We obtain sharp estimates for functions harmonic with respect to $x$-dependent rectilinear stable processes in balls, under the assumption that the Dirichlet exterior data are radial about the center. The main idea of the proof is based on the construction of global barrier functions for the $x$-dependent rectilinear fractional Laplacian in balls.

Concurrent game frames are a standard semantic framework for logics of strategic reasoning. Two notions of coalition power can be derived from such frames: alpha powers and actual powers. An alpha power of a coalition is a set of possible futures such that the coalition has an action that forces the resulting future to lie in that set. An actual power of a coalition is a set of possible futures satisfying the following condition: the coalition has an action such that (1) the action forces the resulting future to lie in the set, and (2) every future in the set is compatible with that action. In two papers, Li and Ju argued that standard concurrent game frames rely on three assumptions that may be too strong: seriality, independence of agents, and determinism. They therefore considered eight classes of general concurrent game frames, determined by which of these three properties hold, and studied the corresponding coalition logics. In this paper, assuming two agents, we prove that for actual powers, the eight classes of general concurrent game frames are representable by eight corresponding classes of neighborhood frames. Building on this result, we show that for alpha powers, the same eight classes of general concurrent game frames are likewise representable by eight corresponding classes of neighborhood frames.

We systematically investigate the thermodynamic landscape of the 38-atom Lennard--Jones cluster LJ$_{38}$ using Population Annealing (PA), a method suited for systems with challenging double-funnel energy landscapes. By employing an adaptive temperature schedule, we demonstrate that thermodynamic observables, such as internal energy and heat capacity, converge robustly when the population size is sufficiently large. To gain deeper insights into the competing basins, we introduce an integrated framework that combines PA reweighting factors with structure-resolved analysis. Using quenched configurations characterized by potential energy and Steinhardt's bond-orientational order parameters, we identify three structural basins, FCC-like, icosahedral, and liquid-like, via dimensionality reduction and clustering. This framework enables the direct computation of structure-resolved free energy differences from population fractions, providing a quantitative mapping of the thermodynamic competition between the funnels. The resulting structural crossovers are consistent with the heat-capacity peak, demonstrating PA as a promising and scalable framework for structure-resolved thermodynamics in complex molecular systems.

In this letter, we explore the phenomenological impact of inflationary gravitational particle production in the physics of Dark Matter (DM). Large-scale DM fluctuations generated during inflation behave as gravitational particles upon their post-inflationary horizon reentry and alter the conventional Boltzmann dynamics of DM with a non-conserving source term, thereby producing significant phenomenological consequences. Within this framework, we analyze four distinct types of DM classified according to their production mechanisms. Dark matter may be completely non-interacting with the thermal bath, behaving as Inert Dark Matter. Alternatively, depending on the strength of its interactions with bath particles, DM may exhibit WIMPy, UFOy, or FIMPy behavior, sharing characteristics with their conventional counterparts. The late-time enhancement of the DM number density, driven by the successive horizon reentry of gravitationally produced low-momentum modes, enlarges the viable parameter space for both thermal and non-thermal DM scenarios. Remarkably, this expanded parameter space remains consistent with current constraints from $ΔN_{\rm eff}$ and Lyman-$α$ bound.

Chromium nitride (CrN) is a thermoelectric transition metal nitride whose properties are strongly influenced by stoichiometry, substrate choice, and defect chemistry. CrN is routinely synthesized by physical vapor deposition (PVD), its growth by chemical vapor deposition (CVD) has been limited by the lack of suitable chromium precursors capable of producing carbon-, oxygen-, and chlorine-free films. CVD of contamination-free Cr compounds is notoriously difficult, with carbon-free Cr compounds thought unattainable below 1000 C. Here, we report epitaxial, carbon- and chlorine-free CrN thin films synthesized by thermal CVD. Single-phase CrN films (~110 nm) were deposited on c-plane alpha-Al2O3 using in-situ generated chromium chlorides. Films exhibit n-type conduction with a Seebeck coefficient of -36 uV/K, comparable to PVD-grown CrN. These results present a routeto highly crystalline rock-salt CrN films for defect engineering, doping, and alloying with reduced implantation-related damage capabilities previously largely confined to PVD-based techniques.

A computationally efficient and accurate machine-learned (ML) interatomic potential is developed for Ti$_{n+1}$C$_n$ MXenes. With a diverse set of structures computed with density functional theory, the trained ML potential demonstrates good accuracy and robustness to a wide range of bond distances and environments, making it a useful tool for molecular dynamics simulations of MXenes subjected to mechanical load or irradiation. The ML potential is applied to simulations of light and heavy ion irradiation, gathering insight into the statistics and probabilities of sputtering, reflection, defect creation, and implantation into Ti$_{n+1}$C$_n$ MXene sheets. The results provide guidelines for defect engineering of MXenes through ion irradiation and implantation. Additionally, the ML potential development provides a landmark recipe for enabling machine-learning-driven atomistic simulations of other MXenes.

Many new physics models, such as the Sequential Standard Model, Grand Unified Theories, models of extra dimensions, or models like leptoquarks or vector-like leptons, predict heavy mediators at the TeV energy scale. We present recent results of such searches in leptonic final states obtained using data recorded by the CMS experiment during Run-II of the LHC.

Structural and functional heterogeneity are hallmarks of cortical circuits, from broad degree distributions in the mouse connectome to diverse intrinsic neuronal timescales. Yet a mechanistic link between connectivity heterogeneity and functional diversity is lacking. To bridge this gap, we introduce a random recurrent network in which connectivity is generated by a configuration model with tunable degree heterogeneity and synaptic weights exhibiting varying levels of correlation. Using generating-functional methods, we derive a heterogeneous dynamical mean-field theory (hDMFT) with degree-conditioned stochastic dynamics. The theory shows that the interaction of partial symmetry in the weights and degree heterogeneity induces a non-Markovian memory term in the form of an emergent self-coupling whose strength scales with degree and produces a broad distribution of activity timescales. We obtain analytic stability criteria demonstrating that degree heterogeneity lowers the critical gain and localizes unstable modes onto hubs. The resulting rich dynamical landscape includes silent, chaotic, and multistable regimes, which we uncover via spectral, replica, and Lyapunov exponent analyses. We highlight the computational benefits of the observed timescale heterogeneity by revealing that, under an external input drive featuring a broadband spectrum, slow hub neurons act as integrators, demixing slow input components. Finally, instantiating the model with the empirically measured topology from the MICrONS cubic-millimeter mouse connectome explains the broad range of single-neuron timescales and their positive correlation with in-degree observed in resting-state recordings. Our results provide a mechanistic link between connectome topology, neural dynamics, and computation, identifying hubs in partially symmetric networks as a natural substrate for multiplexed processing across timescales.

The property of non-stabiliserness, or ``magic'', is of interest in quantum computing due to its role in developing fault-tolerant quantum algorithms with genuine computational advantage over classical counterparts. There has been much interest in quantifying magic in various physical systems, in order to probe how to produce and enhance it. The production of magic has previously been quantified in gluon and graviton scattering, in the so-called helicity basis relating particle spins with momentum directions. For a basis-independent statement, one should instead use the recently developed concept of non-local non-stabiliserness, and our aim in this paper is to derive how this varies for gluon and graviton scattering processes. Our results show that, for many initial states, including those produced with polarised beams, the helicity basis coincides with a basis in which the non-local magic is manifest, providing a physical motivation for using the helicity basis to study quantum information quantities. However, this property breaks upon adding additional operators to the Yang-Mills Lagrangian, as would be the case in new physics scenarios.

New refractory alloys are being continuously designed and characterised for applications requiring good high-temperature mechanical properties and stability. Computational design from atomistic simulations is limited by interatomic potentials missing key elements, being too inaccurate, or computationally too slow for large-scale simulations. Here we present development of a refractory alloy database and two computationally efficient and general-purpose machine-learned potentials (tabGAP and NEP). We also design a cross-sampling strategy for effective sampling of training data using predictions from two potentials with completely different underlying architecture. The potentials support arbitrary alloy compositions of elements in groups four to six in the periodic table (Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W). The database is diverse yet multitargeted to enable simulations of refractory metals and alloys across different pure-metal, solid-solution, intermetallic, and glassy phases. We demonstrate the usefulness of the potentials by reproducing known pressure-, temperature-, and solute-induced phase transitions, grain boundary segregation, and simulations of radiation damage in the WTaCrVHf metallic glass.

We explore what fraction of delta sunspots in which the polarity inversion line (PIL) is sharp in photospheric magnetograms are made from a writhe kink in an emerging twisted flux rope. We searched simultaneous full-disk magnetograms and continuum images from Helioseismic and Magnetic Imager (HMI) on Solar Dynamics Observatory (SDO) to find 28 random sharp-PIL delta sunspots that are born well on the disk. Only one of these is made from a single newly emerged bipolar magnetic region (BMR) and therefore is a candidate for being made from a single emerging writhe-kinked flux rope. That outcome indicates that few, if any, sharp-PIL delta sunspots are made by a single emerging writhe-kinked flux rope. That is the main new finding of this paper. Each of the other 27 is made by merging of two or more emerging or emerged BMRs. We name delta-sunspot genesis from a single BMR Type I genesis. We identify another three genesis types among the other 27 delta sunspots: Type II, Type III, and Type IV. We present an observed example genesis for each of the four genesis types, and for each example present schematic drawings depicting our scenario(s) for the cause of that example genesis. The core idea of each scenario is that the delta sunspot is made by packing opposite-polarity magnetic flux together by advection into a convection downflow.

In conventional optical Stark-shift spectroscopy, molecules are exposed to spatially homogeneous static electric fields that shift the energies of their spectral lines. These shifts are attributed to the molecular electronic properties, such as variation of dipolar moment and polarizability of the molecule associated with photo(de)excitation. In realistic environments containing structural defects and nanoscale heterogeneities, however, molecules experience internal electric fields that vary strongly on the molecular scale, rendering the standard Stark selection rules inapplicable. Here we develop an extended theory of atomic-scale Stark shift, addressing such scenarios. Specifically, we present a detailed theoretical analysis of an experimentally relevant configuration where the atomically sharp tip of a light-assisted scanning tunneling microscope is used to controllably apply inhomogeneous electrostatic fields to representative molecular dyes spanning several molecular families. We decompose the total Stark shift into linear and quadratic contributions and show that they contain different information about the molecular properties. Concretely, spatial variations of the linear Stark shift as the tip scans across the molecule enable subnanometric mapping of the charge redistribution between ground and excited electronic states, with high sensitivity to molecular composition and chemical functionalization. The quadratic Stark contribution, in contrast, reflects changes in the conventional dipolar polarizability upon excitation. Together, these results establish nanoscale Stark-shift spectroscopy as a powerful tool for resolving excited-state charge dynamics in single molecules under realistic, strongly inhomogeneous electric fields.

Advances in epitaxy of transition metal oxides with perovskite structure allow novel insights into transport mechanisms of strongly correlated electron systems, which are of interest for future transparent electronics. In this study, we investigate magnetotransport properties of thin epitaxial CaVO$_3$ films, grown coherently strained on LaAlO$_3$, and demonstrate for the ultraclean limit quantum oscillations. Fermi liquid behavior is detected in the temperature-dependent resistivity $ρ(T) \sim T^2$ up to 20 K, with effective mean free paths exceeding up to 20 times the film thickness (38 nm). Shubnikov-de Haas oscillations and a non-linear Hall resistance reveal two electron-like (1 and 2) and one hole-like (h) carriers, reflecting the three-fold Fermi surface of orthorhombic CaVO$_3$, with effective charge carrier densities and mobilities at 4.2 K of: (1) $n_1 \approx 9.3 \cdot 10^{21}{}cm^{-3}$ with low mobility $μ_1 \approx 926{}cm^{2}V^{-1}s^{-1}$, (2) $n_2 \approx 7.2 \cdot 10^{19}{}cm^{-3}$ with high mobility $μ_2 \approx 6600 {}cm^{2}V^{-1}s^{-1}$, and (h) $n_h \approx 2.2 \cdot 10^{18}{}cm^{-3}$ with $μ_h \approx 1500{}cm^{2}V^{-1}s^{-1}$. A non-saturating linear magnetoresistance dominates at low temperatures, exceeding the value for single crystals by 30$\%$. Our findings on epitaxial films demonstrate the delicate interplay of multiple carriers with correlations stemming from a non-spherical nested Fermi surface of a perovskite structure with orthorhombic distortion.

We obtain some properties of the q-Narayana polynomials for q=-1 and compare them to corresponding properties for q=1.

We report a study of the semileptonic decays $D^0 \rightarrow \bar{K}^0π^-\ell^+ν_{\ell}$ (where $\ell=e,~μ$) based on a sample of $20.3~\mathrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected at a center-of-mass energy of 3.773~GeV with the BESIII detector at the BEPCII collider. Based on an investigation of the decay dynamics in $D^0 \rightarrow \bar{K}^0π^-\ell^+ν_{\ell}$ decays, a $\mathcal{D}-$wave component of $D^0\rightarrow K_2^*(1430)^-\ell^+ν_{\ell}$ is observed for the first time with a statistical significance of $8.0σ$, in addition to the dominant $K^*(892)^-$ and $\mathcal{S}-$wave components. The $\mathcal{D}-$wave component is determined to account for $(0.092 \pm 0.028_{\rm stat} \pm 0.018_{\rm syst})\%$ of the total decay rate. The branching fractions of the dominant $K^*(892)^-$ components are measured as $\mathcal{B}(D^0\rightarrow K^{*}(892)^-e^+ν_{e}) = (2.043 \pm 0.018_{\rm stat} \pm 0.012_{\rm syst})\%$ and $\mathcal{B}(D^0\rightarrow K^{*}(892)^-μ^+ν_μ) = (1.964 \pm 0.018_{\rm stat} \pm 0.012_{\rm syst})\%$, which are the most precise measurements to date and represent significant improvements over the previous world averages. The hadronic form-factor parameters are measured to be $r_{V} = V(0)/A_1(0) = 1.444 \pm 0.026_{\rm stat} \pm 0.010_{\rm syst}$, $r_{2} = A_2(0)/A_1(0) = 0.752 \pm 0.020_{\rm stat} \pm 0.004_{\rm syst}$, and $A_1(0)=0.618\pm0.002_{\rm stat} \pm0.004_{\rm syst}$, where $V(0)$ is the vector form factor and $A_{1,2}(0)$ are the axial-vector form factors evaluated at $q^2=0$. This is the most precise determination of the form-factor parameters to date measured in a $D\rightarrow K^*(892)$ transition. In addition, we report the first model-independent measurement of the $\mathcal{S}-$wave phase shift in the hadronic $\bar{K}^0π^-$ system.

Impacts between droplets and solid surfaces can commonly cause erosion problem in Engineering applications, including aircraft surface erosion, wind blade leading-edge erosion and steam turbine blade erosion. In practice, the impacted solid surfaces have varied material softness, ranging from stiff metallic coatings to soft materials. An analytical-numerical coupled model (ANCM) for simulating droplet impacts on surfaces, and corresponding material analysis, has been developed in the literature. However, the analytical impact pressure solution of the ANCM model has been derived assuming rigid solid surface. In the current study, we investigate the performance of the ANCM model for droplet impacts on soft materials made of urethane gel phantom, by comparing the ANCM computations to lab-based experiments and numerical simulations based on Smoothed Particle Hydrodynamics (SPH). Parametric studies explore the applicability limit of the ANCM model for droplet impacts on very soft materials at low Young's modulus. It was found that for materials at Young's modulus of $47,400$ Pa or stiffer, which covers most engineering applications, the developed ANCM model performs as expected for an assumed rigid surface. For softer solid materials, the SPH-modeled liquid interacts with the evolving surface geometry and mitigates impact intensity as deformation occurs. The analytical impact loads estimated by ANCM are independent of surface geometry, and hence provide conserved impact impulse in a non-physical way. Results show a critical value of Young's modulus at $E=10,000$ pa for the ANCM model, below which the model exhibits overshoot in total contact force and surface deformation, leading to the formation of steep wall craters.

This paper evaluates the radiation tolerance of commercial off-the-shelf (COTS) electronics components for use in the Thin Gap Chamber (TGC) frontend electronics of the ATLAS experiment at the High-Luminosity LHC (HL-LHC). The ATLAS experiment has accumulated more than 450 fb^-1 of data as of 2025. Its luminosity upgrade, the HL-LHC scheduled to begin operation in 2030, will deliver 3000-4000 fb^-1 over ten years and lead to substantially higher radiation levels in detector electronics. The radiation levels for the TGC frontend electronics are estimated to be 4.1-7.3 Gy in terms of Total Ionizing Dose (TID) and 1.1-2.2 x 10^11 n_1MeV cm^-2 in terms of Non-Ionizing Energy Loss (NIEL). To evaluate component suitability under these conditions, TID tests were conducted using Cobalt-60 gamma rays at Nagoya University, and NIEL tests were performed with the Tandem Accelerator at Kobe University. Various COTS components, including SFP+ optical transceivers, clock jitter cleaners, optical fibers, voltage references, operational amplifiers, analog-to-digital converters, digital-to-analog converters, SD cards, flash memories, and low-dropout regulators, were tested and evaluated against the required radiation levels. The results demonstrate that all evaluated components meet the TID and NIEL tolerance requirements for application in the TGC frontend electronics at the HL-LHC.

We prove optimal regularity results for solutions to linear kinetic Fokker-Planck equations in bounded domains. Our contributions are two-fold. First, we establish the sharp $C^{1/2}$ regularity for either diffuse reflection or prescribed in-flow boundary conditions. Previously, in this setting, it was only known that solutions are $C^α$ for some small $α> 0$. Second, we provide a complete characterization of the solution behavior near the grazing set by proving higher order expansions beyond the critical regularity threshold of $\frac{1}{2}$. These results demonstrate for the first time that solutions maintain higher smoothness up to the grazing set near the incoming boundary.

Impacts of liquid droplets on wind turbine blade surfaces, for example sea sprays, can result in material damage through erosion. In this study, we derive an explicit, closed-form analytical approximation for droplet impact and subsequent spreading on a solid surface in inertia-dominated regimes of large Reynolds and Weber numbers. The formulation extends an existing theoretical framework based on inviscid potential flow for a rising expanding disk in an infinite liquid domain. The modified solution provides full spatio-temporal pressure distributions and impact force histories on the impact surface over the entire impact duration, capturing both the early-time self-similar flow and the inertia-driven lamella spreading following the peak impact force. The predicted pressure and force profiles show good agreement with analytical, numerical and experimental results reported in the literature, including accurate reproduction of the well-known ring-shaped pressure distribution. Key quantities, such as the radial location and magnitude of peak pressure, as well as the timing and magnitude of the peak impact force, are predicted analytically with reasonable accuracy. To enable solid material erosion analysis, the analytical liquid-phase solution is coupled with a finite-element (FE) simulation for the solid response. This analytical-numerical coupled method (ANCM) eliminates the need to explicitly simulate droplet fluid dynamics, which is conventionally performed using smoothed particle hydrodynamics (SPH). As a result, for the purpose of material response analysis, the proposed approach achieves grid independence at substantially lower mesh resolutions and reduces computational cost by more than 97% compared to SPH-based simulations, while maintaining or improving numerical accuracy.

Within the framework of SU(2) chiral perturbation theory, we derive the general solution of the QCD $θ$-vacuum for an arbitrary vacuum phase, explicitly incorporating isospin-breaking effects from the light quark mass difference, and compute the temperature dependence of the topological susceptibility, higher-order cumulants, and the domain wall tension up to next-to-leading order. We find that the topological susceptibility agrees with lattice data at low temperatures but deviates at higher temperatures as expected from the breakdown of the chiral expansion; moreover, we demonstrate that the normalized fourth-order cumulant and the domain wall tension decrease monotonically with increasing temperature, while the normalized sixth-order cumulant exhibits the opposite behavior. These results extend earlier analyses by showing how isospin breaking reshapes the full hierarchy of topological charge cumulants and the dynamics of $θ$-vacuum domain walls, thereby offering new theoretical input on the $θ$-vacuum properties, which are relevant for axion-related effective theories in hot QCD matter.

We study quantum entanglement in a system comprising two quantum dots interconnected through the short topological superconducting nanowire, which hosts overlapping boundary Majorana modes. Inspecting the fermionic negativity, we analyze the variation of entanglement against the position of the energy levels of quantum dots and their hybridization with the topological superconducting nanowire. In the absence of electron correlations, the optimal entanglement occurs when the energy levels coincide with the zero-energy Majorana modes, whereas upon increasing the hybridizations, the entanglement is gradually suppressed. Such monotonous behavior is no longer valid when the quantum dot levels are detuned from the zero-energy. Under these circumstances, the quantum dots become maximally entangled for a certain optimal hybridization. Moreover, we study the thermal concurrence to explore the entanglement properties at finite temperatures. We also compute the quantum mutual information and propose recipes for robust finite-temperature entanglement transmission via Majorana modes.

The CMS muon system is undergoing substantial upgrades to meet the challenges of the High-Luminosity LHC (HL-LHC), including the installation of the new Muon Endcap 0 (ME0) detector. Large-scale production started in 2024. ME0 is a six-layer station designed to extend pseudo-rapidity coverage to |η| = 2.8 from the previous maximum of |η| = 2.4, enhancing sensitivity to forward physics processes. Each endcap will host 18 ME0 stacks, with each stack comprising six triple-layer gas electron multiplier (GEM) chambers. The system adds up to six additional hits per track, which significantly improves muon identification, spatial resolution, and robust track reconstruction at the first trigger level. Chamber production and quality control across multiple international sites ensure scalability and timely delivery. The ME0 design incorporates lessons learned from earlier GEM deployments, with improvements in electronics robustness, grounding, and segmentation to withstand high background rates and minimize damage from discharges. This contribution provides a comprehensive overview of the ME0 detector concept, assembly strategy, quality assurance procedures, current production status, and its pivotal role in strengthening CMS muon reconstruction during HL-LHC operations.

Multiconfigurational short-range density functional theory (MC-srDFT) rigorously combines ground state wavefunction theory with DFT. Unlike single-reference range-separated hybrid functionals, MC-srDFT has lacked theoretically grounded protocols for choosing the system-specific range-separation parameter. To address this problem, we introduce an optimal-tuning scheme based on enforcing the correct exponential decay of the electron density. We show that the range-separation parameter can be determined from the ionization potential given by the smallest-magnitude eigenvalue of the Extended Koopmans' Theorem matrix constructed for the model Hamiltonian. We validate this approach for static and dynamic dipole polarizabilities of ground-state molecular systems using MC-srDFT within both full linear response and its extended random phase approximation (ERPA) variant. Optimal tuning substantially improves polarizabilities relative to the commonly used universal $μ= 0.4\,\mathrm{bohr}^{-1}$ parameter.

In this work, we employ a machine-learning-assisted high-throughput density functional theory framework to systematically investigate the stability, electronic structure, and magnetic ground states of 234 M$_4$X$_3$T$_x$ MXenes. The machine learning model predicts lattice parameters with up to 94% accuracy using a relatively small training dataset and significantly reduces structural optimization time in high-throughput calculations. Based on total energy and density-of-states analyses, we classify the magnetic nature of MXenes across different transition- metal compositions and surface terminations. Ti-, Zr-, Hf-, Nb-, and Ta-based MXenes are found to be non-magnetic metals for all functional groups considered, while Sc- and Y-based systems exhibit a range of behaviors including weak ferromagnetism and semiconducting character. V- and Fe-based MXenes are identified as antiferromagnetic metals, whereas Cr- and Mn-based MXenes yield 16 ferromagnetic systems with spin polarization ranging from 50% to 100%.

At the end of the Cassini mission, Saturn's rings have been claimed to be spectacularly young compared to the age of the Solar System: their unusual ice-rich composition corresponds to initially pure ice rings polluted by interplanetary dust particles for 100 to 400 Myr. Since then, this exposure age has been commonly accepted as the real age of the rings. In this paper, we review the processes that are involved in determining the exposure age. We aim to see how the exposure age depends on various parameters and how relevant it is to define the real rings age. First, a new expression for the gravitational focusing onto planar rings, important parameter but crudely defined in the literature, is derived. Then, an analytical formula describing how the dust fraction varies with time in static or viscously evolving rings is provided, including possible vaporisation at impact. Finally, we introduce a cleaning process from space weathering to possibly alter dust and reduce its amount to make rings look younger than they are. We first found that the gravitational focusing is 5 times less important than previously thought, which automatically increases the exposure age from 0.5 to 2 Gyr. Moreover, depending on the impact properties (vaporisation rate, space weathering efficiency), several billion years can easily be reached. Finally, we find that the dust fraction in the rings converges towards a finite value which, in particular with an efficient space weathering mechanism, can be close to the observed one in the current rings. In this case, neither the age nor the initial composition of the rings can be derived, and the measure of the dust fraction and bombardment rate only constrains the physical parameters of the impacts and the efficiency of the space weathering. As long as the latter parameters are not known, the exposure age argument in favour of young rings is completely undercut.

As global demand for food and clean energy intensifies, agrivoltaic systems have emerged as a vital solution for land-use optimization. However, current designs overwhelmingly treat incident light as a classical photon flux, overlooking the quantum mechanical nature of photosynthetic energy transfer. We introduce spectral bath engineering-the strategic spectral filtering of sunlight through semi-transparent organic photovoltaic (OPV) panels to exploit non-Markovian quantum coherence in biological light-harvesting. Using Process Tensor HOPS (PT-HOPS) and Spectrally Bundled Dissipators (SBD) to simulate the Fenna-Matthews-Olson complex, we demonstrate that selective filtering at vibronic resonance wavelengths (750nm and 820nm) enhances the electron transport rate (ETR) by 25% relative to standard Markovian models. This quantum advantage is driven by vibronic resonance-assisted transport, which extends coherence lifetimes by 20% to 50% and nearly doubles pairwise concurrence (89%). Multi-objective Pareto optimization identifies OPV configurations reaching 18.8% power conversion efficiency while sustaining an 80.5% system ETR, potentially generating an additional USD 470 to 3000 $ha^{-1}$$yr^{-1}$ in revenue. Environmental simulations across nine climate zones, including sub-Saharan Africa, confirm persistent ETR enhancements of 18% to 24%. Finally, eco-design analysis using quantum reactivity descriptors ensures that these technological gains are achieved using sustainable, biodegradable materials. By bridging quantum biology and renewable energy engineering, this work provides a quantitative blueprint for next-generation agrivoltaic materials that co-optimize agricultural productivity and energy yield.

The use of stochastic differential equations in multi-objective optimization has been limited, in practice, by two persistent gaps: incomplete stability analyses and the absence of accessible implementations. We revisit a drift--diffusion model for unconstrained vector optimization in which the drift is induced by a common descent direction and the diffusion term preserves exploratory behavior. The main theoretical contribution is a self-contained Lyapunov analysis establishing global existence, pathwise uniqueness, and non-explosion under a dissipativity condition, together with positive recurrence under an additional coercivity assumption. We also derive an Euler--Maruyama discretization and implement the resulting iteration as a \emph{pymoo}-compatible algorithm -- \emph{pymoo} being an open-source Python framework for multi-objective optimization -- with an interactive \emph{PymooLab} front-end for reproducible experiments. Empirical results on DTLZ2 with objective counts from three to fifteen indicate a consistent trade-off: compared with established evolutionary baselines, the method is less competitive in low-dimensional regimes but remains a viable option under restricted evaluation budgets in higher-dimensional settings. Taken together, these observations suggest that stochastic drift--diffusion search occupies a mathematically tractable niche alongside population-based heuristics -- not as a replacement, but as an alternative whose favorable properties are amenable to rigorous analysis.

In this work, we investigate the time-like pion form factor from lattice QCD in the isosymmetric limit, a quantity that plays an important role in understanding hadron physics with substantial phenomenological applications. This observable can be calculated in the elastic region using the finite-volume approach, up to the first (four-particle) open channel. With the goal of accessing the exclusive two-pion form factor in the inelastic region, starting from a three-point correlator involving the vector current and two (temporally-displaced) pion interpolating operators, we examine the associated underlying spectral density and calculate the form factor using a formalism based on the LSZ reduction. A preliminary analysis on one ensemble generated by the RBC/UKQCD collaboration using domain-wall fermions is presented.

Molecular dynamics (MD) simulates the time evolution of atomic systems governed by interatomic forces, and the fidelity of these simulations depends critically on the underlying force model. Classical force fields (CFFs) rely on fixed functional forms fitted to experimental or theoretical data, offering computational efficiency and broad applicability but limited accuracy in chemically diverse or reactive environments. In contrast, machine learning force fields (MLFFs) deliver near quantum chemical accuracy at molecular-mechanics cost by learning interatomic interactions directly from high level electronic structure data. While MLFFs offer improved accuracy at a fraction of the cost of quantum methods, they introduce significant computational overhead, particularly in descriptor evaluation and neural network inference. These operations pose challenges for parallel hardware due to irregular memory access, minimum data reuse and inefficient kernel execution. This work investigates the hardware performance of such models using poly alanine chains, a novel benchmark molecule system(s) with controllable input size, which used as performance evaluation test cases highlighting the computational bottlenecks of the graphical processor units when scaling out MLFF simulations. The analysis identifies key bottlenecks in descriptor and force computation, memory handling, highlighting the opportunities for improvements in the emerging area of MLFF based MD in drug discovery, that has received limited attention from a computer architecture perspective.

The Steiner Tree Problem (STP) is a well-known NP-hard combinatorial optimization problem, which has wide applications in network design, integrated circuit layout, bioinformatics, and other fields. However, traditional algorithms often struggle to balance efficiency and solution quality when dealing with large-scale STP instances. In this paper, we propose a new quantum annealing-based algorithm for solving the STP: we first model the STP into a quadratic unconstrained binary optimization (QUBO) form suitable for quantum annealing, then design a corresponding encoding strategy, and finally verify the algorithm through experimental tests. The results show that our quantum annealing-based method can obtain high-quality solutions with relatively low computational overhead for moderate-scale STP instances, providing a new feasible path for handling this intractable combinatorial optimization problem.