Bismuth oxide (Bi$_2$O$_3$) is a polymorphic material of considerable technological interest, with applications spanning from heterogeneous catalysis to next-generation nanoelectronics. Despite its relevance, systematic investigations of Bi$_2$O$_3$ thin films remain scarce. Here, we report a comprehensive, multi-technique study of bismuth oxide grown on Au(111). By combining synchrotron-based x-ray photoelectron spectroscopy and diffraction with low-energy electron diffraction and scanning tunneling microscopy, we elucidate the structural evolution of the surface during controlled oxidation and subsequent annealing. We find that Bi deposition induces well-defined surface reconstructions, whereas oxidation triggers the formation of a complex sequence of Bi$_2$O$_3$ domains. High-resolution spectroscopic and diffraction data enable us to propose a structural model consistent with the $(201)$ surface of $β$-Bi$_2$O$_3$. In addition, work function measurements reveal substantial electronic modifications at the interface. These results provide benchmark structural and electronic insights into the Bi oxide/Au(111) system and establish a framework for integrating Bi$_2$O$_3$ in devices in combination to two-dimensional semiconductors exploiting its low contact resistance.

The preservation of cultural heritage is increasingly transitioning towards data-driven predictive maintenance and "Digital Twin" construction. However, the mechanical constitutive models required for high-fidelity simulations remain fragmented across decades of unstructured scientific literature, creating a "Data Silo" that hinders conservation engineering. To address this, we present an automated, two-stage agentic framework leveraging Large Language Models (LLMs) to extract mechanical constitutive equations, calibrated parameters, and metadata from PDF documents. The workflow employs a resource-efficient "Gatekeeper" agent for relevance filtering and a high-capability "Analyst" agent for fine-grained extraction, featuring a novel Context-Aware Symbolic Grounding mechanism to resolve mathematical ambiguities. Applied to a corpus of over 2,000 research papers, the system successfully isolated 113 core documents and constructed a structured database containing 185 constitutive model instances and over 450 calibrated parameters. The extraction precision reached 80.4\%, establishing a highly efficient "Human-in-the-loop" workflow that reduces manual data curation time by approximately 90\%. We demonstrate the system's utility through a web-based Knowledge Retrieval Platform, which enables rapid parameter discovery for computational modeling. This work transforms scattered literature into a queryable digital asset, laying the data foundation for the "Digital Material Twin" of built heritage.

White dwarfs (WDs), the evolutionary endpoints of most stars, can form through both single-star and binary channels. While single-star evolutionary models enable reliable WD age estimates, binary evolution introduces interactions that can accelerate WD formation and result in a variety of exotic WDs, which may exhibit strong magnetic fields, rapid rotation, or even serve as potential gravitational wave sources. Such systems offer valuable insights into magnetic field generation, angular momentum evolution, and compact object physics. Star clusters, with their approximately coeval populations, allow precise age determination of member WDs. If a WD's total age derived from single-star evolution exceeds that of its host cluster, it likely indicates a binary origin. In this study, we use \textit{Gaia} 5D astrometry to identify 439 WD candidates in 117 open clusters, with 244 likely formed via binary evolution. We discuss the possibility of dynamical ejection for WDs meeting only 2D (proper motion space) membership criteria. Spectroscopic observations further reveal a subset with strong magnetic fields and rapid rotation, supporting their binary evolutionary origin.

We consider the thin-film equation with linear mobility and a stabilizing second-order porous-medium type term modeling gravity. The model admits self-similar solutions, and our goal is to analyze their stability. We reformulate the problem in mass-Lagrangian coordinates and exploit the underlying gradient-flow structure of the equation with respect to a weighted $L^2$ inner product, where the weight is given by the self-similar source-type profile. This framework allows us to establish a coercivity result for the Hessian (the linearization around the self-similar solution) in a suitably weighted inner product. As a consequence, we prove the convergence of perturbations toward the self-similar profile at an algebraic rate of order $t^{-\frac 1 5}$, in arbitrary scales of weighted Sobolev norms. The analysis relies on maximal-regularity estimates for the linearized evolution, combined with appropriate estimates for the nonlinear terms. Notably, beyond perturbative regimes and in contrast to previous results for the thin-film equation (convergence to the Smyth-Hill profile) or the porous-medium equation (convergence to the Barenblatt-Pattle solution), our analysis does not rely on an explicit (algebraic) representation of the self-similar profile. Instead, it is based solely on a systematic use of the ordinary differential equation satisfied by the self-similar solution, together with a careful analysis of its boundary asymptotics. As a result, we expect that the approach developed here can serve as a flexible toolbox for the study of more general classes of equations and for the stability analysis of special solutions in future work.

We develop a formula for the equivariant index of a twisted Dirac operator on a compact globally hyperbolic spacetime with timelike boundary on which a group acts isometrically, subject to APS boundary conditions. The formula is the same as in the Riemannian case: the equivariant index for a group element is an integral over the fixed point set of that element plus some boundary terms. The proof uses a surprisingly simple technique for reducing from the equivariant to the non-equivariant regime in order to show an equivariant version of the Lorentzian "index $=$ spectral flow" formula.

This paper presents a Failure-Aware Access Point Selection (FAAS) method aimed at improving hardware resilience in cell-free massive MIMO (CF-mMIMO) networks. FAAS selects APs for each user by jointly considering channel strength and the failure probability of each AP. A tunable parameter \(α\in [0,1]\) scales these failure probabilities to model different levels of network stress. We evaluate resilience using two key metrics: the minimum-user spectral efficiency, which captures worst-case user performance, and the outage probability, defined as the fraction of users left without any active APs. Simulation results show that FAAS maintains significantly better performance under failure conditions compared to failure-agnostic clustering. At high failure levels, FAAS reduces outage by over 85\% and improves worst-case user rates. These results confirm that FAAS is a practical and efficient solution for building more reliable CF-mMIMO networks.

The quantum symmetric simple exclusion process (QSSEP) is a recent extension of the symmetric simple exclusion process, designed to model quantum coherent fluctuating effects in noisy diffusive systems. It models stochastic nearest-neighbor fermionic hopping on a lattice, possibly driven out-of-equilibrium by boundary processes. We present a direct formulation in the continuum, and establish how this formulation captures the scaling limit of the discrete version. In the continuum, QSSEP emerges as a non-commutative process, driven by free increments, conditioned on the algebra of functions on the ambiant space to encode spatial correlations. We actually develop a more general framework dealing with conditioned orbits with free increments which may find applications beyond the present context. We view this construction as a preliminary step toward formulating a quantum extension of the macroscopic fluctuation theory.

We report on a measurement of collective behavior in a relativistic electron-positron pair plasma produced in the laboratory. Using the Fireball platform at CERN's HiRadMat facility, 440 GeV protons were used to generate an ultra-relativistic, charge-neutral electron-positron pair beam that propagated through an ambient RF discharge plasma. Magnetic-field amplification due to a beam-plasma instability was diagnosed using a high-sensitivity Faraday-rotation probe, supported by detailed characterization of the diagnostic impulse response. The measured path-integrated magnetic field agrees quantitatively with predictions from particle-in-cell simulations. The results provide a critical benchmark for models of relativistic beam-plasma interactions in astrophysical contexts such as blazar jets and pulsar-wind nebulae.

This paper introduces a class of generalised linear models (GLMs) driven by latent processes for modelling count, real-valued, binary, and positive continuous time series. Extending earlier latent-process regression frameworks based on Poisson or one-parameter exponential family assumptions, we allow the conditional distribution of the response to belong to a bi-parameter exponential family, with the latent process entering the conditional mean multiplicatively. This formulation substantially broadens the scope of latent-process GLMs, for instance, it naturally accommodates gamma responses for positive continuous data, enables estimation of an unknown dispersion parameter via method of moments, and avoids restrictive conditions on link functions that arise under existing formulations. We establish the asymptotic normality of the GLM estimators obtained from the GLM likelihood that ignores the latent process, and we derive the correct information matrix for valid inference. In addition, we provide a principled approach to prediction and forecasting in GLMs driven by latent processes, a topic not previously addressed in the literature. We present two real data applications on measles infections in North Rhine-Westphalia (Germany) and paleoclimatic glacial varves, which highlight the practical advantages and enhanced flexibility of the proposed modelling framework.

Caratheodory's axiom of adiabatic inaccessibility states that, in any neighborhood of a thermodynamic state, certain states remain unreachable via adiabatic processes. Non-arbitrage mirrors this topological restriction in finance. Preserving this constraint in resource-limited systems identifies the exponential family not as a modeling convenience but as the requisite geometric structure unifying both domains.

Electrokinetic phenomena in nanopore sensors and microfluidic devices require accurate simulation of coupled fluid-electrostatic interactions in geometrically complex domains with irregular boundaries and adaptive mesh refinement. We develop an equal-order virtual element method for the Stokes--Poisson--Boltzmann equations that naturally handles general polygonal meshes, including meshes with hanging nodes, without requiring special treatment or remeshing. The key innovation is a residual-based pressure stabilization scheme derived by reformulating the Laplacian drag force in the momentum equation as a weighted advection term involving the nonlinear Poisson--Boltzmann equation, thereby eliminating second-order derivative terms while maintaining theoretical rigor. Well-posedness of the coupled stabilized problem is established using the Banach and Brouwer fixed-point theorems under sufficiently small data assumptions, and optimal a priori error estimates are derived in the energy norm with convergence rates of order $\mathcal{O}(h^k)$ for approximation degree $k \geq 1$. Numerical experiments on diverse polygonal meshes -- including distorted elements, non-convex polygons, Voronoi tessellations, and configurations with hanging nodes -- confirm optimal convergence rates, validating theoretical predictions. Applications to electro-osmotic flows in nanopore sensors with complex obstacle geometries illustrate the method's practical utility for engineering simulations. Compared to Taylor--Hood finite element formulations, the equal-order approach simplifies implementation through uniform polynomial treatment of all fields and offers native support for general polygonal elements.

Free-space optical communications with moving targets, such as satellite terminals, demand ultrafast wavefront sensing and correction. This is typically addressed using a Shack-Hartmann sensor, which pairs a high-speed camera with a lenslet array, but such systems add significant cost, weight, and power demands. In this work, we present a hybrid opto-electric neural network (OENN) wavefront sensor that enables ultra-high-speed operation in a compact, low-cost system. Subwavelength diffractive metasurfaces efficiently encode the incoming wavefront into tailored irradiance patterns, which are then decoded by a lightweight multilayer perceptron (MLP). In simulation and experiment, the hybrid approach achieves average Strehl ratio (SR) improvements exceeding 60% and 45%, respectively, for unseen wavefronts compared to purely electronic sensors with few-pixel inputs. Although larger MLPs allow purely electronic sensors to match the hybrid's SR under static conditions, transient atmosphere modeling shows that their added latency leads to rapid SR degradation with increasing Greenwood frequency, while the hybrid system maintains performance. These results highlight the potential of hybrid OENN architectures to unlock scalable, high-bandwidth free-space communication systems and, more broadly, to advance optical technologies where real-time sensing is constrained by electronic latency.

Measurements of the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) neutrino mixing parameters have entered a precision era, enabling increasingly stringent tests of neutrino oscillations. Within the framework of quantum estimation theory, we investigate whether flavor measurements, the only observables currently accessible experimentally, are optimal for extracting the oscillation parameters. We compute the Quantum Fisher Information (QFI) and the classical Fisher Information (FI) associated with ideal flavor projections for all oscillation parameters, considering accelerator muon (anti)neutrino and reactor electron antineutrino beams propagating in vacuum. Two main results emerge. First, flavor measurements saturate the QFI at the first oscillation maximum for $θ_{13}$, $θ_{23}$, and $θ_{12}$, demonstrating their information-theoretic optimality for these parameters. In contrast, they are far from optimal for $δ_{CP}$. In particular, only a small fraction of the available information on $δ_{CP}$ is extracted at the first maximum; the sensitivity improves at the second maximum, in line with the strategy of ESS$ν$SB, a planned facility. Second, the QFI associated with $δ_{CP}$ is approximately one order of magnitude smaller than that of the mixing angles, indicating that the neutrino state intrinsically encodes less information about CP violation. Nevertheless, this quantum bound lies well below current experimental uncertainties, implying that the present precision on $δ_{CP}$ is not fundamentally limited. Our results provide a quantitative framework to disentangle fundamental from practical limitations and establish a benchmark for optimizing future neutrino facilities.

Exciton-phonon interactions govern the optical response of semiconductors, yet disentangling multiple coupling channels in lead halide perovskites remains challenging. We investigate CsPbBr3 microcrystals using photoluminescence, Raman and reflectance spectroscopy at low temperature, revealing the simultaneous presence of high-energy and Rashba excitons, each accompanied by distinct phonon replica series. High-energy exciton replicas are uniquely spaced by approximately 9 meV, whereas Rashba exciton replicas exhibit a characteristic approximately 6 meV spacing, indicating the specificity of the exciton-phonon coupling. Unsupervised machine learning applied to a large low-temperature photoluminescence dataset reveals these replica features are prevalent. With increasing temperature, replica features broaden and merge, evolving into a dominant longitudinal optical phonon coupling regime at room temperature. This work establishes direct spectroscopic evidence for concurrent, exciton-specific phonon coupling within a single material, offering new pathways to engineer light-matter interactions for optoelectronic and phonon-photon-based quantum device applications.

Determining if an input undirected graph is Hamiltonian, i.e., if it has a cycle that visits every vertex exactly once, is one of the most famous NP-complete problems. We consider the following generalization of Hamiltonian cycles: for a fixed set $S$ of natural numbers, we want to visit each vertex of a graph $G$ exactly once and ensure that any two consecutive vertices can be joined in $k$ hops for some choice of $k \in S$. Formally, an $S$-Hamiltonian cycle is a permutation $(v_0,\ldots,v_{n-1})$ of the vertices of $G$ such that, for $0 \leq i \leq n-1$, there exists a walk between $v_i$ and $v_{i+1 \bmod n}$ whose length is in $S$. (We do not impose any constraints on how many times vertices can be visited as intermediate vertices of walks.) Of course Hamiltonian cycles in the standard sense correspond to $S=\{1\}$. We study the $S$-Hamiltonian cycle problem of deciding whether an input graph $G$ has an $S$-Hamiltonian cycle. Our goal is to determine the complexity of this problem depending on the fixed set $S$. It is already known that the problem remains NP-complete for $S=\{1,2\}$, whereas it is trivial for $S=\{1,2,3\}$ because any connected graph contains a $\{1,2,3\}$-Hamiltonian cycle. Our work classifies the complexity of this problem for most kinds of sets $S$, with the key new results being the following: we have NP-completeness for $S = \{2\}$ and for $S = \{2, 4\}$, but tractability for $S = \{1, 2, 4\}$, for $S = \{2, 4, 6\}$, for any superset of these two tractable cases, and for $S$ the infinite set of all odd integers. The remaining open cases are the non-singleton finite sets of odd integers, in particular $S = \{1, 3\}$. Beyond cycles, we also discuss the complexity of finding $S$-Hamiltonian paths, and show that our problems are all tractable on graphs of bounded cliquewidth.

This paper studies the Model Selection Confidence Set (MSCS) methodology for univariate time series models involving autoregressive and moving average components, and applies it to study model selection uncertainty in the Italian electricity load data. Rather than relying on a single model selected by an arbitrary criterion, the MSCS identifies a set of models that are statistically indistinguishable from the true data-generating process at a given confidence level. The size and composition of this set reveal crucial information about model selection uncertainty: noisy data scenarios produce larger sets with many candidate models, while more informative cases narrow the set considerably. To study the importance of each model term, we consider numerical statistics measuring the frequency with which each term is included in both the entire MSCS and in Lower Boundary Models (LBM), its most parsimonious specifications. Applied to Italian hourly electricity load data, the MSCS methodology reveals marked intraday variation in model selection uncertainty and isolates a collection of model specifications that deliver competitive short-term forecasts while highlighting key drivers of electricity load like intraday hourly lags, temperature, calendar effects and solar energy generation.

Massive O-type stars lose a significant fraction of their mass through radiation-driven winds, a process that critically shapes their evolution and feedback into the interstellar medium. Accurate predictions of mass-loss rates are essential for models of stellar structure and population synthesis. We computed wind parameters for O-type stars using a self-consistent approach that couples the hydrodynamics of the wind with detailed calculations of the line acceleration. This approach follows the theory of radiation-driven stellar winds and allows us to derive mass-loss rate distributions for different atomic configurations of the stellar flux. We used the TLUSTY code for stellar atmosphere models to compute non-local thermodynamic equilibrium models; these models served as input radiation fields for the calculation of the line-force parameters, for which we used the LOCUS code. These line-force parameters were then iteratively coupled with the HYDWIND code to solve the wind hydrodynamics. The procedure was applied across a grid of stellar parameters for three chemical configurations. We obtain self-consistent wind parameters for a broad set of O-type stellar models. The results show a systematic decrease in mass-loss rates with the inclusion of more elements in the radiation field, which is attributed to a strong effect on the UV region of the spectral energy distribution. As more elements are included, resulting in a larger number of spectral lines, the contribution from the UV diminishes, leading to lower mass-loss rates. We fitted three theoretical prescriptions for $\dot{M}$ using a Bayesian approach; this yielded Pearson correlation values greater than 0.92 for all three model grids. It also allowed for the estimation of the wind momentum-luminosity relationships for each of the grids, yielding results similar to those based on observations of O-type stars.

In this paper, we consider five models of heavy-tailed queues involving Mittag-Leffler distributions that generalize the classical $M/M/1$ queues. These models are suitable modifications of previously defined models in such a way that the classical $M/M/1$ queue can be recovered by a suitable selection of parameters. We provide the distribution of inter-arrival and service times of both the original and modified queueing models. We then study the scaling limits of all the proposed models and we argue that the behaviour of the limiting processes can be used to characterise the traffic regime of the queues.

Polarized emission from interstellar dust grains is commonly used to infer information about the underlying magnetic field from the diffuse interstellar medium to molecular cloud cores. Therefore, the ability to accurately determine properties of the magnetic field requires a thorough understanding of the dust alignment mechanism. We investigate the influence of anisotropic radiation fields on the alignment of dust particles by magnetic fields, known as radiative torque (RAT) alignment. Specifically, we take advantage of the unique spatial configuration of the molecular cloud core L43, which contains an embedded yet optically visible star acting as a local source of anisotropic illumination. Based on polarization maps obtained at wavelengths of $154 μ\mathrm{m}$ (SOFIA/HAWC+), as well as $450 μ\mathrm{m}$ and $850 μ\mathrm{m}$ (JCMT/SCUBA-2), which show variations in the degree and angle of polarized emission across all wavelengths, we applied the differential measure analysis method to infer magnetic field strengths and analyze the global polarization spectrum of this source. We derived plane-of-sky magnetic field strengths ranging from approximately 13 to 60 $μ\mathrm{G}$, varying with wavelength, and find a negative slope of the polarization spectrum. Compared to 3D radiative transfer simulations, this finding can be attributed, at least partially, to variations in dust properties and temperatures along the line of sight. However, the additional influence of variations in the magnetic field orientation along the line of sight cannot be ruled out. Our results favor radiative torques as the primary alignment mechanism, as they indicate that the degree of polarization is dependent on temperature and hence the strength of the local radiation field.

In the planar one-round discrete Voronoi game, two players $\mathcal{P}$ and $\mathcal{Q}$ compete over a set $V$ of $n$ voters represented by points in $\mathbb{R}^2$. First, $\mathcal{P}$ places a set $P$ of $k$ points, then $\mathcal{Q}$ places a set $Q$ of $\ell$ points, and then each voter $v\in V$ is won by the player who has placed a point closest to $v$. It is well known that if $k=\ell=1$, then $\mathcal{P}$ can always win $n/3$ voters and that this is worst-case optimal. We study the setting where $k>1$ and $\ell=1$. We present lower bounds on the number of voters that $\mathcal{P}$ can always win, which improve the existing bounds for all $k\geq 4$. As a by-product, we obtain improved bounds on small $\varepsilon$-nets for convex ranges. These results are for the $L_2$ metric. We also obtain lower bounds on the number of voters that $\mathcal{P}$ can always win when distances are measured in the $L_1$ metric.

We study deterministic port based teleportation in the presence of noise affecting both the entangled resource state and the measurement process. We focus on a physically motivated model in which each Bell pair constituting the resource interacts with an identical local environment, corresponding to independently distributed entangled links. Two noisy scenarios are analyzed: one with decoherence acting solely on the resource state and ideal measurements, and another with noisy, noise adapted measurements optimised for the given noise model. In the first case, we derive an analytical lower bound and later a closed-form expression for the entanglement fidelity of the teleportation channel and analyze its asymptotic behaviour. In the second, we combine semi analytical and numerical methods. Surprisingly, we find that noise-adapted measurements perform worse than the noiseless ones. To connect the abstract noise description with microscopic physics, we embed the protocol in a spin boson model and investigate the influence of bath memory and temperature on the teleportation fidelity, highlighting qualitative differences between different environments.

Let X be a smooth complex irreducible projective variety of dimension $n \geq 2$ and $H$ be an ample line bundle on $X$. In this paper, we construct families of $μ_H$-stable vector bundles on $X$ having fixed determinant and rank $r$, which are generated by $r+1$ global sections, parametrized by Grassmanian varieties. This gives into the corresponding moduli spaces special subvarieties birational to Grassmannian.

Systems of instantaneously annihilating or coalescing Brownian motions on the line are considered. The extreme points of the set of entrance laws for this process are shown to be Pfaffian point processes at all times and their kernels are identified.

We study a competitive nonlinear Schrödinger system in $\mathbb{R}^N$ whose nonlinear potential is localized in small regions that shrink to isolated points. Within a variational framework based on a fully sign-changing Nehari constraint and Krasnosel'skii genus, we construct, for all $\varepsilon>0$, a sequence of sign-changing solutions with increasing and unbounded energies, and after suitable translations they converge to a sequence of sign-changing solutions of the associated limiting system as $\varepsilon\to 0$ in $H^1$-norm. Moreover, these sign-changing solutions concentrate around the prescribed attraction points both in $H^1$-norm and $L^q$-norm for $q\in [1,\infty]$.

The vast majority of biological sequences encode unknown functions and bear little resemblance to experimentally characterized proteins, limiting both our understanding of biology and our ability to harness functional potential for the bioeconomy. Predicting enzyme function from sequence remains a central challenge in computational biology, complicated by low sequence diversity and imbalanced label support in publicly available datasets. Models trained on these data can overestimate performance and fail to generalize. To address this, we introduce GRIMM (Genetic stRatification for Inference in Molecular Modeling), a benchmark for enzyme function prediction that employs genetic stratification: sequences are clustered by similarity and clusters are assigned exclusively to training, validation, or test sets. This ensures that sequences from the same cluster do not appear in multiple partitions. GRIMM produces multiple test sets: a closed-set test with the same label distribution as training (Test-1) and an open-set test containing novel labels (Test-2), serving as a realistic out-of-distribution proxy for discovering novel enzyme functions. While demonstrated on enzymes, this approach is generalizable to any sequence-based classification task where inputs can be clustered by similarity. By formalizing a splitting strategy often used implicitly, GRIMM provides a unified and reproducible framework for closed- and open-set evaluation. The method is lightweight, requiring only sequence clustering and label annotations, and can be adapted to different similarity thresholds, data scales, and biological tasks. GRIMM enables more realistic evaluation of functional prediction models on both familiar and unseen classes and establishes a benchmark that more faithfully assesses model performance and generalizability.

We consider quantum-to-classical mapping for an arbitrary system of interacting spins at finite temperatures. We prove that, in the large-$S$ limit, the asymptotic form of the partition function coincides with that of a classical model for spins of length $S_C=\sqrt{S(S+1)}$. Quantum corrections to the leading term form a series in powers of $1/[S(S+1)]$. This representation provides a rigorous basis for classical modeling of realistic magnetic Hamiltonians. As an application, the classical Monte Carlo simulations are performed to compute transition temperatures for several topical materials with known interaction parameters, including MnF$_2$, MnTe, Rb$_2$MnF$_4$, MnPSe$_3$, FePS$_3$, FePSe$_3$, CoPS$_3$, CrSBr, and CrI$_3$. The resulting transition temperatures show good agreement with experimental data.

The Asset Administration Shell (AAS) is an emerging technology for the implementation of digital twins in the field of manufacturing. Software is becoming increasingly important, not only in general but specifically in relation to manufacturing, especially with regard to digital manufacturing and a shift towards the usage of artificial intelligence. This increases the need not only to model software, but also to integrate services directly into the AAS. The existing literature contains individual solutions to implement such software-heavy AAS. However, there is no systematic analysis of software architectures that integrate software services directly into the AAS. This paper aims to fill this research gap and differentiate architectures based on software quality criteria as well as typical manufacturing use cases. This work may be considered as an interpretation guideline for software-heavy AAS, both in academia and for practitioners.

Identifying individual mediators is a central goal of high-dimensional mediation analysis, yet pervasive dependence among mediators can invalidate standard debiased inference and lead to substantial false discovery rate (FDR) inflation. We propose a Factor-Adjusted Debiased Mediation Testing (FADMT) framework that enables large-scale inference for individual mediation effects with FDR control under complex dependence structures. Our approach posits an approximate factor structure on the unobserved errors of the mediator model, extracts common latent factors, and constructs decorrelated pseudo-mediators for the subsequent inferential procedure. We establish the asymptotic normality of the debiased estimator and develop a multiple testing procedure with theoretical FDR control under mild high-dimensional conditions. By adjusting for latent factor induced dependence, FADMT also improves robustness to spurious associations driven by shared latent variation in observational studies. Extensive simulations demonstrate the superior finite-sample performance across a wide range of correlation structures. Applications to TCGA-BRCA multi-omics data and to China's stock connect study further illustrate the practical utility of the proposed method.

The nearby quasar, PG 1211+143, has one of the prototype examples of an ultra fast outflow (UFO), as seen in several past XMM-Newton and Chandra observations. In December 2024, PG 1211+143 was observed simultaneously with XRISM Resolve and XMM-Newton, allowing both the Fe K and soft X-ray outflows to be examined at high resolution simultaneously. The Resolve spectrum revealed a forest of Fe K band absorption lines from the UFO (Mizumoto et al. 2026), comprising of up to six discrete velocity components ranging from $v/c=-0.074$ to $v/c=-0.40$. Here we present the simultaneous XMM-Newton RGS (Reflection Grating Spectrometer) spectrum, where three lower ionization counterparts of the Fe K velocity zones are observed; at $v/c=-0.074, -0.12$ and $-0.33$. The soft X-ray absorbers tend to be somewhat less ionized than their Fe K counterparts, with their opacity mainly arising from Fe L shell lines and highly ionized Oxygen. From comparing the Resolve and RGS absorbers, we show that the outflow can be parameterized with a density profile varying with radius as $r^{-5/3}$, while the lower ionization zones likely originate from denser clumps of gas. Pure electron scattering appears insufficient to provide enough thrust to power the wind, unless sufficient low ionization gas capable of radiative line driving exists outside of the line of sight. Overall, PG 1211+143 provides further evidence for the clumpy nature of accretion disk winds, as was recently revealed in the quasar PDS 456 with XRISM.

We critically examine proposals for the so-called warp-drive spacetimes and classify these models based on their various restrictions within the framework of General Relativity. We then provide a summary of general formalism for each class, and in the process, we highlight some misconceptions, misunderstandings, and errors in the literature that have been used to support claims about the physicality and feasibility of these models. On the way, we prove several new no-go theorems. Our analysis shows that when the principles of General Relativity are applied correctly, most claims regarding physical warp drives must be reassessed, and it becomes highly challenging to justify or support the viability of such models, not merely due to the violation of energy conditions.