Papers

Here we discuss direct links of the number of fundamental dimensions to the fundamental natural constants using simple arguments of dimensional analysis \corr{based on Maxwell's dimensions length (L), time (T) and mass (M) as well as the constants $G$, $c$, $\hbar$ and $e$}. We find that the \corr{form} of the fine-structure constant is a direct consequence of this connection. Additionally, our approach emphasises that gravity is a quite distinct area of physics which is not yet successfully quantised, i.e. not yet combined with quantum mechanics. We also discuss different unit systems based on dimensional analysis and natural constants.

Large Language Models (LLMs) are increasingly deployed as scientific AI as- sistants, and a growing body of benchmarks evaluates their capabilities across knowledge retrieval, reasoning, code generation, and tool use. These evaluations, however, typically assume the scientific problem is already well-posed, whereas practical scientific assistance often begins with an ill-posed user request that must be refined through dialogue before any computation, analysis, or experiment can be carried out reliably. We introduce SCICONVBENCH, a benchmark for multi- turn clarification in scientific task formulation across four computational science problem domains: fluid mechanics, solid mechanics, materials science, and par- tial differential equations (PDEs). SCICONVBENCH targets two complementary capabilities: eliciting missing information (disambiguation) and detecting and correcting erroneous requests containing internally contradictory information (in- consistency resolution). Our benchmark pairs a structured task ontology with a rubric-based evaluation framework, enabling systematic measurement of LLM per- formance across three dimensions: clarification behavior, conversational grounding, and final-specification fidelity. Current frontier models perform relatively well on inconsistency resolution, but even the best model resolves only 52.7% of the disambiguation cases in fluid mechanics. We further find that frontier LLMs fre- quently make silent assumptions and perform implicit specification repairs that are not grounded in the conversation with users. SCICONVBENCH establishes a foundation for evaluating the upstream conversational reasoning that a reliable computational science assistant requires. The code and data can be found at https://github.com/csml-rpi/SciConvBench.

We study the near-kernel sector of the Hamiltonian constraint operator in the one-vertex model of quantum reduced loop gravity using variational Monte Carlo methods with neural quantum states. The analysis is based on the symmetric Hamiltonian containing both Euclidean and Lorentzian contributions, and on the variational minimization of the positive quadratic operator $\hat{\mathcal Q}=\hat C \hat C^\dagger$ in truncated Hilbert spaces with spin cutoff up to $j_{\mathrm{max}}=1001$. The resulting near-kernel states are found to organize into three qualitatively distinct classes. At low cutoffs, we find solutions that do not factorize across the three edge degrees of freedom. At larger cutoffs, we find two different factorized branches, both described to very high accuracy by products of one-edge wavefunctions but localized in different spin regimes. One of these branches is matched with near-unit fidelity by reduced Thiemann coherent states, providing evidence for an emergent semiclassical organization of the near-kernel sector. The other is likewise strongly factorized, but its one-edge factors are not well described by the same coherent-state family.

Human-generated randomness is constrained by cognitive, motor, and strategic biases. This study examines how these constraints appear in individual behavior and how they may be modified through interaction with another human. We analyzed repeated rock-paper-scissors data from 9 participants, yielding 108 human-human matches and 216 individual player sequences. Using Lempel-Ziv complexity (LZC), we compared human-human sequences with the RNG-opponent condition. In the RNG-opponent condition, the maximum human LZC value was 84, which we used as an empirical reference. In the human-human condition, most sequences remained below this value, but a small number exceeded it, producing a small high-complexity tail that was not present in the RNG-opponent condition. We introduced a sensitivity measure that captures whether a player responds to the opponent's recent frequency bias by choosing the move that beats the opponent's most frequent recent move. Partial regression showed that focal-player sensitivity positively predicted future entropy in the opponent's move sequence after controlling for the opponent's current entropy. Circular-shift surrogate analyses indicated that this relation was most clearly interaction-specific when the opponent was in a low-entropy state, where the recent move distribution contained a clear frequency bias. These results suggest that human randomness is not only an isolated individual capacity, but can be shaped by interaction in a state-dependent manner. The findings identify a local mechanism by which interaction may destabilize biased behavior and increase entropy, providing a concrete basis for future causal experiments and generative models of high-complexity human behavior.

The low-field carrier mobilities in <100> silicon were quantified as a function of the 1$\,$MeV neutron-equivalent fluence up to $10^{18}\,$cm$^{-2}$ and for temperatures between 230$\,$K and 260$\,$K. Current measurements were fitted using a mobility model for scattering at ionized impurities. Technology-aided design (TCAD) simulations were compared to measurements and used to estimate the carrier concentrations, which are parameters in the fit. The fit model describes the data very well, both as a function of fluence and the temperature. At a fluence of $6 \cdot 10^{17}\,$cm$^{-2}$, which is expected for the innermost detector layers at the proposed Future Circular Hadron Collider (FCC-hh), the sum of the mobilities of electrons and holes was found to decrease by $\sim60$%.

Triangular-lattice systems attract a lot of attention due to various frustration-induced and strongly correlated effects. Here, we focus on the charge-ordering phenomenon by means of investigation of the extended Hubbard model with dynamical mean-field theory (DMFT). By considering the intersite nearest-neighbor interaction we have found a very rich phase diagram that contains large number of features, phases, and phase transitions. Among them are pinball-liquid (PL) phases where we distinguish charge-transfer-driven and Mott-localization-driven PLs; phase transitions that change their order as model parameters evolve (from discontinuous to continuous); very strong particle-hole asymmetry. Various features of the phase diagram are found to be better understood by means of the simple mean-field approximation (MFA). Moreover, besides helping with interpretation of the phase diagram, the MFA results together with results for the atomic-limit model are found to be able to set rather good expectations on how the DMFT phase diagram should look like. Nevertheless, a few features were not expected and are found within the DMFT, such as a small-region intermediate metallic phase on an electron-doped side of the phase diagram.

The computation of electronic excited states and real-time quantum dynamics of many-fermion systems is among the most promising applications of near-term quantum computing. In this work, we generalize the reinforcement learning contracted quantum eigensolver (RL-CQE), previously developed for ground-state problems, to electronic excited states and real-time quantum dynamics, in which a deep Q-network agent adaptively selects the two-body operators at each iteration, yielding more compact ansätze and improved robustness with respect to critical hyperparameters. A key feature of the algorithm is a scalable state representation based on the ACSE residuals, whose dimension grows with the one-particle basis but remains independent of the number of targeted excited states. We also verify the equivalence of sign-free qubit operators in the excited-state setting, extending a result previously established for ground-state problems. Our RL-CQE for time evolution derives from a constant-scaling ansatz that represents the wave function with a fixed number of unitary transformations independent of simulation time $t$, enabled by the shared unitary structure of the purified ensemble treatment of excited states. Benchmarks on chemical systems demonstrate chemical accuracy with minimal operator counts across a range of bond lengths.

Free-particle Green's function plays a central role in the theoretical description of electron scattering and autoionization processes in quantum physics and chemistry. Recently, Gaussian basis set approaches have become increasingly important in applications to unbound and metastable electronic states. However, the practical use of such methods has been limited by the lack of efficient and compact analytical expressions for matrix elements of the free-particle Green's function in Gaussian-based representations. Here we present a novel, general analytical framework for the evaluation of one- and two-center matrix elements of the free-particle Green's function over spherical Gaussian basis functions and plane-wave-modulated spherical Gaussians. The derivation is based on the Fourier transform of Gaussian functions together with the addition theorem of harmonic polynomials, leading to compact closed-form expressions and efficient recurrence relations. We also analyze the asymptotic behavior of the free-particle Green's function matrix elements, which is essential in the description of low-energy continuum electrons using finite Gaussian basis sets.

Fringe projection profilometry (FPP) is a widely used technique for measuring object surface form and three-dimensional (3D) geometry, capable of delivering high-precision, high-resolution measurements when paired with suitable cameras and projectors. However, in practical deployments, identifying parameter configurations that maximise precision while satisfying real-world constraints remains challenging. To address this, we present an automated digital twin framework implemented in Blender, an open-source 3D software package that provides a ray-traced rendering environment that enables accurate simulation of physical systems. We replicated the physical setup in our digital twin by matching characterisation quality, gamma response, and characterisation images. Accurate system characterisation using Zhang's method [1], to obtain intrinsic and extrinsic parameters, is shown to be critical for achieving high precision. Using this digital twin, we then demonstrate systematic exploration and optimisation of key parameters, including phase-shift count, camera-projector spacing, and fringe density. These parameters span both system geometry (e.g. camera-projector positioning) and algorithmic choices, such as 2D phase-shifting and unwrapping methods [2]. Three measurement artefacts, representative of real world metrology scenarios, were used to benchmark the system. The symmetrical mean Chamfer distance (SMCD), computed between ground-truth and reconstructed meshes, was used to evaluate reconstruction quality. After optimisation within the digital twin, transferring the optimal parameters to the physical system reduced the number of required images per measurement by 48% (from 36 to 21). A reduction of 74.0% mean SMCD was also achieved for fringe pattern stripe count alteration. A 36.9% mean SMCD was obtained for adjusting the camera and projector spacing purely in the digital-twin.

We extend the recently-developed explicit, energy-conserving particle-in-cell (PIC) scheme of [1] to the relativistic Vlasov-Maxwell system. As in the non-relativistic case, the method is built on an optimization problem that is analytically solvable, local to each particle, and designed to enforce exact energy conservation. Although the solution to this optimization problem is not guaranteed to be real, we show that such instances are rare enough for practical simulation parameters to permit dramatic improvements in energy conservation over traditional explicit PIC schemes. We show that, as in the non-relativistic case, the scheme is compatible with popular field-solvers for electromagnetic PIC schemes, including the Yee/FDTD and pseudo-spectral analytic time-domain (PSATD) methods. The scheme is verified on standard relativistic test problems, where its conservation properties are confirmed.

Understanding ion-transport under molecular confinement is essential for developing next-generation energy technologies, where ionic motion often occurs within nanoscale or angstrom-scale channels. In this study, we use the model system of 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide ([EMIM]+[TFSI]-) confined within angstrom-scale slit-shaped 2D channels fabricated via van der Waals assembly to exemplify a broader class of confined ionic liquids.This system provides a well-defined platform to unravel generic features of ion transport under extreme confinement. By systematically varying the channel height h, we demonstrate a non-monotonic conductivity dependence on confinement, with a maximum 26.7 S/m at confining height, 1.02 nm, over 30 times of the bulk value for these ionic liquids. The variation of conductivity with confinement arises from structural rearrangements of ionic layers in the slit channel. Enhanced values of conductivity occur under confinements that promote the breakup of ion pairs and larger clusters, thereby increasing the number of free ions. Stronger confinement (h, 0.68 nm) also leads to steric hindrance, lowering conductivity below bulk values. Furthermore, introducing co-solvents with a higher dielectric constant and lower viscosity, such as acetonitrile (ACN), amplifies conductivity to ~145 S/m. Comparative studies using ACN, dimethyl carbonate and diethyl carbonate highlight that both large dielectric constant and low viscosity critically govern ion transport under confinement, as also supported by molecular dynamics simulations. Overall, this work establishes confined [EMIM]+[TFSI]- as a representative system for probing mechanisms of nano- and angstrom-scale ion transport, demonstrating how nanoconfinement and the solvent environment can be systematically tuned to manipulate ionic conductivity at the molecular level.

We present a systematic experimental comparison of single-pass second-harmonic generation (SHG) in bismuth triborate (BiBO) and lithium triborate (LBO) nonlinear crystals, driven by a 1.3 ps, 91 kHz laser at 1030 nm with up to 57 W of average input power. Both crystals yielded 32 W of second harmonic (SH) output at 515 nm, corresponding to a conversion efficiency of 56 %, which to the best of our knowledge represents the highest SH output power reported in the green spectral region using a BiBO crystal. Power dependence, long-term stability, beam quality, pulse duration, spectral properties, thermal effects, and angular acceptance bandwidth are characterized and directly compared for both crystals. These results provide quantitative performance benchmarks to guide the selection of nonlinear crystals for high-average-power, ultrashort-pulse frequency conversion near 1030 nm.

A key factor in any RF system is the mechanism for coupling the RF power from an amplifier into an accelerating cavity. Any tranmission line will experience reflections if there is a mismatch in the impedance between the line and its load. In accelerating cavities due to their high quality factors there is often a large mismatch between the cavity shunt impedance and the tranmission line. This lecture will look at how to overcome this mismatch and ensure efficient coupling without steady-state reflections in both standing-wave and travelling-wave cavties.

The intertropical convergence zone (ITCZ) is a central component of tropical climate, but the conditions under which a tropical rain belt remains zonally extended or becomes unstable to zonal organization are not well understood. We investigate this problem using idealized nonrotating kilometer-scale simulations forced by a prescribed sea surface temperature (SST) distribution that varies only in the meridional direction. This setup produces an ITCZ-like rain belt while allowing spontaneous zonal convective self-aggregation (ZCSA) to emerge. A parameter sweep shows that ZCSA occurs preferentially when both the peak SST and the meridional SST amplitude are large. ZCSA cases exhibit a temporary weakening of the meridional near-surface convergence. Boundary-layer momentum and thermodynamic analyses link this weakening to enhanced lower-tropospheric stability over the cool subsiding region, a shallower boundary layer, and stronger effective frictional damping of the meridional inflow. However, weak convergence alone is not sufficient for ZCSA. Aggregating cases also have a large meridional contrast in moist static energy forcing, implying a strong demand for meridional energy transport. Consistently, ZCSA reorganizes meridional moist static energy transport, including enhanced stationary eddy export from the warm region, and is accompanied by growing zonal moisture variability and weakening meridional moisture contrast. These results suggest that zonal symmetry breaking of an ITCZ-like rain belt is favored when weakened meridional inflow coincides with a large imposed meridional MSE-forcing contrast.

Rogue waves, characterized by their abrupt and extreme localization in space and time, have evolved from maritime folklore to subjects of intense study across diverse fields, from hydrodynamics and nonlinear optics to plasmas and condensed matter physics. In mechanical systems, however, experimental realization remains elusive despite theoretical and numerical predictions. This gap stems from the stringent requirements for controllable nonlinearity, the high-fidelity initialization of the system, and the necessity to overcome inherent energy dissipation. Here, we report the experimental formation of mechanical rogue waves in a precisely engineered one-dimensional metamaterial lattice with tailored nonlinearity and minimal dissipative losses. Using a precision electromagnetic release system, we prescribe initial strain profiles that trigger a transition from dispersive decay to extreme wave focusing. Our parametric analysis reveals that the emergence of these extreme events is strictly contingent upon a synergy between high nonlinearity and a broad spatial energy reservoir within the initial seed. Crucially, neither factor alone is sufficient to overcome dispersion and trigger the observed focusing. These findings establish a robust platform for studying transient nonlinear wave focusing phenomena in mechanical systems and offer insights for harnessing extreme wave localization for applications such as energy harvesting, waveguiding, and mechanical signal processing.

Low-dimensional materials have emerged as promising hosts for quantum emitters, whose emission typically arises from either strain-induced band bending or defect-induced two-level systems. Among these materials, transition metal dichalcogenide (TMD) monolayers have attracted particular attention; however, their performance is limited by strong photoluminescence (PL) quenching at room temperature. As TMDs transition from a direct to an indirect bandgap when moving from monolayers to multilayers, we herein propose a strategy to overcome this quenching limitation by exploiting the indirect bandgap of TMD bilayers in combination with a point defect doping. The indirect gap suppresses excitonic PL, while specific defects enable robust defect-mediated quantum emission. Using hybrid-functional density functional theory, we investigate substitutional carbon defects at chalcogen sites (S and Se) in WS2, WSe2, MoS2, and MoSe2 bilayers and comprehensively characterize their optical properties. Both neutral and singly negative charge states are found to be thermodynamically stable. Neutral defects exhibit singlet configurations with emission in the O- and C-band telecommunication windows, whereas negatively charged defects adopt doublet configurations featuring spin-selective transitions and near-infrared emission. The electron-phonon coupling strength, radiative lifetime, and dipole orientation are found to depend sensitively on both the host material and defect site, providing distinct fingerprints for experimental identification. Our findings, therefore, establish carbon-doped TMD bilayers as promising platforms for room-temperature defect-based quantum emitters operating at telecommunication wavelengths.

The characteristic impedance and the propagation coefficient are among the most important parameters for evaluating the acoustic performance of porous materials. This work investigates the influence of cylindrical modes in an impermeable tube and applies multiple microphones distributed along the tube circumference within the three-microphone framework to extend the valid frequency range of characteristic impedance measurement. During the extended broadband measurements, discontinuities or phase jumps are observed in the experimentally measured propagation coefficient of the porous material under test. A Bayesian inference is applied in a sequential manner to estimate the unwrapped propagation coefficient and characteristic impedance. The results demonstrate that the inferred parameters accurately capture the behavior of the transfer function, allowing accurate parameter estimation.

Printed circuit board (PCB) modelling is an important part of the PCB production process, in which the designer aims to optimize the desired output characteristics prior to physical PCB manufacturing. Due to the specific shape of PCBs, namely, thin and highly conductive components enclosed within a relatively simply shaped dielectric host, the PCB modelling problem is amenable to solution by the so-called 2.5D Method of Moments (MoM) applied to the integral equation solution of Maxwell's equations. For this purpose, an analytic expression for the Green's function of the host medium needs to be derived. Many studies exist in which expressions are derived for the transverse Green's function components in a waveguide, used for modelling planar metallization layers in shielded layered media. Works containing the full Green's function that allows modelling of both longitudinal and transverse currents are much fewer. In this study, we propose a tool to solve the shielded PCB modelling problem involving both transverse and longitudinal currents, with the Green's function in a layered waveguide derived using the S-matrix formalism. Our approach combines a straightforward, intuitive way of calculating the complete dyadic Green's function in a layered waveguide with the inherent numerical stability of the S-matrix method. The Green's function is expressed in terms of three sets of S-matrices associated with the PCB layers in which the electric current source and the electric field observation point are located. The MoM is implemented using surface rooftop, volume pulse, and linear basis functions, for which we provide the overlap integrals, to model planar metallization layers and wire-like vertical interconnects. The validity of the method is demonstrated on two numerical examples. The method can be extended to other bases to model objects of various shapes.

We present SPARC-atomSFE, a spectral finite-element package for accurate and efficient atomic structure calculations within the framework of Kohn-Sham density functional theory. The package supports both all-electron and norm conserving pseudopotential calculations across a comprehensive hierarchy of exchange-correlation approximations, spanning local, semilocal, and nonlocal functionals. The latter includes hybrid functionals and the many-body random phase approximation, for which we implement both the generalized Kohn-Sham approach and the optimized effective potential (OEP) method, with OEP necessary for eigenvalue-dependent functionals. Spatial discretization is based on an adaptive grid with element nodes distributed according to the Legendre–Gauss–Lobatto scheme, high-order $C^{0}$-continuous Lagrange polynomial basis functions, and Gauss–Legendre quadrature for numerical integration. We present systematic convergence studies and identify the computational parameters required to achieve target accuracies. We validate the accuracy of SPARC-atomSFE through representative calculations spanning the various exchange-correlations approximations, obtaining results that generally agree with values in the literature to within $1~\mu\text{Ha}$ or better.

Tropical cyclone (TC) inner-core surface wind vectors underpin intensity forecasting and storm-surge prediction, yet direct observations remain scarce: routine aircraft reconnaissance is confined to the North Atlantic and Eastern Pacific and, even there, samples each storm only episodically. CYGNSS is the only satellite that penetrates heavy precipitation to measure inner-core surface winds, but delivers directionless scalar wind speeds and is assimilated by no operational analysis system. Here we show that the full 10 m vector wind field inside the TC inner core can be reconstructed globally at 1.5 km resolution from sparse CYGNSS scalar observations alone, by generalising score-based diffusion assimilation to a nonlinear observation operator and injecting three TC boundary-layer constraints; we further propose a CYGNSS-intrinsic Observation Coverage Sufficiency (OCS) criterion that flags reliable reconstructions without external references. Applied to 4,955 snapshots of 249 TCs across all six active basins (2020-2022), the reconstructions reduce systematic Vmax bias against IBTrACS best-track by ~79% and ~75% relative to ERA5 and CCMP. Independent Tail Doppler Radar validation (47 storms) yields a wind speed RMSE of 6.9 m/s on the 23 coverage-sufficient cases (7.5 m/s overall); ablation across the full sample shows that the physical constraints cut wind-direction RMSE by 60% without degrading speed accuracy. The framework further supports joint assimilation of heterogeneous observations: adding only 11 dropsonde vectors to CYGNSS for TC FIONA (2022) reduces the cross-eye profile RMSE by 42%, outlining a practical pathway for fusing CYGNSS with SFMR, SAR and scatterometer data. The result is a globally consistent, observation-anchored kilometre-scale description of TC inner-core vector winds across all six active basins, including those without routine aircraft reconnaissance.

Electric double-layer capacitors (EDLCs), consisting of an ionic fluid between two metallic electrodes, are electrochemical energy storage devices complementary to batteries, allowing for a faster charge/discharge. The charging dynamics in response to a voltage step features a variety of regimes and relaxation timescales, depending on the applied voltage and the various lengths characterizing the system, most importantly the inter-electrode distance and the Debye length over which electrostatic effects are screened in the electrolyte. Inspired by recent works on "shortcut to adiabaticity" in colloidal systems, here we investigate the possibility to control the charge and discharge of planar EDLCs using time-dependent voltages. Specifically, our aim is to achieve a full charge or discharge within a finite time shorter than their intrinsic relaxation timescales. Within the Poisson-Nernst-Planck model and the small-voltage regime, we derive time-dependent protocols that can eliminate an arbitrary number of relaxation modes. This permits to approach the equilibrium charged state within a finite time, that can be in practice an order of magnitude faster than the natural equilibration time. We illustrate the relevance and efficacy of the method on polynomial drivings and show that the surface charge density, charge-density profiles, and global deviation from equilibrium (quantified by a Kullback-Leibler-like divergence) can all be significantly accelerated, even for driving times comparable to or shorter than the natural RC time of the system.

In this work, we investigate the role of spacers in tuning singlet-fission (SF) activity in aza-BODIPY dimers using a diabatic electronic-structure framework. A series of nine spacer-linked dimers is analyzed employing high-level multireference methods to evaluate the energetics and couplings of the locally excited (LE), charge-transfer (CT), and multiexcitonic (ME) states. The introduction of spacers reduces excessive state mixing that was observed in C-C bonded aza-BODIPY dimers and promotes the emergence of more distinct ME states, while preserving the CT-mediated interactions required for efficient SF. Furthermore, the substitution pattern, degree of conjugation, and introduction of acetylene units strongly modulate the relative state energetics and effective couplings, thereby tuning the SF rate over several orders of magnitude. These findings establish clear structure–property relationships and provide molecular design guidelines for optimizing SF activity in aza-BODIPY-based chromophores.

Stochastic resonance (SR) could amplify weak electric-field signals in nonlinear systems by means of the externally injected noises. Here we propose and experimentally demonstrate a modified SR method, termed squeezing-induced SR, implemented in the system involving a trapped ion behaving as a Duffing oscillator. We find that squeezing the phase noise of the oscillator results in amplified fluctuation of the corresponding amplitude, which helps achieve the SR. Since no auxiliary noise source is needed, the squeezing-induced SR may enhance the signal-to-noise ratio by 4.28 $\pm$ 0.39 dB compared to the conventional noise-induced SR under identical conditions of the electric-field detection. This technique offers a promising approach for developing atomic ion sensors for detecting weak electric-field signals.

Three-dimensional (3D) recirculating flows are often interpreted qualitatively from selected streamline visualizations. In separated flows, such recirculating motion is central to the drag modulation, but the local orientation of recirculation remains difficult to quantify in a field-based form. This work introduces an Eulerian binormal-axis diagnostic that locally evaluates the orientation of streamline turning at each point in the velocity field, yielding a spatially resolved field of the recirculating direction. Motivated by the Frenet-Serret binormal direction of a curved streamline, the diagnostic uses the velocity vector and its convective acceleration to extract the local streamline-turning axis without requiring explicit streamline integration. The resulting direction is encoded with barycentric RGB weights to visualize streamwise, spanwise, and wall-normal turning axis contributions. The diagnostic is first applied to Hill's spherical vortex, which provides a controlled analytic example of 3D recirculating motion for interpreting the binormal-axis direction and the associated barycentric RGB encoding. It is then applied to the mean field of a pressure-gradient-induced 3D separation bubble. The resulting visualizations show that the diagnostic reveals orientation changes that are not apparent from streamline visualization. The proposed diagnostic therefore converts qualitative streamline impressions into a spatially resolved measure of local streamline-turning orientation, providing a quantitative complement to conventional 3D flow visualization.

The Super Tau-Charm Facility (STCF) is a proposed high-luminosity electron-positron collider operating in the beam energy range of 1-3.5 GeV, targeting a peak luminosity larger than $0.5\times10^{35}\ \mathrm{cm^{-2}s^{-1}}$ at 2 GeV. In this regime, the combination of beam-beam interaction in the crab-waist scheme and low beam energy imposes stringent constraints on dynamic aperture, momentum acceptance, and Touschek lifetime. In this paper, we present an alternative one-fold lattice design for the STCF collider rings, developed within a systematic optimization framework. The approach consists of three stages: (i) lattice-agnostic global parameter optimization using a parameter optimization model that consistently incorporates luminosity performance, beam-beam limits, and collective effects; (ii) optics design based on a compact interaction region with local chromatic correction and crab-waist sextupoles; and (iii) global nonlinear optimization combining analysis-driven methods and tracking-based refinement. The optimized lattice achieves the more ambitious luminosity of $1\times10^{35}\ \mathrm{cm^{-2}s^{-1}}$ while maintaining a Touschek lifetime of about 600 s at 2 GeV, with sufficient dynamic aperture and momentum acceptance for stable operation. The results highlight the critical role of local nonlinear control in the interaction region and demonstrate that the proposed optimization strategy provides an effective and general methodology for the design of high-luminosity low-energy colliders.

In contrast to dyadic interactions, higher-order interactions may contain one another, with subgroups naturally embedded within larger groups. These containment patterns arise empirically in ecology, sociology, computer science and the science of science, and have been studied under the names nestedness, simpliciality, encapsulation, and inclusion. In this chapter, we review each of these measures and unify them through a mathematical object known as the encapsulation directed acyclic graph, formulating each measure as a function of its properties. We demonstrate that nested structure is prevalent in social systems across several domains, show that different measures capture complementary aspects of this structure, and find that the absence of nestedness can itself be a powerful indicator of the mesoscale organization of a system.

Addressing the need for efficient and integrated multiscale crystallographic and defect analyses of advanced materials, this paper presents the implementation of a new multi-configuration detection system, integrating a single Timepix3-based direct electron detector (DED) in a scanning electron microscope (SEM). By combining precise translation and rotation movements, this system enables, for the first time, the use of the same detector to realize all principal diffraction geometries. These include conventional Electron BackScatter Diffraction (EBSD), off-axis Reflexion Kikuchi Diffraction (RKD), and Transmission Kikuchi Diffraction (TKD) in on-, off- and near-axis configurations. Furthermore, transitions between all these geometries are accomplished without hardware modification. On the other hand, this work presents efficient reconstruction of electron images using the detector data-driven feature, extending thus its applicability to BackScattered Electron imaging (BSE), Electron Channelling Contrast Imaging (ECCI) and Scanning Transmission Electron Imaging in SEM (STEM-in-SEM) characterizations. High-quality Kikuchi patterns easily indexable were acquired across all geometries as well as micrographs of dislocations in both reflection and transmission modes. This is achieved thanks to the flexibility of the implemented detector, the optimizations made in acquisition parameters, such as energy filtering settings, and the efficiency of the developed custom approach used for electron data post-processing. Through this work, it is demonstrated that with a single DED assisted by an orientable support, it is possible to perform multiple advanced microstructural characterizations of both bulk samples and thin foils in the same SEM.

University extension activities play a fundamental role in bridging the gap between academia and society by fostering the socialization of scientific knowledge. This study reports and analyzes an outreach activity conducted in a public space, involving undergraduate students enrolled in Physics I, Physics III, and Physics IV courses within the Physics Teacher Education Program at the State University of the Tocantina Region of Maranhao (UEMASUL). The activity was developed through the design and presentation of didactic experiments using low-cost materials. Its main objectives were to disseminate fundamental physics concepts to the community, stimulate public interest in science, and provide pre-service teachers with a formative experience integrating theory, practice, and social responsibility. Data were collected from questionnaires adminisvelopment of communication skills, and the strengthening of the university's social role, while also fostering scientific curitered to visitors (n = 52). The results indicate that the activity significantly contributed to student learning, the deosity among participants.

Distributed Acoustic Sensing (DAS) converts existing fibre-optic cables into dense seismic arrays at near-zero deployment cost, but measures strain rate rather than particle velocity – the quantity required by virtually all seismological analysis tools. Converting strain rate to particle velocity by numerical integration is ill-posed: the integration constant is undefined and noise accumulates without bound. We present DANTE (DAS-to-velocity via physics-informed neural operator for Acoustic-wave recoNstruction in heTErogeneous media), a Fourier Neural Operator (FNO) trained entirely on synthetic data that enforces two physics constraints: (i) the exact kinematic relation between DAS strain rate and the spatial gradient of particle velocity, and (ii) the one-dimensional elastic wave equation. These constraints resolve the undetermined integration constant and suppress noise without requiring co-located seismometers. On a test set of 200 heterogeneous synthetic wavefields, DANTE achieves a mean output SNR of $15.3 \pm 8.8$ dB, Pearson correlation $r = 0.907$, and SSIM $= 0.976$, corresponding to a mean SNR improvement of approximately $+15$ dB over the best conventional baseline (trace stacking, $n = 10$, $0.02 \pm 0.06$ dB), and up to $+28.8$ dB on the most challenging samples. Zero-shot inference on seven real microseismic events from the Utah FORGE 2019 DAS dataset yields a kinematic residual of 0.003–0.005, five times lower than the synthetic test baseline, confirming generalisation to real field data with no fine-tuning and no seismometers.

Presently, there is a steady state approach in Computational fluid dynamics (CFD) to obtain a steady solution directly from the steady state governing equations. Whereas, for obtaining a time-periodic flow solution, the present unsteady governing equations-based CFD approach starts from an initial condition and requires a large computational time during the initial non-periodic transient phase before reaching the periodic state. For obtaining the periodic flow directly, without transient simulations that may not be of interest, our objective is to propose a Physics Informed Neural Network (PINN)-based periodic CFD approach. The motivation is a substantial reduction in computational time by a meshless PINN-based periodic CFD solver as compared to the present mesh-based transient-to-periodic solver. Proof-of-concept, for the periodic CFD approach, is demonstrated here for 2D periodic heat diffusion and fluid flow problems. The proposed PINN-based periodic solver primarily focuses on the time-periodic state, optimizing the neural network model's trainable parameters to precisely fit a smaller time window (one time-period) rather than the temporal domain starting from the initial condition. After presenting a verification study, effect of the PINN-related various hyperparameters such as the number of collocation points, neural network architecture, and point spacing for numerical differentiation, on computational time and accuracy are presented. Our results demonstrate that the PINN-based periodic solver takes substantially less computational time to achieve almost same accuracy as that obtained by the traditional transient-to-periodic solver.