Identifying architecturally relevant entities in textual artifacts is crucial for Traceability Link Recovery (TLR) between Software Architecture Documentation (SAD) and source code. While Software Architecture Models (SAMs) can bridge the semantic gap between these artifacts, their manual creation is time-consuming. LLMs offer new capabilities for extracting architectural entities from SAD and source code to construct SAMs automatically or establish direct trace links. This paper extends our ICSA 2025 paper [19], which introduced Extracting Architecture (ExArch) for LLM-based architecture component name extraction. The extension contributes the novel Architecture Traceability with Entity Matching via Semantic inference (ArTEMiS) approach, an extended evaluation with additional LLMs, configurations, a revised benchmark, and a combined evaluation of both approaches. Specifically, this paper presents the following approaches: ExArch extracts component names as simple SAMs from SAD and source code to eliminate the need for manual SAM creation, while ArTEMiS identifies architectural entities in documentation and matches them with (manually or automatically generated) SAM entities. Our evaluation compares against state-of-the-art approaches SWATTR, TransArC and ArDoCode. TransArC achieves strong performance (F1: 0.87) but requires manually created SAMs; ExArch achieves comparable results (F1: 0.86) using only SAD and code. ArTEMiS is on par with the traditional heuristic-based SWATTR (F1: 0.81) and can successfully replace it when integrated with TransArC. The combination of ArTEMiS and ExArch outperforms ArDoCode, the best baseline without manual SAMs. Our results demonstrate that LLMs can effectively identify architectural entities in textual artifacts, enabling automated SAM generation and TLR, making architecture-code traceability more practical and accessible.

In the era of precision measurements in high-energy heavy-ion physics, there is an increasing expectation towards phenomenological and theoretical studies to provide a better description of data. In recent years, multiple experiments have confirmed through two-pion Bose-Einstein correlation measurements that the shape of the two-pion pair source can be well described by Levy-stable distributions. However, direct comparisons of new phenomenological results with the data are still needed to understand the underlying phenomena and learn more about the nature of pion emission. In this paper, we present a three-dimensional analysis of the two-pion source in Monte-Carlo simulations of Au+Au collisions at 200 GeV per nucleon collision energy, and discuss a detailed comparison with the most recent centrality-dependent measurements from the PHENIX Collaboration.

We present near-infrared scattered-light observations of the disks around two stars of the Corona Australis star-forming region, V721 CrA, and BN CrA, obtained with VLT/SPHERE, in the H band, as part of the DESTINYS large programme. Our objective is to analyse the morphology of these disks, and highlight their main properties. We adopt an analytical axisymmetric disk model to fit the observations and perform a regression on key disk parameters, namely the dust mass, the height profile, and the inclination. We use RADMC-3D code to produce synthetic observations of the analytical models, with full polarised scattering treatment. Both stars show resolved and extended disks with substructures in the near-IR. The disk of V721 CrA is vertically thicker, radially smaller (120 au), and brighter than BN CrA (190 au). It also shows spiral arms in the inner regions. The disk of BN CrA shows a dark circular lane, which could be either an intrinsic dust gap or a self-cast shadow. Both disks are compatible with the evolutionary stage of their parent subgroup within the CrA region: V721 CrA belongs to the "on-cloud" part of CrA, which is dustier, denser and younger, whereas BN CrA is found on the outskirts of the older "off-cloud" group.

We investigate a generalized quantum perceptron architecture characterized by an oscillating activation function with a tunable frequency ranging from zero to infinity. Employing analytical techniques from statistical mechanics, we derive the optimal storage capacity and demonstrate that the classical result is recovered in the limit of vanishing frequency. As the frequency increases, however, the architecture exhibits enhanced quantum storage capabilities. Notably, this improvement stems solely from the specific form of the activation function and, in principle, could be emulated within a classical framework. Accordingly, we refer to this enhancement as a pseudo quantum advantage.

We give uniform proofs of tightness and exponential tightness of the sequences of stationary queue lengths in generalised Jackson networks in a number of setups which concern large, normal and moderate deviations.

The notion of acceptable bundles plays a fundamental role in the Simpson--Mochizuki theory. This paper presents a detailed study of acceptable bundles on a punctured disk. In addition to its expository aspects, we introduce a new invariant and provide arguments that differ from those of Simpson and Mochizuki.

For the first time, correlations among mixed-order moments of two or three flow harmonics$-$($v_{n}^{k},v_{m}^{l}$) and ($v_{n}^{k},v_{m}^{l}, v_{p}^{q}$), with $k$, $l$, and $q$ denoting the respective orders$-$are measured in xenon-xenon (XeXe) collisions and compared with lead-lead (PbPb) results, providing a novel probe of collective behavior in heavy ion collisions. These measurements compare a nearly spherical, doubly-magic ${}^{208}$Pb nucleus to a triaxially deformed ${}^{129}$Xe nucleus, emphasizing the sensitivity to initial-state geometry fluctuations arising from nuclear deformation. The dependence of these results ($v_{n}$, $n$ = 2, 3, 4) on the shape and size of the nuclear overlap region is studied. Comparisons between $v_{2}$, $v_{3}$, and $v_{4}$ demonstrate the importance of $v_{3}$ and $v_{4}$ in exploring the nonlinear hydrodynamic response of the quark-gluon plasma (QGP) to the initial spatial anisotropy. The results constrain initial-state model parameters that influence the evolution of the QGP. The CMS detector was used to collect XeXe and PbPb data at nucleon-nucleon center-of-mass energies of $\sqrt{s_\mathrm{NN}}$ = 5.44 and 5.36 TeV, respectively. Correlations are extracted using multiparticle mixed-harmonic cumulants (up to eight-particle cumulants) with charged particles in the pseudorapidity range $\lvertη\rvert$ $\lt$ 2.4 and transverse momentum range 0.5 $\lt$ $p_\mathrm{T}$ $\lt$ 3 GeV/$c$.

We examine the quantum dynamics of both a single large spin and a pair of spins in the presence of static and rotating magnetic fields, which can be important for qudit-based quantum technologies. Notably, we find resonant, periodic oscillations between two maximally stretched states, irrespective of how large the spin is. Additionally, we observe periodic transitions between sublevels with magnetic quantum numbers of opposite signs. The dynamics also exhibit a periodic transfer of the spin to the maximally stretched state, starting from the ground state of the initial Hamiltonian. For a pair of spins, we derive various resonance conditions and further analyze the entanglement generated by dipole-dipole interactions. In the case of two spin-1/2 particles, the entanglement dynamics reveal resonances and kinks in the maximum entanglement, and their criteria can be obtained from the energy spectrum. Strikingly, we show that the kink can be exploited to engineer the entanglement dynamics. Finally, we briefly discuss the regime of weak dipolar interactions, which are relevant for dipolar Bose-Einstein condensates.

Let $K$ be a number field, and let $A$ be an Abelian variety over $K$ which has no CM isogeny-factors over $\overline{K}$. We prove that $A$ has only finitely many torsion points over the maximal $n$-step-solvable extension of $K$ for any $n$ and only finitely many torsion points of prime order over the maximal prosolvable extension of $K$.

We consider the mirrors model in $d$ dimensions on an infinite slab and with unit density. This is a deterministic dynamics in a random environment. We argue that the crossing probability of the slab goes like $κ/(κ+N)$ where $N$ is the width of the slab. We are able to compute $κ$ perturbatively by using a multiscale approach. The only small parameter involved in the expansion is the inverse of the size of the system. This approach rests on an inductive process and a closure assumption adapted to the mirrors model. For $d=3$, we propose the recursive relation for the conductivity $κ_n$ at scale $n$ : $κ_{n+1}=κ_n(1+\frac{κ_n}{2^{n}}α)$, up to $o(1/2^n)$ terms and with $α\simeq 0.0374$. This sequence has a finite limit.

The integration of converter-interfaced generation introduces new transient stability challenges to modern power systems. Classical Lyapunov- and scalable passivity-based approaches typically rely on restrictive assumptions, and finding storage functions for large grids is generally considered intractable. Furthermore, most methods require an accurate grid dynamics model. To address these challenges, we propose a model-free, nonlinear, and dissipativity-based controller which, when applied to grid-connected virtual synchronous generators (VSGs), enhances power system transient stability. Using input-state data, we train neural networks to learn dissipativity-characterizing matrices that yield stabilizing controllers. Furthermore, we incorporate cost function shaping to improve the performance with respect to the user-specified objectives. Numerical results on a modified, all-VSG Kundur two-area power system validate the effectiveness of the proposed approach.

We construct a unitarily invariant Hermitian matrix ensemble whose fixed-time eigenvalue law coincides with the Karlin--McGregor law for non-intersecting Brownian bridges with arbitrary finite multiplicities at both endpoints. This provides an explicit matrix-ensemble realization of the known mixed-type multiple orthogonal polynomial and Riemann--Hilbert description of the general multi-start/multi-end problem. We then derive several exact finite-$n$ consequences of this construction. These include a path-space lift as an orbital Hermitian Brownian bridge and a reduction of the partition function to a single compact HCIZ integral with explicit $t$-dependence. We also compare the one-sided reduction with the Gaussian external-field ensemble, showing that, although the two ensembles are spectrally equivalent, their angular statistics are different. Finally, we derive fixed-time Schwinger--Dyson identities and associated resolvent relations for the dressed ensemble.

A novel integrability condition for the Riccati equation, the simplest form of nonlinear ordinary differential equations, is obtained by using elementary quadrature method. Under this condition, the analytic general solution is presented, which can be extended to second-order linear ordinary differential equation. This result may provide valuable mathematical criteria for in-depth research on quantum mechanics, relativity and dynamical systems.

Active particles are entities that sustain persistent out-of-equilibrium motion by consuming energy. Under certain conditions, they exhibit the tendency to self-organize through coordinated movements, such as swarming via aggregation. While performing non-cooperative foraging tasks, the emergence of such swarming behavior in foragers, exemplifying active particles, has been attributed to the partial observability of the environment, in which the presence of another forager can serve as a proxy signal to indicate the potential presence of a food source or a resource patch. In this paper, we validate this phenomenon by simulating multiple self-propelled foragers as they forage from multiple resource patches in a non-cooperative manner. These foragers operate in a continuous two-dimensional space with stochastic position updates and partial observability. We evolve a shared policy in the form of a continuous-time recurrent neural network that serves as a velocity controller for the foragers. To this end, we use an evolutionary strategy algorithm wherein the different samples of the policy-distribution are evaluated in the same rollout. Then we show that agents are able to learn to adaptively forage in the environment. Next, we show the emergence of swarming in the form of aggregation among the foragers when resource patches are absent. We observe that the strength of this swarming behavior appears to be inversely proportional to the amount of resource stored in the foragers, which supports the risk-sensitive foraging claims. Empirical analysis of the learned controller's hidden states in minimal test runs uncovers their sensitivity to the amount of resource stored in a forager. Clamping these hidden states to represent a lesser amount of resource hastens its learned aggregation behavior.

Recently, building upon the research findings of E. L. Medeiros, we have extended the alpha-particle non-locality effect to the two-potential approach (TPA). This extension demonstrates that the integration of the alpha-particle nonlocality effect into TPA yields relatively favorable results. In the present work, we employ machine learning methods to further optimize the aforementioned approach, specifically utilizing three classical machine learning models: decision tree regression, random forest regression, and XGBRegressor. Among these models, both the decision tree regression and XGBRegressor models exhibit the highest degree of agreement with the reference data, whereas the random forest regression model shows inferior performance. In terms of standard deviation, the results derived from the decision tree regression and XGBRegressor models represent improvements of 54.5% and 53.7%, respectively, compared to the TPA that does not account for the coordinate-dependent effective mass of alpha particles. Furthermore, we extend the decision tree regression and XGBRegressor models to predict the alpha-decay half-lives of 20 even-even nuclei with atomic numbers Z=118 and Z=120. Subsequently, the superheavy nucleus half-life predictions generated by our proposed models are compared with those from two established benchmarks: the improved eight-parameter Deng-Zhang-Royer (DZR) model and the new empirical expression (denoted as "New+D") proposed by V. Yu. Denisov, which explicitly incorporates nuclear deformation effects. Overall, the predictions from these models and formulas are generally consistent. Notably, the predictions of the decision tree regression model show a high level of consistency with those of the New+D expression, while the XGBRegressor model exhibits deviations from the other two comparative models.

Optimization problems with convex quadratic cost and polyhedral constraints are ubiquitous in signal processing, automatic control and decision-making. We consider here an enlarged problem class that allows to encode logical conditions and cardinality constraints, among others. In particular, we cover also situations where parts of the constraints are nonconvex and possibly complicated, but it is practical to compute projections onto this nonconvex set. Our approach combines the augmented Lagrangian framework with a solver-agnostic structure-exploiting subproblem reformulation. While convergence guarantees follow from the former, the proposed condensing technique leads to significant improvements in computational performance.

The electric field of biological membranes has long been treated as a one-dimensional quantity, defined solely by the component normal to the bilayer (E_VERT). Here, we present a bioelectronic platform that enables controlled generation of a horizontal electric field within the hydrophobic core of a lipid bilayer (E_HORZ). The device incorporates micrometer-scale electrodes embedded within the bilayer torus, allowing sustained E_HORZ actuation. Applied E_HORZ selectively and reversibly accelerates slow inactivation of a voltage-gated potassium channel while leaving activation essentially unchanged. Physical considerations further indicate that E_HORZ arises naturally wherever membrane potential varies spatially, including at action potential wavefronts, suggesting broader physiological relevance. This platform provides experimental access to vector-resolved membrane electric fields and establishes a generalizable strategy for multidirectional electrical control of soft-matter biointerfaces.

Let $G$ be a two-step nilpotent Lie group, identified via the exponential map with the Lie-algebra $\mathfrak g=\mathfrak g_1\oplus\mathfrak g_2$, where $[\mathfrak g,\mathfrak g]\subset \mathfrak g_2$. We consider maximal functions associated to spheres in a $d$-dimensional linear subspace $H$, dilated by the automorphic dilations. $L^p$ boundedness results for the case where $H=\mathfrak g_1$ are well understood. Here we consider the case of a tilted hyperplane $H\neq \mathfrak g_1$ which is not invariant under the automorphic dilations. In the case of Métivier groups it is known that the $L^p$-boundedness results are stable under a small linear tilt. We show that this is generally not the case for other two-step groups, and provide new necessary conditions for $L^p$ boundedness. We prove these results in a more general setting with tilted versions of submanifolds of $\mathfrak g_1$.

We propose a model based on density functional theory (DFT) and quantum electrodynamics (QED) to study the dynamical characteristics of graphene quantum dots (GQDs). We assume the GQD edges are saturated with hydrogen atoms, effectively making it a polycyclic aromatic hydrocarbon (PAH) such as coronene. By combining the GQD spectrum calculated from a time-dependent DFT (TDDFT) with the dynamical behavior of a QD model derived from QED, we calculate the main optical characteristics of the GQD, such as its transition frequencies, the dipole moment associated to each of those transitions, life-time and the population dynamics of the molecular levels. Owing to the close match between the calculated spectrum and experimental results, our results represent a significant contribution to research on quantum treatments of light-matter interactions in realistic 2D nanomaterials.

Random telegraph signal (RTS) analysis is increasingly important for characterizing meaningful temporal fluctuations in physical, chemical, and biological systems. The simplest RTS arises from discrete stochastic switching events between two binary states, quantified by their transition amplitude and dwell times in each state. Quantitative analysis of RTSs provides valuable insights into microscopic processes such as charge trapping in semiconductors. However, analyzing RTS becomes considerably complex when signals exhibit multi-level structures or are corrupted by background white or pink noise. To address these challenges and support high-throughput RTS characterization, we propose a modular, training-free signal processing pipeline that integrates adaptive dual-tree complex wavelet transform (DTCWT) denoising with a lightweight Bayesian digitization strategy. The adaptive DTCWT denoiser incorporates autonomous parameter selection rules for its decomposition level and thresholds, optimizing white noise suppression without manual tuning. Our Bayesian digitizer formulates RTS level assignment as a probabilistic latent-state inference problem incorporating temporal regularization without iterative optimization, effectively resolving binary trap states even under residual notorious background pink noise. Quantitative benchmarking on large synthetic datasets with known ground truth demonstrates improved RTS reconstruction accuracy, trap-state resolution, and dwell-time estimation across diverse noise regimes and multi-trap scenarios, while achieving up to 83x speedups over classical and neural baselines. Qualitative validation on experimental RTS data when no ground truth is available illustrates practical usability and flexibility for real-time or large-scale analysis in real measurement settings.

Estimating posteriors and the associated model evidences, with desired accuracy and affordable computational cost, is a core issue of Bayesian model updating, and can be of great challenge given expensive-to-evaluate models and posteriors with complex features such as multi-modalities of unequal importance, nonlinear dependencies and high sharpness. Bayesian Quadrature (BQ) equipped with active learning has emerged as a competitive framework for tackling this challenge, as it provides flexible balance between computational cost and accuracy. The performance of a BQ scheme is fundamentally dictated by the acquisition function as it exclusively governs the active generation of integration points. After reexamining one of the most advanced acquisition function from a prospective inference perspective and reformulating the quadrature rules for prediction, four new acquisition functions, inspired by distinct intuitions on expected rewards, are primarily developed, all of which are accompanied by elegant interpretations and highly efficient numerical estimators. Mathematically, these four acquisition functions measure, respectively, the prediction uncertainty of posterior, the contribution to prediction uncertainty of evidence, as well as the expected reduction of prediction uncertainties concerning posterior and evidence, and thus provide flexibility for highly effective design of integration points. These acquisition functions are further extended to the transitional BQ scheme, along with several specific refinements, to tackle the above-mentioned challenges with high efficiency and robustness. Effectiveness of the developments is ultimately demonstrated with extensive benchmark studies and application to an engineering example.

This paper presents a new, significantly simpler proof of one of the main results of applied pi-calculus: the theorem that the concepts of observational and labeled equivalence of extended processes in applied pi-calculus coincide.

Multipartite entanglement has a much more complex structure than bipartite entanglement. A state that lacks generic multipartite entanglement is 2-producible, i.e. it can be written as a tensor product of at most 2-partite entangled states. Recently, it has been proved that a tripartite pure state is 2-producible if and only if the gap between the entanglement of purification and its lower bound vanishes. Here, we show that the entanglement of purification gap is insufficient to detect more than tripartite entanglement in 4-partite stabilizer states. We then generalize entanglement of purification to the multipartite cases, and demonstrate that a multipartite pure state is 2-producible if and only if all the generalized entanglement of purification gaps vanish. The generalized entanglement of purification gap quantifies the quantum communication cost for redistributing one part of the system to the others, and also relates to the local recoverability of a multipartite state and the relative entropy between that state and 2-producible states. Moreover, we calculate the generalized entanglement of purification for states satisfying the general Schmidt decomposition, which implies that 4-partite stabilizer states do not necessarily have a general Schmidt decomposition. Our results provide a quantitative characterization of multipartite entanglement in multipartite system, which will promote further investigations and understanding of multipartite entanglement.

Convolutional Neural Networks (CNNs) achieve remarkable accuracy in vision tasks, yet their computational complexity challenges low-power edge deployment. In this work, we present COMET, a framework of CNN models that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performance and resource utilization. The approach formulates CNN inference using OBC representations applied separately to inputs (Scheme A) and weights (Scheme B), enabling exploitation of bit-width asymmetry. The shift-accumulate operation is modified by incorporating offset-term with the pre-scaled bias. Leveraging symmetries in Schemes A and B, we introduce four look-up table (LUT) techniques -- parallel, shared, split, and hybrid -- and evaluate their efficiency. Building on this foundation, we develop a general matrix multiplication core using the im2col transformation for efficient CNN acceleration. We consider LeNet-5 and All-CNN-C to demonstrate that the OBC-GEMM core efficiently supports modern workloads. Evaluation shows that COMET enables efficient FPGA deployment compared to state-of-the-art designs, with negligible accuracy loss, demonstrating its efficiency and scalability across diverse network architectures.

Background: The locus of the gene PTK2B encoding the tyrosine kinase Pyk2 has been associated with the risk of late-onset Alzheimer's disease, the predominant form of dementia. Pyk2 is primarily expressed in neurons where it is involved in excitatory neurotransmission and synaptic functions. Although previous studies have implicated Pyk2 in amyloid-beta and Tau pathologies of Alzheimer's disease, its exact role remains unresolved, with evidence showing both detrimental and protective effects in mouse models. Here, we investigate the role of Pyk2 in hippocampal hyperactivity, Tau synaptic localization and synaptic loss associated with Alzheimer's disease-related alterations occurring in the early stages of the disease. Methods: Pyk2's involvement in amyloid-beta oligomer-induced hippocampal neuronal hyperactivity was investigated using whole-cell patch clamp in hippocampal slices from WT and Pyk2 KO mice. Various Pyk2 mutants were overexpressed in cultured cortical neurons to study Pyk2's role in synaptic loss. Pyk2 and Tau interaction was assessed with bimolecular fluorescence complementation assays in cultured neurons and co-immunoprecipitation in mouse cortex. To evaluate the impact of Pyk2 on Tau expression in synapses, cellular fractionation was performed on hippocampi from WT and Pyk2 KO mice. Results: Genetic deletion of Pyk2 prevented amyloid-beta oligomer-induced hippocampal neuronal hyperactivity and synaptic loss. Overexpression of Pyk2 in neurons decreased dendritic spine density independently of its autophosphorylation or kinase activity, but through its proline-rich motif 1. Furthermore, Pyk2 interacted with Tau in synapses, while Pyk2 deletion decreased Tau synaptic localization in the hippocampus. Conclusions: Pyk2 contributes to hippocampal neuronal hyperactivity and synaptic loss, two early events in Alzheimer's disease pathogenesis. It is also involved in Tau synaptic localization, a process known to be detrimental in Alzheimer's disease. These findings highlight Pyk2 as a critical player in Alzheimer's disease pathophysiology and suggest its potential as a promising therapeutic target for early intervention.

In this contribution, we construct a supersymmetric extension of axionic electrodynamics by adopting the superspace/superfield approach. In terms of component fields, the resulting Lagrangian describes the interactions among the axion, the photon, and their respective supersymmetric partners, the axino and the photino. The model exhibits quartic fermionic couplings and self-couplings, as well as a non-polynomial interaction involving the axino, the photino, and a scalar partner of the axion. We also pay special attention to the dispersion relations in both the bosonic and fermionic sectors, and analyze the effective masses of the different particles. Finally, with the help of computational methods, we investigate the solutions to the bosonic field equations. As a result, we identify a class of axionic and electromagnetic-field configurations whose profiles resemble magnetic vortices.

We provide explicit formulas for computing the motivic Chern and Hirzebruch classes of degeneracy loci, especially those coming from the symplectic and odd orthogonal Grassmannians. The Chern-Schwartz-MacPherson classes, K-theory classes, and Cappell-Shaneson L-classes arise as specializations of the motivic Chern and Hirzebruch classes. Our results are inspired by, and partially extends, those of Anderson--Chen--Tarasca in the case of ordinary Grassmannian degeneracy loci to isotropic and odd orthogonal Grassmannians as well as maximal even orthogonal Grassmannians. As applications, we obtain the motivic Chern and Hirzebruch classes of orthogonal and symplectic orbit closures in flag varieties.

Phononic materials (PMs) are periodic media that exhibit novel elastodynamic responses. While PMs have made progress in vibration-mitigation applications, recent studies have demonstrated the potential of PMs to passively and adaptively modulate flow behavior through fluid-structure interaction (FSI). For example, PMs have been shown to delay laminar-to-turbulent transition and mitigate unsteadiness in shock-boundary layer interactions. However, a systematic framework to relate the effect of specific PM behaviors to the FSI dynamics is lacking. Such a framework is essential to systematically investigate the complex and nonlinear coupled dynamics of the FSI. Further, parameters that are not typically considered in PM models become critical, such as the vibration amplitude. This article addresses this gap by proposing FSI-relevant ``behavioral'' parameters, distinct from the structural parameters of the PM, but with a clear mapping provided to them. We use high-fidelity, strongly coupled simulations to quantify the FSI between a novel configuration of laminar flow past a flat plate, equipped with a PM. Our study proposes four critical PM behavioral parameters -- effective stiffness, truncation resonance frequency, a quantity representing the dynamic displacement amplitude, and unit cell mass -- that influence the spectral characteristics of the vortex-shedding process inherent to the flat plate system. Results show connections between each parameter and distinct behavior in the lift coefficient in FSI. While the focus of this work is on the PM-FSI dynamics in an aerodynamic flow, we argue that identifying these behavioral parameters is key to unlocking scientific study and design with phononic materials in fluid flows more broadly.

We perform milliarcsecond X-ray astrometry of the quadruply lensed radio-quiet quasar GraL J065904.1+162909 (J0659). This $z = 3.083$ quasar is lensed into four images and was discovered with the second Data Release of the $Gaia$ Space Observatory ($Gaia$ DR2). Our J0659 study exploits strong gravitational lenses as high resolution telescopes. This technique shows promise to elucidate the origin of optical and X-ray emission in distant lensed quasars at spatial scales beyond the reach of current instruments. In our study, we use $Gaia$ DR3 and $HST$ observations of J0659 to infer a mass model for the deflector. Our model reproduces the $Gaia$ DR3 quasar lensed image positions to one milliarcsecond and determines the position of the optical source in J0659 to within this precision. Next, we analyze $Chandra$ observations of J0659 and conduct a Bayesian test evaluating whether the X-ray emission region coincides with the optical source. We then constrain the origin of the X-ray emission to within a $0.''020 \times 0.''010$ ellipse centered $0.''014$ away from the optical source at the $1σ$ level. We demonstrate that our approach can be extended to pinpoint the distinct origins of the soft and hard X-ray emission regions in lensed quasars. We discuss the potential of upcoming broadband and spectrally resolved X-ray astrometric studies to probe complex quasar morphology and AGN multiplicity at sub-kiloparsec scales otherwise inaccessible at high redshifts.

Accurate modeling of aqueous monovalent ions is essential for understanding the function of biomolecules, such as nucleic acid stability and binding of charged drugs to protein targets. The 1D and 3D reference interaction site models (1D- and 3D-RISM) of molecular solvation, as implemented in the AmberTools molecular modeling suite, are well suited for modeling mixtures of ionic species around biomolecules across a wide range of concentrations. However, the available ion model parameters were optimized for molecular dynamics simulations, not for the RISM framework, which includes a closure approximation. To address this, we optimized the Lennard-Jones 12-6 model for monovalent ions for 1D-RISM with the partial series expansion of order 3 closure by fitting to experimental values of ion-oxygen distance (IOD), hydration free energy (HFE), partial molar volume (PMV) and mean activity coefficient. The new parameter set demonstrated significant improvement in HFE, IOD, and mean activity coefficients, whereas no overall change was observed for the PMV. A second optimization step was necessary to account for the cation-anion interactions that affect the mean activity coefficients. The new parameters were validated at finite salt concentrations against experimental data for 16 ion pairs and showed improved accuracy for 14 of them, while the results for CsI and CsF were the second best. 1D-RISM results obtained with the new NaCl parameters were used to calculate the preferential interaction parameter of the ions around the 24L B-DNA using 3D-RISM. The new parameters demonstrated better agreement with experiment at physiological and higher concentrations. At lower concentrations, the results primarily depended on the closure with little effect from the ion parameters. Overall, the ion parameters specifically developed for RISM show improved accuracy at infinite dilution and finite concentrations.