Papers

Updated reference data and an analytic parameterisation of elastic electron and X-ray scattering are presented for all 118 neutral atoms. The reference electron densities for the multi-electron elements $Z=2$–$118$ are computed with the relativistic B-spline Dirac–Fock code atomx, while hydrogen is constructed from the exact relativistic one-electron Dirac $1s$ solution; the electron scattering factor $f_e(g)$, X-ray scattering factor $f_x(g)$ and radial moments are derived from these densities. The reported parameterisation extends the fixed-size Lobato–Van Dyck hydrogenic expansion while retaining closed-form expressions for $f_x(g)$, $\rho(r)$, the electrostatic potential $V(r)$ and the projected potential $V(R)$. These extensions are an element-adaptive basis size $n_t(Z)$, a simultaneous real- and reciprocal-space fit, an exact $\langle r^4\rangle$ constraint in place of the non-relativistic Kato cusp, and a charge-carrying Dirac–Padé basis term that adds a polynomial-times-exponential shape channel without replacing the hydrogenic basis by a tabulated Dirac radial function or assigning the term to a physical shell. For the same total parameter count, the Dirac–Padé-enriched basis improves on the parameter-matched non-relativistic basis for 111 of the 118 elements, lowering the mean total cost by 39%. Relative to a controlled fixed five-term refit on the same reference grid and objective, the element-adaptive bases improve the median reciprocal-space deviations by about three to four orders of magnitude and resolve shell structure in $4\pi r^2\rho(r)$ that the fixed five-term basis cannot. The largest changes occur near the nucleus and in the reciprocal-space tail beyond the legacy 12 inverse angstroms range, which is directly relevant to quantitative high-angle scattering and electron-diffraction measurements.

The Bode-Fano bound sets a fundamental trade-off between bandwidth and reflection in passive, linear, time-invariant matching networks. We show that an aperiodically time-modulated reactive element can emulate the non-Foster response required to match a prescribed pulse, achieving reflectionless, nearly distortionless energy transfer beyond the Bode-Fano limit. The approach introduces a new constraint: a minimum dc bias that scales with pulse bandwidth, derived from the requirement that the modulated capacitance remain positive at all times. This modulation-bias bound replaces the classical bandwidth-reflection trade-off with a bandwidth-energy trade-off. A realistic circuit simulation confirms broadband matching with preserved waveform fidelity, demonstrating that the scheme is physically realizable and not merely a mathematical circumvention.

Particulate matter with aerodynamic diameter less than 2.5 {\mu}m (PM2.5) is of great concern for human health. Here, for the first time, we examine the impact of sulfate aerosol geoengineering on PM2.5, using the output from the Geoengineering Large Ensemble (GLENS) project. GLENS is an ensemble of climate model simulations injecting SO2 into the stratosphere to balance RCP8.5 forcing using the Community Earth System Model, version 1. GLENS geoengineering reduces global averaged surface PM2.5 mass concentrations compared with RCP8.5 and also changes PM2.5 composition with more percentages of organic carbon and sulfate. The total reduction of PM2.5 is a result of less dust and sea salt concentrations. Dust emission is declined under GLENS geoengineering because of increased soil moisture and leaf area index over desert regions, and less emission of sea salt is due to slower wind speeds when geoengineering is applied. Excluding dust and sea salt, there is more global averaged PM2.5 under GLENS geoengineering relative to RCP8.5, predominantly due to more aerosol phase secondary organic aerosol (SOA). Since gas precursors of SOA are prescribed in the simulations, a cooler environment with geoengineering tends to transfer more gas phase SOA to aerosol phase. Changes in PM2.5 concentration and composition with applied geoengineering may have potential human health impact. Another new finding of this study is that the large amount of injected SO2 does not increase surface sulfate aerosol as a component of PM2.5, as the majority of sulfate aerosol reaching the boundary layer is in the coarse mode, which represents a very small fraction of the PM2.5. But the difference of deposition spatial distribution between geoengineering and RCP8.5 may have potential impacts on ecosystem.

Partial coherence is commonly viewed as a mechanism that reduces contrast or smooths intensity structure. Here we show that it can also encode hidden transverse-flow topology. Starting from the cross-spectral density, we formulate a generalized transverse flux, an effective velocity field, and the associated flux trajectories for quasi-monochromatic partially coherent paraxial beams. This construction converts two-point correlation phases into local transport information and reduces to the usual coherent energy-flow picture in the single-mode limit. Because the generalized flux is obtained through a local differential operation on the cross-spectral density, the proposed trajectories can, in principle, be reconstructed from measurements of the complex second-order coherence function, without requiring direct measurement of individual optical paths. Two analytical beam families expose this hidden topology. In twisted Gaussian Schell-model beams, a Gaussian intensity hides a distributed rotational flow with nonzero vorticity. In Laguerre-Christoffel-Darboux beams, sources with identical intensity profiles can have different flux topology, producing either spiral or purely radial trajectories. Thus, intensity and coherence magnitude do not exhaust the physical information contained in the cross-spectral density: partial coherence can reorganize the hidden topology of transverse optical transport.

ESPResSo++ is an open-source software package for molecular dynamics (MD) simulations with a particular emphasis on coarse-grained (CG) models of soft matter systems. Written in C++ with a flexible Python interface, it is designed for high-performance computing (HPC) environments and supports massively parallel simulations through MPI. The package enables simulations of polymers, membranes, colloids and complex fluids with a wide range of interaction models and advanced algorithms.

In thermodynamics, reversible adiabatic expansion can be understood as a limit of stepwise irreversible processes. We make this idea concrete by studying an ideal gas in an insulating cylinder with a frictionless piston supporting a load divided into $N$ blocks. If blocks are removed one at a time, the resulting expansion is a stepwise, irreversible process, but we prove that for a fixed total load, the work done by the gas approaches the reversible limit as the largest block mass tends to zero. On the way to this limit, an interesting work optimization question arises at finite $N$: how does the work done by the gas depend on the order and sizes of the removed blocks? Guided by numerical experiments accessible to advanced undergraduates, we motivate and then prove general answers to these questions. Our main finite-$N$ result proves that for fixed $N$, the optimal stepwise expansion corresponds to a geometric progression of equilibrium pressures, settling a conjecture previously made by Andresen, Berry, Nitzan, and Salamon.

Pretrained machine-learning interatomic potentials, so-called universal or foundation models offer an appealing starting point for atomistic simulations, but their accuracy for material-specific observables often remains limited without additional reference data (fine-tuning). Here, we systematically quantify how much first-principles data are required to convert universal models into ab initio-accurate material-specific potentials, and ask whether fine-tuning is necessarily preferable to training from scratch. We compare five universal MLIP frameworks, MACE-MP-0, SevenNet-0, GRACE-1L-OAM, MatterSim-v1-5M and ORB-v2, across seven chemically diverse systems incorporating rare and reactive events. Fine-tuning on only 10 AIMD-derived configurations is insufficient for the investigated systems; 200 configurations succeed in favorable cases, but the outcome remains strongly system-dependent. By contrast, 2000 AIMD configurations constitute a robust default, yielding low force and energy errors and reproducing the target material-specific observables. Moderately dense sub-sampling of the AIMD trajectory reduces the required trajectory length tenfold with little loss in model quality. Training from scratch on the same datasets is competitive with, and often slightly more accurate than, naive fine-tuning for MACE and SevenNet, whereas GRACE requires more data. The energy profile for a sulfur-vacancy jump in MoS$_2$ reveals that low trajectory-level errors do not guarantee a correct reaction profile, highlighting the need for observable-level validation. Finally, we show that averaging independently trained models improves predictions in scarce-data regimes at no additional first-principles cost. Together, these results provide practical guidelines for converting limited AIMD reference data into reliable material-specific MLIPs for nanosecond-timescale simulations at near-DFT accuracy.

Large language models enable the creation of autonomous agents that interact in social environments, raising the question of whether agent-based platforms reproduce the organizational properties of human social networks. We compare Moltbook, a social network populated by AI agents, with early Reddit, focusing on how communities organize and differentiate semantic content, using network analysis and NLP methods to characterize semantic coherence and diversity within and between communities, and their relationship to user activity. We find a systematic difference between the two platforms. Reddit communities show stronger semantic coherence, closer alignment with community names, and greater semantic diversity, with individual communities spanning broader content and communities more differentiated from one another. This combination distinguishes Reddit from Moltbook, whose communities are more homogeneous, less differentiated, and increasingly misaligned with their names over time. Users on Reddit also participate across communities that are more semantically related than those connected by activity in Moltbook. At the interaction level, comment-network motif analysis shows Moltbook dominated by non-reciprocal, broadcast-like exchanges, whereas Reddit shows more reciprocal, chained interaction patterns. These results indicate that Reddit combines semantic coherence with diversity across organizational levels, a pattern not reproduced by the AI-agent network.

This study presents a hybrid computational framework for simulating non-reacting inviscid high-speed flows of nitrogen gas (N$_2$) around a circular cylinder. Owing to the strongly convection-dominated nature of the compressible Euler equations, the compressible-flow streamline-upwind/Petrov–Galerkin (SUPG) formulation is combined with the YZ$\beta$ shock-capturing technique to stabilize the finite element discretization in the presence of strong discontinuities. Building upon the stabilized solution, a physics-informed neural network (PINN) is employed as a post-processing correction stage (\underline{P}INN-\underline{A}ugmented \underline{S}UPG with \underline{S}hock-\underline{C}apturing—PASSC). The network is anchored to the finite element solution through a shock-weighted data-consistency loss, while the governing equations are enforced in a conservative space–time control-volume form supplemented by macroscopic conservation windows, an entropy-admissibility penalty, and the boundary conditions of the underlying problem. Two-dimensional simulations are performed for free-stream Mach numbers ranging from $2.0$ to $12.0$, and the results are assessed against analytical normal-shock and stagnation relations, the semi-empirical Billig correlation, and reference solutions from the literature. The correction is designed to improve the numerical representation of shocks by reducing localized discretization-induced oscillations and mesh-scale serrations while preserving the agreement of the stabilized solution with the analytical and semi-empirical reference quantities.

Machine-learned exchange-correlation functionals correct band gaps at near-semilocal cost, while density-functional tight binding reaches the $10^3$-$10^6$-atom regime; combining them assumes that a better parent yields a better parameterization, but we show it does not. Current-generation functionals are orbital-dependent generalized Kohn-Sham operators, whereas the parameterization channel is built on a multiplicative potential, preventing exact representation. Using the transfer ratio, the surviving fraction of a parent-level change, we find anti-transfer: coherently negative ratios across four covalent semiconductors move the gap in the wrong direction, consistent with a molecular proxy and an r$^2$SCAN control. The minimal-basis overgap is dominated by the on-site convention rather than basis incompleteness; correcting the on-site block removes most of it, while one $d$-polarization shell closes a further $16$-$40%$, depending on the placement of the empty $d$ level, which no free-atom eigenvalue uniquely fixes. Occupied-manifold enhancements, ionic and closed-shell repulsive potentials, and rocksalt-oxide gaps inherit, whereas elemental and III-V covalent networks inherit neither gaps nor repulsive potentials and oxide networks inherit only the latter. We screen 23 elements and release the parameter sets, showing that the transfer ratio provides a cheap pre-test before any parameterization campaign.

Mechanical separation of enantiomers is an attractive alternative to synthetic methods for producing enantiopure samples. Shear flow that produces solution vorticity has been shown to be a viable means for separating chiral objects on the micro- to nano-scale due to the tensorial nature of the interactions between chiral objects and the surrounding fluid. A recently-developed theory of molecular pitch characterizes these interactions using the resistance tensor and predicts the shear-induced separation of drug-like molecules from their optimized molecular geometries. We present a molecular dynamics study on the effects of incorporating molecular flexibility into the molecular pitch framework. We also evaluate the potential for enantiomeric separation of two drug molecules: bicalutamide (Casodex) and montelukast sodium (Singulair). Simulations reveal the emergence of flexibility-induced pitch distributions that result from conformational changes occurring in a realistic solvent environment. However, these distributions are weakly influenced by the solvent identity and the shearing process, producing mean scalar pitch values that are close to those from optimized gas phase structures. Despite the opposing effect of translational diffusion at the molecular scale, racemic mixtures of flexible enantiomers show linear rates of separation at the 10 ns timescale, and we predict that cm-scale separation can be achieved within hours. Additionally, we provide estimates for parameters of a Taylor-Couette device for generating laminar shear flow, as well as considerations for future experiments.

Recent experimental work carried out by Yettapu, Manning, and Cyran on ice / vapor interfaces revealed changes in sum frequency generation (SFG) signals at ~ 3170 cm$^{-1}$ depending on whether acetone or methanol was adsorbed onto the ice at 223 K. They linked the transformation of that signal to the induced melting of the ice crystal. We present molecular dynamics modeling of their experiments using methanol and acetone layers adsorbed on the basal facet of a proton-ordered Ice-Ih crystal at temperatures of 223 K and 265 K. The width of the quasi-liquid layer (QLL), O-H bond frequencies, calculated SFG signals, and hydrogen bonding statistics all indicate changes due to the presence of the adsorbates. While we see no significant adsorbate-based differences in QLL formation, we find at 223 K that acetone preserves under-coordinated waters that are associated with a secondary water with a low frequency O-H bond, while methanol integrates more fully into the hydrogen bonding network, resulting in a narrower distribution of O-H bond frequencies. This change in hydrogen bonding produces a local modification of the electric field, which may contribute to the difference in the experimental SFG signals at low frequencies.

Nonthermal radio filaments (NTFs), first discovered at 20-centimeter wavelength more than four decades ago, are among the most enigmatic structures at the Galactic Center. They still defy a clear explanation. These striking narrow features trace intense magnetic fields and often stand in bold contrast to the Galactic plane. Recent discoveries have revealed surprising associations: some NTFs align well with X-ray threads that seem to exhibit Fe He-$\alpha$ emission. Here, I present preliminary results from an ongoing, collaborative, multi-wavelength study aimed at understanding the origins of these filaments, focusing on testing the magnetic reconnection scenario of these associations and shedding new light on the high-energy processes and magnetic phenomena operating under extreme conditions at the heart of our Galaxy.

Localized detector inefficiencies produce incomplete calorimeter data that limit the ability to perform precision measurements. We address this problem in relativistic heavy-ion collisions from a Bayesian perspective using pretrained calorimeter diffusion models as priors to reconstruct the missing signal conditioned on surrounding measurements. In this work, we conduct a systematic comparison of several diffusion-based inpainting algorithms, whose performance is evaluated using Bayesian posterior diagnostics of energy response, spatial bias, and uncertainty calibration. The reconstruction fidelity is also analyzed across collision centralities and masked region sizes. This study establishes a general validation strategy for probabilistic reconstruction of missing detector information.

We implemented on GPU a mixed quantum-classical Liouville molecular dynamics simulation based on a momentum-jump-free theory. The trajectory spawning that was previously implemented on CPU for sampling enhancement was eliminated to avoid the overhead of thread divergence and dynamic memory allocation on the GPU. This achieved a speedup of an order of magnitude compared to the CPU computation with spawning, as well as a linear scaling with respect to the number of sampling trajectories.

Laser-matter interaction at relativistic intensities is central to almost every scientific case for multi-PW laser facilities. In particular, the progress in laser-driven ion acceleration brings this interaction closer to multiple applications, ranging from material science to biomedical research. One of the most important questions regarding ion acceleration is how to obtain high charge ion beams with relativistic energies. Increasing laser energy and intensity usually leads to a number of limitations on maximum achievable ion energies rooted in the fundamental properties of charged particle interaction with strong electromagnetic fields. Following advances in staged laser-plasma electron acceleration, staged ion acceleration could offer a path to relativistic energies. Here, such scheme is explored and the possibility of scaling the acceleration of ion beams to relativistic energies is discussed.

Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free energy calculations require ensemble averages. We introduce the thermodynamic interatomic potential (TIP), which extends an interatomic potential from its static energy to a thermodynamically consistent Gibbs free energy model, with thermodynamic responses following from temperature and pressure by automatic differentiation. We implement TIP[UMA] using the universal potential UMA, train it on free energies from quasi-harmonic to molecular dynamics fidelity, and calibrate it to higher-resolution calculations or experiment. From a single evaluation, it returns the equation of state of a crystal and locates phase transitions among competing branches, including dynamically stabilized phases. Fine-tuning extends the model to alloy solubility limits and miscibility gaps. TIP makes the free energy as accessible as the potential energy, opening finite-temperature phase stability to high-throughput discovery.

Theoretical arguments have for decades anticipated substantial weakening of tropical convection as the surface warms, yet convection-resolving models show no robust change in column-integrated convective mass transport and an increase in transported volume. Here we reconcile this apparent discrepancy by demonstrating that the two main theoretical arguments identify a redistribution, rather than a straightforward reduction, of convective motions. A commonly invoked hydrological argument accordingly captures a weakening at a single altitude that is offset by strengthening aloft as the troposphere expands upward. Further, reduced convective mass flux following atmospheric structures that rise with warming simply reflects decreasing air density aloft. Convective motions and transported water are shown to be highly invariant to surface warming along isotherms, and the ability to predict convective responses at individual altitudes is a manifestation of this invariance. Together, these findings recast and simplify long-standing expectations of how the tropical atmosphere will respond to warming.

Effective Hamiltonians connect microscopic interactions to measurable collective behavior in quantum materials, but determining their parameters directly from experiment remains a challenging inverse problem. We introduce a supervised machine-learning framework that infers Hamiltonian parameters from nonlinear terahertz two-dimensional coherent spectra. A calibrated forward model generates spectra from candidate Hamiltonians, a common preprocessing pipeline maps simulated and experimental spectra into the same representation, and a neural network learns the inverse map from spectral fingerprints to microscopic parameters. We demonstrate the approach for rare-earth orthoferrites using a two-sublattice Landau–Lifshitz–Gilbert spin model with exchange, Dzyaloshinskii–Moriya interaction, anisotropies, and damping. Synthetic benchmarks show that nonlinear spectra encode parameters beyond those fixed by the linear response, with inference accuracy tracking the physical spectral sensitivity and robustness against noise improved by using multiple inter-pulse delays. Applied to experimental THz-2DCS data from Sm$_{0.4}$Er$_{0.6}$FeO$_3$, the inferred parameters yield physically reasonable forward simulations, while remaining discrepancies identify limitations of the reduced model. These results establish THz-2DCS as a data-rich platform for effective-Hamiltonian inference and model refinement, enabling experimentally driven identification of microscopic interactions while providing a foundation for understanding, predicting, and ultimately controlling the emergent properties of quantum materials.

Research-based assessments (RBAs) have proven to be valuable tools in PER, supporting both instructional reform and foundational research. In the rapidly-growing field of Quantum Information Science (QIS), the lack of suitable RBAs limits the field's ability to make evidence-based decisions about curricula and program development. In this paper, we introduce the Quantum Computing Conceptual Survey (QCCS), an instrument developed to help address this gap by measuring student conceptual understanding in the foundations of quantum computing. Using pilot data from over 50 courses and 700 students, we present evidence supporting the validity of the QCCS for use in introductory QIS courses, drawing on analyses from both classical test theory and the Rasch model. We detail the potential uses for the QCCS, intending for it to serve as both a practical tool for instructors and a means of facilitating measurement-driven QIS education research.

Cellular activity drives epithelial fluidization — a widespread phenomenon observed during tissue development, remodeling, and repair both in vivo and in vitro. Yet the physical origins and spatial organization of active forces vary widely across biological systems and are often represented by a single generic mechanism in theoretical models. Here, using an active vertex model, we systematically compare four modes of epithelial activity spanning subcellular to tissue scales: apolar motility, polar motility, fluctuating contractility, and mechanochemical regulation. Although all four mechanisms drive the same global transition from a solid-like rectangular tissue to a fluid-like circular morphology, they reach this state through distinct pathways — differing in the rates and topology of junctional rearrangements, cell elimination, and collective motion and leave distinguishable signatures in tissue architecture, cell dynamics, and mechanical relaxation. Among these observables, spatial velocity correlations directly capture the spatial organization of activity: their correlation length and functional form together resolve all four mechanisms. The robustness of these signatures across activity strengths suggests that spatial velocity correlations offer an experimentally accessible means of identifying the physical origin of epithelial activity from live-cell imaging alone.

Quantifying forced and internal variability in climate data is fundamental to detecting the climate change signal and assessing uncertainty in climate projections. We propose a metric that quantifies the relative magnitude of internal variability in single-model initial-condition large ensembles (SMILEs). We measure forced and internal variability by two 1-Wasserstein distances, a form of optimal transport cost. The proposed metric is the distance for internal variability divided by the sum of the two. Computing this ratio requires only sorting the samples and differencing the resulting quantiles, with no additional parameters. The metric reflects the entire distribution shape and applies to non-Gaussian variables, because the 1-Wasserstein distance quantifies the difference between any two probability distributions. We validate the metric with synthetic climate data from Gaussian, uniform, and lognormal distributions. Unlike the two existing metrics, the proposed metric gives stable estimates irrespective of the distribution shape and the presence of outliers, provided that the ensemble has about 40 members or more. We then apply the proposed and existing metrics to the 2 m air temperature and total precipitation of the Community Earth System Model Large Ensemble (CESM-LE) under two different forcing scenarios. All the metrics indicate that the relative contribution of internal variability decreases as the forcing increases, but the proposed metric shows this response most clearly. The 1-Wasserstein distance thus provides a simple and useful tool for analyzing large-ensemble datasets.

Measuring the angular displacement of a mechanical oscillator is a ubiquitous task; however, the multimode nature of angular optomechanical coupling makes coherent signal enhancement challenging. Here we demonstrate coherently enhanced angular displacement readout with an integrated guided mode resonance (GMR) structure, applying it to precision readout of a nanomechanical oscillator. Specifically, we fabricate subwavelength gratings into 100-nm-thick Si$_3$N$_4$ membranes and record their vibration by direct transmission measurements. The narrow linewidth $\approx 2.5\;\text{mrad}$ of the GMR enables a shot-noise-limited displacement imprecision of $ 10^{-9}\;\text{rad}/\sqrt{\text{Hz}}$ with nanowatts of optical power, sufficient to resolve the thermal motion of a $Q\approx 10^6$ torsion mode with a signal-to-noise ratio of 47 dB. Control experiments based on polarization and wavelength detuning confirm that the measured signal arises from GMR-mediated transduction. These results establish guided-mode resonance as an on-chip approach to angular displacement readout in quantum optomechanical sensors.

Hypersonic flows involve extreme thermochemical non-equilibrium, where strong energy dissipation leads to tightly coupled chemical reactions, radiation, and energy exchange. In this regime, surface chemistry, particularly catalytic wall reactions, can significantly affect boundary-layer composition and surface heat transfer. Accurate simulations of such flows may require repeated evaluations of detailed thermochemical libraries, which represent a major computational bottleneck in high-fidelity reactive-flow simulations. To mitigate this cost, we employ the data-driven reduced-order framework introduced by Scherding et al. (2023), which combines nonlinear dimensionality reduction, community clustering, and local surrogate models to efficiently approximate high-dimensional thermochemical mappings. In this work, this framework is extended for the first time to hypersonic reactive flows with localized catalytic surface discontinuities, introducing sharp variations in wall chemistry and heat transfer. To address the increased complexity of the thermochemical state space, the dimensionality reduction method is enhanced with a Sammon-type stress penalty that mitigates topological folding of the latent manifold and improves the robustness of the clustering and surrogate stages. The resulting model accurately captures the effects of discontinuous catalytic properties, including sharp gradients in wall species mass fractions, diffusion fluxes, and surface heat transfer, while reducing the overall simulation cost by 50% without compromising accuracy.

MRI reconstruction from undersampled k-space measurements is an ill-posed inverse problem. Physics-driven deep learning (PD-DL) methods have shown strong performance for this task by combining the MRI forward model with learned image regularization within algorithm-unrolling frameworks. However, most existing PD-DL methods reconstruct complex-valued images directly, thereby implicitly coupling magnitude and phase within a single learned representation. This coupled regularization may be suboptimal in reconstruction settings where accurate phase modeling plays an important role, such as partial Fourier (PF) imaging, where recovery of the omitted asymmetric k-space measurements depends on the underlying image phase. In such scenarios, explicit modeling of magnitude and phase as separate components may reduce the reliance on externally estimated or predefined phase information. To this end, we propose UMPIRE-Net (Unrolled Magnitude-Phase In REgularization Network), a PD-DL method that introduces separate learned regularizers for magnitude and phase components, together with a novel data-fidelity formulation that enforces measurements consistency. We evaluate UMPIRE-Net for accelerated MRI with PF across different datasets and acceleration factors. Experimental results demonstrate that our proposed method improves reconstruction quality compared with a conventional complex-valued PD-DL baseline, yielding sharper images and reduced artifacts. Code available at: https://github.com/MahdiSaberii/UMPIRE-Net

Textile-integrated capacitive sensors offer flexible and conformable tactile sensing for wearable electronics and human-robot interaction; however, the influence of yarn-level architecture on capacitive transduction characteristics remains insufficiently quantified. This work presents a textile capacitive sensing platform based on silver-coated yarns coated with polydimethylsiloxane and assembled into one-, two-, and four-layer twisted configurations. The influence of effective electrode overlap area and inter-fiber separation on the capacitive response is systematically investigated, enabling architecture-dependent tuning of pressure and proximity sensing characteristics. Pressure was calculated using the localized single-fiber contact area, corresponding to stresses of 0.4-3.9 MPa. Increasing the layer number improved mechanical strength and sensing performance: elongation at break increased from 37.5% to 62.5% and 85.0%, while the maximum load increased from 23.3 to 42.7 and 89.7 N. Sensitivity increased with layer number and frequency, reaching 0.1331 MPa$^{-1}$ for the four-layer sensor at 100 kHz. The four-layer configuration also exhibited low hysteresis, minimal thermal drift from 25 to 90 $^\circ$C, and stable operation over 15,000 cycles. Proximity detection ranges of 60, 50, and 40 mm were obtained for the one-, two-, and four-layer sensors, respectively, revealing an architecture-dependent sensitivity-range trade-off. A 4$\times$4 textile sensing array enabled spatial contact mapping, while robotic-arm integration demonstrated real-time touch and proximity detection with an end-to-end robotic system latency (from detection to robot reaction) of 403 ms. The results establish yarn architecture as a tunable design parameter governing the measurement characteristics of textile-integrated capacitive sensing systems.

What decides whether people do things together or separately? Many activities cannot be carried out alone, and an individual must rank them against the private tasks competing for the same time. We address this within the priority-queue description of human activity by letting each agent choose the priority of a shared task rather than drawing it from a fixed distribution: the value of the joint activity, discounted by the estimated risk that the partner will not take part. Participation becomes strategic, and the model acquires a phase transition. A coupled phase, in which joint activity is sustained, is separated from an absorbing solitary phase by a saddle-node bifurcation that we obtain in closed form. The transition is discontinuous and the solitary phase is absorbing, so collapse is irreversible unless an agent persists unilaterally for of order one memory time, a cost we also compute. The heavy-tailed interevent statistics that motivate queueing models of human dynamics survive only in a narrow window at the transition, and there the exponent is fixed by the fraction of time spent coupled rather than by queue length: the universality classes of the non-strategic model do not survive the introduction of choice. On a network, attention divides as $1/(k+a)$ and fixes a critical degree beyond which no coupled state exists, so the solitary phase percolates according to the Molloy–Reed criterion with the second moment truncated at that degree — formally an attack on hubs, with no attacker. For group activities the critical degree falls as the $(m-1)$th root, implying a maximum group size. The transition organises two quantities already measured in communication records: a finite capacity for keeping ties active, and the decay of ties whose rhythm is interrupted.

Understanding is a generally acknowledged aim of science, but the way in which understanding is pursued and criteria for when it is achieved differ widely across scientific disciplines. This paper offers a comparative analysis of this disciplinary variation, aimed at revealing differences as well as commonalities in methods and criteria for achieving understanding. Starting point is the contextual theory of scientific understanding (De Regt 2017), which was developed to accommodate contextual variation in criteria for understanding. While this work focused on historical variation and used mainly case studies from the history of physics, the current paper applies the contextual theory to disciplinary variation. We present brief descriptive accounts of how understanding figures in nine scientific disciplines: mathematics, theoretical physics, astrophysics, chemistry, geoscience, biology, neuroscience, psychology, and the engineering sciences. Subsequently, we compare and evaluate these accounts in light of the contextual theory of scientific understanding.

Current understandings of the tropical cyclone (TC) warm core primarily relies on statistical averages from soundings and satellite products, which provide a climatological thermal state but cannot adequately characterize the continuous three-dimensional evolution of the warm core throughout the TC life cycle. In this study, Empirical Orthogonal Function (EOF) analysis was applied to three-dimensional temperature anomalies of Western North Pacific (WNP) TCs derived from the ERA5 reanalysis dataset for the period 1979-2024. We found that the three leading EOF modes effectively capture the primary characteristics of the TC thermal structure: the first mode represents the typical warm core structure; the second mode characterizes vertical baroclinicity; and the third pattern captures the horizontal asymmetry of the temperature anomalies. Using the first three principal components (PCs), we established a three-dimensional Cartesian coordinate system. Within this framework, two phase parameters define a thermal phase space to visualize the TC's three-dimensional thermal structure: one diagnoses the vertical barotropic or baroclinic structure, and the other quantifies the horizontal asymmetry of the thermal field. The results demonstrate that the trajectory in the phase diagram effectively captures the observed intensity changes and thermal structural evolution. The EOF-based phase diagrams offer promising insights and provide a novel, objective tool for analyzing and diagnosing structural thermodynamic characteristics and intensity evolution by quantifying key thermal features and their dynamic relationships with TC intensity. This capability thereby holds substantial potential for advancing both theoretical understanding and operational forecasting of TCs.

Understanding charge-carrier recombination in metal halide perovskites is essential for accurately identifying the factors limiting solar cell efficiency, yet it remains challenging due to the interplay of multiple competing processes. Here, we combine time-resolved photoluminescence and excitation dependent photoluminescence quantum yield measurements over a wide range of fluences and repetition rates to investigate recombination dynamics in triple-cation perovskite thin films. By jointly analyzing these multidimensional datasets, we develop a unified model that quantitatively reproduces both photoluminescence decays and absolute quantum yields across all excitation conditions. Our results reveal the coexistence of deep and shallow traps, as well as a second-order nonradiative recombination pathway attributed to Auger-assisted trapping. Importantly, this mechanism dominates under one-sun illumination, making it a critical limiting factor for photovoltaic performance. These findings provide a comprehensive framework for understanding recombination in perovskites and highlight the importance of higher-order defect-mediated processes in determining their efficiency.