Papers

Ferroelectric materials such as BaTiO3 exhibit spontaneous polarization that can be reoriented by an external electric field, forming the basis of various memory, actuator, and sensor applications. The polarization switching behavior, however, is strongly influenced by mechanical boundary conditions due to the intrinsic electromechanical coupling in ferroelectrics. In this study, we employ a machine learning interatomic potential to investigate the effect of uniaxial compressive stress on polarization switching and domain wall evolution in the tetragonal phase of BaTiO3. This study revealed a critical stress about 120 MPa which 90 degree polarization switching occurs. Beyond the critical stress, larger supercells exhibit lower activation energies for polarization switching with 180-degree domain wall formation and weaker constraints from periodic boundary conditions, thereby facilitating domain-wall formation. Besides, Increasing compressive stress reduces both the remnant polarization and the coercive field, while a double hysteresis loop emerges at a stress level of 80 MPa. These findings provide atomistic insights into stress-controlled ferroelectric switching and highlight the crucial role of mechanical loading in designing reliable ferroelectric devices.

Polychromatic electromagnetic fields are typically expanded as integrals over monochromatic fields, such as plane waves, multipolar fields, or Bessel beams. However, monochromatic fields do not belong to the Hilbert space of free Maxwell fields, since their norms diverge. Moreover, the continuous frequency integrals involved in such expansions complicate the treatment of light–matter interactions via the scattering operator. Here, we identify and study a polychromatic basis for free Maxwell fields whose basis vectors belong to the Hilbert space. These vectors are defined as simultaneous eigenstates of four commuting operators with integer eigenvalues. As a consequence, the basis set is countable, and the Hilbert space is separable and isomorphic to $\ell^2$, the Hilbert space of square-summable sequences. Each basis vector represents a polychromatic single-photon wave with quantized energy and a wavelet–like temporal dependence. Three versions of this basis are defined: Regular, incoming, and outgoing. The fields of the regular basis are smooth in both space and time. The incoming and outgoing fields are likewise smooth, except at the spatial origin. These results support and motivate the use of countable bases for both the theoretical description and the practical computation of light–matter interactions.

People carry ritual palanquins, or raths, while describing themselves as following a deity's movement. This study combines western Himalayan ethnography, motor-control research and synthetic mechanics to explain how practical contribution and attributed agency relate. It distinguishes knowing a destination, preparing for a load change, influencing local movement and attributing an episode's purpose. An oracle-mediated travel proposal connects divine initiative to practical responsibility; accounts of rehearsal, shoulder adjustment, upright carrying and a narrated return identify distinct forms of participation. Using assumed parameters and prescribed shoulder motions, a two-support model separates heave from roll, and a four-contact walking extension adds pitch. The extension demonstrates substantial changes in individual loads with negligible rocking and shows how pitch and contact opening affect load sharing. A worked comparison with an upright-carrying account explains how visible posture can conceal changing support. These findings connect ritual authority, bodily preparation and local physical influence while specifying evidence that distinguishes guidance, anticipation, correction and retrospective interpretation. The resulting account treats following as an organised form of participation: people can sustain and adjust a movement while attributing its initiative and purpose to the deity.

Additional clock transitions of ytterbium atoms based on inner-shell orbital transition could benefit the search for new physics beyond the Standard Model. Observation of these transitions with high resolution is a prerequisite for making precise frequency measurements. Here, we observe 4.3 Hz-linewidth spectra of the inner-shell orbital transition at 431 nm in lattice-trapped 174Yb. With high-resolution spectra, we precisely determine the differential static polarizability of the transition to be -2.10(4) kHz/(kV/cm)^2. The magnitude of this polarizability is approximately 1/17 of that of the well-known clock transition in 171Yb at 578 nm, indicating a reduced sensitivity to blackbody radiation. We carry out a frequency ratio measurement between the two clock transitions of ytterbium atoms with an uncertainty of 9E-15. The frequency of the 431 nm transition is determined to be 695 175 030 801 776.5(6.3) Hz. These results represent a step forward in future studies on the search for new physics beyond the Standard Model.

Cyclic molecules are ubiquitous across applications in chemistry and biology. Their restricted conformational flexibility provides structural pre-organization that is key to their function in drug discovery and catalysis. However, reliably sampling the conformer ensembles of ring systems remains challenging. Here, we introduce PuckerFlow, a generative machine learning model that performs flow matching on the Cremer-Pople space, a low-dimensional internal coordinate system capturing the relevant degrees of freedom of rings. Our approach enables generation of valid closed rings by design and demonstrates strong performance in generating conformers that are both diverse and precise. We show that PuckerFlow outperforms other conformer generation methods on nearly all quantitative metrics and illustrate the potential of PuckerFlow for ring systems relevant to chemical applications, particularly in catalysis and drug discovery. This work enables efficient and reliable conformer generation of cyclic structures, paving the way towards modeling structure-property relationships and the property-guided generation of rings across a wide range of applications in chemistry and biology.

We report the first creation of a bulk sample of ultracold heteronuclear p-wave Feshbach molecules in an optically trapped Bose-Bose mixture of 23Na and 87Rb atoms. Using loss spectroscopy and binding energy measurements, we systematically characterize the interspecies p-wave Feshbach resonances near 284 G. Leveraging this understanding, we use magneto-association to form p-wave NaRb Feshbach molecules, producing both pure samples and mixtures of molecules in different p-wave orbitals. We further measure the molecular lifetime and identify atom-molecule and molecule-molecule collisions as the dominant loss mechanisms. This work establishes a previously unavailable ultracold molecule platform that combines orbital anisotropy with heteronuclear constituents, represents a significant step toward realizing tunable p-wave interactions in Bose-Bose mixtures, and provides a foundation for exploring non-zero angular momentum molecules.

Single-Photon Avalanche Diodes (SPADs) are new and promising imaging sensors. These sensors are sensitive enough to detect individual photons hitting each pixel, with extreme temporal resolution and without readout noise. Thus, SPADs stand out as an optimal choice for low-light imaging. Due to the high price and limited availability of SPAD sensors, the demand for an accurate data simulation pipeline is substantial. Indeed, the scarcity of SPAD datasets hinders the development of SPAD-specific processing algorithms and impedes the training of learning-based solutions. In this paper, we present a comprehensive SPAD simulation pipeline and validate it with multiple experiments using two recent commercial SPAD sensors. Our simulator is used to generate the SPAD-MNIST, a single-photon version of the seminal MNIST dataset, to investigate the effectiveness of convolutional neural network (CNN) classifiers on reconstructed fluxes, even at extremely low light conditions, e.g., 5 mlux. We also assess the performance of classifiers exclusively trained on simulated data on real images acquired from SPAD sensors at different light conditions. The synthetic dataset encompasses different SPAD imaging modalities and is made available for download. Project page: https://boracchi.faculty.polimi.it/Projects/SPAD-MNIST.html.

The current study reports the design of advanced light-weight materials for high-energy accelerator beampipe applications. The objective is to optimize the combined requirements of high radiation length and stiffness properties of the designed materials. The present study targets conventional beampipe materials such as aluminum, titanium, and stainless steel as primary performance benchmarks. These conventional beampipes are used at synchrotron radiation sources, such as Indus-1 and Indus-2 in India, the Nuclotron-based Ion Collider Facility in Russia, and the ring synchrotron facility SIS 100/300 at the Facility for Antiproton and Ion Research in Germany. In this context, a series of ternary Mg–Al–Li alloys is systematically investigated to enhance the figure of merit. Two aluminum–rich alloys, A1 ($\mathrm{Al_{61.5}Li_{10.8}Mg_{27.7}}$) and A2 ($\mathrm{Al_{66}Li_{19.4}Mg_{14.6}}$), along with three magnesium-rich alloys, M1 ($\mathrm{Al_{23.9}Li_{29.3}Mg_{46.8}}$), M2 ($\mathrm{Al_{19}Li_{20.6}Mg_{60.4}}$), and M3 ($\mathrm{Al_{39.8}Li_{20.1}Mg_{40.1}}$) are explored. Thermodynamic stability, density, liquidus temperature, and phases are evaluated using Latin hypercube sampling within the Thermo-Calc TC-Python framework. Elastic properties are obtained from density functional theory calculations performed using the Vienna Ab Initio Simulation Package. Our results show that, although the elastic moduli ($E$) of the investigated Mg-Al-Li alloys are comparable to those of conventional beampipe materials, their significantly higher radiation lengths ($X_0$) lead to an overall improvement in the figure of merit $X_0 E^{1/3}$.

The COSINE-100U (upgrade) will enhance the sensitivity of the COSINE-100 dark matter search by operating the detector array immersed in liquid scintillator (LS) at $-30^oC$. To validate the detector design for these conditions, we constructed a module using the COSINE-100U encapsulation and performed a dedicated long-term stability study. The module was first monitored at room temperature for ~110 days in air, followed by a one-week immersion in LAB-based LS to verify initial compatibility. Upon confirming stable optical performance, the temperature was lowered to $-33^oC$. During approximately 150 days of continuous operation at low temperature, we observed no degradation in performance. These results demonstrate the chemical and mechanical robustness of the encapsulation, confirming its suitability for the COSINE-100U physics run.

Pigmentation abnormalities, ranging from hypo- to hyperpigmentation, can serve as biomarkers of developmental disruption in fish exposed to environmental contaminants. However, the mechanistic pathways underlying these alterations remain poorly understood. Studies have shown that pattern formation in fish development requires specific pigment cell interactions. Motivated by experimental observations of pigmentation alterations following contaminant exposure, we investigate how pollutants influence pigment cell self-organization using a continuum reaction-diffusion-advection framework. The model incorporates nonlocal Morse-type kernels to describe short- and long-range interactions among melanophores and xanthophores. Our results show that perturbations to the strengths of adhesion or repulsion can drive transitions between stripes, spots, and mixed patterns, reproducing phenotypes characteristic of fish pigmentation mutants. In particular, homotypic interactions are sensitive to contamination, leading to pronounced changes in melanophore density and resulting pigmentation patterns. Time-dependent simulations indicate that pigment changes from early short-term contaminant exposure may be recoverable, whereas prolonged exposure can lead to sustained pigment loss. In a growing fish, contaminant-induced changes in cell-cell interactions directly influence stripe formation rate, stripe number, and pigmentation levels. Overall, our study provides insight into the mechanistic link between experimentally observed pigmentation alterations and the changes in spatial patterns of adult fish.

We show that the relativistic energy-momentum relation can emerge as an effective ensemble-averaged structure from a multiplicative Hamiltonian when fluctuations of an auxiliary parameter are treated using maximum entropy inference. The resulting probability distribution is uniquely fixed by scale-invariant constraints, which are shown to arise naturally from the Fisher-Rao geometry of the associated statistical manifold. Within this information-geometric framework, the relativistic dispersion relation appears without initially imposing Lorentz symmetry, but as a consequence of statistical averaging and geometric invariance.

Non-Hermitian photonics provides a fertile platform for exploring phenomena with no Hermitian counterparts, including the non-Hermitian skin effect and exceptional points, with direct relevance for integrated photonic technologies. In this work, we investigate the properties of non-Hermitian second-order topological phases by constructing a photonic kagome crystal with balanced gain and loss, and reveal the interplay between higher-order topology and the non-Hermitian skin effect. We demonstrate that non-Hermiticity not only lifts the degeneracy of the topological corner modes but also drives bulk states to accumulate at corners, giving rise to bipolar non-Hermitian skin effect. By defining the point-gap topology, we uncover the fundamental topological origin of the non-Hermitian skin effect. More interestingly, the non-Hermitian skin effect induces a fundamental breakdown of the conventional bulk-boundary correspondence based on the Bloch band theory. Our findings establish a general framework for non-Hermitian higher-order photonic systems and open avenues toward tailorable topological photonic devices exploiting non-Hermitian enhanced localization.

This work develops low-energy spacecraft (SC) trajectories using Venus gravity assists to study asteroids during heliocentric transfer segments between planetary encounters. The study focuses on potentially hazardous asteroids (PHAs) as primary exploration targets. This paper proposes a method for calculating SC trajectories that enable asteroid flybys after a Venus gravity assist. The method involves formulating and solving an optimization problem to design trajectories incorporating flybys of selected asteroids and Venus. Trajectories are calculated using two-body dynamics by solving the Lambert problem. A preliminary search for candidate asteroids uses an algorithm to narrow the search space of the optimization problem. This algorithm uses the V-infinity globe technique to connect planetary gravity assists with resonant orbits. The resonant orbit in this case serves as an initial approximation for the SC's trajectory between two successive planetary flybys. Four flight schemes were analyzed, including multiple flybys of Venus and asteroids, with the possibility of an SC returning to Earth. The proposed solutions reduce flight time between asteroid approaches, increase gravity assist frequency, and enhance mission design flexibility. The use of Venus gravity assists and resonant orbits ensures a close encounter with at least one asteroid during the SC's trajectory between two consecutive flybys of Venus, and demonstrates the feasibility of periodic Venus gravity assists and encounters with PHAs. The developed method was applied to construct trajectories that allow an SC to approach both Venus-resonant asteroids and PHAs via multiple Venus gravity assists. An additional study was carried out to identify asteroids accessible during the Earth-Venus segment in launch windows between 2029 and 2050.

Although solid-state platforms underpin modern electronics, little is known about how intense ultrashort light pulses carrying orbital angular momentum (OAM) interact with solids. This gap persists even though, for more conventional light-matter interactions, the complex underlying electron dynamics can often be confined to a single Brillouin zone and described well within the dipole approximation. Previous studies were restricted to nonlinear, perturbative regimes, largely because the generation of intense ultrashort vortex pulses, particularly in the mid-infrared spectral regime, has remained a long-standing challenge. Consequently, the role of structured light in driving nonlinear, non-perturbative processes in solids, and the associated transfer of angular momentum during these interactions, has not been systematically explored. Here, we investigate solid-state high-harmonic generation (HHG) driven by intense ultrashort structured light using a versatile experimental approach applicable to different materials and geometries. We demonstrate that the OAM of the driving field is coherently transferred to the emitted harmonics. In particular, we show that the OAM is conserved independently of the crystal symmetry, the range of electronic interactions, and the presence of strong spin-orbit coupling. These results establish OAM-resolved HHG as a robust framework for characterizing and controlling angular momentum transfer in solid-state HHG and open new avenues for structured-light-driven quantum technologies and topological materials investigations.

We introduce a novel spatial discretization technique for the reliable and efficient simulation of magnetization dynamics governed by the Landau-Lifshitz (LL) equation. The overall discretization error is systematically decomposed into temporal and spatial components. The spatial error analysis is conducted by formulating the LL equation within the framework of the Localized Orthogonal Decomposition (LOD) method. Numerical examples are presented to validate the accuracy and approximation properties of the proposed scheme.

The global food system has metamorphosed from a loose aggregation of bilateral exchanges into a highly intricate, interdependent Global Food Trade Network (FTN). This comprehensive review synthesizes the extant literature to examine the FTN through the rigorous lens of complex network science, moving beyond traditional economic trade models to quantify the system's topological architecture. We delineate the network's historical transition from a unipolar, efficiency-driven system dominated by Western hegemony to a multipolar, regionalized structure characterized by high clustering and scale-free heterogeneity. Special emphasis is placed on the dual nature of connectivity, which functions simultaneously as a buffer against local production variances and a conduit for global contagion. By conceptualizing the FTN as a multiplex system-distinguishing between the robust topology of wheat, the brittle regionalism of rice, and the polarized "dumbbell" structure of soy-we elucidate the distinct structural vulnerabilities inherent in modern food security. Furthermore, we analyze the impact of recent high-magnitude shocks, specifically the COVID-19 pandemic and the Russia-Ukraine conflict, illustrating the critical trade-off between logistical efficiency and systemic resilience. The review concludes by assessing the future trajectory of the network under anthropogenic climate change, predicting a poleward migration of comparative advantage that necessitates a paradigm shift from isolationist protectionism to cooperative network redundancy.

The physical behavior of discharges in Z pinch devices can be completely deciphered in terms of only three dimensionless parameters. These parameters can be arranged in a way that draw a surface in 3D space. This surface compiles all the accessible information on the macroscopic physical behavior of each possible Z pinch discharge. We analyze the practical problems the drawing of this surface encounters and in view of the situation, we devote the remainder of the article to outline a feasible method for estimating the plasma temperature in Z pinch discharges.

Metasurfaces are extremely useful for controlling and manipulating electromagnetic waves. Full-wave numerical simulation is highly desired for their design and optimization, but it is notoriously difficult, even for two-dimensional metasurfaces, when they comprise a huge number of subwavelength elements. This paper focuses on two-dimensional non-periodic metasurfaces that contain only a relatively small number of distinct subwavelength elements. We develop an efficient numerical method based on Neumann-to-Dirichlet operators, the finite element method and local function expansions. Our method drastically reduces the total number of unknowns and is capable of simulating two-dimensional metasurfaces with $10^{5}$ subwavelength elements on a personal computer. Numerical examples demonstrate that the method maintains high accuracy while offering significant advantages in both computational time and memory usage compared to the classical full-domain finite element method, making it particularly suited for the analysis of large metasurfaces.

Liquid crystal whispering gallery mode (WGM) microlasers show high sensitivity to external stimuli and distinct spectral features, rendering them ideally suited for various sensing applications. They also offer intrinsic anisotropic optical properties, which can be used to shape and manipulate light even inside spatially highly symmetric structures. Here, we report the fabrication of a spherical bipolar liquid crystal polymer microlaser that tightly confines the optical path of WGMs to the equatorial plane. By controlled anchoring of the liquid crystal mesogens followed by polymerization, a fixed refractive index profile is formed within the spherical microcavity. Consequently, only transverse electric (TE) modes oscillating in the equatorial plane experience the high extraordinary refractive index, allowing to confine lasing into a single plane. Furthermore, we observe a characteristic splitting into sub-modes. By combining hyperspectral imaging, optical modeling, and theoretical calculations, we show that the observed sub-modes originate from the lifting of the energy degeneracy of azimuthal WGMs. The unique ability to confine lasing into a single plane makes these spherical microlasers independent of the exact position of the pump beam, which allows consistent localized sensing especially in combination with fast point scanning microscopes or inside highly dynamic biological environments.

A gauge theory with an indefinite metric without negative probabilities is given by extending quantum mechanics, where a general metric is introduced, and the invariance under the general linear transformation is imposed on the space of quantum states. On this basis, we construct and investigate a chiral sextet model, which has one more Lorentz symmetry in the gauge space, to derive much properties of the standard electroweak theory, and also Einstein gravity, when the double Lorentz symmetry spontaneously fuses into one.

Scientific attention is unevenly distributed, creating inequities in recognition and distorting access to opportunities. Using citations as a proxy, we quantify disparities in attention by gender and institutional prestige. We find that women receive systematically fewer citations than men, and that attention is increasingly concentrated among authors from elite institutions – patterns not fully explained by underrepresentation alone. To explain these dynamics, we introduce a model of citation network growth that incorporates homophily (tendency to cite similar authors), preferential attachment (favoring highly cited authors) and group size (underrepresentation). The model shows that disparities arise not only from group size imbalances but also from cumulative advantage amplifying biased citation preferences. Importantly, increasing representation alone is often insufficient to reduce disparities. Effective strategies should also include reducing homophily, amplifying the visibility of underrepresented groups, and supporting equitable integration of newcomers. Our findings highlight the challenges of mitigating inequities in asymmetric networks like citations, where recognition flows in one direction. By making visible the mechanisms through which attention is distributed, we contribute to efforts toward a more responsible web of science that is fairer, more transparent, and more inclusive, and that better sustains innovation and knowledge production.

The use of polymer nanocomposites as gas barrier materials has seen increasing interest, including applications involving hydrogen transport and storage. Better understanding of gas transport through those polymeric systems requires 3D nanoscale detection of distributions and the possible trapping of gas molecules within nanoparticles and polymer/nanoparticle interfaces While atom probe tomography (APT) offers promising means for such nanoscale characterisation, its use for polymers has been mainly limited to thin organic layers deposited onto substrates or pre-fabricated metal needle shaped specimens. This work provides the very first application of APT to bulk polymer nanocomposites. Particularly, site specific atom probe sample preparation by Focused Ion Beam (FIB) liftout has been shown for the first time in a model system of hexagonal boron nanoparticles within a PVDF polymer matrix, using a variety of FIB workflows including Xe FIB, Ga FIB, cryogenic Ga FIB and deuterium charging. Mass spectra from the bulk polymer and the nanoparticle were collected using pulsed laser atom probe using standard conditions and compared. Several challenges encountered during this research including damage of the polymeric matrix during sample preparation were extensively discussed in this paper. Once those challenges have been resolved (e.g. by developing site specific sample preparation protocols), the application of APT to polymer nanocomposites can open new options for nanoscale characterisation of those systems.

The problem of spatially selective radio-frequency (RF) pulse design in magnetic resonance imaging (MRI) is typically stated in the form of determining, analytically or numerically, RF waveforms to be applied in synchrony with one or more predetermined gradient waveforms. In most cases, the dynamics of the nuclear spin magnetization under the RF and gradient fields is described in a global rotating frame that cancels the effect of the static (main) magnetic field B0. In this work, we consider an alternative frame of reference, which can be called a local rotating frame where total longitudinal magnetic field (B0 plus gradient) in every voxel is zero. In this frame, the effect of time-dependent gradient field is integrated out, and the remaining magnetization dynamics, governed by much weaker RF fields, becomes both simpler and slower. We show that recasting existing RF design methods in such a frame provides useful insights and techniques that are not obvious in the conventional description. The methods we consider include (i) two-dimensional spatial RF pulse design in the excitation k-space, (ii) Shinnar-Le Roux RF design, (iii) residual phase calculation in slice-selective excitation, and (iv) iterative and numerical solutions for multi-coil RF pulse design. In particular, we show that the new formalism can substantially reduce the Bloch simulation time which can greatly benefit iterative pulse designs in parallel transmit. In all, the proposed framework provides considerable theoretical insights and practical utility for RF pulse design in MRI.

Combining high-speed photography with direct force measurements, we investigate the impact dynamics of drops of cornstarch-water mixtures – a premier example of shear-thickening fluids – across a wide range of impact conditions. Our study identifies three distinct impact regimes. In addition to the liquid-like and solid-like behaviors generally expected for the impact-induced response of shear-thickening fluids, we uncover a counterintuitive regime in which high-concentration cornstarch-water mixtures display a liquid-like response at the onset of impact when shear rates are high and only transition to a solid-like behavior at later times as shear rates reduce. By integrating the classic drop-impact theory with the Reynolds-Darcy mechanism for dilatancy, we develop a unified model that quantitatively describes the impact dynamics of shear-thickening drops across all regimes. Our work reveals the unexpected response of shear-thickening fluids to ultra-fast deformation and advances fundamental understanding of drop impact for complex fluids.

Active arterial mechanics, governed by vascular smooth muscle contraction, are critical to physiological regulation, cardiovascular disease progression, and clinical diagnosis. Although various in vivo methods have been developed to assess arterial stiffness, most cannot distinguish the contribution of smooth muscle tone; therefore, quantitative characterization of arterial activity remains challenging. In this study, we developed a pressure-area analysis framework integrating ultrasound imaging, blood pressure measurement, neural network-based segmentation of arterial cross-sectional area, and biomechanical model-driven inversion to infer active mechanical properties. A total of 233 volunteers (aged 18-65 year) were recruited to acquire cross-sectional ultrasound videos of the right common carotid artery for training the neural network. The segmentation results demonstrate good spatial and temporal performance of the neural network. We further recruited 10 additional volunteers (aged 25 +/- 3 year) to perform a 1-minute step test, followed by pressure-area measurements over a 30-minute recovery period. Using the proposed approach, we quantified post-exercise changes in carotid arterial active mechanics relative to baseline (i.e., the resting state). Results showed that active mechanics remained elevated for approximately 15 minutes compared to baseline (p < 0.05), whereas systolic pressure differed significantly only within the first approximately 5 minutes post-exercise (p < 0.001). These results indicate a dissociation between blood pressure and smooth muscle recovery, which may offer new insight into vascular smooth muscle regulation during physiological stress.

Accurate determination of transition states is central to an understanding of reaction kinetics. Double-endpoint methods where both initial and final states are specified, such as the climbing image nudged elastic band (CI-NEB), identify the minimum energy path between the two and thereby the saddle point on the energy surface that is relevant for the given transition, thus providing an estimate of the transition state within the harmonic approximation of transition state theory. Such calculations can, however, incur high computational costs and may suffer stagnation on exceptionally flat or rough energy surfaces. Conversely, methods that only require specification of an initial set of atomic coordinates, such as the minimum mode following (MMF) method, offer efficiency but can converge on saddle points that are not relevant for transition of interest. Here, we present an adaptive hybrid algorithm that integrates the CI-NEB with the MMF method so as to get faster convergence to the relevant saddle point. The method is benchmarked for the Baker-Chan (BC) saddle point test set using the PET-MAD machine-learned potential as well as 59 transitions of a heptamer island on Pt(111) from the OptBench benchmark set. A Bayesian analysis of the performance shows a reduction in energy and force calculations of 57% [95% CrI: -64%, -50%] relative to CI-NEB for the BC set, while a 31% mean reduction is found for the transitions of the heptamer island. These results establish this hybrid method as a highly effective tool for high-throughput automated chemical discovery of atomic rearrangements.

The development of lightweight flex PCBs and nanowire-based thermal interfaces for low-mass, high-performance detector modules are presented. A novel manufacturing approach targeting flex circuits with double-sided pad access, assembled using ACF and gold studs. Signal integrity was simulated and validation trials conducted on test structures. For thermal management, sintered nanowire interfaces were evaluated. These results contribute quantitative input relevant to minimal-mass, scalable packaging in HEP detectors.

Many cities promote walkability through concepts such as the compact city and 15-minute city to enhance urban livability, yet few methods link spatial walkability features to empirically measured livability and account for temporal dynamics. The method developed for this study uses mobile phone data from the Helsinki Metropolitan Area (Finland) to assess whether commonly used, literature-derived livability indicators (diversity, density, proximity, accessibility) predict observed human activity patterns across different times of day. We constructed two key dimensions of livability: attractiveness and walkability with quantifiable sub-indicators that were selected based on literature. Our analysis shows that walkability, and even more so the combined livability index, correlates with activity patterns, outperforming the pure attractiveness perspective. However, this relationship is temporally unstable, significantly weakening at night and fluctuating daily. Moreover, based on Geographically Weighted Regression analysis, our results reveal significant spatial variation in the relationship between livability and the intensity of human activities. The findings suggest that traditional urban planning goals, such as functional diversity to enhance walkability, contribute to livability but have a limited impact on the 15-minute city's overall sustainable mobility objectives, necessitating a larger-scale perspective and more functionally profiled approaches for urban development.

Measuring radon emanation from detector materials is a key method for controlling radon, a significant background in rare-event physics experiments. Methods for measuring radon emanation are well-established but have predominantly focused on the 222Rn isotope, the dominant radon isotope for these backgrounds. However, measurements of 220Rn (thoron), the second most abundant radon isotope, remain relatively unexplored. 220Rn emanation measurements are challenging because the 220Rn must be transferred from the emanation chamber to the active detector within its short 55 s half-life. In this study, a direct in-chamber approach for measuring 220Rn emanation is presented in which the sample is placed directly within the active detector chamber, thereby minimising losses during transfer. The method was demonstrated with a DURRIDGE RAD8 electrostatic radon detector, which measured 220Rn emanation from low-activity thoriated rods with an activity of 76 +/- 20 mBq. Compared with a conventional flowthrough 220Rn emanation setup, the in-chamber method increased sensitivity by a factor of 3. Using helium as the carrier gas provided a further sensitivity increase, giving an overall sensitivity gain of ~5. These results indicate that in-chamber 220Rn emanation measurements provide an effective tool for low-background experiments and have the potential to accelerate radon studies by exploiting the shorter half-life of 220Rn.

Hybrid-Vlasov simulations resolve ion-kinetic effects in the solar wind-magnetosphere interaction, but even 5D (2D + 3V) configurations are computationally expensive. We show that graph-based machine learning emulators can learn the spatiotemporal evolution of electromagnetic fields and lower-order moments of the ion velocity distribution function in near-Earth space from four 5D Vlasiator runs, each driven by steady solar wind conditions. The upstream ion number density is systematically varied between the runs, while the grid spacing is held constant, to scan the ratio of ion inertial length to grid size. Using a graph neural network (GNN) operating on the 2D spatial simulation grid comprising 670k cells, we demonstrate that both a deterministic forecasting model (Graph-FM) and a probabilistic ensemble forecasting model (Graph-EFM) based on a latent variable formulation produce accurate predictions of future plasma states. A divergence penalty is incorporated to encourage divergence-freeness in the magnetic fields. For the probabilistic model, a continuous ranked probability score objective is added to improve the calibration of the ensemble forecasts. In terms of wall time per output step, the trained emulators run over two orders of magnitude faster on a single GPU than the Vlasiator simulations on 100 CPUs. Most forecasted fields have Pearson correlations above 0.95 at 50 seconds lead time. Fields that exhibit degenerate (near-zero) distributions in the 5D setting are more challenging for the emulator to keep well correlated. The ensemble forecasts remain underdispersive, with spread-skill ratios of approximately 0.2-0.3, and thus provide spatially structured relative uncertainty estimates. Overall, these results demonstrate that GNNs provide a viable framework for rapid ensemble generation in hybrid-Vlasov modeling and highlight promising directions for future work.