In this paper, we prove that a \(p\)-block with abelian defect group is inertial if it covers a \(p\)-block of a normal subgroup of \(p\)-power index having only one irreducible Brauer character orbit.

For risks that cannot be accepted, sufficiently mitigated, or eliminated, continuous observation is a viable approach but requires a model that can be operationalized. The Hagenberg Risk Management Process bridges this gap between qualitative risk analysis, using contextualized polar heatmaps (triage), and realtime risk management by extending Bowtie diagrams into a formal probabilistic runtime model. We introduce Realtime Risk Studio, a domain-specific modeling tool that (i) transforms Bowtie structures (causes, top event, barriers, consequences) into a directed acyclic graph (DAG) suitable for Bayesian inference, (ii) adds explicit safe-state semantics, and (iii) designates Activation Nodes as intervention points. Bowtie models are qualitative; however, Bayesian inference requires actual probabilities. As a second contribution, we present Probability Capture, a tool that complements our Realtime Risk Studio by automatically generating questionnaires from a DAG model so experts can provide estimates. The tool analyzes disagreement and aggregates conditional-probability assessments using both descriptive dispersion analysis and prior-regularized methods. Causal analysis can then provide insights into the DAG model, for example, via d-separation, adjustment-set inspection, do-calculus for what-if analysis, local independence checks, evidence updating, and impact-oriented searches for effective interventions. This workflow is illustrated with an instant-payments gateway scenario, demonstrating (a) explicit safe-state semantics, (b) Bowtie-to-DAG operationalization, (c) probability capture with visible expert noise, and (d) causal what-if analysis on a transformed and enriched model. Rather than presenting a statistical validation, the paper contributes a method and prototype system that transforms partially mitigated risks into observable, probabilistic, and intervention-ready models.

The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets. Recent advances in machine learning (ML) provide unprecedented opportunities to address many fundamental challenges posed by these heterogeneous and hyper-dimensional datasets. This review chapter highlights innovative ML methodologies that were developed and used by NCCR PlanetS members to address three overarching challenges in (exo)planetary science. The first challenge is sequence modelling, which encompasses the intricate analysis of one-dimensional data such as time series of radial velocities and light curves, among other examples. Secondly, there is pattern recognition that involves studying correlations, leveraging convolutional neural networks for feature extraction, mapping and cross correlation among other examples., anomaly detection through variational autoencoders, and unsupervised clustering of mass spectrometric data. Lastly, there are generative models and emulation-based Bayesian analysis, which encompass the development of predictive models for planetary interior structure, employing Deep Neural Networks to understand planet formation mechanisms. These innovative ML methodologies herald a paradigm shift in the processing of data and numerical models that represent inherent challenges in planetary and exoplanetary science, paving the way for revolutionary discoveries and ideas in this field.

We present a systematic study of the electromagnetic and spin polarizabilities of spin-1/2 singly charmed baryons at $\mathcal{O}(p^4)$ within the framework of heavy baryon chiral perturbation theory. Our results show that the higher-order corrections to the electric polarizability are small, while those to the magnetic polarizability are relatively larger due to the small mass splitting of singly charmed baryons and are closely related to transition magnetic moments. Furthermore, we find that the spin polarizabilities of singly charmed baryons, except for $γ_{M1M1}$, are much smaller than those of the nucleons. We have also calculated the polarizabilities for singly bottom baryons, with the results showing generally larger values than those of singly charmed baryons.

We apply discrete Morse theory, global topology, and persistent homology to characterize the impact of massive neutrinos on the multiscale cosmic web, focusing on filaments. The topology of the cosmic web is sensitive to neutrino imprints, and persistence diagrams provide more information than commonly used summary statistics by quantifying the longevity of topological features across densities. This scale-adaptive, parameter-free formalism is powerful, as massive neutrinos affect halos, walls, filaments, and voids in distinct ways. Within this framework, we simultaneously assess their impact on tracers and skeleton structures and capture their multiscale signals across cosmic time. Discrete Morse theory is also well suited for particle-based neutrino implementations, often affected by Poisson shot noise, as it preserves the salient features of the underlying smooth field. Using two independent sets of N-body simulations, we present filament statistics and persistence diagrams in massive-neutrino cosmologies. Our results show that neutrinos leave distinct imprints on filaments and skeleton connectivity, producing mass-dependent signatures most pronounced at high redshift (z~2) and detectable at the few-percent level for masses as small as $M_ν\sim 0.1$ eV. Filaments thus provide an ideal environment for isolating neutrino effects. We also compare two implementations of massive neutrinos to assess systematics. Our study establishes a promising avenue for leveraging cosmic web topology, persistent homology, and environment-based statistics to constrain or directly detect neutrino mass and infer the mass hierarchy - a long-standing challenge in particle physics and a major objective of ongoing and upcoming galaxy redshift surveys (e.g., DES, DESI, Euclid, Rubin-LSST).

Hierarchical matrices are data-sparse approximations of dense matrices that are widely used for fast matrix computations. Hierarchical matrices are built using a tree data structure, with low-rank blocks identified by various admissibility conditions, such as standard admissibility and weak admissibility. This paper introduces a novel hierarchical matrix framework, namely $\mathcal{H}_h$, based on a hybrid admissibility condition: we use the standard admissibility at the coarser levels (larger blocks) and the weak admissibility at the finer levels (smaller blocks). This hybrid strategy confines dense blocks only along the diagonal. We provide a criterion that ensures lower storage cost for $\mathcal{H}_h$-matrices compared to $\mathcal{H}$-matrices under the standard admissibility condition. We carry out a rounding error analysis of $\mathcal{H}_h$-matrices and show that the admissible blocks of $\mathcal{H}_h$-matrices can be represented in low precision (precision lower than the working precision) without degrading the overall approximation quality. We provide an explicit rule for dynamically selecting the precision of a given admissible block, thereby proposing an adaptive mixed precision algorithm for constructing and storing $\mathcal{H}_h$-matrices. Furthermore, we show that the use of mixed precision does not compromise the numerical stability and accuracy of the resulting $\mathcal{H}_h$-matrix-vector product. We perform a range of numerical experiments to validate our theoretical findings. Our numerical results show that the proposed adaptive mixed precision $\mathcal{H}_h$-matrices achieve significant storage reductions (up to $11 \times$) compared with uniform double precision standard admissibility-based $\mathcal{H}$-matrices, without compromising accuracy.

Occasionally, individuals immersed in a Virtual Reality (VR) environment may experience distractions that disrupt their sense of presence, a phenomenon referred to as a break in presence (BIP). Better understanding BIPs is crucial to designing VR applications that keep their users present. BIPs have been studied using a variety of methods, exploring their origins or trying to detect them from physiological or behavioral measurements. However, despite the importance of understanding how they are actually lived and managed by VR users, very few studies focused on their phenomenological characterization. We employed micro-phenomenology to collect the descriptions of BIPs experienced by users (n=14) of a height exposure VR application. We precisely modeled 57 BIP episodes, bringing to light a variety of experiences and behaviors. Four generic diachronic patterns of BIP episodes emerge: reflected-upon, discarded, self-preservation, and contradictory mediation BIPs. We discuss these in light of the PI/Psi model of presence, propose an awareness-based definition of BIPs, as well as three BIP-related design opportunities.

Quantum key distribution (QKD) networks provide information-theoretically secure keys for distant parties, emerging as a vital alternative to classical cryptography infrastructures threatened by quantum computing. In QKD networks, the immediacy of key supply service is crucial to the security and performance of applications, as their data must be encrypted before transmission. While key buffering can enable instant key supply services, existing schemes rely on heuristic solutions that incur prohibitive key resource consumption, thus significantly hindering practical deployment. To address this issue, we propose QuIKS, an instant key supply scheme based on adaptive buffering, offering the dominant advantage of near-zero key supply latency while consuming ultra-low key resources (i.e., ultra-low buffer size). Specifically, it is built upon a novel analytical model that determines the minimum buffer size required to guarantee near-zero-latency key supply performance. Guided by this model, QuIKS introduces a lightweight two-phase control algorithm that dynamically determines key relaying requests and adjusts the buffer size by probing real-time application patterns and network conditions. Experiments on a real QKD network testbed demonstrate that QuIKS achieves near-zero key supply latency while providing a more than 10-fold reduction in key buffer size compared to state-of-the-art schemes.

We study cosmological solutions of (pseudo)scalar theories with periodic potentials, in the presence of arbitrary cosmological fluids -- including a cosmological constant of either sign. Independently of the initial misalignment angle and field velocity, we derive an analytic bound that the axion mass parameter and decay constant fulfill as the universe decreases its acceleration rate, finding a natural application in models of thawing quintessence. As a first application, we illustrate the analytic handle our bound provides in bounding axion dark energy, after observational inputs from DESI and various supernovae data sets are taken into account. As a second application, we argue that our analytic bounds in combination with proposed quantum gravity constraints on axions exclude vast regions of parameter space. The combined constraints push the axion masses to be much larger than the Hubble scale, in tension with basic models of axion quintessence.

Combining X-ray diffraction with density-functional theory and electron topology calculations we found that pressure substantially modifies the bonding in K2Zn(IO3)4.2H2O. We discovered that under compression there is a progressive change from primary covalent I-O bonds and secondary halogen I-O interactions towards O-I-O electron-deficient multicenter bonds. Because of this, iodine hypercoordination converts IO3 trigonal pyramids towards IO6 units. The formation of these IO6 units breaks the typical isolation of iodate molecules forming an infinite two-dimensional iodate network. Hypercoordination influences the hydrogen atoms too, such that multicenter O-H-O bonds are also promoted with increasing pressure. We have determined that K2Zn(IO3)4.2H2O is one of the most compressible iodates studied to date, with a bulk modulus of 22(3) GPa. The pressure-induced structural changes strongly modify the electronic structure as shown by optical-absorption measurements and band-structure calculations. The band-gap energy closes from 4.2(1) eV at ambient pressure to 3.4(1) eV at 20 GPa.

The extragalactic background Light (EBL) from ultraviolet to infrared comprises the emission from all stars, galaxies, and actively accreting black holes in the observable Universe. A precise measurement of the EBL is critically important to probe models of star formation and galaxy evolution. The EBL can be measured via the absorption imprint left on the spectra of gamma-ray blazars. In this work, we rely on 15 years of {\it Fermi}-LAT data and 1576 blazars to measure the EBL optical depth in the $0<z<4.3$ range. We detect the EBL attenuation with $\sim23σ$ significance and measure the optical depth in 19 redshift bins, extending the coverage and improving on our previous results. This allows us to reconstruct the EBL evolution and find general consistency with recent EBL models. These results represent the most precise determination of the EBL with GeV $γ$ rays to date.

Drug instructions are crucial for guiding the rational use of medication. We conduct a visualization design study to enhance the comprehension of over-the-counter (OTC) drug instructions, targeting both the general public and medical professionals. We devise two tailored drug instruction designs for different audience groups through an iterative design process. A controlled user study reveals that our design outperforms traditional text-based instructions in terms of response time and usability, and the availability of two versions is also found to be beneficial. This study also motivates a taxonomy based on a systematic classification of OTC drug instructions sampled from an official drug database, which received positive expert feedback. Finally, this study summarizes a workflow for a visualization design strategy based on our design exploration and user study feedback, which can be generalized to other OTC drug instructions.

In this paper, our main aim is to extend a classical theorem of Phelps on norm-attaining functionals from the space of scalar-valued continuous functions $C(Ω)$ to its vector-valued counterpart $C(Ω, X)$. One of our main results provides a complete characterization of norm-attaining functionals on $C(Ω, X)$ under the assumption that $X^*$ has the Radon-Nikodým property (RNP). For a general Banach space $X$, we further investigate norm attainment at points of weak$^*$-to-weak continuity for the identity map $Id : (C(Ω, X)_1^*, w^*) \to (C(Ω, X)_1^*, w)$.

Benchmark problems play a central role in assessing the performance of numerical optimization algorithms. However, many existing constrained multiobjective optimization benchmark problems rely on overly restricted constructions or lack formal analysis of their optimal solution sets, limiting their relevance for systematic algorithm evaluation. In this work, we introduce a class of analytically tractable constrained multiobjective optimization problems whose Pareto sets can be formally characterized. The construction is based on convex-quadratic functions with positive definite Hessians, combined through multipeak formulations in which each objective is defined as the minimum over several convex-quadratic components. This approach preserves analytical structure while enabling multimodality (non-convexity), ill-conditioning and non-separability. The constraints are built as sublevel sets of multipeak functions giving rise to problems with potentially disconnected feasible regions. Building on these results, we propose COBI, a scalable generator of constrained bi-objective test problems designed for benchmarking derivative-free optimization algorithms. We provide a reference Python implementation that enables straightforward integration of COBI instances into benchmarking workflows.

Dimensionally or analytically regulated Feynman integrals lead to relative twisted period integrals. We present a recent extension of the Griffiths-Dwork pole reduction algorithm for deriving the D-module of differential operators acting on the twisted differential forms from Feynman integrals. We illustrate the application of this algorithm by providing twisted Picard-Fuchs operators for hypergeometric, elliptic and Calabi-Yau differential motives arising from families of Feynman integrals.

Flexible-geometry arrays based on movable antennas have shown considerable potential for improving wireless communication performance. In this letter, we investigate a multiuser multiple-input single-output (MU-MISO) downlink secure communication system aided by a flexible cylindrical array (FCLA) and artificial noise (AN), where each antenna element rotates along circular tracks while the circular slices move along a vertical axis. To guarantee transmission security, we aim to maximize the achievable sum rate at multiple legitimate information receivers by jointly optimizing transmit beamforming, AN covariance matrix, and antenna placement under secrecy constraints for an eavesdropper. While the resulting problem is intractable to solve, we develop a block coordinate descent (BCD)-based framework that combines the Lagrangian dual transform, tight semidefinite relaxation (SDR), and Nesterov-accelerated projected gradient descent (PGD). Numerical results show that the proposed algorithm converges rapidly and achieves significant sum-rate gains over benchmark schemes by exploiting the geometry flexibility of the array.

We analyze non-invertible topological interfaces and defects in the two-dimensional compact boson, focusing on the more exotic ones obtained by gauging continuous symmetries with flat connections on a half-space. These include interfaces between mutually irrational radii and T-duality symmetries at arbitrary boson radius. Using the modified Villain discretization on both a Euclidean two-dimensional square lattice and a quantum one-dimensional chain, we show that all these topological interfaces survive discretization and give rise to non-compact edge modes localized at the defect sites. Such non-compact edge modes imply a continuous defect spectrum and an infinite quantum dimension. In the special case of rational radii, we show how the defect action or Hamiltonian can be modified in order to compactify the edge modes and produce more standard defects with finite quantum dimension.

The nucleosynthetic heterogeneity between different asteroids and planets is well established. These isotopic variations manifest themselves at the part per millions level or larger, in isotopes that were synthesised in various stellar environments. To escape homogenisation, some of these isotopic signatures must have been preserved in dust, which ended up being heterogeneously distributed in the solar protoplanetary disc. The origin of the nucleosynthetic heterogeneity is still poorly constrained, potentially reflecting inherited isotope variations from the Sun's parental molecular cloud and/or processing and redistribution during the subsequent protoplanetary disc phase with thermal processing and size sorting as major processes. This chapter aims to provide a broad review of the dynamical, collisional, and thermal processes in protoplanetary discs -- from initial infall to gas dispersal -- that may have influenced the distribution and survival of the anomalous carrier phases, which finally accreted into asteroids and planets. While several of these mechanisms have been considered in past studies, they are often examined in isolation, which impedes the assessment of how their effects may be altered or amplified by additional disc processes. Size sorting in particular has received little attention, and here we highlight that this process likely occurred in the disc and can induce nucleosynthetic heterogeneity. By placing previous studies within the context of a comprehensive overview, we aim to clarify the broader physical framework in which anomalous carrier transport occurs and identify previously underexplored mechanisms that may have contributed to the final isotopic structure of the Solar System we see today.

While much prior work examines Large Language Models (LLMs) for solo development tasks (e.g., coding), far less is known about how LLMs shape collaborative group work in software engineering. This study focuses on one such collaborative task, namely software design. It presents the results of an exploratory laboratory study of 18 pairs of software professionals who could use an LLM however they saw fit, to design a University campus bicycle parking application. Our findings reveal that introducing an LLM leads to distinct patterns of joint use: shared-instance use facilitated shared understanding, whereas parallel use across separate instances sometimes led to ''context drift''. We also observe wide variation in reliance, from non-use to treating the LLM as an information source or producer. Across these modes, professionals scrutinized and reflected on LLM responses, often yielding design insights; however, early anchoring sometimes curtailed exploration. We provide implications for tools to aid designers while retaining the human-centricity important to design.

We study real triality structures through their intrinsic tensor algebra. Starting from a single triality symbol, we construct the associated Lie algebra of two-triality operators, prove the Jacobi identity, and identify the resulting algebra uniformly with the corresponding entry of the magic square. We then examine the natural invariant bilinear forms and the Clifford-theoretic structures arising from this construction. In low dimension, the triality formalism also recovers classical arithmetic data: in the \(2\times2\times2\) case, the associated binary quadratic forms have a common discriminant and fit naturally into the Bhargava cube picture.

Model Predictive Control (MPC) offers safe and near-optimal control but suffers from high computational costs. Approximate MPC (AMPC) mitigates this by learning a cheaper surrogate policy, typically by training a neural network on state-MPC input pairs. Generating training data is a major bottleneck, requiring solving the MPC for numerous states sampled from its feasible set. Since this feasible set is implicitly defined and unknown, efficient sampling is nontrivial but crucial. We propose the linear MPC Hit-and-Run (LMPC-HR) sampler for linear MPC with polyhedral constraints. We identify the feasible set boundaries along search directions, a crucial step within HR, by formulating the problem as a convex linear program, replacing expensive iterative searches with a single optimization step. A numerical study demonstrates that LMPC-HR achieves an order of magnitude reduction in computation time for generating uniformly distributed samples from the feasible set compared to naive baselines.

Polat generalised Menger's theorem -- the maximum number of vertex-disjoint paths between two sets $A$ and $B$ equals the minimum size of an $A$-$B$ separator -- to ends of undirected graphs. In this paper we extend Menger's theorem to ends of digraphs. As an application, we characterise the combined degree of ends of digraphs.

We present a domain decomposition method for the fast simulation of large lattice structures described by level set functions. The method does not rely on homogenization or multiscale techniques, and therefore avoids their underlying assumptions such as scale separation and periodicity. Individual cells are defined through level set functions and mapped into physical space using arbitrary order mappings, allowing the creation of complex graded designs with varying geometries and topologies. The discretization is based on unfitted p-FEM, where each cell is approximated by a single high order element. This choice naturally handles the implicit geometric description and provides high accuracy with a moderate number of degrees of freedom. The solver is built on the Balanced Domain Decomposition by Constraints (BDDC) method, where each cell corresponds to one subdomain. To accelerate the assembly of the cell stiffness matrices, we combine a fast assembly technique that separates the contributions of the geometric mapping from the trimmed domain with a reduced order model (ROM) based on the matrix discrete empirical interpolation method (MDEIM). The ROM surrogate is trained offline and reused for any geometric mapping, restricting the expensive quadrature on cut elements to the training stage. A stabilization term ensures the scalability of the solver when using the ROM approximation, at the cost of a small and controllable error. We validate the method through numerical experiments and demonstrate its performance on a complex 2D problem with more than 17,000 cells of varying geometry, solved in approximately 30 seconds on a standard laptop. The number of solver iterations remains bounded as the number of subdomains grows, provided the ratio between subdomain and mesh sizes is kept constant, in agreement with classical BDDC scalability properties.

Selecting appropriate physical models is a critical yet difficult step in many areas of computational science and engineering. In multiphase Computational Fluid Dynamics (CFD), practitioners must choose among numerous closure model combinations whose performance varies strongly across flow conditions. Sub-optimal choices can lead to inaccurate predictions, simulation failures, and wasted computational resources, making model selection a prime candidate for data-driven decision support. This work formulates closure model selection as a cold-start recommender system problem in a high-cost scientific domain. We propose a hybrid recommendation framework that combines (i) metadata-driven case similarity and (ii) collaborative inference via matrix completion. The approach enables case-specific model recommendations for entirely new CFD cases using their descriptive features, while leveraging historical simulation results from similar cases. The methodology is evaluated on 13,600 simulations across 136 validation cases and 100 model combinations. A nested cross-validation protocol with experiment-level holdout is employed to rigorously assess generalisation to unseen flow scenarios under varying levels of data sparsity. Recommendation quality is measured using ranking-based metrics and a domain-specific regret measure capturing performance loss relative to the per-case optimum. Results show that the proposed hybrid recommender consistently outperforms popularity-based and expert-designed reference models and reduces regret across the investigated sparsities. These findings demonstrate that recommender system methodology can effectively support complex scientific decision-making tasks characterised by expensive evaluations, structured metadata, and limited prior observations.

Detecting video deepfakes has become increasingly urgent in recent years. Given the audio-visual information in videos, existing methods typically expose deepfakes by modeling cross-modal correspondence using specifically designed architectures with publicly available datasets. While they have shown promising results, their effectiveness often degrades in real-world scenarios, as the limited diversity of training datasets naturally restricts generalizability to unseen cases. To address this, we propose a simple yet effective method, called AVPF, which can notably enhance model generalizability by training with self-generated Audio-Visual Pseudo-Fakes.The key idea of AVPF is to create pseudo-fake training samples that contain diverse audio-visual correspondence patterns commonly observed in real-world deepfakes. We highlight that AVPF is generated solely from authentic samples, and training relies only on authentic data and AVPF, without requiring any real deepfakes.Extensive experiments on multiple standard datasets demonstrate the strong generalizability of the proposed method, achieving an average performance improvement of up to 7.4%.

The most dangerous error in clinical trial interpretation is equating p > 0.05 with no effect. This review provides a practical, algorithm-based framework for classifying randomized controlled trial (RCT) results into six distinct categories positive, imprecise (+), neutral, inconclusive, negative, and harmful using confidence interval (CI) position relative to the minimal clinically important difference (MCID) as the primary tool, augmented by Bayesian posterior probabilities. We demonstrate that the same p > 0.05 result can represent three fundamentally different conclusions (inconclusive, negative, or neutral), show how Bayesian reanalysis can rescue benefit signals missed by frequentist thresholds, and illustrate the framework with real-world examples from critical care and cardiology trials. The framework synthesizes guidance from Altman, Harrell, Pocock, Zampieri, the ASA, and ICH E9 into a single coherent decision algorithm.

In this paper we will provide an overview of the hadron colliders built to date and the design and operational challenges that each of these machines has faced. Many of these are inherent to the ongoing effort to optimise the instantaneous and integrated luminosity of the machines, which inevitably lead to many technological challenges that must be met and overcome. We will summarise how these challenges have been successfully met in the past and present machines and outline the role they could play in ambitious future accelerator projects such as the HL-LHC upgrade and the FCC project.

A $G$-normal random variable $X\sim \mathcal{N}(0,[\underlineσ^2,\overlineσ^2])$ does not admit a unique probability law due to volatility uncertainty. For a given test function $ϕ$, the $G$-expectation admits the stochastic control representation$$\mathbb{E}[ϕ(X)] = \sup_{σ\in[\underlineσ,\overlineσ]} {E}\!\left[ϕ(X_T^σ)\mid X_0^σ=0\right] ={E}\!\left[ϕ(X_T^\ast)\mid X_0^\ast=0\right].$$ This formulation interprets the nonlinear expectation as a linear expectation under the law induced by the optimally controlled diffusion $X^\ast$, namely, the terminal law of $X_T^\ast$. This observation motivates the notion of a \emph{responsive distribution}, a measurement-dependent probability density $f_ϕ$ such that, for a given test function $ϕ$, $$\mathbb{E}[ϕ(X)] = \int_{\mathbb{R}} ϕ(x)\,f_ϕ(x)\,dx.$$ Based on this viewpoint, we propose a coupled backward--forward trinomial tree framework for computing the $G$-expectation and constructing the corresponding responsive distribution. The backward trinomial tree discretizes the associated stochastic optimal control problem and yields approximations of the value function (i.e., the $G$-expectation) and the optimal feedback control, while the forward trinomial tree propagates the induced transition probabilities and produces a discrete approximation of the responsive distribution. We establish rigorous convergence results for both components of the method. Numerical results not only validate the theoretical convergence of the coupled schemes but also provide a powerful, practical sampling tool to visualize the complex responsive distributions under various measurements.

In real-time systems, both individual task execution and data propagation must meet strict timing constraints. Cause-effect (CE) chains are widely used to analyze such behaviors by end-to-end latency. However, timing anomalies (TAs) can distort it, where a local reduction in execution times leads to an increase in the overall end-to-end latency. As a result, precisely analyzing the upper bounds of the latency becomes challenging, and such systems typically exhibit larger upper bounds than TA-eliminated systems. Existing studies either eliminate TAs by completely sacrificing average latency to simplify analysis or, despite adopting complex safe analysis methods, do not eliminate TAs effectively, still having high latencies. To address this issue, we identify two basic causes of TAs in end-to-end latency. Based on these causes, we propose the first treatment that eliminates TAs in the latency with negligible average latency loss using Deterministic Data Flow (DDF). We further formally prove its TA-free property. Therefore, we can get a precise upper bound for latency when all jobs execute with their worst-case execution times. Experimental results show that it effectively reduces the maximum end-to-end latency, the average latency, and latency jitter compared with the state-of-the-art (SOTA) method.

The present article studies solutions to the compressible Navier-Stokes equations for ideal gases in one dimension when thermal conductivity is present but very weak, while viscosity is positive and constant. The main novelty is the establishment of bounds that do not explode when the conductivity coefficient approaches zero. The conductivity coefficient is assumed to be constant and the framework is that of ''{à} la Hoff'' solutions. More precisely, the velocity is initially assumed to be regular, while the density and temperature are only in L^infini and far from zero. A new proof of a stability result for cases without conductivity is given. Then, the proof of the zero-conductivity limit to the Navier-Stokes system without conduction is established in the ''{à} la Hoff'' framework.