Papers

The superconducting Transition-Edge Sensor (TES) is a critical technology for next-generation X-ray spectrometers, known for its exceptional energy resolution. In the last decade, TESs based on AlMn alloy films have been extensively used in several cosmic microwave background (CMB) experiments. The advantages of simple fabrication process and easily tunable critical temperature make them an alternative to bilayer TESs. However, they have rarely been applied to X-ray detection until now. We developed an annular AlMn TES for X-ray detection and tested it in a dilution refrigerator with a Superconducting Quantum Interference Device (SQUID) amplifier, achieving an Full Width at Half Maximum (FWHM) of 12.1 +- 0.3 eV at 17.48 keV. To the best of our knowledge, this is the first demonstration of an AlMn TES achieving an energy resolution below 0.1%, highlighting its potential for high-resolution X-ray detection.

We introduce potential-energy gating, a method for robust state estimation in systems governed by double-well stochastic dynamics. The observation noise covariance of a Bayesian filter is modulated by the local value of a known or assumed potential energy function: observations are trusted when the state is near a potential minimum and progressively discounted as it approaches the barrier separating metastable wells. This physics-based mechanism differs from statistical robust filters, which treat all state-space regions identically, and from constrained filters, which bound states rather than modulating observation trust. The approach is especially relevant in non-ergodic or data-scarce settings where only a single realization is available and statistical methods alone cannot learn the noise structure. We implement gating within Extended, Unscented, Ensemble, and Adaptive Kalman filters and particle filters, requiring only two additional hyperparameters. Monte Carlo benchmarks (100 replications) on a Ginzburg-Landau double-well with 10% outlier contamination show 57-80% RMSE improvement over the standard Extended Kalman Filter, all statistically significant (p < 10^{-15}, Wilcoxon test). A naive topological baseline using only well positions achieves 57%, confirming that the continuous energy landscape adds ~21 percentage points. The method is robust to misspecification: even with 50% parameter errors, improvement never falls below 47%. Comparing externally forced and spontaneous Kramers-type transitions, gating retains 68% improvement under noise-induced transitions whereas the naive baseline degrades to 30%. As an empirical illustration, we apply the framework to Dansgaard-Oeschger events in the NGRIP delta-18O ice-core record, estimating asymmetry gamma = -0.109 (bootstrap 95% CI: [-0.220, -0.011]) and showing that outlier fraction explains 91% of the variance in filter improvement.

High-throughput quantum-chemical calculations underpin modern molecular modelling, materials discovery, and machine-learning workflows, yet even semi-empirical methods become restrictive when many molecules must be evaluated. Here we report the first hardware-native realisation of semi-empirical electronic structure theory on a field-programmable gate array (FPGA), implementing as a proof of principle Extended Hückel Theory (EHT) and non-self-consistent Density Functional Tight Binding (DFTB0). Our design performs Hamiltonian construction and diagonalisation on the FPGA device through a streaming dataflow, enabling deterministic execution without host intervention. On a mid-range Artix-7 FPGA, the DFTB0 Hamiltonian generator delivers a throughput over fourfold higher than that of a contemporary server-class CPU. Improvements in eigensolver design, memory capacity, and extensions to nuclear gradients and excited states could further expand capability. Combined with the inherent energy efficiency of FPGA dataflow, this work opens a pathway towards sustainable, hardware-native acceleration of electronic-structure simulation and direct hardware implementations of a broad class of methods.

Portable backscatter imaging systems (PBI) integrate an X-ray source and detector in a single unit, utilizing Compton scattering photons to rapidly acquire superficial or shallow structural information of an inspected object through single-sided imaging. The application of this technology overcomes the limitations of traditional transmission X-ray detection, offering greater flexibility and portability, making it the preferred tool for the rapid and accurate identification of potential threats in scenarios such as borders, ports, and industrial nondestructive security inspections. However, the image quality is significantly compromised due to the limited number of Compton backscattered photons. The insufficient photon counts result primarily from photon absorption in materials, the pencil-beam scanning design, and short signal sampling times. It therefore yields severe image noise and an extremely low signal-to-noise ratio, greatly reducing the accuracy and reliability of PBI systems. To address these challenges, this paper introduces BSoNet, a novel deep learning-based approach specifically designed to optimize the image quality of PBI systems. The approach significantly enhances image clarity, recognition, and contrast while meeting practical application requirements. It transforms PBI systems into more effective and reliable inspection tools, contributing significantly to strengthening security protection.

Coupled resonators form band-like optical states that support rich nonlinearities beyond what is possible in single resonators. In these systems, four-wave mixing mediates interband coupling, displaying multimode dynamics that span both spatial and spectral degrees of freedom. In this study, we propose a framework describing the onset and control of hybridized optical parametric oscillation in three coupled silicon nitride microring resonators. In a symmetric configuration, we observe the emergence of diverse phase-matching pathways defined by the dispersive band structure. We develop an analytical model that captures the parametric gain of these interband processes and derive closed-form expressions for the dominant gain maxima; the analytical framework itself readily extends to more complex coupled networks. We further report an asymmetric design that co-engineers mode overlap and dispersion to operate on a compact 7-GHz spacing, free from mode competition. Our findings establish design principles for engineering nonlinear dynamics in coupled-resonator platforms, with implications for coherent photonic computing and quantum information processing.

Quantum coherence offers potential for energy conversion technologies. It influences light absorption and emission, affecting energy conversion limits and efficiency. As a result, quantum coherence is being harnessed to boost performance in quantum heat engines, photocells, and photosynthetic-inspired platforms. Of particular interest in this context is the generation of Fano coherences, i.e., the formation of quantum coherences due to the interaction with the continuum of modes characterizing an incoherent process. We aim to formalize mathematically the possibility of achieving steady-state Fano coherence in a V-type three-level quantum system using polarized incoherent radiation, without requiring the energy difference between the excited levels to tend to zero. We perform this analysis by deriving the Bloch-Redfield equation from first-principles by quantizing the incoherent radiation. The resulting reduced dynamics of the system are analysed, so as to determine the lifetime of Fano coherence and identify the conditions under which it becomes stationary. We characterise distinct dynamical regimes, ranging from weak to strong pumping, in which steady-state Fano coherence emerges, and we quantitatively determine its magnitude. For each regime, we analyse the generation of Fano coherence as a function of both the intensity of the incoherent pumping and the energy splitting between the excited levels. We also assess how obtaining Fano coherence is modified by symmetric or asymmetric decay rates. These findings indicate that a three-level quantum system driven by polarized incoherent light can act as a robust resource for coherence-assisted energy conversion and storage. Finally, we discuss the experimental challenges associated with the implementation of the proposed model using an ensemble of Rubidium atoms.

We investigate the use of tool-using coding agents to automate end-to-end workflows in the open-source CFD package OpenFOAM. Building on general-purpose coding agent interfaces, we introduce a lightweight configuration that guides an agent toward tutorial reuse and log-driven repair to improve case setup and execution. We evaluate this approach on the FoamBench-Advanced benchmark, covering both tutorial-derivative and planar 2D obstacle-flow tasks. For tutorial-derivative cases, prompt guidance dramatically increases execution completion rates and reduces unnecessary tool calls. For obstacle-flow cases, stronger language models such as GPT-5.2 markedly improve mesh generation and overall task completion compared to earlier models. Our findings show that coding agents can correctly execute a range of CFD simulations with minimal configuration and that model capability significantly influences performance on tasks requiring geometry and mesh creation. These results suggest that coding agents have practical utility for automating portions of CFD workflows while highlighting areas that require further investigation.

We demonstrate a synthetic gauge phase in Rydberg electromagnetically induced transparency (EIT) using room-temperature rubidium vapor. By exploiting polarization selection rules in a ladder-type system involving ground, intermediate, and Rydberg states, multiple Zeeman sublevels form closed-loop transitions that acquire a gauge phase. We show that the relative polarization angle between the linearly polarized probe and coupling lasers directly controls this gauge phase, which modulates the EIT transmission and Rydberg state population, consequently controlling the linewidth of EIT due to Rydberg dipole-dipole interactions between atoms. Our approach provides a simple polarization-based method for realizing synthetic gauge physics and manipulating many-body interactions in atomic ensembles without requiring laser cooling and dipole traps.

Supercontinuum generation (SCG) on integrated photonic platforms is a pivotal technology for developing next-generation chip-scale systems for precision spectroscopy and metrology. While significant progress has been made with silicon (Si) and silicon nitride ($Si_3N_4$) platforms, they are often constrained by two-photon absorption (TPA) or moderate nonlinear coefficients, necessitating a trade-off between energy efficiency and bandwidth. Tantalum pentoxide ($Ta_2O_5$), possessing both high nonlinearity and a wide bandgap, emerges as a promising candidate; however, current implementations remain challenged by high pump energy consumption. Here, we report a low-loss $Ta_2O_5$ integrated waveguide fabricated via the Damascene process. It enables the generation of a two-octave-spanning spectrum with a low pulse energy of only 92.9 pJ (60 fs, 1550 nm). Notably, the corresponding peak power is a mere 1.36 kW, which is nearly an order of magnitude lower than that of state-of-the-art comparable broadband sources. Furthermore, at the maximum pump energy, our spectrum exhibits an ultrabroad coverage from 450 nm to 3400 nm, spanning nearly three octaves. Supported by numerical simulations, we analyze the dynamics of soliton fission. Furthermore, a Michelson interferometry system developed using this source exhibits superior performance, achieving not only micrometer-scale axial resolution but also a 6 dB sensitivity roll-off length of 3.1 mm. This exceptional roll-off performance, combined with a displacement measurement sensitivity of 346 nm, underscores the immense potential of the $Ta_2O_5$ platform for applications in biomedical imaging and precision metrology.

Accurate and efficient wavefield modeling underpins seismic structure and source studies. Traditional methods comply with physical laws but are computationally intensive. Data-driven methods, while opening new avenues for advancement, have yet to incorporate strict physical consistency. The principle of reciprocity is one of the most fundamental physical laws in wave propagation. We introduce the Reciprocity-Enforced Neural Operator (RENO), a transformer-based architecture for modeling seismic wave propagation that hard-codes the reciprocity principle. The model leverages the cross-attention mechanism and commutative operations to guarantee invariance under swapping source and receiver positions. Beyond improved physical consistency, the proposed architecture supports simultaneous realizations for multiple sources. This yields an order-of-magnitude inference speedup at a similar memory footprint over a conventional neural operator on a realistic multi-source configuration. We demonstrate the functionality using the reciprocity relation for particle velocity fields under single forces. This architecture is also applicable to pressure fields under dilatational sources and travel-time fields governed by the eikonal equation, paving the way for encoding more complex reciprocity relations.

Learning solution operators on arbitrary geometries remains a central challenge in scientific machine learning, especially for many-query simulation, physics-informed learning, and evolving geometries requiring accurate, geometry-aware predictions at arbitrary spatial locations. Existing operator-learning methods often rely on structured discretizations, explicit geometry parameterizations, or point-cloud formulations that couple geometric representation with solution-query sampling, limiting flexibility on irregular and non-parameterized domains. We propose the Arbitrary Geometry-encoded Transformer (ArGEnT), a geometry-conditioned attention framework that decouples geometry encoding from query-point evaluation. We develop three variants: self-attention, cross-attention, and hybrid-attention. ArGEnT can be used independently or integrated with neural operators to incorporate non-geometric physical inputs. In the cross-attention variant, geometry is represented by an independently sampled point cloud used to construct keys and values, while arbitrary solution-query points construct queries. This design enables mesh-independent field prediction, reduces sensitivity to query-point distribution, and allows compact geometric representations to condition large-scale solution evaluations. Across benchmarks in fluid dynamics, solid mechanics, and electrochemical systems, ArGEnT consistently improves accuracy and generalization over standard DeepONet, point-cloud-based operator learning, and geometry-aware transformer baselines. In several cases, it reduces prediction errors by more than an order of magnitude while requiring substantially lower training cost than transformer-based baselines. These results demonstrate that decoupled geometry-query attention provides an accurate, scalable, and flexible framework for operator learning on arbitrary geometries.

This study introduces Differentiable Graph Neural Network Simulators (Diff-GNS) as a physics-informed and automated framework for estimating post-liquefaction residual strengths ($S_r$). Traditional approaches to estimate $S_r$ rely on simplified physics, manual iterations, and assumptions about runout development. Diff-GNS overcomes these limitations by integrating a Graph Neural Network Simulator (GNS) that simulates granular flows, with gradient-based optimization through automatic differentiation. GNS accelerates forward runout simulations that are otherwise computationally intensive with conventional numerical methods, while gradient-based optimization automates the inversion to back-calculate $S_r$. The GNS is trained on simulations with the material point method on geometries informed by case-history runout failures, enabling focused learning of realistic runout mechanisms and the ability to simulate slopes across small and large scales. The Diff-GNS framework is validated using two well-documented liquefaction-induced flow failure case histories: the Lower San Fernando dam and La Marquesa dam. In the two cases, the inferred $S_r$ agrees closely with published estimates and reproduces physically consistent runout behaviors. The framework also has the ability to jointly infer multiple interacting parameters, extending beyond single-parameter back-analyses. By embedding the physics of runout processes, minimizing manual intervention, and accelerating the inversion process to estimate $S_r$, Diff-GNS provides an efficient, reproducible, and physically grounded approach for geotechnical analysis of liquefaction-induced flow failures.

Dispersion-based photonics-assisted microwave measurement systems provide immense potential for real-time analysis of wideband and dynamic signals. However, they face two critical challenges: a difficulty in achieving high frequency resolution over a wideband analysis bandwidth, and a reliance on large-bandwidth-and-high-sampling-rate oscilloscopes to capture the resulting ultra-narrow pulses. We introduce a dynamic dispersion accumulation technique to overcome these limitations. By circulating the optical signal in fiber loops containing a dispersion-compensating fiber, we achieve a high accumulated dispersion of -215700 ps/nm. This high dispersion relaxes the required chirp rate of the chirped optical signal, enabling two distinct advantages: When the analysis bandwidth is fixed, a lower chirp rate enables a longer temporal period, yielding a record-high frequency resolution of 27.9 MHz; When the temporal period is fixed, a lower chirp rate enables a smaller bandwidth, generating a wider pulse and thus relaxing pulse sampling requirements at the expense of analysis bandwidth. This sacrifice in analysis bandwidth can be compensated by a duty-cycle-enabling technique, which holds the potential to extend the analysis bandwidth beyond 100 GHz. This work breaks the performance and hardware limitations in dispersion-based systems, paving the way for high frequency resolution, wideband microwave measurement systems that are both real-time and cost-effective.

Simulating chemical reactions exhibits a pronounced unevenness in computational difficulty: while equilibrium reactant and product geometries are often tractable, transition-state (TS) geometries frequently display strong multi-reference character that challenges both classical solvers and coherent quantum state-preparation methods. We introduce a dissipative continuation protocol for preparing electronic ground states near TS geometries within a hybrid classical–quantum workflow. In the intended setting, classical electronic-structure methods supply an approximate TS geometry, a computationally motivated continuation path, and a locally compatible active-space representation along that path. Starting from a warm start at a tractable geometry on the same aligned path, the quantum routine transports the state toward the TS using orbital-gauge-aligned Hamiltonians and engineered dissipative cooling primitives that repeatedly contract population into the instantaneous low-energy sector. We prove that, for continuation paths satisfying a Lipschitz smoothness condition and a localized Eigenstate Thermalization Hypothesis (ETH)-motivated downward-drift condition within the relevant energy window, the ground state at the target geometry can be prepared to total energy error $\epsilon_E$ with total ideal cooling-step complexity $\widetilde{O}(C_{\mathrm{DK}}^2 N_o^2 / \epsilon_E)$. Here $C_{\mathrm{DK}}$ quantifies ground-state rotation along the aligned path. The corresponding logical gate count is obtained by multiplying this primitive count by the cost of implementing one dissipative step, which is polynomial in the block-encoding size of the Lindbladian under standard Lindblad-simulation algorithms. This identifies a structured regime in which dissipative continuation provides a conditional route to ground-state preparation at strongly correlated TS geometries.

We present a collaboration ring model – a network of players playing the prisoner's dilemma game and collaborating among the nearest neighbours by forming coalitions. The microscopic stochastic updating of the players' strategies are driven by their innate nature of seeking selfish gains and shared intentionality. Cooperation emerges in such a structured population through non-equilibrium phase transitions driven by propensity of the players to collaborate and by the benefit that a cooperator generates. The robust results are qualitatively independent of number of neighbours and collaborators.

We present a detailed characterization of the thermal neutron sensitive transparent glass scintillator SG101, benchmarked against the conventional LiF ZnS(Ag)based scintillator EJ426. The detection efficiency, energy resolution, and pulse shape discrimination (PSD) performance ofSG101 were evaluated under AmBe neutron irradiation. When coupled with organic scintillators(EJ200 or EJ276),the SG101 EJ200 system achieves a figure of merit (FOM) of 3.81 for thermal neutron/gamma separation, while the SG101 EJ276 configuration resolves three distinct particle populations gamma rays, fast neutrons, and thermal neutrons with FOM values of 3.46 and2.21, respectively. Correlation analysis reveals that the number of fast thermal neutron coincidence events significantly exceeds the accidental background, and the count of gamma fast thermal neutron triple-coincidence events is also far higher than the expected accidental rate, confirming significant physical correlations for both event types within a 100 us time window. These results demonstrate that SG101 is a promising candidate for applications requiring high-efficiency thermal neutron detection and precise event tagging coupling with a scintillator with PSD approach

Quantum spin rings represent fundamental model systems that exhibit distinctive quantum phenomena-such as quantum critical behavior and quasiparticle excitations-arising from their periodic boundary conditions and enhanced quantum fluctuations. Here, we report the on-surface synthesis and atomic-scale characterization of antiferromagnetic S=1/2 quantum spin rings composed of pristine and unmodified [2]triangulene units on a Au(111) surface. Using stepwise on-surface synthesis followed by STM tip-induced dehydrogenation, we precisely constructed cyclic five- and six-membered spin rings and investigated their spin states via scanning probe microscopy and multireference calculations. Nc-AFM imaging reveals that the six-membered ring retains a planar geometry, whereas the five-membered ring exhibits pronounced structural distortion. The six-membered ring hosts a uniform excitation gap that can be accurately described by a Heisenberg spin model and multireference CASCI calculations. In contrast, the distorted five-membered ring displays spin ground states with asymmetric spatial distributions due to degeneracy lifting induced by structural distortion. Our findings establish a versatile molecular platform for exploring correlated magnetism and quantum spin phenomena in cyclic organic magnetic architectures with disorder.

While Rydberg-Rydberg interactions are essential for quantum nonlinear optics and quantum information processing, their role in microwave and radio-frequency sensing remains poorly understood. Here we experimentally investigate Rydberg interaction-induced nonlinearity in cold-atom Rydberg electromagnetically induced transparency (EIT). In a three-level EIT system, increasing photon-photon interactions produces nonlinear spectral broadening accompanied by resonance shifts, while a microwave-dressed four-level system exhibits pronounced nonlinear broadening without detectable spectral shifts. Our three-level data can be explained by a conditional superatom model, whereas our four-level observations are surprisingly captured by a simple dephasing model. Comparisons with three representative models provide key insights to the role of many-body interactions in Rydberg EIT spectroscopy. Furthermore, our results clarify the conditions under which microwave field characterization can be performed in the nonlinear regime without introducing systematic bias. Our study advances both fundamental understanding of many-body physics and practical development of atomic sensors.

Reaction-diffusion equations describe various spatially extended processes that unfold as traveling fronts moving at constant velocity. We introduce and solve analytically a model that, besides such fronts, supports solutions advancing as the square root of time. These sublinear fronts preserve an invariant shape, with an effective diffusion constant that diverges at the transition to linear spreading. The model applies to dense cellular aggregates of nonmotile cells consuming a diffusible nutrient. The sublinear spread results from biomass redistribution slowing due to nutrient depletion, a phenomenon supported experimentally but often neglected. Our results provide a potential explanation for the linear rather than quadratic increase of colony area with time, which has been observed for many microbes.

In this paper, we propose a switchable high-Q light absorber based on a reconfigurable metasurface enabled by a lowloss phase-change material (PCM). By leveraging the coupling between guided-mode resonance and Fabry-Perot modes, mediated by the phase-transition dynamics of the embedded PCM, the resonance Q factor can be actively tuned. This allows the system to switch from a perfect dark state, governed by the physics of bound states in the continuum, to a critically coupled resonance with a finite Q factor. Consequently, the metasurface exhibits perfect absorption in the amorphous state and a reflection-dominated response in the crystalline state. The proposed metasurface holds significant potential for diverse nanophotonic applications, including photodetection and thermal emission control.

Amontons' law postulates a monotonic relationship between frictional force and the normal load applied to a sliding contact. This empirical rule, however, fails in systems where internal degrees of freedom - such as structural or electronic order - play a central role. Here, we demonstrate that friction can emerge entirely from magnetically driven configurational dynamics in the absence of physical contact. Using a two-dimensional array of rotatable magnetic dipoles sliding over a commensurate magnetic substrate, we observe a pronounced non-monotonic dependence of friction on the interlayer separation, and thus on the effective load. The friction peaks at an intermediate distance where competing ferromagnetic and antiferromagnetic interactions induce dynamical frustration and hysteretic torque cycles during sliding. Molecular dynamics simulations and a simplified two-sublattice model confirm that energy dissipation is governed by collective magnetic reorientations and their hysteresis. Our results establish the occurrence of sliding-induced changes in collective magnetic order, which has a strong impact on friction, and thus open new possibilities for contactless friction control, magnetic sensing, and the design of reconfigurable, wear-free frictional interfaces and metamaterials.

Volumetric lithography offers a path to scalable fabrication of complex three-dimensional (3D) micro- and nanoscale architectures, yet existing approaches are limited by quasi-two-dimensional exposure physics or slow serial writing. We present a single-exposure volumetric fabrication strategy that enables creation of ultrahigh-aspect-ratio 3D structures with 6 um minimum features. An inverse-designed volumetric (holographic) phase mask generates an extended-depth-of-field intensity distribution inside a photoresist volume while preserving high transverse resolution, enabling uniform polymerization of the full volume in a single exposure. With exposure times of approximately 20 s, we fabricate lattices, Penrose tilings, and micromechanical elements with feature sizes down to 6 um over volumes up to 800 x 800 x 720 um^3, achieving aspect ratios exceeding 120:1. Quantitative analysis of capillary flow in hollow lattices demonstrates controlled fluid transport with an effective capillary transport coefficient of 176.3 um/(ms)^(1/2). In situ nanoindentation-based micro-compression reveals that the printed 3D hexagonal close-packed lattices exhibit a well-defined linear elastic regime with an effective Young's modulus of 5.7 GPa, followed by progressive buckling and densification characteristic of mechanically robust cellular architectures. Overlapping, tilted and multi-mask exposures further enable quasi-3D complex geometries with potential for reconfigurability. This approach establishes a new regime of high-throughput volumetric fabrication.

Quantitative imaging is an important feature of spectral X-ray and CT systems, especially photon-counting CT (PCCT) imaging systems, which is achieved through material decomposition (MD) using spectral measurements. In this work, we present a novel framework that makes the PCCT imaging chain end-to-end differentiable (differentiable PCCT), with which we can leverage quantitative information in the image domain to enable cross-domain learning and optimization for upstream models. Specifically, the material decomposition from maximum-likelihood estimation (MLE) was made differentiable based on the Implicit Function Theorem and inserted as a layer into the imaging chain for end-to-end optimization. This framework allows for an automatic and adaptive solution of a wide range of imaging tasks, ultimately achieving quantitative imaging through computation rather than manual intervention. The end-to-end training mechanism effectively avoids the need for direct-domain training or supervision from intermediate references as models are trained using quantitative images. We demonstrate its applicability in two representative tasks: correcting detector energy bin drift and training an object scatter correction network using cross-domain reference from quantitative material images.

The Zeroth Law of Thermodynamics states that if two systems are in thermal equilibrium with a third one, then they are also in equilibrium with each other. This study explores not only the final state of thermal equilibrium between ideal gases separated by heat-conducting walls, but also the intermediate stages leading up to equilibrium, using classical molecular dynamics simulations. Two- and three-region models with argon atoms are analyzed. Fluctuations, correlations, and temperature distributions are observed, highlighting how heat conduction between regions influences the time to reach equilibrium. This work is distinguished by its detailed analysis of the intermediate stages that occur until the system reaches thermal equilibrium, in accordance with the Zeroth Law of Thermodynamics.

Amino acids (AAs) are a key target in the search for life beyond Earth due to their extensive role in the machinery of all known life, persistence over geologic timescales, and analytical detectability. However, AAs can also arise from abiotic processes on planets and in space. For example, material from asteroid Bennu contained 33 AAs, including 15 of the 20 proteinogenic AAs that are fundamental to life's functions. Distinguishing life from non-life based on AAs in a sample remains an unsolved problem, particularly when their isotopic and structural signatures (e.g., chirality) could be altered via physicochemical processes. Here we introduce LUMOS (Life Unveiled via Molecular Orbital Signatures), a statistical framework that distinguishes life from non-life by analyzing the distribution of abundance-weighted HOMO-LUMO gap (HLG) values of AAs within a sample. Compilation of AAs datasets from diverse environments and provenances revealed that abiotic samples display highly uniform distributions of AAs HLGs. In contrast, biotic samples show greater variance and preference towards AAs with lower HLG, likely reflecting the need for life to control when, where, and how chemical reactions occur. LUMOS achieves >95% accuracy in distinguishing biotic versus abiotic provenance across diverse environmental and extraterrestrial conditions. These results suggest that varied molecular reactivity within biochemical systems may be a universal feature of life, representing an agnostic biosignature unlinked to the specific set of AAs used by life as we know it. LUMOS is compatible with existing analytical instrumentation, applicable to returned samples or in situ analyses. Broader characterization of abiotic and biotic environments will further refine the chemical boundaries separating biotic from abiotic chemical systems.

We investigate the thermodynamic behavior of a spherically symmetric Anti-de Sitter black hole in Mod(A)Max electrodynamics surrounded by a cloud of strings. Within the extended phase-space framework, we treat the cosmological constant as a pressure and interpret the black-hole mass as enthalpy, which enables a unified discussion of local stability, global phase structure, and Joule–Thomson expansion. We analyze the Hawking temperature, Gibbs free energy, and heat capacity, and show how the string-cloud parameter, the Mod(A)Max deformation, and the electric charge reshape the physical domain, the stability windows, and the small/large black-hole transition pattern. We further characterize the critical behavior and demonstrate that a van der Waals–like phase structure arises only in the physical sector, while the alternate branch does not admit a genuine critical point. For the Joule-Thomson process, we determine the inversion curve and the corresponding isenthalpic trajectories, highlighting how the model parameters control the cooling/heating regimes and can generate terminating isenthalpic behavior at sufficiently large charge. Finally, we examine the sparsity of Hawking radiation and discuss how the underlying parameters influence the temporal discreteness of the emitted flux, particularly near extremality and in the large-radius AdS regime.

Designing a city-wide public transport network poses a dual challenge: achieving computational efficiency while ensuring spatial equity for different population groups. We investigate whether AI-based optimization hybrid neuroevolutionary methods combining graph neural networks with evolutionary algorithms - can scale Transit Network Design Problem (TNDP) solutions from synthetic tests to real urban networks while preserving social fairness. Our contribution is to introduce a transport connectivity-aware accessibility metric that bases optimization on principles of equitable accessibility rather than traditional trade-offs between passenger and operator costs. The results show a noticeable improvement in network resilience by improving algebraic connectivity on synthetic datasets, and highlight the ambiguity of applying network generation to real data.

Black carbon and mineral dust are key absorbing aerosols that influence atmospheric radiation and increasingly threaten global cryospheric stability. This study examines the long-range transport and seasonal variability of these aerosols over Pakistan and their movement toward the western Himalayas. Using satellite-derived Absorption Aerosol Optical Depth (AAOD) data from 2019 to mid-2025, we analyse their spatiotemporal behaviour across Pakistan's urban lowlands and high-altitude regions. Fifteen-day aggregated AAOD fields are used to track seasonal transport into glaciated terrain, where deposited aerosols can darken snow and ice and accelerate melt. For high-AAOD events, a probabilistic forecasting approach based on machine learning (ML) was developed. Using geographical, seasonal, and lagged indicators, a histogram-based gradient boosting classifier was trained to predict AAOD exceedance one step in advance. ROC-AUC, PR-AUC, and the Brier score were used to assess the model's performance. The results show high predictive capacity and good probability calibration, with values of 0.791, 0.269, and 0.028, respectively. Forecasts indicate that areas adjacent to Himalayan glaciers consistently exhibit the highest probability of increasing AAOD, signalling an elevated risk of aerosol-induced snowmelt.

Our world of power and national security is increasingly probabilistic: like a quantum wavefunction, it encodes multiple plausible futures until policy choices and shocks collapse them into observable outcomes. Quantum technologies have moved from laboratory curiosities to strategic infrastructure, with an approaching 'event horizon' reflected in recent United States strategic assessments – incl. the U.S. –China Economic and Security Review Commission's (USSC) call for a Quantum First posture by 2030- and in parallel White House initiatives aimed at securing critical inputs and accelerating trusted innovation. Government research further documents that China's quantum program is centrally mobilized under military-civil fusion, and that its consequential advantages may arise not only from computing milestones but also from sensing and cryptanalytic applications, thereby sharpening the need for a values based deterrence by denial governance posture. The Article's central claim is that the U.S. and its allies should pursue security-sufficient openness, operationalized through a least-trade-restrictive, security-sufficient, innovation-preserving (LSI) test that disciplines both state and private action. The LSI test integrates emerging instruments of economic statecraft to create secure closed loop enclaves for high-sensitivity collaborative R&D. The Article's contribution is an implementable coalition playbook, offering empirically anchored criteria, templates, and differentiated guardrails – incl. red zone domains where denial is the default – to avoid both over-securitization and under-securitization. Properly applied, LSI reduces the risk of a self-defeating Silicon Curtain while establishing standards-first interoperability as a stabilizing eigenstate of the international order and enabling RQT by design to shape trusted adoption pathways beyond the coalition, incl. in the majority world.

The morphologies of phase separating systems have formal distinctions such as symmetry groups, but the analysis protocol for labeling a particular phase field with a morphology requires manual expertise, arbitrary thresholds, or established signatures. In this work, it is investigated if the angular power spectrum of the 3D structure factor can discriminate between morphologies. The 3D structure factor is computed on configurations of phase separating block copolymers generated by coarse-grained molecular dynamics simulations. The shell of structure factor values containing the primary peaks is isolated. This 2D field on a sphere is decomposed into spherical harmonic modes of even polynomial degree $\ell\le 12$, then further reduced to the rotationally invariant angular power spectrum. It is found that these few coefficients for low $\ell$ discriminate robustly between different morphologies. This analysis serves as an automatic tool for flagging novel structures, without a need to enumerate the plausible morphologies in advance.