We present a combined density-functional theory and dynamical mean-field theory (DFT+DMFT) study of the full structural phase space of rutile-based vanadium dioxide (VO$_2$), including also the less studied M2 and T phases, using an unconventional bond-centered orbital basis. The use of bond-centered orbitals allows us to treat all main phases of VO$_2$, and the structural transitions between them, using one consistent approach with moderate computational cost and without pre-pattering of the structure into dimerized and undimerized V--V pairs. We obtain two distinct insulating states on the two different types of vanadium chains in the M2 phase, a singlet-insulator on the dimerized chains and a Mott-insulator on the zigzag-distorted chains, which, however, are strongly coupled in the M2 phase and thus the metal-insulator transition always occurs concomitantly for both types of sites. We also demonstrate that the M2 phase corresponds to a local energy minimum in the structural phase space of VO$_2$, the stability of which, apart from the internal structural distortion, depends crucially on the unit cell strain relative to the undistorted rutile phase. Our calculations further indicate that the symmetry-distinct triclinic T phase corresponds electronically to either an M1 or an M2-type insulator with an abrupt transition as a function of distortion. Finally, we disentangle the effect of the dimerization and zigzag distortions by constructing hypothetical structures that contain only one site type, finding that the zigzag distortion strongly favors emergence of the Mott-insulating state, both as function of distortion and on-site interaction.

Despite hydrogen being central to Europe's decarbonisation strategy, only a small share of renewable hydrogen projects reached final investment decision. A key barrier is uncertainty about how future hydrogen markets will be designed and operated, particularly under Renewable Fuels of Non-Biological Origin requirements. This study investigates the extent to which future hydrogen market design can be adapted from existing natural gas markets, and the challenges it must address. The analysis was based on a survey targeting European gas transmission system operators, structured around five components: market design principles, trading frameworks, capacity allocation, tariffs, and balancing. The survey produced two outputs: an assessment of mechanism transferability and an identification of challenges for early hydrogen market development. Core market design principles and trading frameworks are broadly transferable from natural gas markets, as entry-exit systems and virtual trading points. Capacity allocation requires targeted adaptation to improve coupling with electricity markets. Tariffs require adaptation through intertemporal cost allocation, distributing infrastructure costs over time to protect early adopters. Balancing regimes should be revisited to reflect hydrogen's physical characteristics and different linepack flexibility usages. Key challenges for early hydrogen markets include: temporal mismatches between variable renewable supply and expected relatively stable industrial demand, limited operational flexibility due to scarce storage and reduced pipeline linepack, fragmented regional hydrogen clusters, and regulatory uncertainty affecting long-term investment decisions. These findings provide empirical input to the hydrogen network code led by the European Network of Hydrogen Network Operators and offer guidance to policymakers designing hydrogen market frameworks.

This monograph studies $KK$-theory in its unbounded model. The central object is the $KK$-bordism group obtained by imposing the $KK$-bordism relation on unbounded $KK$-cycles. In the paradigm of noncommutative geometry, an unbounded $KK$-cycle is a noncommutative geometry in its own right and our approach allow for the study of mildly noncommutative geometries (orbifolds, foliations et cetera) as if they were closed manifolds. The techniques we introduce enable us to directly import manifold techniques and arguments into the important yet technical field of unbounded $KK$-theory. Recent decades has seen a tremendous progress in the study of the unbounded model for $KK$ as well as secondary invariants, the first motivated by refining computational tools in Kasparov's $KK$-theory and the second by applications to geometry and topology. The aim of this work is to provide a common framework for these two areas: equipping unbounded $KK$-cycles with a geometrically motivated relation recovering Kasparov's $KK$-theory that is computationally tractable for working with secondary invariants.

We present the first spectral analysis of Scorpius X-1 (Sco X-1) using intentional stray light (SL) observations taken by NuSTAR. Unlike focused observations that have high telemetry load when observing bright sources, intentional SL observations can help reduce the telemetry and reduce the effect of dead time, thereby maximizing the on-source exposure time; all of which are critical for extremely bright sources that exhibit short timescale variability like Sco X-1. The intentional SL observation of Sco X-1, taken in 2023, captured the source primarily in the flaring branch (FB) of the Z track. We performed spectral modeling of the continuum and reprocessed emission. A combination of thermal and Comptonization components (modeled with thcomp) provided a robust fit to the continuum. We test both scenarios for Comptonized emitting regions arising from the accretion disk and close to the neutron star, which provides comparable fit statistics. Reflection was modeled with the relxillNS model, enabling measurements of disk inclination consistent with prior radio and IXPE studies and comparison of inner disk radius to the emission radii of the thermal components. Overall, the results from the intentional SL data provide comparable results to literature on the focused NuSTAR data of Sco X-1 in the FB or taken contemporaneously. The success of this observation demonstrates the capability of SL data to yield high-quality spectral constraints comparable to focused observations, offering a powerful avenue for studying bright X-ray binaries with NuSTAR.

Damping of structures and systems is often dominated by frictional dissipation in connections, the prediction of which remains a longstanding scientific challenge. Previous studies have shown that the actual topography of contact interfaces may have a strong effect on the dynamics of jointed structures. The multi-scale nature of manufactured surfaces makes finite element (FE) simulations computationally challenging or even infeasible, especially for long-duration transient dynamic simulations. We recently proposed a multi-scale method to enable topography resolving simulations. In that method, the contact region is modeled using half-space theory implemented on a fine grid of boundary elements (BE), whereas the underlying bodies are described using a relatively coarse FE model. So far, this FE-BE multi-scale method has been limited to quasi-static analysis. In the present work, we extend the method dynamic analysis, in the form of time integration and Harmonic Balance. As numerical benchmark system, the well-known S4 Beam is used, for which actual topography measurements are available. The proposed method demonstrates high robustness and efficiency, permits relatively large and mesh-independent time steps, and shows no evidence of numerical damping. The simulation results are in overall very good agreement with explicit and implicit full-FE analyses. In the partial slip regime, some discrepancy is found to be of physical origin: Depending on the load history, the system settles to a slightly different equilibrium, which is associated with a distinct residual contact stress field.

Nanoscale surface analysis of 1 micrometer thick high entropy alloys (HEAs) was carried out using nano-IR for hyperspectral imaging and single point spectroscopy in the 700-1700 1/cm spectral range. Nano-IR is based on the detection of scattered light from an oscillating metal coated nano-tip in one of the arms of the Fourier transform infrared spectrometer and has a resolution defined by the tip radius of the probe, 20 nm, regardless of the excitation wavelength. HEA CuPdAgPtAu showed an absorption and reflection increase at 900-1100 1/cm band, which is consistent with Drude-Lorenz modeling of permittivity, however, could also signify oxide formation as tested by X-ray photoelectron spectroscopy of CuPdAgPtAu and CrFeCoNiCuMo. Realization of polarization analysis for nano-IR nano-spectroscopy in the plane perpendicular to the sample's surface is discussed and modeled. The currently available modality of surface analysis with the excitation-detection mode of the p-pol. antenna can be extended to full 3D analysis of the orientational dependencies of local absorbance and refractive index.

In analogy with the spectral theory of geometrically finite hyperbolic manifolds, we initiate the study of resonances on geometrically finite (q+1)-regular graphs of groups. We prove the meromorphic continuation of the resolvent of the adjacency operator on such spaces and give a geometric characterization of the resonant states. In contrast to the hyperbolic surfaces setting, geometrically finite graphs have only finitely many resonances and may be computed explicitly, yet exhibit many of the same qualitative phenomena as in the hyperbolic manifolds setting. Particularly interesting examples arise from algebraic curves over finite fields.

Black holes (BHs) and wormholes (WHs) are characterized by distinct spacetime geometries, whose differences become pronounced close to the central objects. A useful way to probe such differences is via the dynamics of stellar tidal disruption events in the regime of strong gravity. Here, using a general relativistic smoothed particle hydrodynamics code inspired from an algorithm developed by Liptai and Price, we perform a suite of numerical simulations of solar mass polytropic stars in the background of supermassive Schwarzschild BHs and similar mass exponential WHs. Important differences between the two geometries near the BH event horizon or the WH throat is provided by the distinct outcomes of such events. For a given impact parameter, BH backgrounds lead to greater tidal stripping compared to WHs ones and further, the critical impact parameter, beyond which the star undergoes full tidal disruption is higher for WH backgrounds compared to BHs. We further study the differences in observable peak fallback rates in the two backgrounds. We also provide a quantitative explanation for the tendency of stars in partial tidal disruptions to retain larger cores around more massive centers, by computing tidal stresses in a Fermi normal coordinate system and introducing an appropriate measure of stellar compactness. Finally, we suggest a way to observationally distinguish BH and WH backgrounds, based on the properties of different observables.

With the staggering increase of edge compute applications like Internet-of-Things (IoT) and artificial intelligence (AI), the demand for fast, energy-efficient on-chip memory is growing. While the fast and mature static random-access memory (SRAM) technology is the standard choice, its volatility requires a constant supply voltage to operate and store data. Especially in edge AI and IoT devices that often idle, the leakage power consumes a significant portion of the constrained power budget. For this, emerging non-volatile memory (NVM) technologies such as Resistive RAM and ferroelectric FET (FeFET) offer zero-standby power consumption but suffer from integration and performance tradeoffs. To harness the benefits of the different technologies, hybrid architectures have been proposed, combining SRAM with NVM devices. This work proposes a hybrid non-volatile SRAM (nvSRAM) architecture based on recently demonstrated PMOS FeFETs (p-FeFETs). By replacing the two PMOS pull-up transistors with p-FeFETs, we achieve non-volatility without additional transistors. The design supports seamless power-down and restore operation, thus eliminating standby leakage. SPICE simulations in a commercial 28 nm technology show read latency comparable to conventional SRAM, and on-silicon measurements show robust restore behavior. With this, we are the first to demonstrate a fabricated 6T nvSRAM cell design. The resulting cell achieves an area footprint of 99 $μm^2$. The read path remains identical to baseline SRAM, enabling high-speed operation while being non-volatile, making it ideal for IoT and edge systems.

Nonlinear magnetoconductivity (NLMC) is a nonreciprocal transport response arising in non-centrosymmetric materials. However, this ordinary NLMC signal vanishes at zero magnetic field, limiting its potential for applications. Here, we report the observation of an anomalous NLMC controlled by internal order parameters such as the magnetization or Néel vectors. We achieve this response by breaking both inversion and time-reversal symmetry in artificial van der Waals heterostructures based on the magnetic CrSBr and insulating hBN. The nonreciprocal signal can be tuned between two different states in ferromagnetic monolayer CrSBr and among four different states in antiferromagnetic bilayer CrSBr, thanks to its metamagnetic transition. Remarkably, this output signal in the ferromagnetic (antiferromagnetic) state of CrSBr is three (one) orders of magnitude higher than those previously measured. A conductivity scaling analysis reveals the Berry connection polarizability as the origin of the anomalous NLMC. Our results pave the way for high-frequency rectifiers with magnetically switchable output polarity as well as for an efficient electrical readout of the magnetic state of antiferromagnetic materials.

Modern GPU-rich HPC systems are increasingly becoming energy-constrained. Thus, understanding an application's energy consumption becomes essential. Unfortunately, current GPU energy attribution techniques are either inaccurate, inflexible, or outdated. Therefore, we propose Wattchmen, a flexible methodology for measuring, attributing, and predicting GPU energy consumption. We construct a per-instruction energy model using a diverse set of microbenchmarks to systematically quantify the energy consumption of GPU instructions, enabling finer-grain prediction and energy consumption breakdowns for applications. Compared with the state-of-the-art systems like AccelWattch (32%) and Guser (25%), across 16 popular GPGPU, graph analytics, HPC, and ML workloads, Wattchmen reduces the mean absolute percent error (MAPE) to 14% on V100 GPUs. Furthermore, we show that Wattchmen provides similar MAPEs for water-cooled V100s (15%) and extends to later architectures, including air-cooled A100 (11%) and H100 (12%) GPUs. Finally, to further demonstrate Wattchmen's value, we apply it to applications such as Backprop and QMCPACK, where Wattchmen's insights enable energy reductions of up to 35%.

In this paper, a new tool for Doppler tomography, Tomo-V (https://tomo-v.inasan.ru) that is developed based on the algebraic reconstruction technique (ART) has been presented. Previously, the ART method has not been widely used in tomography, as its direct implementation was computationally complex. The author has developed a fast version of this algorithm, which allowed it to be implemented within a web application that runs at acceptable speed in a browser on a personal computer. This method can be used to obtain sharp tomographic images from blurred profiles. Furthermore, the method has demonstrated excellent results in reconstructing images from noisy data, from a small number of profiles, and from profiles contaminated by absorption lines and emission from the expanding envelope. Tomo-V also includes tools for analyzing the resulting tomograms, allowing the position of accretion disks and Roche lobes to be displayed on the tomogram, as well as back-projecting the tomographic image onto flow elements in spatial coordinates. The paper is partially based on a report presented at the Modern Stellar Astronomy 2025 conference.

Matrix differential Riccati equation (DRE) typically exhibits transient and steady-state phases, posing challenges for fixed-step time integration methods, which may lack accuracy during transients or oversample in steady regimes. In this work, we propose adaptive low-rank matrix-valued exponential integrators for large-scale stiff DRE. The methods combine embedded exponential Rosenbrock-type schemes and adaptive step-size control, enabling an automatic adjustment to the evolving solution dynamics. This improves the accuracy during rapid transient phases while maintaining high accuracy in the steady state. Numerical experiments on benchmark problems demonstrate that the proposed adaptive integrators consistently improve accuracy and computational efficiency compared with fixed-step low-rank schemes.

Starting from the definition of the Gromov-Hausdorff distance via distortion of correspondences, we add the requirement of semicontinuity of each correspondence and its inverse. It turns out that in the case of lower semicontinuity we obtain the same classical Gromov-Hausdorff distance, while for upper semicontinuity we are able to prove coincidence with the classical one only in cases where the spaces are either totally bounded or boundedly compact.

Detecting faint objects in cislunar space using ground-based optical telescopes is difficult because of their low brightness, strong lunar background, and complex, nonlinear apparent motion. Traditional shift-and-stack techniques based on linear motion assumption suffer signal trailing loss due to significant nonlinear motion during long integrations, thus producing a degraded signal-to-noise ratio (SNR). In this paper, we first derive a theoretical criterion based on the point spread function to determine the maximum applicable integration time for linear-motion stacking. We then propose a quadratic shift-and-stack (QSS) method to correct for the first-order nonlinear motion, namely the angular acceleration of cislunar targets. Simulations of typical cislunar orbits verify this theoretical criterion and show that the QSS method significantly improves SNR from stacking and can enhance the detection limit by up to 1 stellar magnitude compared with the linear-motion stacking method. Furthermore, tests using observational data of the cislunar object Tiandu-1 confirm that while linear stacking degrades after a 29-minute integration due to trajectory curvature, the QSS method achieves continuous SNR improvement over a 46-minute integration, outperforming the peak SNR of the linear method by 31%.

We present a reconstruction of UKRI's Gateway to Research (GtR) database that links funding opportunities to their resulting project proposals through panel meeting outcomes. Unlike existing work that focuses primarily on funded projects and their outcomes, we close the complete funding lifecycle by integrating three previously disconnected data sources: the GtR project database, UKRI funding opportunities, and competitive funding decision records across UKRI's research councils. We describe the technical challenges of data collection, including navigating inconsistent publication formats and restricted access to panel decisions. The resulting dataset enables a holistic interrogation of the entire funding process, from opportunity announcement to research outcomes. We release the database and associated code.

The changes in magnetic properties of Ce(Fe0.9Co0.1)2 compound with increasing disorder are discussed in the paper. Homogeneous alloys are known to undergo the phase transition from ferromagnetic to antiferromagnetic state accompanied by the structural distortion of the cubic Laves C15 phase into the rhombohedral one. Various stimuli, like the structural disorder, or applied magnetic field, can force the emergence of ferromagnetism at low temperatures. We initially introduced the structural disorder using rapid quenching. Further changes were made by severe plastic deformation. The presence of a ferromagnetic phase in a low-temperature region is reported here and accompanies the deterioration of a first-order phase transition. We show, based on electronic calculations, that the structural motifs arising from various distortions of the initial MgCu2-type structure, caused by the partial replacement of Fe with Co atoms, are characterized by stable antiferromagnetic order. This neglects simple structural distortions as the source of ferromagnetism. The presence of a strongly defective structure understood as a topologically disordered volume, reduced the fraction transformed from a ferromagnetic to an antiferromagnetic state. Therefore, a strong reduction of isothermal entropy changes was also observed, as it decreased from 1.94 Jkg-1K-1 and -1.43 Jkg-1K-1 (Δμ0H = 4 T) to 0.30 Jkg-1K-1 and -0.96 Jkg-1K-1 for antiferromagnetic-ferromagnetic and ferromagnetic-paramagnetic transition, respectively.

We consider a fluid-structure interaction problem in the Eulerian, phase-field formulation. The problem is described using the Navier--Stokes equations for a viscous, incompressible fluid, coupled with the incompressible hyperelasticity system, both written in the Eulerian coordinates. This allows the problem to be written in a unified formulation, using a single field for the fluid and structure velocities. To track the position of the domain, we use a phase-field approach, resulting in a coupled Cahn--Hilliard--Navier--Stokes-type of problem for the diffuse interface fluid-structure interaction. Under certain assumptions, we prove the convergence of the diffuse interface model to the sharp interface fluid-structure interaction problem. To solve the problem numerically, we propose a novel, strongly coupled, second-order partitioned computational method where the system is decoupled into the Cahn--Hilliard problem, the transport problem for the left Cauchy--Green deformation tensor, and the Navier--Stokes problem. The problems are solved iteratively until convergence at each time step. The performance of the method is illustrated on two computational examples.

We study a stationary 3D/2D fluid-structure interaction problem between an elastic structure described by the linear plate equation and a fluid described by the compressible Navier-Stokes equations with hard-sphere pressure and inflow/outflow boundary data. This problem is motivated by wind-tunnel configuration and by the need for physically relevant steady states about which compressible flow-plate dynamics can be linearized. The main difficulty in the analysis is the lack of uniform estimates, both for approximate and weak solutions. In particular, the fixed-point construction for approximate solution yields a density estimate depending on approximate parameter, while the pressure estimate for the weak solution is only finite and non-quantifiable. As a result, large pressure loads can drive outward volume growth, while low pressure regions may lead to contact and therefore domain degeneration. This necessitates a novel approach based on a Lipschitz \emph{domain-correction} (barrier) mechanism that provides a framework in which solutions can be constructed without volume blow-up or degeneration of the domain. Constrained by the possibly very large fluid pressure load, our main result is the existence of a weak solution for a sufficiently large plate stiffness. Keywords: fluid-structure interaction, compressible Navier-Stokes, stationary weak solutions, hard-sphere pressure, inflow/outflow, linear plate, mathematical aeroelasticity

Equipping laypeople with the capabilities to seek legal information has been an important goal for Legal Empowerment in modern society. However, unlike general information-seeking behaviors, legal information seeking is characterized by high stakes, urgency, and a critical need for emotional support, which traditional text-based searching platforms struggle to satisfy. In recent years, people have been increasingly turning to Video-Sharing Platforms (VSPs) for access to legal information and to fulfill their legal needs. Despite the importance of this shift, such VSP-mediated legal information-seeking practices remain underexplored. Through an observational analysis of legal content on two VSPs (Douyin and Bilibili) and interviews with 20 Chinese information seekers, this study examined the practices and challenges associated with seeking, comprehending, and evaluating legal information on VSPs. We further revealed the formation of trust and engagement on the VSP-based legal knowledge-sharing community, highlighting how VSP affordances helped mitigate seekers' epistemic discomfort and satisfy their needs for emotional support. In the discussion, we provided insights on balancing heuristic and systematic processing to encourage information cross-validation, and offered implications for designing trustworthy civic information systems and fostering an accessible, safe, and efficient information-seeking environment in digital space.

Planetary magnetospheres exhibit diverse environments where Ultra-low frequency (ULF) pulsations induce nonlinear ponderomotive effects. Since suprathermal populations modeled by Kappa distributions are ubiquitous in these regions, their significant influence on the ponderomotive force (PF) induced by electromagnetic ion cyclotron (EMIC) waves must be accounted for. We investigate field-aligned plasma density redistribution driven by the PF of traveling EMIC waves across different planetary magnetospheres. We apply a generalized slow-time-scale force balance equation to model stationary density solutions in low-beta plasmas ($β\ll 1$) with isotropic Kappa distributions. To enable systematic comparison, wave modulation is described using the WKB approximation in a dipole magnetic field, neglecting first-order curvature effects. The plasma response varies significantly with magnetospheric parameters: decreasing the kappa parameter and increasing plasma beta counteract plasma accumulation towards the equator. In low-beta environments, non-thermal effects substantially reduce the nonlinear response to short-period pulsations, though preserving the qualitative behavior of Maxwellian models. Furthermore, we characterize how the critical parameter governing the phase transition between equatorial density minima and maxima depends on the specific combination of plasma beta, kappa, and L-shell. Our study demonstrates that non-thermal plasma properties are a governing factor in field-aligned density redistribution driven by ULF waves, highlighting the necessity of incorporating them to accurately model ponderomotive phenomena across multifaceted planetary magnetospheres.

Federated Learning (FL) enables collaborative training while keeping sensitive data on clients' devices, but local model updates can still leak private information. Hybrid Homomorphic Encryption (HHE) has recently been applied to FL to mitigate client overhead while preserving privacy. However, existing HHE-FL systems rely on a single homomorphic key pair shared across all clients, which forces them to assume an unrealistically weak threat model: if a client misbehaves or intercepts another's traffic, private updates can be exposed. We eliminate this weakness by integrating two alternative key protection mechanisms into the HHE-FL workflow. The first is masking, where client keys are blinded before homomorphic encryption and later unblinded homomorphically by the server. The second is RSA encapsulation, where homomorphically encrypted keys are additionally wrapped under the server's RSA public key. These countermeasures prevent key misuse by other clients and extend HHE-FL security to adversarial settings with malicious participants. We implement both approaches on top of the Flower framework using the PASTA/BFV HHE scheme and evaluate them on the MNIST dataset with 12 clients. Results show that both mechanisms preserve model accuracy while adding minimal overhead: masking incurs negligible cost, and RSA encapsulation introduces only modest runtime and communication overhead.

We introduce weighted Markovian graphs, a random walk model that decouples the transition dynamics of a Markov chain from (random) edge weights representing the cost of traversing each edge. This decoupling allows us to study the accumulated weight along a path independently of the routing behavior. Crucially, we derive closed-form expressions for the mean and variance of weighted first passage times and weighted Kemeny constants, together with their partial derivatives with respect to both the weight and transition matrices. These results hold for both deterministic and stochastic weights with no distributional assumptions. We demonstrate the framework through two applications, highlighting the dual role of variance. In surveillance networks, we introduce the surprise index, a coefficient-of-variation metric quantifying patrol unpredictability, and show how maximizing it yields policies that are both efficient and hard to anticipate. In traffic networks subject to cascading edge failures, we develop a minimal-intervention framework that adjusts speed limits to preserve connectivity under three increasingly flexible regulatory policies.

We introduce a novel deep convolutional neural network (NN) -enhanced Bayesian global analysis of bulk observables in highest-energy heavy-ion collisions, using relativistic 2+1 D second-order viscous hydrodynamics with a dynamical freeze-out, and with perturbative QCD and saturation -based initial conditions from the event-by-event EKRT-model. Our analysis has 13+2 free parameters for the QCD-matter properties + initial state, which are constrained by the experimental data from $\sqrt{s_{NN}}=200$ GeV Au+Au collisions at RHIC and $2.76$ TeV Pb+Pb, $5.02$ TeV Pb+Pb, and $5.44$ TeV Xe+Xe collisions at the LHC. We replace the computationally demanding hydrodynamical simulations by NNs, which predict bulk observables directly from the initial energy density profiles, event-by-event, and account for the QCD-matter properties. With the NN output, we train the Gaussian process emulators for obtaining centrality-class averaged observables and their uncertainties. The NNs reduce the computing time significantly, enabling us to include also statistics-hungry flow observables like $v_4$ and the normalized symmetric cumulant $NSC(4,2)$ in the analysis. In this paper, we demonstrate the feasibility of the NN based Bayesian global analysis. We find the data favoring a specific shear viscosity $η/s$ with a minimum-value plateau at temperatures $150\lesssim T \lesssim 230$ MeV, with $0.12 \lesssim (η/s)_{\mathrm{min}} \lesssim 0.18$. The bulk viscous coefficient $ζ/s$ is non-zero at $200\lesssim T \lesssim 300$ MeV. The Knudsen number at the freeze-out is $0.8-2.3$, while the ratio of the mean free path to the system size at freeze-out is in the range $0.3-1.2$, implying that the freeze-out indeed happens at the expected limit of the applicability of hydrodynamics.

Predicting how protein mutations affect drug binding remains a major challenge, particularly when the mutations are distal from the binding site. In this study, we introduce a coupled simulation workflow that combines long-time-scale molecular dynamics (MD) with high-throughput quantum mechanical (QM) analysis to reveal the electronic structure signatures of mutation induced drug resistance in the HIV-1 protease. Our workflow leverages GPU-accelerated MD to generate conformational ensembles, and performs in-operando linear-scaling density functional theory (DFT) calculations on selected frames parallelized on a coupled partition of CPU nodes. This design enables efficient, massively parallel quantum analysis of protein-ligand complexes at atomic resolution. Using this approach, we investigate resistance to the antiviral Darunavir in a multi-mutant HIV-1 protease variant. By mapping the network of electronic interactions across the binding interface, our results highlight the critical role of conformational sampling and quantum insight in understanding distal mutation effects, and demonstrate a scalable computational strategy for studying complex biophysical mechanisms of drug resistance. We argue that such kind of analysis may pave the way for designing inhibitors that maintain binding stability against systemic, mutation-induced destabilization.

In this paper we present an attack on a recently proposed code-based Private Information Retrieval (PIR) scheme. Indeed, the server can retrieve the index of the desired file with high probability in polynomial time. The attack relies on the fact that random codes over finite rings are free with high probability and that the dimension of the rowspan of the query matrix decreases when the rows corresponding to the desired index are removed.

We analyse the gravitational-wave emission from 60 two-dimensional core-collapse supernova simulations. The models cover a range of progenitors and equations of state. We focus on the narrow frequency interval in the gravitational-wave spectrum where the emitted power is strongly suppressed (the power gap) and how its central frequency relates to the physical properties of the simulations. We find that the power-gap frequency exhibits strong and systematic correlations with the properties of the inner core of the forming neutron star, for example the sound speed, suggesting that the gap encodes information about the behaviour of matter at extreme densities. We further examine how well several mechanisms proposed in the literature account for the presence and evolution of the gap in our simulations. Finally, we explore a scenario in which the gap arises from destructive interference between a narrow oscillation mode and a broadband background signal, demonstrating that such an interaction can produce a sharp minimum in the emitted gravitational-wave power.

Parameter estimation for gravitational-wave signals is computationally demanding due to the high dimensionality of the parameter space and the cost of repeated waveform generation in traditional Bayesian inference. These analyses require on the order of 10^8 likelihood evaluations and waveform generations, resulting in inference times of hours to days per event. Furthermore, discrepancies between waveform models introduce systematic uncertainties that can bias inferred source properties. To address these challenges, we propose a novel framework based on Simulation-based Inference (SBI) and Neural Posterior Estimation (NPE) and apply it to signals from Intermediate-Mass Black Holes (IMBH). In this framework, we train a single amortised neural posterior estimator on a large simulated dataset generated using two state-of-the-art waveform approximants, IMRPhenomXPHM and SEOBNRv5PHM. By treating the waveform model index as a latent variable, the network learns to produce posterior distributions that are naturally marginalized over the discrepancies of the two waveform models. Once trained, the model enables direct posterior sampling in milliseconds per event, eliminating the need for likelihood evaluations while simultaneously accounting for model systematics. We demonstrate that this approach recovers accurate posterior distributions for IMBH signals injected into Gaussian noise, achieving close agreement with traditional nested-sampling results while reducing inference time by several orders of magnitude. Our results show that NPE can robustly incorporate waveform-model systematics within a unified framework, offering a scalable path toward rapid, systematics-aware gravitational-wave inference. Establishing these methods as promising alternatives to classical likelihood-based pipelines for current and future high-mass gravitational-wave observations.

Altermagnetism has so far been associated with compensated magnetic moments carried by atoms. Here we introduce Stoner instability induced interstitial-electron altermagnetism, a distinct mechanism in which altermagnetic order is carried instead by interstitial anionic electrons in electrides. We show that, owing to the quasi-nucleus-free nature of interstitial electrons, the Stoner instability in electrides hosting two interstitial electrons can naturally stabilize an altermagnetic state rather than the conventional ferromagnetic one. This mechanism leads to a practical design principle for two-dimensional materials, from which we identify monolayers Zr2N and Ti2N as representative candidates. The strong sensitivity of interstitial electrons to cavity size enables efficient strain control of the altermagnetic order and a pronounced piezo-altermagnetic effect. Moreover, we investigate the evolution of the magnetism in Zr2N under ultrafast laser excitation, which exhibits dynamics distinct from those in all previously reported magnetic materials where magnetism is carried by real atoms. Our work not only offers a novel pathway to realize altermagnetism but also reveals an efficient non-magnetic route for its control.

In this paper, we investigate the continuity of solutions to the Dirichlet problem for complex Hessian-type equations associated with $(ω, m)-β$-subharmonic functions on a ball in $\mathbb{C}^n$, where $ β=d d^c\|z\|^2=\frac{i}{2} \sum_{j=1}^n d z_j \wedge d \bar{z}_j $ is denoted the flat metric on $\mathbb{C}^n$.