Hyperscale AI data centers induce spatially and temporally correlated load fluctuations that violate classical independence assumptions and are not captured by time-averaged spectral methods. These correlations are episodic and non-stationary, requiring analysis that resolves transient structure. This paper applies Dynamic Mode Decomposition (DMD) to the temporal evolution of pairwise inter-bus correlation coefficients to form a low-dimensional state representation that enables modal analysis without a stationarity assumption. DMD eigenvalues encode the correlation regime: their location in the complex plane distinguishes sustained coherence, decaying transients, and intensifying events, while oscillation frequency maps to underlying physical coupling mechanisms. Using an IEEE 39-bus Real-Time Digital Simulator (RTDS) testbed with three converter-interfaced AI data center loads driven by synthetic workload profiles, global DMD provides a time-averaged modal baseline in a slow thermal band ($f \approx 0.005$\,Hz, $|\mu| = 0.91$) captures 93.6\% of total correlation energy. A sliding-window DMD formulation identifies transient intensification events: 51 of 775 windows (6.6\%) satisfy the $|\mu_k^{(n)}| > 1$ criterion, which aligns with stochastic workload coincidences. Cross-validation with RTDS voltage coherence confirms elevated coupling during these intervals. The proposed modal growth indicator provides an early-warning signal of correlation intensification prior to peak pairwise coherence.

We provide an ergodic theory framework to study statistical properties of smooth sequences over the odd alphabet {1,3}. The arithmetic nature of this alphabet yields a partition of the subshift of smooth sequences based on their local structure, defining a notion of type for those sequences. We describe the substitutive structure of the smaller subshifts obtained by fixing the sequence of types of the successive derivatives of smooth sequences, from which we obtain the unique ergodicity of all these subshifts. A direct consequence is that the asymptotic frequency of any finite pattern in a smooth sequence over {1,3} is always well-defined and depends on its type sequence. Finally, we characterize the minimality of these subshifts.

We give the conjectured solution for the generating function of k-convex polyominoes, enumerated by semi-perimeter. The solution was obtained from the analysis of enumeration data that we generated.

Ramulator 2.1 is a major overhaul of Ramulator 2.0 that substantially improves the simulator in three directions: 1) support of modern and emerging DRAM and memory-controller features, 2) better usability and extensibility of the simulator, and 3) more comprehensive tests and validation workflows. Ramulator 2.1 adds support for advanced features in recent and emerging DRAM standards and memory controllers, including HBM3/4, LPDDR5/6, and GDDR7. To improve usability and extensibility, Ramulator 2.1 introduces a Python-based modeling and configuration interface backed by a two-way code-generation framework that 1) hides low-level C++ code behind high-level DRAM specifications written in Python, and 2) automatically creates Python proxies for all components of the simulator. Doing so enables users to rapidly create variants of DRAM standards and automate design-space-exploration workflows. To improve trustworthiness in simulation results, Ramulator 2.1 provides a comprehensive testing and validation infrastructure that covers both 1) fine-grained validation of specific DRAM timing constraints and memory-controller scheduling behavior, and 2) system-level performance evaluation using latency-throughput curves. To aid performance analysis and debugging, Ramulator 2.1 also includes an easy-to-use and high-performance DRAM command trace visualizer. Ramulator 2.1 is open-source on GitHub and under active development.

GenUI is an emergent class of AI tools that use large models to generate UI mock-ups based on users' high-level descriptions, promising to democratize UX design exploration for a broader audience. Most GenUI designs to date tend to inherit the conventions of conversational large models, such as ChatGPT and Gemini, where a user describes their design needs primarily via an unstructured prompt, and the tool then takes a depth-first approach, delving into the design right away and producing a high-fidelity prototype. In this research, we rethink how well this unstructured, depth-first, and high-fidelity GenUI design can support early-stage, 0-to-1 design exploration. To probe this question, we propose a contrastive design with structured input, breadth-first exploration, and low-fidelity generation. We then conducted a comparison study with 24 UX designers and product managers who conducted mini design exploration exercises using an existing GenUI tool and our contrastive GenUI tool. Findings reveal participants' perceived benefits and trade-offs of the two GenUI designs: structured input surfaces key facets but requires more work, raising entry barriers to start exploration; breadth-first workflow reveals more possibilities, but previewing UX ideas spanning many screens remains hard; and though low fidelity has value, professionals favor high fidelity because it fits practice and GenAI heightens fidelity expectations. We conclude with design implications for GenUI and similar AI-powered creativity support tools.

The composition of partially ionised plasmas is investigated for densities and temperatures at which the free electrons are degenerate. Based on a quantum statistical approach, the effect of Pauli blocking is addressed. Specifically, one- and two-electron ions are studied. Approximations for deriving an in-medium Schrödinger equation for the ionization potential are indicated. New results regarding the degree of ionisation and the Mott effect are presented. Standard codes for plasma properties do not take Pauli blocking effects into account and are therefore unable to explain the experiments in the high-density regime, where the electrons are degenerate.

We give a new proof of Vinogradov's theorem asserting that free product of biorderable groups is biorderable. Our proof relies on a simple dynamical construction that provides a bi-ordering of the free product that extends the bi-orderings on the factors. Furthermore, if the bi-orders of the factors are invariant under some automorphisms, then the bi-ordering on the product turns out to be invariant under the product automorphism.

Quranic Passage Retrieval (PR) could be a challenging task due to the linguistic complexity and the semantic gap between the Modern Standard Arabic (MSA) used in daily queries and the Classical Arabic (CA) of the Holy Quran. These factors hinder conventional retrieval methods. To handle these limitations and improve multi-verse retrieval and filter the zero-answer queries, this paper proposes a four-phase neural architecture designed to enhance retrieval accuracy and contextual understanding. The methodology combines hybrid candidate retrieval using AraColBERT dense indexing and BM25 sparse retrieval, followed by semantic reranking with a CAMeLBERTmix cross-encoder. A confidence gating mechanism is then applied to filter zero-answer queries, and an AraT5-based refinement module for multi-verse aggregation. The system is evaluated on an expanded version of the Quran QA 2022 dataset. Results show improved performance compared to the baseline models, achieving a Recall@10 of 0.7024 and a Mean Average Precision (MAP@10) of 0.4947. While the system exhibits a marginal tradeoff in absolute top-rank precision (MRR = 0.5807) compared to heavily optimised single models, the proposed architecture provides a substantially more comprehensive, reliable, and context aware solution for multi-verse Quranic passage retrieval.

We consider a partition of the projective space PG(m + n - 1, K), with K any (commutative) field, into one space of dimension m - 1 while all other spaces of the partition have dimension n - 1. Characterizations of particular partitions of this type are given. This research is motivated by some interesting problems on translation generalized quadrangles. In the paper we apply our results to generalized ovoids and generalized quadrangles. Finally we give some ideas for further research.

Many NP-hard graph problems can be modeled as optimal subgraph extraction problems with feasibility constraints. From Network Design to Facility Location, from Robotics to Graph Drawing, the subgraph extraction pattern emerges across diverse domains. Despite this commonality, these problems are typically solved with domain-specific heuristics. Usually, these problems balance competing objectives such as maximizing coverage or minimizing cost while satisfying structural constraints such as connectivity, planarity and reachability. In this work, we introduce $\Delta$Search, a general and fast heuristic framework that exploits the insight of Reward-Penalty optimization for solving a large class of subgraph extraction problems. The framework is easy to use as it only requires feasibility constraints and optimality criteria to be provided by the user to express the subgraph extraction problem. We also show how exact methods can be augmented with $\Delta$Search to improve their performance by aggressive pruning of the search space. We evaluate our framework on monotone graph problems such as Maximum Planar Subgraph (MPS) and Minimum Connected Dominating Set, Weighted Monotone problems such as Maximum Weighted Independent Set and Minimum Weighted Steiner Tree, and non-monotone graph problems such as Prize Collecting Vertex Cover (PCVC) and Uncapacitated Facility Location Problem (UFLP). Our results show that $\Delta$Search matches or surpasses state of the art heuristics for MPS, UFLP and PCVC problems with similar runtime. For the remaining problems, $\Delta$Search achieves approximately 89% of the solution quality of the state-of-the-art algorithms without any problem-specific tuning

Reducible oxides exhibit a rich interplay of electronic, structural, and chemical properties that underpins applications in catalysis, photovoltaics, batteries, and energy storage. This interplay is strongly shaped by excess electrons, often introduced by oxygen vacancies, that localize as small polarons and influence charge transport and surface chemistry. At surfaces, these polarons play a central role in charge localization, mobility, and reactivity, yet their finite-temperature dynamics remain difficult to access from first principles. Ab initio molecular dynamics is typically limited to picosecond time scales, precluding statistically meaningful sampling of polaron hopping dynamics. To overcome this limitation, we extend machine-learning-assisted polaron dynamics [V. Birschitzky et al., Phys. Rev. Lett. 134, 216301 (2025)] to redox-active oxide surfaces, using oxygen-deficient rutile TiO2(110) as a paradigmatic case. By accessing several nanoseconds of dynamics over a range of temperatures, we show that small-polaron mobility at the reduced rutile TiO2(110) surface is suppressed by several orders of magnitude relative to the corresponding bulk material, providing a microscopic interpretation of the lower electron mobilities observed in porous rutile TiO2 compared with single-crystal samples. This suppressed mobility arises from the loss of favorable hopping pathways: surface polaron motion is largely confined to planar inter-row trajectories within the second topmost layers, with only rare interlayer hopping events. Oxygen vacancies further reshape the polaron free-energy landscape by acting as attractive centers for excess electrons, biasing the polaron distribution toward nearby Ti sites and promoting occasional charge transfer to the outermost surface layer. These results establish a transferable machine-learning strategy for investigating polaron dynamics in reducible oxides.

Seeing that ultrafast (picosecond timescale) vibrational electron energy loss spectroscopy (EELS) should soon be experimentally realizable, we present in silico approach capable of providing insight from the computational physics perspective. We present a framework that combines frozen trajectory excitation (FTE) with time auto-correlation of auxiliary wavefunctions (TACAW) to study the time-dependent spectral response of non-equilibrium lattice dynamics in a way comparable directly to experiment - (scanning) transmission electron microscope EELS, (S)TEM-EELS. In this approach, a selected phonon excitation is first introduced into an equilibrium molecular dynamics trajectory using FTE, after which the atomic positions during subsequent relaxations are treated with short-time TACAW analysis performed at different pump-probe delays. This yields momentum- and energy-resolved electron-scattering signals bearing a phonon imprint during the relaxation process, going beyond time-dependent diffuse-scattering intensities alone. We demonstrate the approach for fcc-Ni and 3C-SiC and discuss the observed phonon mode coupling and spectral redistribution during phonon relaxation.

We present a direct collective-spin derivation of the effective uniform-mode Hamiltonian used in cavity magnonics. Starting from a nearest-neighbor Heisenberg ferromagnet coupled to long-wavelength magnetic fields, we show that the relevant dynamics can be restricted to the fully symmetric spin sector, where the exchange interaction contributes only a constant energy shift and the ferromagnet behaves as a macrospin of length $Ns$. Applying the Holstein--Primakoff transformation directly to this total spin yields the usual uniform magnon mode and its leading nonlinear corrections without first introducing site-resolved bosonic operators. This collective formulation makes explicit the interpretation of the ferromagnet as a synthetic large-spin atom and provides a compact route to the effective Hamiltonians used in driven and Floquet cavity magnonics. As a physical consequence, the leading nonlinear correction produces an occupation-dependent reduction of the effective magnon--photon coupling, providing a simple signature of finite-spin saturation under strong uniform-mode driving.

Low-dimensional transition-metal dichalcogenides (TMDCs) provide an ideal platform for studying the emergence of charge density wave (CDW) and superconductivity. The discovery of emergent CDW order in 1T $\mathrm{ZrTe_2}$ monolayer raises an important question: does this instability persist when one $\mathrm{Te}$ chalcogen layer is substituted by $\mathrm{Se}$? In the present work, we investigate the CDW (2$\times$2$\times$1) and superconducting instability in 1T $\mathrm{ZrSeTe}$ Janus monolayer using first-principles calculations. The phonon spectrum exhibits a pronounced anomaly at the $\mathrm{M}$ point of the irreducible Brillouin zone, arising from enhanced electron-phonon interaction together with electronic instabilities originating from both interband and intraband scattering. The resulting lattice distortion reconstructs the electronic structure, opening a small indirect band gap, driving the system from a semi-metallic to a semiconducting state. The energy gain associated with the distortion is significantly smaller than that of $\mathrm{ZrTe_2}$ monolayer, indicating that the replacement of one $\mathrm{Te}$ chalcogen layer with $\mathrm{Se}$ weakens the CDW instability. We have further investigated the effects of electronic correlation and biaxial strain, both acts as effective tuning parameters for the instabilities concerened. In the high temperature undistorted phase, $\mathrm{ZrSeTe}$ exhibits phonon mediated two-gap superconductivity. It originates primarily from the robust coupling between the soft phonon mode at $\mathrm{M}$ point and the electronic bands predominantly derived from $\mathrm{Zr}$ $\mathit{d}$ and $\mathrm{Te}$ $\mathit{p}$ orbitals crossing the Fermi level. Spin-orbit coupling (SOC) further modifies the electronic states and reduces the superconducting transition temperature.

The response of particles to low-Reynolds flow can be compactly predicted with resistance or mobility tensors. However, the difficulty of obtaining accurate values for the elements of these tensors for specific geometries has held back work on particles with complex shapes. Here we show how Stokesian dynamics can be adapted to efficiently compute the resistance and mobility tensors of rigid and flexible aggregates, including confinement by walls. We introduce SHAPES, an implementation of the method, and demonstrate its capabilities for complex geometries including curved fibres, chiral dipoles, interacting aggregates, and active swimmers. Aggregates are represented by assemblies of beads designed to reproduce the geometry and motion of rigid or flexible particles. This coarse-grained description preserves the essential hydrodynamic interactions while substantially reducing computational cost. The method accurately reproduces known exact and approximate solutions, as well as experimental observations. The ability to compute the complete resistance and mobility tensors provides new insight into how aggregate shape controls translation, rotation, and coupling to fluid-velocity gradients. Previous descriptions often relied on simplified models retaining only a few symmetry-allowed couplings. While useful, such reduced descriptions are not always structurally stable under small perturbations of particle shape. Computing the full tensors makes it possible to draw robust conclusions and relate them to shape symmetry and hydrodynamic interactions. In particular, the method allows systematic analysis of non-Jeffery couplings to the strain rate that arise for helicoidal aggregates. SHAPES therefore provides a versatile framework for studying rigid and flexible aggregates in microfluidic, biological, and environmental flows.

We show that the equal-prior Helstrom trace-distance geometry of qubit states is recovered from Connes spectral distance in a finite scalar-qubit-scalar model. The two scalar reference sectors couple isotropically to the qubit block through identity Dirac links, so that the full Bloch ball, including mixed states, inherits its standard chordal trace-distance geometry from the finite spectral metric. The scalar-sector distances serve a distinct calibration role: they determine the individual link lengths, satisfy a Pythagorean consistency relation, and reconstruct the middle-sector scale.

A tokenized U.S. Treasury product lives on two ledgers: an off-chain portfolio of government securities and an on-chain wrapper that claims to represent it. The foundational question is whether the on-chain series carries recoverable information about the underlying -- whether a mapping from token to fiat exists, and with what structure. We proceed in three steps. First, fixed-income measurement conventions are reconciled between the ledgers; these corrections are signed and jointly. Second, on the reconciled series we introduce a price-discovery admissibility criterion: a falsifiable test, based on serial dependence and idiosyncratic dispersion, for whether a product's series is market-informative or administratively generated. Of four products with sufficient history it admits exactly one; the rest are dominated by how net asset value is computed and republished, and treating them as spreads fits artifacts. That most of the universe is inadmissible is our principal finding. Third, for the admitted mapping we give a minimal two-factor affine characterization in which the basis enters additively and orthogonally to rates, recovering a quarterly reversion, a small positive long-run basis, and a sharp March 2026 regime change toward parity. Persistence is weakly identified; we propagate it through a profile likelihood into one consequence, a collateral haircut. The contribution is measurement and identification infrastructure for when on-chain fixed income may be treated as a quantitative object.

Building on two key ingredients, namely the well established gauge-theoretic formulation of gravity and the observation that the tangent space of a curved manifold need not have the same dimension as the manifold, we discuss how all known fundamental interactions can be accommodated within a single unified framework. The unification is realised by enlarging the tangent group of the four-dimensional spacetime manifold to $SO(2,16)$, a choice that simultaneously encompasses both the gauge group underlying the gravitational sector and the $SO(10)$ Grand Unified Theory for the internal interactions. The gravitational theories entering this construction are Conformal Gravity and Fuzzy (Noncommutative) Gravity, each formulated in gauge-theoretic terms.

The study of starburst- and AGN-driven feedback is fundamental for understanding the processes that shape galaxy growth, quench star formation, and drive the co-evolution of galaxies and their central black holes. We present a spatially resolved study of six galaxies at $3 < z < 9$, including starburst- and AGN-dominated systems, observed with JWST/NIRSpec IFU in low- ($R \sim 100$) and high-resolution ($R \sim 2700$) mode. Previous analysis of $R \sim 2700$ data revealed ionized outflows in all these galaxies. We explore possible links between outflows and stellar population properties to assess the mechanisms driving galaxy evolution at early epochs. Combining the spatially resolved stellar population analysis with the outflow properties, we find that the most massive galaxies ($M_\star > 5 \times 10^{10} M_\odot$) with strong AGN-driven ionized outflows show evidence of past quenching episodes occurring within the last $\sim 100$--$300$ Myr, mainly in the nuclear regions ($r < 3$ kpc). In contrast, higher-redshift ($z > 5$) and less massive ($M_\star < 10^{10} M_\odot$) starburst galaxies with powerful (starburst-driven) outflows appear to have experienced continuous growth, with no clear sign of quenching. Massive galaxies with weaker outflows do not show evidence of quenching in their history. One massive AGN host (GS20936) shows evidence for a recent ($10$--$30$ Myr) rejuvenation phase, likely fueled by a recent major merger. These results suggest that (AGN-driven) outflows can already play a key role in shaping the SFHs of massive galaxies at early cosmic epochs.

High resolution echelle spectrographs remain the backbone of precision Doppler radial velocity (RV) programs, detecting almost all known exoplanets within 50 pc. Precision Doppler RV spectrographs have traditionally fallen into two camps. Standard unstabilized echelles observe targets through an iodine absorption cell. The iodine spectrum is embedded on the target spectrum, and provides a wavelength scale and a record of the spectrometer point-spread-function (PSF). Super-stabilized spectrometers are placed inside a vacuum tank, temperature stabilized at the level of 0.001 deg C, and fed by two scrambled fibers. One fiber carries the target, the other the calibration source (ThAr, Fabry-Pérot, laser-comb). Both techniques have found hundreds of planets and produce sub m/s uncertainties on the highest resolution echelles currently available. Both techniques have advantages and disadvantages and can be combined with the goal of reducing the long-term Doppler RV uncertainty to the sub 10 cm/s level. We have designed, built, calibrated, and commissioned an iodine cell for the European Southern Observatory's (ESO) ESPRESSO spectrograph. The design and construction of the cell was carried out in 2022. The cell was calibrated at the National Institute of Standards Technology (NIST) Atomic Spectroscopy laboratory in early 2023 and was commissioned in May 2023. The commissioning run was limited to evening and morning twilight on VLT-UT2. Five main sequence dwarf stars ranging in spectral type from G to early K were observed between 4 and 6 nights spanning a total of ten nights.

Iterative retrieval-augmented generation agents commonly overspend by continuing to retrieve after the model has converged on an answer, incurring calls that change neither the prediction nor the supporting evidence. Existing remedies learn a stopping policy from labeled trajectories, tying the decision to a trained component that requires retraining for each new model or task. We propose TASR (Training-Free Adaptive Stopping Rule), a one-line predicate that fires when the model repeats its previous-round normalized answer and the isotonically calibrated logit margin exceeds 0.25. No classifier or value head is learned; the threshold is fixed across all twenty-four (model, retriever, corpus) configurations we evaluate. On a 3-model x 2-dataset distractor grid, TASR retains 94.8% of fixed-k=5's macro F1 at 62.6% of its calls and exceeds fixed-k=3 by +3.42 F1. The pattern holds on nine open-domain BM25 cells (55.01 F1 at 2.98 calls vs. 54.33 at 3.00 for fixed-k=3) and, with calibration locked from the distractor split, on nine dense-retrieval cells across two retriever families, with zero significant regressions in either extension. The rule was selected from an exhaustive enumeration of 381 candidate stopping rules; no alternative Pareto-dominates it on any evaluated configuration. A signal-quality analysis shows that verbalized 1-5 confidence collapses on RLHF-tuned models (96.5% of values equal 5, entropy 0.182 nats), while the logit margin achieves 44x better class-conditional separation, grounding the design in a measurable model pathology. TASR is an auditable, training-free Pareto baseline against which learned stopping controllers can be compared. Code is publicly available.

Configuration spaces of graphs frequently grow factorially in complexity with the number of particles they parametrize. However, for suitable families of nested graphs $G_\bullet$ with compatible symmetric group actions, Ramos and White prove that, for fixed $k$, the rational homology of the $k$\textsuperscript{th} configuration spaces of $G_\bullet$ has multiplicity stability. In the current work, we derive the stable range and use computer algebra to determine the stable representations on homology for $k=2$ and $G_\bullet$ several families of graphs, including the complete graphs, the complete bipartite graphs on $2n$ vertices, the crown graphs on $2n$ vertices, and the complete tripartite graphs on $2n+1$ vertices. We determine the stable multiplicities for certain irreducible components in the case $k=3$ and $G_\bullet$ the complete graphs.

Business sentiment is a closely watched economic signal, but measuring it is slow and costly: surveys reach only a few hundred firms, arrive periodically, and take time to compile. We show that large language models hold the potential to address these shortcomings. We prompt an LLM to role-play as the CFO of a specific company at a specific date and focus on the economic-optimism question on the Duke-Federal Reserve CFO Survey over 2002-2025. We find that the LLM reproduces individual human responses: the predicted optimism score significantly forecasts the CFO's actual answer, surviving firm and year-quarter fixed effects and a control for the most recent prior response. Predictive accuracy increases with the amount of information supplied, as both respondent history and firm characteristics improve fit, and the relationship persists under quarterly aggregation. With appropriate conditioning, LLMs may be able to serve as credible digital twins of executives, offering scalable, high-frequency expectations data for financial research and policy.

Stabilizer Rényi entropy has emerged as a probe of nonstabilizerness in quantum many-body systems, but its scaling structure beyond critical points remains poorly understood compared with entanglement entropy. Recent field-theory approaches indicate that stabilizer entropy contains universal critical data and boundary-sensitive terms, raising the question of how these structures extend into massive and crossover regimes. We address this problem for a broad class of finite-range spin chains at Rényi index one-half. We derive exact finite-size formulas for both full periodic chains and finite intervals of the infinite chain, making the universal crossover from critical to noncritical behavior analytically accessible. In periodic geometry, the entropy obeys a volume law away from criticality and exhibits a universal finite-size crossover controlled by the competition between system size and correlation length. We also show that the large-scale SRE density develops a cusp across the field-tuned critical line, while the XX endpoint is governed by a distinct scaling regime associated with the saturation point. In the subsystem geometry, the interval entropy separates bulk critical behavior from boundary contributions generated by the way the finite region cuts the infinite chain. The crossover from critical to massive behavior is then encoded in boundary constants and universal functions controlled by the correlation length. Through exact stabilizer-entropy correspondences, the scaling theory extends to internal XY reductions, Finite-range spin chains, and Cluster--Ising representatives. Our results provide an exact lattice benchmark for the emerging QFT description of stabilizer entropy beyond isolated conformal points.

In this study, we present a novel time-stepping scheme for multiscale differential equations describing oscillatory systems with well-separated scales, where the scale separation is controlled by a small parameter $\epsilon$. The time-stepping method is related to a multi-modal WKB approximation and relies on a transformation of variables derived in this work. The analysis reveals that, in the transformed formulation, the leading-order oscillations are either eliminated or appear only at higher asymptotic order. The method is applied to ordinary differential equations, including the well-known van der Pol oscillator. We investigate the accuracy of the proposed method numerically for different parameter regimes, in particular for decreasing values of $\epsilon$, and study how the parameters of the numerical scheme must be adapted as $\epsilon$ is reduced. In the presented numerical tests, the computational cost remains bounded as $\epsilon$ is decreased.

Relativistic Doppler boosting significantly affects the observed emission of astrophysical jets resulting in observational biases. In this work we investigate the observational biases and modelling opportunities which arise due to relativistic boosting using two X-ray binary case studies. Using the one-sided jet ejecta from MAXI J1535-571, we demonstrate that incorporating non-detections of the receding jet ejecta into kinematic modelling can significantly improve parameter estimation, reducing posterior uncertainties by over 40%. For the bipolar jets of MAXI J1820+070, we recover the intrinsic jet rest-frame emission of both approaching and receding jet components, demonstrating that they follow a common powerlaw evolution. Using this rest-frame emission profile as a base model, we show that current observational strategies strongly bias against detecting ejecta with high initial Lorentz factors >5 and receding ejecta components across a broad region of parameter space. These results highlight the importance of observational strategy selection, particularly early-time and late-time observations, and leveraging non-detections in the modelling of relativistic jets. More generally, quantifying observational biases and maximising modelling capabilities by incorporating the non-detection of receding jets can be employed to enhance interpretation of future gravitational-wave/optically-triggered observations of off-axis, extragalactic jetted transients.

We present a detailed study on how the strength of the interlayer magnetic coupling on Fe$_{85}$Co$_{15}$/Ni$_{80}$Fe$_{20}$ bilayers modifies the spin wave behavior of this system. A series of Fe$_{85}$Co$_{15}$/Ni$_{80}$Fe$_{20}$ bilayers deposited on MgO[100] substrates were grown by magnetron sputtering. Magnetic characterization of the samples was performed using a vibrating sample magnetometer and magneto-optical Kerr effect. The in-plane hysteresis loops reveal a cubic magnetic anisotropy of magnetocrystalline origin, with easy and hard axis along the [100] and [110] Fe-Co crystallographic directions, respectively. Ferromagnetic resonance measurements were performed to analyze the in-plane angular dependence of the resonance field, and also the resonance field at several frequencies was determined along the hard axis. By using a bilayer model in the frame of the Landau-Lifshitz-Gilbert magnetization equation of motion, the magnetization precession components were calculated, as well as the dependence of precession area on the Fe-Co layer thickness and the ferromagnetic interlayer coupling. We observe a maximum in the area of the ellipsoid generated by the magnetization precession of the permalloy layer at a certain exchange constant, showing that this effect could be used to maximize the injected spin currents, which could be tuned by changing the interlayer exchange constant in bilayer systems, the saturation magnetization of the materials, or the excitation frequency.

Manual testing, in which testers follow natural language instructions to validate system behavior, remains essential for uncovering issues that are difficult to capture with automation. However, manual test cases often contain test smells, quality issues such as ambiguity, redundancy, or missing checks that reduce reliability, maintainability, and reproducibility. Existing detection approaches largely depend on manually engineered rules and thus struggle to generalize and scale across heterogeneous test suites. In our previous work, we assessed the feasibility of using Small Language Models (SLMs) for test smell detection by evaluating GEMMA-3-4B, LLAMA-3.2-3B, and PHI-4-14B on test steps from 143 real-world Ubuntu test cases, covering seven smell types. PHI-4-14B achieved the best performance. In this article, we investigate whether a contemporary Large Language Model (GEMINI-3-PRO-PREVIEW) available at the time of the study can identify test smells in natural language manual test cases using a prompt-based, whole-test-case analysis strategy. Unlike approaches that analyze individual test steps in isolation, our approach evaluates complete test cases, enabling the model to consider relationships and dependencies among test steps. We evaluate the approach on 100 Ubuntu test cases covering seven test smell types and compare its performance against previously evaluated SLMs, including GEMMA-3-4B, LLAMA-3.2-3B, and PHI-4-14B. Our results show that GEMINI-3-PRO-PREVIEW outperforms the SLMs, while producing actionable explanations that can help practitioners revise manual test cases for greater clarity and consistency. We also find that test smells are pervasive in practice, with nearly one detected test smell per step on average, highlighting the need for scalable and automated quality support for manual testing artifacts.

We present a 13-year Chandra/HRC-I study of the M87 jet, using point-spread-function deconvolution to achieve unprecedented sub-arcsecond X-ray resolution of its kiloparsec-scale structure and temporal evolution. The deconvolved images reveal previously blended structures within the jet, including two components in HST-1 and complex morphology in downstream knots. Flux measurements reveal a global decrease in X-ray emission across the jet of up to 84 %, consistent with synchrotron cooling. Modeling the fading yields minimum magnetic field strengths of ~324-1006 $\mu$G for HST-1 and ~41-115 $\mu$G for knot A. Consistent with synchrotron cooling, multi-wavelength comparisons with ALMA, JWST, and HST show that the principal X-ray structures closely match the jet width and knot locations observed at lower energies, while the X-ray emission is generally shifted upstream. Proper motion measurements show that jet features exhibit both quasi-stationary and superluminal apparent motions, reaching up to 4.8 $c$ for HST-1, and also demonstrate that unresolved component blending can substantially bias inferred velocities. These results demonstrate the unique capability of high-resolution Chandra X-ray imaging over long temporal baselines to resolve the evolving substructure of relativistic jets and to probe the particle acceleration and energy dissipation processes that shape their dynamics.

Securing access control in large-scale Internet of Things (IoT) deployments requires mechanisms that adapt to risk while preserving low latency for benign traffic. Permissioned blockchains such as Hyperledger Fabric offer auditability through smart contracts, but static endorsement policies impose the same validation depth on all requests, regardless of security posture. We propose a risk-adaptive enforcement layer for Hyperledger Fabric that couples an off-chain LSTM-based risk oracle with deterministic on-chain checks. The oracle assigns each request to a tier (Low, Moderate, High) and issues a signed attestation bound to the client identity and target key/version. Endorsing peers verify the attestation in chaincode and enforce tier-conditioned SBE policies without modifying the ordering service or consensus. Experiments on a Fabric testbed show that tier-conditioned endorsement strengthens validation for higher-risk requests while retaining low confirmation latency for benign workloads.