deterministically concentrate energy into targeted detector regions, enabling end-to-end co-design of the optical transformation and a lightweight electronic readout. We experimentally validate the approach on BreastMNIST and Olivetti Faces, achieving accuracies of 82.45% and 95.00%, respectively, competitive with standard digital baselines. These results demonstrate that extreme-wave phenomena, often treated as deleterious fluctuations, can be harnessed as structural nonlinearity for scalable, energy-efficient neuromorphic photonic inference.

The Born-Mayer-Huggins (BMH) potential, which combines Coulomb interactions with dispersion and short-range exponential repulsion, is widely used for ionic materials such as molten salts. However, large-scale molecular dynamics simulations of BMH systems are often limited by computation, communication, and memory costs. We recently proposed the random batch sum-of-Gaussians (RBSOG) method, which accelerates Coulomb calculations by using a sum-of-Gaussians (SOG) decomposition to split the potential into short- and long-range parts and by applying importance sampling in Fourier space for the long-range part. In this work, we extend the RBSOG to BMH systems and incorporate a random batch list (RBL) scheme to further accelerate the short-range part, yielding a unified framework for efficient simulations with the BMH potential. The combination of the SOG decomposition and the RBL enables an efficient and scalable treatment of both long- and short-range interactions in BMH system, particularly the RBL well handles the medium-range exponential repulsion and dispersion by the random batch neighbor list. Error estimate is provided to show the theoretical convergence of the RBL force. We evaluate the framework on molten NaCl and mixed alkali halide with up to $5\times10^6$ atoms on $2048$ CPU cores. Compared to the Ewald-based particle-particle particle-mesh method and the RBSOG-only method, our method achieves approximately $4\sim10\times$ and $2\times$ speedups while using $1000$ cores, respectively, under the same level of structural and thermodynamic accuracy and with a reduced memory usage. These results demonstrate the attractive performance of our method in accuracy and scalability for MD simulations with long-range interactions.

We use large language models (LLMs) to uncover long-ranged structure in English texts from a variety of sources. The conditional entropy or code length in many cases continues to decrease with context length at least to $N\sim 10^4$ characters, implying that there are direct dependencies or interactions across these distances. A corollary is that there are small but significant correlations between characters at these separations, as we show from the data independent of models. The distribution of code lengths reveals an emergent certainty about an increasing fraction of characters at large $N$. Over the course of model training, we observe different dynamics at long and short context lengths, suggesting that long-ranged structure is learned only gradually. Our results constrain efforts to build statistical physics models of LLMs or language itself.

Two-dimensional single-mode Rayleigh-Taylor Instability (RTI) is simulated using an accurate and robust front-tracking/ghost-fluid method (FT/GFM) with high-order weighted essentially non-oscillatory (WENO) scheme. We compare our numerical results with the single-mode RTI experiments of Renoult, Rosenblatt and Carles (2015). The time evolution of the interface between two immiscible fluids and the effects of surface tension on the growth of the amplitude and asymmetry of the perturbed interface are examined for the initial wavelength 1 cm and the Atwood number A=0.29. The important features of RTI flows such as interface profiles, bubble/spike penetration and velocities show good agreement between experiments and simulations of immiscible fluids with surface tension. The velocity vector fields for the bubble and spike in the linear and nonlinear regimes are consistent with the theory for the single wavelength perturbation.

The interaction of vortical structures with boundaries has been extensively studied in Newtonian fluids, where conditions such as no slip walls, free surfaces, or contaminated surfaces dictate whether vortices rebound, dissipate, or generate secondary structures. In this work, we investigate a related but fundamentally different problem: the interaction of a vortex pair with a finite, non uniform layer of polymeric fluid. Numerical simulations employing the finitely extensible nonlinear elastic Peterlin model are used to examine the effects of polymer concentration, relaxation time, polymer layer thickness, and maximum polymer extension on the evolution of kinetic energy and enstrophy. The results show that, while the polymeric fluid dissipates vortical motion, vortex polymer layer interactions can also generate new coherent structures. In particular, the formation of secondary and tertiary vortices coincides with transient increases in kinetic energy, a behavior absent in the Newtonian case. Unlike classical vortex boundary interactions, where the primary vortex survives, we find that under certain conditions it completely dissipates upon interaction with the polymer layer. These findings emphasize that fluids with non-uniform polymer concentrations, act not only as dissipative agents but also as sources of vorticity, extending the traditional view of polymer induced drag reduction and providing new insight into vortex polymer interactions.

Imagine you walk in a plane. You move by making a step of a certain length per time interval in a chosen direction. Repeating this process by randomly sampling step length and turning angle defines a two-dimensional random walk in what we call comoving frame coordinates. This is precisely how Ross and Pearson proposed to model the movements of organisms more than a century ago. Decades later their concept was generalised by including persistence leading to a correlated random walk, which became a popular model in Movement Ecology. In contrast, Langevin equations describing cell migration and used in active matter theory are typically formulated by position and velocity in a fixed Cartesian frame. In this article, we explore the transformation of stochastic Langevin dynamics from the Cartesian into the comoving frame. We show that the Ornstein-Uhlenbeck process for the Cartesian velocity of a walker can be transformed exactly into a stochastic process that is defined self-consistently in the comoving frame, thereby profoundly generalising correlated random walk models. This approach yields a general conceptual framework how to transform stochastic processes from the Cartesian into the comoving frame. Our theory paves the way to derive, invent and explore novel stochastic processes in the comoving frame for modelling the movements of organisms. It can also be applied to design novel stochastic dynamics for autonomously moving robots and drones.

A capacitor consisting of the [bmim][NTf$_2$] ionic liquid (IL) confined in between planar graphite electrodes has been investigated by molecular dynamics based on an all-atom, unpolarizable force field. Despite a few peculiarities due to the size and complexity of the ions, properties such as the density of ions throughout the capacitor, the screening of the surface charge on the electrodes by the IL and exact sum rules for the radial distribution functions of cations and anions generally comply with the results of time honored theories of the electrostatic double layer. This soothing regularity may conceal hidden correlations still compatible with the static screening rules, propagating far inside the IL the information on the state of charge of the capacitor. Evidence in this respect might have been detected by vibrational spectroscopy (see, for instance, Langmuir 2021, vol. 37, 5193-5201) showing changes in optical properties of the IL far from the charged electrodes. We show that grouping the [bmim]$^+$ and [NTf$_2$]$^-$ ions into instantaneous neutral pairs reveals an intriguing long range ordering of ions normal to the interface, driven by the capacitor state of charge. These correlations manifest themselves through the parallel orientation of the dipole moments of the neutral ion pairs. We speculate that this effect changes the average intensity of fluctuating electric fields deep in the IL, while average, static fields vanish in agreement with well established screening laws. This effect, which could change the spectroscopic properties of the IL, is present in the simulated [bmim][NTf$_2$] / graphite capacitor, but too small to be unambiguously confirmed by the present simulations with a safe margin over the error bar. The conceptual interest in these effects, however, will motivate further studies of the same or similar electrode / ionic liquid interfaces.

Spatially entangled photon pairs (biphotons) generated by spontaneous parametric down-conversion offer unique opportunities for quantum imaging, but image-plane biphoton correlations are difficult to observe with camera-based detectors. Previous camera-based biphoton imaging experiments have relied on photon-counting detection, which necessitates operation deep in the photon-sparse regime and requires extremely low dark rates. Here, we demonstrate the detection of spatial biphoton joint probability distributions in both the image plane and the pupil plane (also termed "near-field plane" and "far-field plane" respectively) using a conventional scientific CMOS camera operated in linear mode. We work at mesoscopic intensity levels, corresponding to a photon flux approximately four orders of magnitude higher than typical photon-counting approaches. From the measured image- and pupil plane correlations, we observe position and momentum correlations consistent with an EPR-type entanglement witness. A tailored correlation analysis suited for image plane imaging suppresses detector artifacts and intensity fluctuations, enabling acquisition with significantly fewer frames. Our results demonstrate that spatially entangled-light imaging can be performed efficiently with standard imaging hardware, extending quantum imaging techniques beyond the photon-counting regime.

We establish an exact identity for overdamped Langevin dynamics: the total entropy production rate equals four times the mutual information rate between an infinitesimal displacement and its actual temporal midpoint, plus a mean-flow term. This provides a forward information-theoretic representation of irreversibility and makes the Shannon chain rule act directly on dissipation. For additive bipartitions, the chain rule reveals nonnegative self and interaction channels, with the self channel coinciding with apparent entropy production. This channel structure sharpens the learning-rate bound by showing that learning is constrained solely by the interaction channel. The same structure is also shown to resolve a recently reported coarse-graining reversal in hydrodynamically coupled colloids. Together, these results exhibit that, within this channel decomposition, learning and coarse-graining probe complementary parts of dissipation in overdamped Langevin systems.

Improving hydrogen storage density is essential for reducing the extreme conditions required in applications such as nuclear fusion. However, the recognition of metallic hydrogen as the "Holy Grail" of high-pressure science highlights the difficulty of high-density hydrogen aggregation. Here, we report a solid-state crystal H9@C20 formed by embedding hydrogen atoms into C20 fullerene cages and utilizing chemical precompression, which remains stable under ambient pressure and temperature conditions and exhibits metallic properties. This precompression effect is reflected in the formation of C-H bonds within the cage and C-C bonds between cages, resulting in the transformation of all C atoms from sp2 to sp3 hybridization with inward and outward distortions, while promoting delocalized multicenter bonding within the H9 aggregate. In particular, the hydrogen density inside the C20 cage exceeds that of solid hydrogen, achieving a uniform discrete distribution with H9 as monomers. Further study reveals that filling hydrogen molecules into voids between H9@C20 primitive cells can increase hydrogen content while maintaining structural stability, forming a solid-gas mixed hydrogen storage crystal. Our findings provide a basis for developing high-density hydrogen storage materials under ambient conditions.

We propose a novel variational phase-field model for fracture and fatigue in pseudoelastic shape memory alloys (SMAs). The model, developed in a one-dimensional setting, builds upon the Auricchio-Petrini constitutive formulation for SMAs and couples damage evolution with phase transformation. We study analytically and numerically the homogeneous and localization responses of a bar under both monotonic and cyclic loading, and we investigate various macroscopic behaviors by tuning the constitutive parameters. A key feature of the model is the introduction of a transformation strain limit, beyond which the material is fully martensitic and behaves elastically. This leads to a distinctive behavior in which the region of localized damage widens, yielding a delay of fracture. The capability of the model to predict the fatigue performance is assessed by simulating the uniaxial response of Ni-Ti multi-wire samples under different loading conditions. The results show that the model discriminates between safe and critical loading scenarios, capturing the experimental trend of increased fatigue resistance with higher mean strain at a fixed strain amplitude. Ongoing efforts are aimed at further evaluating its reliability for quantitative fatigue life prediction.

The FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission will provide, for the first time, systematic far-infrared spectral measurements of Earth's outgoing radiation, enabling improved understanding of atmospheric processes and the radiation budget. Retrieving atmospheric states from these observations constitutes a high-dimensional, ill-posed inverse problem, particularly under cloudy-sky conditions where multiple-scattering effects are present. In this work, we develop a data-driven, physics-aware inversion framework for FORUM all-sky retrievals based on latent twins: coupled autoencoders for atmospheric states and spectra, combined with bidirectional latent-space mappings. A lightweight model-consistency correction ensures physically plausible cloud variable reconstructions. The resulting framework demonstrates potential for retrievals of atmospheric, cloud and surface variables, providing information that can serve as a prior, initial guess, or surrogate for computationally expensive full-physics inversion methods. It also enables robust scene classification and near-instantaneous inference, making it suitable for operational near-real-time applications. We demonstrate its performance on synthetic FORUM-like data and discuss implications for future data assimilation and climate studies.

Pure-state manifestations of geometric phase are well established and have found applications across essentially all branches of physics, yet their generalization to mixed-state regimes remains largely unexplored experimentally. The Uhlmann geometric phase offers a natural extension of pure-state paradigms and can exhibit a topological character. However, observation of this invariant is impeded by the incompatibility between Uhlmann parallel transport and Hamiltonian dynamics, as well as the difficulty of preparing topologically nontrivial mixed states. To address this challenge, we report an experimentally accessible protocol for directly measuring the mixed-state topological invariant. By engineering controlled nonunitary dynamics in a photonic quantum walk, we prepare topologically nontrivial mixed states from a trivial initial state. Furthermore, by machine-learning the full density matrix in momentum space, we directly extract the quantized geometric phase of the nontrivial mixed states. These results highlight a geometric phase framework that naturally extends to open quantum systems both in and out of thermal equilibrium.

High-quality reconstruction of Aerosol Optical Depth (AOD) fields is critical for Atmosphere monitoring, yet current models remain constrained by the scarcity of complete training data and a lack of uncertainty quantification.To address these limitations, we propose AODDiff, a probabilistic reconstruction framework based on diffusion-based Bayesian inference. By leveraging the learned spatiotemporal probability distribution of the AOD field as a generative prior, this framework can be flexibly adapted to various reconstruction tasks without requiring task-specific retraining. We first introduce a corruption-aware training strategy to learns a spatiotemporal AOD prior solely from naturally incomplete data. Subsequently, we employ a decoupled annealing posterior sampling strategy that enables the more effective and integration of heterogeneous observations as constraints to guide the generation process. We validate the proposed framework through extensive experiments on Reanalysis data. Results across downscaling and inpainting tasks confirm the efficacy and robustness of AODDiff, specifically demonstrating its advantage in maintaining high spatial spectral fidelity. Furthermore, as a generative model, AODDiff inherently enables uncertainty quantification via multiple sampling, offering critical confidence metrics for downstream applications.

This work presents tunable generation of vortex, vector, and flat-top 1060-nm NIR beams in a few-mode fiber with a dual-force mechanical long-period fiber grating. By varying the applied forces on the fiber grating, the core mode to higher-order mode excitation can be adjusted. Manipulating the beam transformation is achieved by controlling the polarization of the fiber eigenmodes and the mode-coupling efficiency. In addition, our dual-force scheme enables greater flexibility in the mode selection by introducing the force gradient as an additional degree of freedom. By precisely tuning the intensity ratio between fundamental and doughnut modes, we arrive at the generation of propagation-invariant flat-top beams, for which the transverse intensity distribution exhibits a near-uniform profile over a propagation distance of 5 m in free space. The transverse optical field of 532-nm green light from a frequency-doubled Nd-doped yttrium vanadate laser can also be manipulated and coupled into various intensity distributions in a few-mode fiber by using a mechanically induced long-period fiber grating. We show that a doughnut beam, Mexican-hat beam, and crater-lake beam can be generated from the input Gaussian beam via the coupling of the fundamental core mode to a series of co-propagating higher-order modes with properly applied forces and polarizations. Our experimental results establish dual-force mechanically induced LPGs as a flexible and reconfigurable fiber-based tool for structured beam engineering, offering a unified platform for in-situ and robust control of phase, polarization, and amplitude of optical fields across different spectral bands.

This study examines the effect of centre-body geometry on the dynamics of an isothermal, non-reacting swirl combustor through computational fluid dynamics (CFD) simulations. Two different central body shapes were considered in a lab-scale combustor configuration, modelled as transient, incompressible flow using the SST k-omega turbulence model. The numerical model was validated against experimental velocity data from literature to ensure accuracy. Cross-spectral analysis techniques were employed to characterise the coherent dynamics of the flow, providing insight into the influence of geometry on unsteady swirl dynamics.

In this paper, we present a Time Projection Chamber (TPC) system for low-background beta radiation measurements. The system consists of a TPC with two-dimensional-strip readout Micromegas and an anti-coincidence detector with readout pads for cosmic ray veto. The detector system utilize an AGET-based waveform sampling system for data acquisition. The beta detection capability of the system was verified through experimental test using $^{90}$Sr beta source. Additionally, a dedicated simulation program based on Geant4 was developed to model the entire detection process, including responses to both the beta source and background radiation. Simulation results were compared with experimental data for both beta and background samples, showing good agreements. The simulation samples were utilized to optimize and train classification models for beta and background discrimination. By applying the selected model into test data, the system achieved a background rate of 0.49 $\rm cpm/cm^2$ while retaining more than 55% of $^{90}$Sr beta signals within a 7 cm diameter detection region. Further analysis revealed that approximately 70% of the background originates from environmental gamma radiation, while the remaining contribution mainly comes from intrinsic radioactivity of detector materials, particularly the FR-4 based field cage and readout plane. Based on the knowledge gained from the experiments and simulations, an optimization of the TPC system has been proposed, with simulation predicting a potential reduction of the background rate to 0.0012 $\rm cpm/cm^2$.

Accessing the properties of a plant cell interior non-invasively is difficult due to the presence of a cell wall. Nanoparticles larger than 5 nm cannot be readily phagocytosed inside the cell like animal cells. It is here that we realise that Tradescantia zebrina plant cells have prismatic forms of calcium oxalate crystals present inside them naturally. These crystals make a ready choice to study properties of the inner cell surface with the application of optical tweezers. Moreover, out-of-plane rotations in optical tweezers have begun to be explored only recently. The pitch rotation has been detected with high resolution and several applications are explored. In this work, we first study the stable configuration while trapped in linearly polarized optical tweezers and then explore the other out-of-plane configurations to detect the roll rotation at high resolution. Then a micro-rheological analysis is performed to obtain the frictional properties of the inner surface of the plasma membrane of the leaf cell. The size of the particle is about 5 $\mu$m along the diagonal, so that the contact length with the surface is about 200 nm. We measure a frictional force of 18.5 pN at a sensitivity of about 200 fN without averaging.

Nanoscale mechano-optical surfaces enable electronics-free strain sensing, attractive for skin-interfaced devices, yet reported implementations require laser/spectrometer interrogation, negating this advantage. Here, we report electrically passive mechanical transduction of arterial pulsation into diffractive colour shifts read by unmodified smartphone cameras, enabled by a dual-function monolithic poly-dimethylsiloxane (PDMS) film. Using large-area double-sided nanoimprinting, we achieve a strain-sensitive nanophotonic surface on one face of the film and a bio-inspired 3D structural adhesive on the other. We measure strain-dependent optical response and reproduce it in colour-mixing optical simulations. In uniaxial cyclic loading tests, 2% strain produces a 9% RGB-intensity modulation, stable over 1000 cycles. Further, 3D structuring improves adhesive shear strength by 65% on skin over flat PDMS. Hand-held smartphone recordings in humans ($n=13$) resolve sub-beat hemodynamics in agreement with clinical reference (per-beat waveform $\rho=0.94 \pm 0.03$), exceeding established non-invasive techniques such as active reflectance photoplethysmography (PPG), imaging PPG, and piezoelectric pulse-force sensors in simultaneous recordings. Importantly, mechanical transduction at the elastomer–air interface presents an optical cardiovascular monitoring approach agnostic to dermal-melanin, a known PPG confounder. Together, these advances establish camera-readable mechanochromic elastomers as versatile materials platforms for wearable cardiovascular monitoring, point-of-care diagnostics, and electronics-free human-machine interfaces.

We present a generalizable scale-bridging computational framework that enables predictive modeling of insertion-type electrode materials from atomistic to device scales. Applied to sodium manganese hexacyanoferrate, a promising cathode material for grid-scale sodium-ion batteries, our methodology employs an active-learning strategy to train a Moment Tensor Potential through iterative hybrid grand-canonical Monte Carlo–molecular dynamics sampling, robustly capturing configuration spaces at all sodiation levels. The resulting machine learning interatomic potential accurately reproduces experimental properties including volume expansion, operating voltage, and sodium concentration-dependent structural transformations, while revealing a four-order-of-magnitude difference in sodium diffusivity between the rhombohedral (sodium-rich) and tetragonal (sodium-poor) phases at 300 K. We directly compute all critical parameters – temperature- and concentration-dependent diffusivities, interfacial and strain energies, and complete free-energy landscapes – to feed them into pseudo-2D phase-field simulations that predict phase-boundary propagation and rate-dependent performances across electrode length scales. This multiscale workflow establishes a blueprint for rational computational design of next-generation insertion-type materials, such as battery electrode materials, demonstrating how atomistic insights can be systematically translated into continuum-scale predictions.

We investigate the statistical accuracy of temporally interpolated spatiotemporal flow sequences between sparse, decorrelated snapshots of turbulent flow fields using conditional Denoising Diffusion Probabilistic Models (DDPMs). The developed method is presented as a proof-of-concept generative surrogate for reconstructing coherent turbulent dynamics between sparse snapshots, demonstrated on a 2D Kolmogorov Flow, and a 3D Kelvin-Helmholtz Instability (KHI). We analyse the generated flow sequences through the lens of statistical turbulence, examining the time-averaged turbulent kinetic energy spectra over generated sequences, and temporal decay of turbulent structures. For the non-stationary Kelvin-Helmholtz Instability, we assess the ability of the proposed method to capture evolving flow statistics across the most strongly time-varying flow regime. We additionally examine instantaneous fields and physically motivated metrics at key stages of the KHI flow evolution.

This study presents the design, simulation, and experimental validation of a dual-tuned concentric multimodal surface coil for 7T 1H/31P magnetic resonance spectroscopic imaging (MRSI), developed to significantly enhance 31P B1 efficiency while improving 1H performance. The coil architecture utilizes two interleaved sets of three concentric loop resonators. Intra-nucleus electromagnetic coupling within each three-loop set generates a spectrum of eigenmodes; the operational modes for 1H and 31P were specifically selected because their co-directed current distributions reinforce the magnetic field at the center, yielding B1 patterns that resemble those of conventional single-loop surface coils but with superior efficiency. Full-wave electromagnetic simulations and bench measurements on a fabricated prototype were conducted to characterize the multimodal resonance behavior, scattering parameters, B1 distribution, and 10-g local SAR, using size-matched conventional single-tuned loops as references. The results confirmed that the design reproducibly generated the predicted eigenmode ordering with sufficient spectral separation to prevent interference from parasitic or undesired modes. Notably, the multimodal design achieved an 83% boost in 31P B1 efficiency and a 21% boost in 1H B1 efficiency at the coil center compared to same-sized single-tuned references. Sufficient inter-nuclear decoupling was achieved to prevent signal leakage between channels, and simulations with a human head model confirmed that the peak 10-g local SAR remained comparable to conventional designs. These findings demonstrate that this multimodal concentric design offers a robust and highly efficient solution for multinuclear MRSI at ultrahigh fields, effectively mitigating the sensitivity limitations of X-nuclei without compromising proton-based imaging capabilities.

This paper introduces a novel framework for analysing equilibrium in structured production systems that incorporate a static social division of labour, distinguishing between consumption goods traded in competitive markets and intermediate goods exchanged through bilateral relationships. We develop the concept of viability – the requirement that all producers earn positive incomes, as a foundational equilibrium prerequisite. Our main theorem establishes that a structured production system is viable if and only if it is coherent, admitting no circular conversion processes that yield no net output, which in turn is equivalent to the non-singularity of its matrix representation. We further investigate completely viable systems, in which viable prices exist for all consumption good price vectors. We show that complete viability stands exactly between two input restrictions: it is guaranteed in coherent systems where no consumption good is used as an input in production, and it requires that no consumption good is used as an input in the production of another consumption good. The analysis thus reveals fundamental relationships between the architectural design of production systems and their economic sustainability. Our framework also contributes to the literature on the existence of positive output price systems and the Hawkins-Simon condition in input-output analysis.

The classical Prats' problem of flow instability in a horizontal porous channel saturated by a fluid subject to a buoyancy force is reconsidered. In the original formulation, the driving buoyancy force results from thermal diffusion. This study, however, substitutes thermal diffusion with mass diffusion. Furthermore, the usual scheme of mass diffusion is extended to comprehend also the anomalous phenomena of superdiffusion or subdiffusion. Such phenomena are modelled via a time-dependent mass diffusivity which yields a significant change in the formulation of the stability eigenvalue problem. In particular, the ordinary differential equations governing the time evolution of the perturbations acting on the base throughflow become non-autonomous. This makes a significant difference in the discussion of the conditions leading to instability, with a marked effect of the anomaly in the mass diffusion process. The transition from convective to absolute instability for subdiffusion processes is also addressed.

Synchronization systems with effective inertia, such as power grid networks and coupled electromechanical oscillators, are commonly modeled by the second-order Kuramoto model. In the forward process, numerical simulations exhibit a staircase-like growth of global synchrony, reflecting temporal oscillations induced by secondary synchronized clusters of whirling oscillators. While this behavior has been observed previously, its governing conditions have not been quantitatively determined in terms of analytical criteria. Here, we develop a self-consistent theoretical framework that explicitly characterizes the secondary synchronized clusters. This analysis identifies an onset crossover mass $\tilde{m}^* \simeq 3.865$ for the emergence of secondary clusters and yields quantitative criteria for predicting both the crossover mass and the termination coupling strength at which they vanish. As a result, we determine the oscillatory regimes of coupling strengths over which global synchrony shows temporal oscillations, providing practical guidance for controlling and avoiding undesirable oscillatory behavior in inertial synchronization systems, such as power grids.

The transition of the Divertor Tokamak Test (DTT) facility from its initial commissioning phase (Day-0, plasma current $I_{p}=2$ MA) to the full power scenario ($I_{p}=5.5$ MA) introduces a critical shift in the dynamics of runaway electrons (REs) generation. While previous predictive studies of the low-current scenario indicated a robust safety margin against RE beam formation, this work reveals that the exponential scaling of the RE avalanche gain with plasma current severely narrows the safe operational window in the full power scenario. Using the non-linear magnetohydrodynamic code JOREK, we perform comprehensive 2D simulations of the current quench (CQ) phase of several disruption scenarios, systematically scanning initial RE seed currents and injected impurity levels. The results demonstrate that in the full power scenario, the avalanche multiplication factor is sufficiently high ($G_\text{av} \approx 1.3 \cdot 10^5$) to convert a mere 5.5 A seed current into macroscopic RE beams of $\approx 0.7$ MA when large amounts of impurities are present. For even higher RE seeds, the RE current can peak at $ \approx 3.2$ MA, constituting up to $\approx$ 80% of the total plasma current during the CQ. These findings suggest that, unlike the Day-0 phase, the disruption mitigation strategy for the full power scenario involves a careful balance between thermal load mitigation and RE avoidance, necessitating a well-chosen quantity of injected impurities. This work provides the baseline needed for future estimations of RE loads on the plasma-facing components of DTT, which will be essential for designing and positioning mitigation components like sacrificial limiters.

The spatiotemporal evolution of pulsating turbulent pipe flow was predicted by deep learning. A convolutional neural network (CNN) and long short-term memory (LSTM) were employed for long-term prediction by recursively predicting the local temporal evolution. To enhance prediction, physical components such as wall shear stress were informed into the training process. The datasets were obtained from direct numerical simulation (DNS). The model was trained exclusively on a limited set of sinusoidal pulsating flows driven by pressure gradients defined by their period and amplitude. Subsequently, 36 pulsating flows with arbitrary non-sinusoidal acceleration and deceleration were predicted to evaluate the generalization capability, defined as the predictive performance on unseen data during training. The model successfully predicted drag reduction rates ranging from $-1\%$ to $86\%$, with a mean absolute error of 9.2. This predictive performance for unseen pulsations indicates that local temporal prediction plays a central role, rather than learning the global profile of the pulsating waveforms. This implication was quantitatively verified by analyzing the differences in periodic $C_f$–$Re_b$ trajectories between the training and test datasets, demonstrating that flows exhibiting local similarity to the training data are more predictable. Furthermore, it was demonstrated that flows exhibiting intermittent laminar–turbulent transition and relaminarization become predictable when such regimes are incorporated into the training data. The results indicate that accurate prediction is achievable provided that the training data sufficiently cover the local flow-state space, highlighting the importance of appropriate training data selection for generalized flow prediction.

The nonlinear waves excited by the movement of a charged body in the dusty plasma medium are studied. A charged body moving through a dusty plasma medium can generate diverse nonlinear waves, such as precursors and pinned solitons. These wave excitations under weakly nonlinear and dispersive limits are described theoretically by the forced Korteweg-de Vries (fKdV) type equation. We have examined the role of the driver in shaping and evolving these wave excitations. In particular we studied the effect of primarily three source parameters, namely, amplitude, width, and flow speed, on the evolution of nonlinear structures. The driver generates a perturbation in the stable system configuration, which couples with medium characteristics and eventually evolves into propagating excitations. Our finding shows that the excitation of nonlinear structure by a moving body in a plasma medium is not just dictated by the mach number but also the features of the source such as amplitude and width. As a novel finding apart from pinned and precursor solitons, we observe another nonlinear structure that lags behind the source term, maintaining its shape and speed as it propagates. These features are the first ever theoretical depiction of such lagging structures.