Current density perturbations induced by radiative collapse, which is a possible mechanism governing tokamak plasma disruptions, have been investigated using a reaction-diffusion model. The reaction term of the current diffusion equation, which depends on the first and second radial derivatives of the electrical resistivity profile, produces a strong disturbance in the current density profile in a narrow layer of the cold front. While the current density locally increases in the region where the electron temperature gradient is steep, it decreases behind the cold front in the region where the electron temperature profile exhibits a pronounced concave-down curvature. The electrothermal dynamics driven by such a shape of the current density perturbation and the competition between Ohmic heating and impurity radiation are simulated by the tokamak transport code INDEX.

Volumetric videoconferencing enables immersive six Degrees of Freedom interactions by jointly transmitting visual appearance and 3D geometry. However, delivering volumetric video over today's networks remains challenging due to high bandwidth demands, strict real-time latency constraints, and frequent packet loss. Packet loss not only degrades visual quality but also corrupts geometric structure, leading to severe artifacts and video freezes that significantly degrade Quality of Experience. Existing solutions either optimize volumetric videos assuming reliable networks or focus on loss recovery for 2D video, and are insufficient for volumetric videoconferencing. In this paper, we present ReVo, a loss-resilient volumetric videoconferencing system that jointly recovers RGB and depth content under packet loss while meeting real-time constraints on desktop-grade hardware. ReVo leverages the insight that effective recovery requires a cross-layer, modality-aware design. It decouples volumetric video into RGB and depth streams, selectively protects critical content using network-layer FEC, and reconstructs corrupted non-critical frames using a post-decode neural recovery module. ReVo is implemented end-to-end over WebRTC and supports both traditional and neural video codecs. Our evaluations using real-world loss traces show that ReVo improves median SSIM by up to 32% (resp. 13%) for RGB (resp. depth) content and reduces video freezes by up to 95.7% compared to existing techniques.

Interior-gap superconductivity has long been discussed as an exotic paired state in the presence of Fermi-surface mismatch, but its realization in canonical strongly correlated models has remained elusive. Here we present evidence that the superconducting phase of one-dimensional Kondo-Heisenberg models realizes an interior-gap pair-density-wave (PDW) state generated by strong correlations. Combining infinite density-matrix-renormalization-group (iDMRG) and finite DMRG calculations for $S=1/2$ and $S=3/2$ chains, we show that the PDW correlation is the dominant bulk superconducting correlation in the spin-gapped regime and that the momentum distribution function $n(k)$ exhibits a reconstructed structure characteristic of interior-gap physics. In particular, while the feature in $n(k)$ for the $S=1/2$ chain is only hump-like, the corresponding structure in the $S=3/2$ chain develops into a clear dip, strongly supporting the interpretation in terms of an interior-gap-like dip structure. Unlike conventional interior-gap scenarios based on a mismatch between preexisting Fermi surfaces, the present system starts from a single bare conduction-electron Fermi surface, and the additional low-energy single-particle structure emerges dynamically together with the dominant PDW correlation through the Kondo coupling. Finite DMRG data further demonstrate that boundary effects can substantially modify real-space correlations in this gapless one-dimensional system, making a direct thermodynamic-limit calculation essential for identifying the intrinsic bulk momentum structure and the dominant correlation channel.

Multi-talker automatic speech recognition (ASR) in conversational recordings remains an open problem, particularly in scenarios with large portion of overlapping speech where identifying and transcribing a target speaker is difficult from audio alone. Visual cues can help resolve speaker ambiguity, yet their integration into long-context audio-visual (AV) ASR systems has been limited. The CHiME-9 MCoRec task addresses this challenge by requiring transcription of audio-visual recordings of heavily-overlapped parallel conversations, followed by clustering the participants into conversational groups. In this work, we present the BUT system based on a long-context target-speaker AV-ASR model capable of processing long-form recordings in a single decoding pass. Our architecture conditions a pre-trained NVIDIA Parakeet-v2 ASR model on visual representations from a pre-trained AV-HuBERT model. To cluster participants into conversation groups, we employ Qwen3.5-122B LLM to estimate transcript topic similarity followed by hierarchical agglomerative clustering. On the MCoRec development set, the proposed system achieves 33.7% WER and a clustering F1 score of 0.97, improving over the official baseline by 16.2% WER and 0.15 F1 absolute. On the eval set, our team ranked second, being 0.16% WER and 0.5% F1 worse than the best system.

This paper introduces the Structural Dissolution Framework to explain how artificial intelligence restructures the coordination architecture of traditional industries. We argue that AI dissolves the boundaries that once separated firms, markets, experts, and consumers by internalizing human multimodal interfaces, including language, vision, and behavioral data, into computational systems. This process is not merely an efficiency gain but a qualitative transformation of production relations. It generates four major shifts: the erosion of firm and industry boundaries; the movement of value creation from physical resources and human collaboration to continuous token flows produced through data refinement loops; the rise of domain-specific data refinement infrastructure as the new basis of positional control; and the emergence of regional data sovereignty entities as organizational forms that replace the coordinating role of firms and markets. We define this mechanism as Interface Internalization, through which inter-agent coordination is absorbed into intra-system computation. The framework challenges the Coasian view that organizational boundaries are determined by transaction cost minimization, arguing instead that AI makes such boundaries economically obsolete. Firms may continue to exist as legal and physical entities, but their coordinating function is displaced as they become data nodes within regionally governed AI infrastructure. Using resource-dependent regional economies as an illustrative case, the paper shows how AI adoption can both transform seasonal industries into continuous economic infrastructure and replace intermediate coordination roles and traditional employment structures.

Large Language Models (LLMs) have been widely applied to student-facing educational tools, this work explores their use in supporting instructors by presenting a practical adaptation of the Framework for Adaptive Content using Educational Technology (FACET) system to generate personalized instructional materials for an Introduction to Computer Programming (CS1) course. We conducted a mixed-methods study with 409 first-year computer science (CS) students, focusing on regular expressions (RegEx). Students were assessed on their knowledge and motivation, classified into one of four learner profiles, and assigned either LLM-personalized (treatment) or standard non-adaptive (control) exercises. Personalized materials varied in scaffolding, instructional explicitness, and tone based on learner profiles grounded in Bloom's Taxonomy and Self-Determination Theory. Quantitative analysis reveals that standard exercises resulted in task incompletion among low-knowledge learners, with approximately 25-30% incompletion, whereas personalized materials sustained near-universal completion (>99%) across all profiles. While high-performing students experienced ceiling effects, Low Knowledge/Low Motivation students achieved significantly higher correctness (+18.2%) with personalized support. Survey data indicate that students prioritize structural scaffolding (logical sequence, difficulty pacing) over motivational tone and perceive the adaptive tasks as equally challenging as standard exercises. These findings suggest that learner-profile-driven LLM personalization primarily serves as a retention scaffold, preventing task abandonment among at-risk students without diminishing the task's "desirable difficulty". The results demonstrate that instructor-facing LLM systems can effectively close engagement gaps in CS1 by tailoring instructional explicitness to student needs.

The pressure for Water Resource Recovery Facilities (WRRF) operators to efficiently treat wastewater is greater than ever because of the water crisis, produced by the climate change effects and more restrictive regulations. Technicians and researchers need to evaluate WRRF performance to ensure maximum efficiency. For this purpose, numerical techniques, such as CFD, have been widely applied to the wastewater sector to model biological reactors and secondary settling tanks with high spatial and temporal accuracy. However, limitations such as complexity and learning curve, prevent extending CFD usage among wastewater modeling experts. This paper presents HydroSludge, a framework that provides a series of tools that simplify the implementation of the processes and workflows in a WRRF. This work leverages HydroSludge to preprocess existing data, aid the meshing process, and perform CFD simulations. Its intuitive interface proves itself as an effective tool to increase the efficiency of wastewater treatment

Data-driven methods for computer simulations are blooming in many scientific areas. The traditional approach to simulating physical behaviors relies on solving partial differential equations (PDE). Since calculating these iterative equations is highly both computationally demanding and time-consuming, data-driven methods leverage artificial intelligence (AI) techniques to alleviate that workload. Data-driven methods have to be trained in advance to provide their subsequent fast predictions, however, the cost of the training stage is non-negligible. This paper presents a predictive model for inferencing future states of a specific fluid simulation that serves as a use case for evaluating different training alternatives. Particularly, this study compares the performance of only CPU, multiGPU, and distributed approaches for training a time series forecasting deep learning (DL) model. With some slight code adaptations, results show and compare, in different implementations, the benefits of distributed GPU-enabled training for predicting high-accuracy states in a fraction of the time needed by the computational fluid dynamics (CFD) solver.

This paper presents an efficient tool for managing dynamic resources in production high-performance computing (HPC) settings, focusing on flexibility, adaptability, and user-friendliness. We introduce a unified dynamic resource management application programming interface (API) that supports a wide range of HPC applications, allowing seamless integration without direct interaction with Dynamic Management of Resources (DMR). The DMR framework, evolved from the DMRlib structure, now supports various dynamic resource managers and includes the Proteo reconfiguration engine to enhance malleability strategies. This integration addresses previous limitations by allowing diverse reconfiguration methods without respawning all processes or lacking RMS support. The paper also showcases the solution's performance and coding productivity with the MPDATA (Multidimensional Positive Definite Advection Transport Algorithm) application. Key contributions include an enhanced modular DMR framework supporting different reconfiguration managers, upgraded DMRlib with the Proteo reconfiguration engine, offering extensive reconfiguration strategies, and a malleable version of the MPDATA solver.

Most galaxies have supermassive black holes (SMBH) at their centres, surrounded by stars with binary systems also present in this environment. We use two schemes - post-Newtonian (PN) and a scalar perturbation to a background metric to numerically solve the three-body problem of a binary with a SMBH. We test three different PN formulations for the PN scheme: The Einstein-Infeld-Hoffman equation, pair-wise implementation of two-body PN-terms for three bodies and the Arnowitt-Deser-Misner Hamiltonian. We compare these approaches for one million solar mass and one billion solar mass black holes, and find a statistical match between the two approximations for stellar mass binary interacting with a million solar mass black hole. We also perform a statistical study for encounters with this black hole, and find that the higher order PN formulation matches with metric-with-perturbation scheme. However, we find a decrease in separation of the binary, and eccentricity variations between different schemes around the billion solar mass black hole. This behaviour is not present if binary has a large separation or is further away from the black hole due to decreased general-relativistic effects. We find that the pair-wise PN method results in a decrease in separation at pericentre in all test cases irrespective of the distance from the black hole or mass of the black hole, making this the least reliable method for solving this problem. Our work highlights the need for caution when interpreting the results in different formulations around SMBHs. This also shows that when understanding extreme mass ratio inspirals (EMRIs) using simulations, one should beware as the binary gets closer to the black hole.

Sound speed heterogeneities can create aberrations in B-mode ultrasound images by inducing tissue-dependent delays and diffractive effects that conventional beamforming does not incorporate. By using the Fourier split-step method to simulate pressure fields in heterogenous sound speed media, reverse-time migration (RTM) can reconstruct the B-mode image by cross-correlating transmitted and received pressure fields. As a result, RTM is differentiable with respect to sound speed. This enables the reconstruction of the sound speed profile that minimizes the aberration in the B-mode image. In seismic imaging, this form of diffraction tomography, known as wave-equation migration velocity analysis, can roughly be understood as a type of full-waveform inversion (FWI) that acts in the image domain rather than errors in the received channel data. This is the first work applying WEMVA to medical pulse-echo ultrasound imaging. Phantom experiments show dramatic improvements in image quality with measured improvements in point target resolution from 1.22$\pm$1.01 to 0.32$\pm$0.07 mm and lesion contrast from 3.05 to 4.39 dB.

Convex maximization encompasses a broad class of optimization problems and is generally NP-hard, even for low-rank objectives. This paper investigates structural conditions under which convex maximization becomes polynomially solvable. From a geometric perspective, we introduce comonotonicity, a structural property of the feasible region crucial for problem tractability, and establish mathematical characterizations of this property. Under comonotonicity and mild additional assumptions, we develop a unified enumerative framework showing that fixed-rank convex maximization is polynomially solvable. This viewpoint recovers several known tractability results that previously required separate analyses, such as fixed-rank convex matroid maximization and sparse principal component analysis (SPCA). Furthermore, for the more structured class of standard comonotone feasible regions, we refine the analysis via a lifting technique to achieve a square-root improvement in the complexity bound. Finally, applications to SPCA and its variants illustrate the broad applicability and effectiveness of the proposed framework.

Bilocal holography provides a constructive approach to the higher-spin gravity theories dual to vector-model conformal field theories. Its central advantage is that it is completely gauge fixed and formulated entirely in terms of physical degrees of freedom. We derive a remarkably local bulk reconstruction formula and demonstrate its agreement with standard bulk reconstruction, after the same boundary data and gauge-fixed variables have been identified. We further clarify how subregion duality is realized in this framework.

A vertex subset of a graph is called a distance-$k$ independent set if the distance between any two of its distinct vertices is at least $k + 1$. For all $n,k \geq 1$, we determine the minimum possible number of inclusion-wise maximal distance-$k$ independent sets among all $n$-vertex trees. It equals $n$ if $n \leq k + 1$, and $n - \bigg\lfloor \frac{n - (k \bmod 2)}{\lfloor k/2 \rfloor + 1} \bigg\rfloor + 1$ otherwise. We also completely describe the class of trees attaining this bound and determine the growth rate of the number of such $n$-vertex trees for a fixed $k \geq 1$. If $k$ is odd and $(k+1)/2$ does not divide $n-1$, then the number of non-isomorphic $n$-vertex trees with the minimum possible number of maximal distance-$k$ independent sets grows linearly with $n$. Otherwise, it is bounded above by the number of unlabeled $k^2$-vertex trees.

The Solar System Notification Alert Processing System, SNAPS, is a downstream broker that ingests moving object data from ZTF and LSST and serves these data and derived properties to the public. This document describes how users can access our SNAPS data and products. This is intended to be a living document that will be updated on the arXiv when significant improvements are made to our data access schemes, and will therefore always contain the most up to date information about interacting with our databases and infrastructure. This is version 1.0.

Exceptional points (EPs), non-Hermitian degeneracies where both eigenvalues and eigenvectors coalesce, play a central role in the topology of non-Hermitian spectra. Recent advances have enabled the controlled creation and manipulation of EPs in a wide range of physical systems, raising the question of what new band topology emerges when EPs become mobile under cyclic modulation. Here we show that mobile EPs generate momentum-space switching domains that partition the Brillouin zone into regions with distinct band-switching behavior. Using a minimal two-band lattice model, we introduce a band-permutation invariant that determines whether eigenmodes exchange after one modulation cycle. The boundaries between switching regions arise from the projection of EP trajectories in an extended parameter space combining crystal momentum and the modulation parameter. As the modulation strength increases, the switching domains expand and eventually cover the entire Brillouin zone, resulting in global band switching. The predicted switching-domain structure is further demonstrated in a photonic crystal with lossy materials. These results open a new avenue within non-Hermitian topology by enabling the engineering of EP-driven phenomena through their controlled motion.

In this article, we first explain a group theoretic interpretation of the derivation of the relation between the flat coordinates of the polynomial prepotential $(H_3)$ and those of the algebraic prepotential $(H_3)'$ given in \cite{KMS2} constructed by M. Feigin, D. Valeri and J. Wright \cite{FVW}. By the same idea explained in the case of $(H_3)$, we will show a relation between the flat coordinates of the polynomial prepotential $(H_4)$ and those of the algebraic prepotential $H_4(9)$ given in \cite{Se}.

The simple form of the optical theorem of scattering theory, $σ_{\rm tot}^{\rm pw} = (4π/k)\,\Im f(0)$, is valid for an incident plane wave or for a wave packet whose Fourier components possess azimuthal symmetry about the incident wave vector $\vec{k}$. Previous work has shown that this expression can break down for structured beams of light which possess orbital angular momentum (OAM), despite the fact that there is clearly no violation of unitarity, and the relevant modifications have been worked out for the case of massless photons. We present a form of the optical theorem involving neutron OAM states for the case of the scattering of massive nonrelativistic particles. We apply this form to Ryndin's theorem on the application of time reversal symmetry to forward scattering, indicate how the statement of the null condition for T violation in forward scattering is modified, and show that this effect is negligible compared with other sources of systematic error in neutron optics transmission experiments.

Choosing the optimal observable to model dynamical systems for which we do not know the driving equations is nearly always an ad hoc art. Takens' Delay Embedding Theorem guarantees a diffeomorphism between delay-coordinate vectors built from generic scalar observables and the underlying invariant attractor, but is agnostic to optimal observable choice, and formal bounds on reconstruction quality across observables are not known. Here we prove that, under modest technical conditions, the Kolmogorov-Sinai entropy of an observable predicts its reconstruction error of the underlying dynamics in chaotic, ergodic systems. Using the Oseledets Multiplicative Ergodic Theorem, we show that the tangent bundles of reconstructed manifolds admit an invariant Oseledets filtration diffeomorphically related across admissible observables, with Lyapunov exponents controlling the propagation of perturbations. We bound reconstruction error by a quantity monotonically related to the sum of positive Lyapunov exponents and, by the Ruelle inequality, the Kolmogorov-Sinai entropy. We validate this empirically on the Lorenz-63 attractor, the Hastings-Powell food chain, and a tetracosane molecular-dynamics trajectory, recovering Spearman rank correlations between $h^{KS,UB}$ and reconstruction RMSE up to $ρ=+0.89$ ($p=5.5\times 10^{-8}$) for the realistic tetracosane case, sharpening to $ρ=+0.97$ under added measurement noise. This provides a rigorous foundation for observable selection in chaotic systems, applicable as an a priori data-selection criterion for any data-driven modeling pipeline.

In diagnostic test accuracy meta-analysis (DTA-MA), standard inference methods using bivariate random-effects models for jointly synthesizing sensitivity and specificity can be sensitive to outlying studies and may yield misleading conclusions. In this article, we propose frequentist outlier-robust statistical inference methods for DTA-MA based on density power divergence. The proposed methods automatically downweight influential outlying studies by modifying the estimating function using the robust divergence with a tuning parameter. To achieve robust yet statistically efficient inference in the presence of outlying studies, the proposed methods incorporate practical strategies for selecting the tuning parameter, including a data-adaptive criterion based on the Hyvärinen score. We also quantify the contributions of individual studies to the robust pooled estimates, facilitating interpretation of how outlying studies affect the results. We illustrate the effectiveness of the proposed methods through an application to a DTA-MA of the Mini-Mental State Examination. Simulation studies showed that the proposed methods reduced bias and root mean squared error relative to existing methods and improved coverage probability in the presence of outliers. The proposed methods enable a sensitivity analysis to assess whether the main results obtained using standard methods are driven by outlying studies.

Personalized cancer modeling for clinical applications requires robust and efficient parameter calibration, particularly in settings with limited patient data. This need is especially critical for medical digital twins (MDTs), which are virtual representations of disease continuously updated using longitudinal patient measurements. In this work, we propose a novel parameter personalization framework for dynamical cancer models under data scarcity. Our approach decomposes selected model parameters into a common component, shared across patients, and a personalized component, which is patient-specific and can be updated as new data become available. The common component captures population-level structure and is estimated once, providing an informed prior that enables rapid and accurate personalization. We demonstrate the effectiveness of this framework using synthetic data generated from canonical dynamical systems, such as logistic growth models with optimized treatment interventions. Our results show that parameter decomposition significantly improves calibration performance in limited-data regimes, facilitating fast and reliable personalization and supporting the development of patient-specific cancer models and MDTs.

We propose a regularized Hessian-free Newton-type method for minimizing smooth convex functions with Lipschitz continuous Hessians. The algorithm constructs an approximate Hessian by finite differences and selects the regularization parameter through an adaptive criterion that ensures sufficient decrease and gradient control. We prove that the method achieves a global $\mathcal{O}(k^{-2})$ convergence rate, matching the best known bound for second-order methods. A modified variant incorporating the exact Hessian when available enjoys local quadratic convergence under standard assumptions. Despite its simplicity, this variant is computationally faster than the \emph{Regularized Newton Method} of Mishchenko (2023) across several convex benchmark problems. Our analysis also provides explicit bounds on the regularization sequence and a worst-case iteration complexity of order $\mathcal{O}(\varepsilon^{-2})$. The proposed framework thus unifies regularized and Hessian-free Newton-type schemes, offering a theoretically sound and practically efficient alternative for smooth convex optimization.

We study stochastic differential equations on the $d$-dimensional flat torus $\mathbb{T}^d$ with drift and perturbation coefficients in $L^{\infty}(\mathbb{T}^d;\mathbb{R}^d)$ and additive non-degenerate noise. For the associated transfer operators, we analyse the dependence of the stationary measure and of the expectation of a given observable on small perturbations of the drift. In this framework, we prove a linear response formula for the invariant density and for the expectation of a given observable. We then address an optimal response problem, namely the determination of admissible perturbations that maximise the first-order variation of a prescribed observable. We establish existence of optimal perturbations and, in a Hilbert space framework, prove uniqueness and provide an explicit characterisation of the optimiser. This yields a practical Fourier-based numerical method, which we implement in several numerical examples, including both low and high-dimensional settings.

We propose a knowledge-driven approach to speech target extraction in the presence of background sound effects already recorded in cinematic audio. The specific knowledge sources studied are manners of articulation that are detected in speech frames and adopted to form a knowledge vector as a part of features to enhance speech separation and target speech extraction because some short speech segments are often difficult to separate from mixed background sounds. Testing on the recent Sound Demixing Challenge data for cinematic audio source separation (CASS) shows that utilizing articulator-aware knowledge sources produces better separation results than those obtained without using any knowledge, especially for speech segments buried in unspecified background sound events.

In this paper, we utilize our previous results on mod p monodromy of cyclic coverings of the projective line to realize a large series of groups of the form PSL(n, q) and PSU(n, q) as Galois groups over Q. We achieve for the first time a fully explicit infinite series of such groups where simultaneously the field can have arbitrarily large degree over the prime field and the group does not coincide with PGL(n, q) or PGU(n, q), respectively.

Alberto Isidori's framework of geometric nonlinear control, and particularly of feedback linearization, is the inspiration behind PDE backstepping: apply a transfromation of the state to cast the plant into a canonical form, bring all the non-canonical effects within the "span" of (boundary) control, and close the design with a feedback that makes the closed loop evolve in accordance with well-studied stable dynamics. The specificity of this approach is that, for PDEs, there is not one canonical form (like Brunovsky for ODEs) but the canonical forms are PDE-class-specific. When conducting this process for nonlinear PDEs, where the "transformation of the state" is performed using a nonlinear Volterra series indexed by the spatial variable, enormous technical challenges arise. One has to deal with kernels governed by PDEs on simplex domains growing in dimension to infinity, capture the growth rates of these kernels of the "direct transformation," and conduct the same for the "inverse transformation" without directly studying its Volterra kernels. So far, this agenda has been executed only once, two decades ago: for parabolic PDEs by Vazquez and Krstic [Automatica, 2008]. Generalization attempts have not followed because of the immense complexity involved in feedback-linearizing nonlinear PDEs. In this paper, dedicated to Professor Isidori, we convert the PDE feedback-linearizing methodology of 2008 from the parabolic to a hyperbolic class and, for a transport-adapted subclass of Chen-Fliess series, construct controllers without kernel PDEs.

Optical time projection chambers (OTPCs) are well suited for applications that require the highest spatial resolution for particle track reconstruction. The MIGDAL experiment uses a glass GEM-based OTPC and observes a systematic excess in both the intensity and width of particle tracks in its optical readout, when compared with charge readout simulations. One hypothesis is that scintillation light produced inside a GEM hole during the avalanche propagates through the GEM substrate and exits neighboring holes. We present lab measurements testing this hypothesized optical broadening effect in three types of GEM substrates: glass, ceramic, and FR4. Our observations quantify this optical broadening and demonstrate it to be strongest in glass GEMs. Additionally, we use Geant4 simulations to both reproduce our observations and quantify optical broadening effects in realistic charge avalanches. Applying our glass GEM effects to simulated particle tracks yields increases of track intensity and widths by up to around 26% and 31%, respectively. This may explain the larger than expected intensity and track widths observed in the MIGDAL OTPC and is expected to be an observed effect in all GEM-based OTPCs.

In a paper of Mathews, an isomorphism is constructed between two-component complex spinors and horospheres in H^3 carrying `spin decorations'. A recent arXiv preprint of Mathews and Varsha arXiv:2412.06572 extends this result to the case of `quaternionic spinors' and spin decorated horospheres in H^4. The following work generalises these results to an equivariant correspondence between two-component `Lipschitz spinors' with entries drawn from the Lipschitz group of a Clifford algebra, null multiflags in generalised Minkowski space, and higher-dimensional horospheres that carry an extension of the Mathews spin decoration. This correspondence allows spinors to be applied to horospheres in any dimension of hyperbolic space.

A model for mildly relativistic Runaway Electrons (REs) is developed in a reduced-kinetic form and qualitatively compared with radiation characteristics observed in KSTAR ohmic startup. The mildly relativistic correction not only alleviates runaway current overestimation but also accounts for the partial parallel confinement of the initial runaway seed under an open-field configuration during early burn-through. The model is self-consistently integrated in the state-of-the-art predictive plasma initiation code DYON (Hyun-Tae Kim et al 2022 Nucl. Fusion 62 126012), hereafter referred to as DYON-RE. DYON-RE provides an improved RE confinement model during the transition from an open to a closed magnetic configuration by employing a model-based description of closed flux surface formation validated in multi machines. We show prediction capability of DYON-RE in two representative discharges among KSTAR ohmic startups. DYON-RE reliably predicts key plasma parameters such as plasma current, density, and temperature and also implies the characteristic behavior of the radiative temperature measured by electron cyclotron emission diagnostics in agreement with experimental results. The proposed model offers a framework for designing runaway-free ohmic startup scenarios in CPD and ITER. Future experimental validation will further refine its predictive capabilities and broaden its practical application.

In this paper, we prove a big monodromy theorem for the monodromy of cyclic coverings of projective line for cohomology with Fp-coefficients. This is a direct generalization of the results of Achter and Pries, where such a theorem is proved for cyclic coverings of degree 2 and 3. Instead of generalizing their methods, we adapt the proof of the analogous theorem for integral cohomology. In our subsequent work, we will apply this theorem to construct in infinitely many cases Galois extensions of Q with Galois group PSL(n, q) and PSU(n, q), where q can be an arbitrarilty large prime power.