RF photonic transversal signal processors, which combine reconfigurable electrical digital signal processing and high-bandwidth photonic processing, provide a powerful solution for achieving adaptive high-speed information processing. Recent progress in optical microcomb technology provides compelling multi-wavelength sources with compact footprint, yielding a variety of microcomb-based RF photonic transversal signal processors implemented by either discrete or integrated components. Although operating based on the same principle, processors in these two forms exhibit distinct performance. This letter presents a comparative investigation into their performance. First, we compare the performance of state-of-the-art processors, focusing on the processing accuracy. Next, we analyze various factors that contribute to the performance differences, including tap number and imperfect response of experimental components. Finally, we discuss the potential for future improvement. These results provide a comprehensive comparison of microcomb based RF photonic transversal signal processors implemented using discrete and integrated components and provide insights for their future development.

The utilization and engineering of thermo-optic effects have found broad applications in integrated photonic devices, facilitating efficient light manipulation to achieve various functionalities. Here, we perform both an experimental characterization and theoretical analysis of these effects in integrated micro-ring resonators in high index doped silica (HIDS), which has had many applications in integrated photonics and nonlinear optics. By fitting the experimental results with theory, we obtain fundamental parameters that characterize their thermo-optic performance, including the thermo-optic coefficient, the efficiency for the optically induced thermo-optic process, and the thermal conductivity. The characteristics of these parameters are compared to those of other materials commonly used for integrated photonic platforms, such as silicon, silicon nitride, and silica. These results offer a comprehensive insight into the thermo-optic properties of HIDS based devices. Understanding these properties is essential for efficiently controlling and engineering them in many practical applications.

This paper is a continuation of a series of works, devoted to various aspects of the June 30, 1908 Tunguska event. In those days, various sky optical anomalies were observed over a large area. In the presented paper, the main focus is on bright nights. Bright nights are the glow (airglow) of the night sky, visible to the naked eye. In the author's opinion, atmospheric gravitational waves played a large role in their appearance in 1908, as evidenced by the behavior of atmospheric pressure in late June and early July 1908. Some other geophysical peculiarities of that period, which could have affected the appearance of bright nights, and possibly other optical sky anomalies, are also considered.

Purpose: Lung cancer poses a significant global health challenge, necessitating improved prognostic methods for personalized treatment. This study introduces a censor-aware semi-supervised learning (SSL) that integrates clinical and imaging data, addressing biases in traditional models handling censored data. Methods: We analyzed clinical, PET, and CT data from 199 lung cancer patients from TCIA and BC Cancer Agency, focusing on overall survival (OS) time as the primary outcome. Handcrafted (HRF) and Deep Radiomics features (DRF) were extracted after preprocessing using ViSERA software and were combined with clinical features (CF). Feature dimensions were optimized using Principal Component Analysis (PCA), followed by the application of supervised learning (SL) and SSL. SSL incorporated pseudo-labeling of censored data to improve performance. Seven regressors and three hazard ratio survival analysis (HRSA) algorithms were optimized using five-fold cross-validation, grid search and external test bootstrapping. Results: For PET HRFs, SSL reduced the mean absolute error (MAE) by 26.54%, achieving 1.55 years with PCA+decision tree regression, compared to SL's 2.11 years with PCA+KNNR (p<0.05). Combining HRFs (CT_HRF) and DRFs from CT images using SSL+PCA+KNNR achieved an MAE of 2.08.45 years, outperforming SL's 2.26 years by 7.96% (p<0.05). In HRSA, CT_HRF applied to PCA+Component Wise Gradient Boosting Survival Analysis achieved an external c-index of 0.65, effectively differentiating high- and low-risk groups. Conclusion: This study demonstrated that the SSL strategy significantly outperformed SL across PET, CT, and CF. Thereby, censor-aware SSL applied to HRFs from PET images significantly improved survival prediction performance by 26.54% compared to the SL approach.

Because it allows a rigorous separation between reversible and irreversible processes, the concept of available potential energy (APE) has become central to the study of turbulent stratified fluids. In ocean modelling, it is fundamental to the parameterisation of meso-scale ocean eddies and of the turbulent mixing of heat and salt. However, how to apply APE theory consistently to local or regional subdomains has been a longstanding source of confusion due to the globally defined Lorenz reference state entering the definition of APE and of buoyancy forces being generally thought to be meaningless in those cases. In practice, this is often remedied by introducing heuristic `localised' forms of APE density depending uniquely on region-specific reference states, possibly diverging significantly from the global Lorenz reference state. In this paper, we argue that this practice is problematic because it cannot consistently describes the turbulent APE cascades associated with the inter-scale energy transfers between the APE produced at large scales -- which depends on the global Lorenz reference state -- and the APE of smaller scales. To avoid this difficulty, we argue that localised forms of APE density should be defined as the eddy APE component of an exact mean/eddy decomposition of the APE density. The eddy APE density thus defined exhibits a much weaker dependency on the global Lorenz reference state than the mean APE, in agreement with physical intuition, but with a different structure than that of existing heuristic localised APE forms. The results are important, because they establish a rigorous physical basis for linking parameterised energy transfers to observable viscous and diffusive dissipation rates, which is a pivotal goal of energetically consistent ocean models.

We derive a relativistic field equation for the trace of the metric perturbation beyond the weak field approximation to the Einstein field equations. The dynamics is governed by a massive Klein-Gordon equation on curved space-time, where the effective mass of the field is associated with the material and the dark energy content via the cosmological term. We solve the equation in the case of a Schwarzschild black hole and show that it can be cast into an effective Schr\"odinger form with an effective geometric potential which binds the zero angular momentum states. The non-zero angular momentum states experience a positive potential peak before the event horizon pointing to gravitational waves scattering. Black holes scatter gravitational waves and thus we provide an unambiguous testable prediction of black hole existence. The Newtonian limit for this equation points to the possibility of reflecting gravitational waves at interfaces with sharp density boundary, thus opening up gravitational wave propulsion physics. We discuss this type of propulsion in the light of Newton's third law of Mechanics. Compelling questions such as the existence of quanta of this field which may account for the dark matter content are also addressed.

We deduce the quantum mechanical prediction of $-{\bf a}\cdot{\bf b}$ for the singlet spin state employing local measurement functions following Bell's approach. Our derivation is corroborated through a computational simulation conducted via the Mathematica programming environment.

This work presents a novel approach to distribute orbitals into subspaces within electron-pairing-based natural orbital functionals (NOFs). This approach modifies the coupling between weakly and strongly occupied orbitals by applying an alternating orbital sorting strategy. In contrast to the previous orbital sorting that enforced electron pairing within subspaces of contiguous orbitals, the new approach provides greater flexibility, enabling a calculation scheme where the size of the subspaces can be gradually expanded. As a consequence, one can start using subspaces of only one weakly occupied orbital (perfect pairing) and progressively enlarge their size by incorporating more weakly occupied orbitals (extended pairing) up to the maximum size allowed by the basis set. In this way, the alternate orbital sorting allows solving first a simpler problem with small subspaces and leverage its orbital solution for the more intensive problem with larger subspaces, thereby reducing the overall computational cost and improving convergence, as we observed in the DoNOF program. The efficiency provided by the new sorting approach has been validated through benchmark calculations in H2O, H2O2, and NH3. In particular, we compared three strategies: i) solving directly the calculation with the largest subspaces (one-shot strategy), as was usually done before this work, ii) starting with perfect pairing and stepwise increasing the number of orbitals in the subspaces one by one until reaching the maximum size (incremental strategy), and iii) starting with perfect pairing and transitioning directly to the maximum subspace size (two-step strategy). Our results show that the two-step approach emerges as the most effective strategy, achieving the lowest computational cost while maintaining high accuracy.

Hurricane track forecasting remains a significant challenge due to the complex interactions between the atmosphere, land, and ocean. Although AI-based numerical weather prediction models, such as Google Graphcast operation, have significantly improved hurricane track forecasts, they currently function as atmosphere-only models, omitting critical land and ocean interactions. To investigate the impact of land feedback, we conducted independent simulations using the physics-based Hurricane WRF experimental model to assess how soil moisture variations influence storm trajectories. Our results show that land surface conditions significantly alter storm paths, demonstrating the importance of land-atmosphere coupling in hurricane prediction. Although recent advances have introduced AI-based atmosphere-ocean coupled models, a fully functional AI-driven atmosphere-land-ocean model does not yet exist. Our findings suggest that AI-NWP models could be further improved by incorporating land surface interactions, improving both forecast accuracy and explainability. Developing a fully coupled AI-based weather model would mark a critical step toward more reliable and physically consistent hurricane forecasting, with direct applications for disaster preparedness and risk mitigation.

A generalized kinetic ion induced electron emission (IIEE) model is developed to obtain the emitted electron energy spectrum for a distribution of ion impacts on a metallic surface. This framework is implemented as a boundary condition for the continuum kinetic Boltzmann equation. The IIEE model is used to study how emissions affect sheath formation near biased Z-pinch electrodes. 1X-1V (one spatial and one velocity dimension) Boltzmann-Poisson simulations are performed for a proton-electron plasma doubly bounded by two biased copper electrodes with and without IIEE at bias potentials from 0 kV to 9 kV. The ions are accelerated to higher energies by the sheath potentials at the electrodes inducing electron emission. The secondary electron yield (SEY), defined as the ratio of the flux of emitted electrons to impacting ions, increases with bias potential at both electrodes, but more significantly at the cathode. Despite the SEY crossing 1 at 7 kV, a classical sheath, rather than a space-charge limited or inverse sheath, forms for all cases. The emitted electrons present as a beam that is accelerated by the sheath potential into the domain resulting in increased electron temperatures due to collisions. For bias potentials greater than 2 kV, the potential difference at the cathode is sufficiently strong for emissive heating to increase the plasma potential compared to emissionless simulations. The emitted electrons increase the current in the domain from 130 kA to 199 kA closely matching the experimental value of 200 kA.

Kinetic plasma studies often require computing integrals of the velocity distribution over a complex-valued pole. The standard method is to solve the integral in the complex plane using the Plemelj theorem, resulting in the standard plasma dispersion function for Maxwellian plasmas. For non-Maxwellian plasmas, the Plemelj theorem does not generalize to an analytic form, and computational methods must be used. In this paper, a new computational method is developed to accurately integrate a non-Maxwellian velocity distribution over an arbitrary set of complex valued poles. This method works by keeping the integration contour on the real line, and applying a trapezoid rule-like integration scheme over all discretized intervals. In intervals containing a pole, the velocity distribution is linearly interpolated, and the analytic result for the integral over a linear function is used. The integration scheme is validated by comparing its results to the analytic plasma dispersion function for Maxwellian distributions. We then show the utility of this method by computing the Thomson scattering spectra for several non-Maxwellian distributions: the kappa, super Gaussian, and toroidal distributions. Thomson scattering is a valuable plasma diagnostic tool for both laboratory and space plasmas, but the technique relies on fitting measured wave spectra to a forward model, which typically assumes Maxwellian plasmas. Therefore, this integration method can expand the capabilities of Thomson scatter diagnostics to regimes where the plasma is non-Maxwellian, including high energy density plasmas, frictionally heated plasmas in the ionosphere, and plasmas with a substantial suprathermal electron tail.

This article reviews the concepts and methods of variational path sampling. These methods allow computational studies of rare events in systems driven arbitrarily far from equilibrium. Based upon a statistical mechanics of trajectory space and leveraging the theory of large deviations, they provide a perspective with which dynamical phenomena can be studied with the same types of ensemble reweighting ideas that have been used for static equilibrium properties. Applications to chemical, material, and biophysical systems are highlighted.

The binding energies of the five bound rotational levels $J=0-4$ in the highest vibrational level $v=14$ in the X$^1\Sigma_g^+$ ground electronic state of H$_2$ were measured in a three-step ultraviolet-laser experiment. Two-photon UV-photolysis of H$_2$S produced population in these high-lying bound states, that were subsequently interrogated at high precision via Doppler-free spectroscopy of the F$^1\Sigma_g^+$ - X$^1\Sigma_g^+$ system. A third UV-laser was used for detection through auto-ionizing resonances. The experimentally determined binding energies were found to be in excellent agreement with calculations based on non-adiabatic perturbation theory, also including relativistic and quantum electrodynamical contributions. The $s$-wave scattering length of the H + H system is derived from the binding energy of the last bound $J=0$ level via a direct semi-empirical approach, yielding a value of $a_s$ = 0.2724(5) $a_0$, in good agreement with a result from a previously followed theoretical approach. The subtle effect of the $m\alpha^4$ relativity contribution to $a_s$ was found to be significant. In a similar manner a value for the $p$-wave scattering volume is determined via the $J=1$ binding energy yielding $a_p$ = -134.0000(6) $a_0^3$. The binding energy of the last bound state in H$_2$, the ($v=14$, $J=4$) level, is determined at 0.023(4) cm$^{-1}$, in good agreement with calculation. The effect of the hyperfine substructure caused by the two hydrogen atoms at large internuclear separation, giving rise to three distinct dissociation limits, is discussed.

We study the transport and deposition of inhaled aerosols in a mid-generation, mucus-lined lung airway, with the aim of understanding if and how airborne particles can avoid the mucus and deposit on the airway wall -- an outcome that is harmful in case of allergens and pathogens but beneficial in case of aerosolized drugs. We adopt the weighted-residual integral boundary-layer model of Dietze and Ruyer-Quil (J. Fluid Mech., vol. 762, 2015, pp. 68-109) to describe the dynamics of the mucus-air interface, as well as the flow in both phases. The transport of mucus induced by wall-attached cilia is also considered, via a coarse-grained boundary condition at the base of the mucus. We show that the capillary-driven Rayleigh-Plateau instability plays an important role in particle deposition by drawing the mucus into large annular humps and leaving substantial areas of the wall exposed to particles. We find, counter-intuitively, that these mucus-depleted zones enlarge on increasing the mucus volume fraction. Particles spanning a range of sizes (0.1 to 50 microns) are modelled using the Maxey-Riley equation, augmented with Brownian forces. We find a non-monotonic dependence of deposition on size. Small particles diffuse across streamlines due to Brownian motion, while large particles are thrown off streamlines by inertial forces -- particularly when air flows past mucus humps. Intermediate-sized particles are tracer-like and deposit the least. Remarkably, increasing the mucus volume need not increase entrapment: the effect depends on particle-size, because more mucus produces not only deeper humps that intercept inertial particles, but also larger depleted-zones that enable diffusive particles to deposit on the wall.

This paper aims to provide physics teachers with tools to help deepen the understanding of the laws of electromagnetism. The fundamental contributions of our proposal are: a) to use quotes from mythical characters in the history of science as a motivating educational resource; b) to promote the discussion of striking and fundamental topics; c) to mention diverse approaches and stimulate the search for correct answers to provocative questions. Citations from Einstein refer to principal contributions made by Newton, Maxwell and himself. Emphasis is placed on the cognitive value of differential (local, infinitesimal) analysis of fundamental concepts (field structure, causality, field relativistic transformations). The unity of electromagnetism is analyzed from the point of view of special relativity. It is clarified that descriptions suggesting that the magnetic field is dispensable are contrary to the Einstenian approach: they assume that, to describe the interaction between moving charges, there is a preferred coordinate system for each particular problem. An introductory presentation of the tensor form of Maxwell equations is provided.

We investigate the feasibility of using rocket launches, specifically rocketquakes, as a seismic source to image subsurface velocity and geology of planetary bodies. Toward this goal, we record the seismic vibrations excited by a Falcon 9 rocket launch from Vandenberg Space Force Base (SFB) near Lompoc, California. Nine passive three-component (3C) seismometers were deployed every 18.75 meters along a 45-degree line from the launch site starting at the offset of about 7 km kilometers from the launch pad. Results show that coherent body waves can be recorded with a P-velocity of more than 2.0 km/s and a penetration depth of 1 km or deeper. Stronger Rayleigh waves were also recorded and inverted to give an S-velocity profile to a depth of 60 m. Notably, a 3C recorder placed approximately 15 km from the launch site did not capture any discernible body wave arrivals. The imaging techniques employed for rocketquake seismology integrate inversion methods from earthquake and exploration seismology, yielding the P- and S-velocity profiles of the subsurface. These results suggest that rocket launches as seismic sources will provide unprecedented opportunities for identifying the subsurface hazards, faults, tunnels, water ice, and mineral deposits of planetary bodies and their moons.

Two-stage exams, which pair a traditional individual exam with a subsequent collaborative exam, are widely popular with both students and faculty for fostering deep engagement, collaboration, and immediate feedback. Over the last decade, this assessment model has gained substantial traction in STEM college courses; however, holding both stages during class sessions often limits the full potential of two-stage exams, deterring many instructors from adopting them. To accommodate these constraints, instructors must either reduce the exam's content depth and coverage, or hold evening, in-person exam sessions at fixed times, which can often be impractical for students and faculty with external commitments. In this paper, we introduce an alternative asynchronous approach that allows the collaborative portion to be completed unsupervised and outside of regular class hours, within 48 hours of the individual exam. This method not only eases logistical constraints but also allows more time for collaboration, potentially enhancing feedback quality. Our findings show that students equate their engagement, collaboration, and feedback quality during the asynchronous collaborative exam with that of standard in-class exams and show signs of retention of learning. This cost-effective approach requires no extra time or resources and could promote widespread adoption of this effective assessment method, especially in online-only and general STEM courses.

Particularly for aneutronic fusion schemes, it is advantageous to manipulate the fuel species differently from one another, as well as expel ash promptly. The ponderomotive effect can be used to selectively manipulate particles. It is commonly a result of particle-wave interactions and has a complex dependence on the particle charge and mass, enabling species-selectivity. If the plasma is rotating, e.g. due to $\mathbf{E} \times \mathbf{B}$ motion, the ponderomotive effect can be generated using static (i.e., time-independent) perturbations to the electric and magnetic fields, which can be significantly cheaper to produce than time-dependent waves. This feature can be particularly useful in rotating mirror machines where mirror confinement can be enhanced by rotation, both through centrifugal confinement and additionally through a ponderomotive interaction with a static azimuthal perturbation. Some static perturbations generate a ponderomotive barrier, other perturbations can generate either a repulsive barrier or an attractive ponderomotive well which can be used to attract particles of a certain species while repelling another. The viability of each of these effects depends on the specifics of the rotation profile and temperature, and the resultant dispersion relation in the rotating plasma.

Recent theoretical studies in quantum spectroscopy have emphasized the potential of non-classical correlations in entangled photon pairs for selectively targeting specific nonlinear optical processes in nonlinear optical responses. However, because of the extremely low intensity of the nonlinear optical signal generated by irradiating molecules with entangled photon pairs, time-resolved spectroscopic measurements using entangled photons have yet to be experimentally implemented. In this paper, we theoretically propose a quantum spectroscopy measurement employing a time-resolved fluorescence approach that aligns with the capabilities of current photon detection technologies. The proposed quantum spectroscopy affords two remarkable advantages over conventional two-dimensional electronic spectroscopy. First, it enables the acquisition of two-dimensional spectra without requiring control over multiple pulsed lasers. Second, it reduces the complexity of the spectra because the spectroscopic signal is contingent upon the nonlinear optical process of spontaneous emission. These advantages are similar to those achieved in a previous study [Fujihashi et al., J. Chem. Phys. 160, 104201 (2024)]. However, our approach achieves sufficient signal intensities that can be readily detected using existing photon detection technologies, thereby rendering it a practicable. Our findings will potentially facilitate the first experimental real-time observation of dynamic processes in molecular systems using quantum entangled photon pairs.

Airborne Gamma-Ray Spectrometry (AGRS) is a critical tool for radiological emergency response, enabling the rapid identification and quantification of hazardous terrestrial radionuclides over large areas. However, existing calibration methods are inherently limited to only a few gamma-ray sources, excluding most gamma-ray emitting radionuclides released in severe nuclear accidents and nuclear weapon detonations, which in turn compromise effective response and accurate risk assessments in such incidents. Here, we present a high-fidelity Monte Carlo model that overcomes these limitations, offering full-spectrum calibration of any gamma-ray source. Unlike previous approaches, our model integrates a detailed mass model of the entire aircraft and a calibrated non-proportional scintillation model, enabling accurate event-by-event prediction of the spectrometer's scintillation response to arbitrarily complex gamma-ray fields. Through a series of validation measurements in near-, mid-, and far-field scenarios, we show that the developed model not only effectively addresses the large deficiencies of previous approaches, but also achieves the accuracy and precision required to supersede traditional empirical calibration methods. These results mark a major advancement in AGRS. The developed methodology not only allows for the quantification of any gamma-ray source, but also reduces calibration time and costs, minimizes reliance on high-intensity calibration sources, and eliminates the generation of related radioactive waste. In addition, these advancements offer promising new capabilities for AGRS applications beyond emergency response, including the quantification of the cosmic-ray induced gamma-ray flux in the atmosphere and probing trace-level airborne radionuclides.

Dielectric structures can support low-absorption optical modes, which are attractive for engineering light-matter interactions with excitonic resonances in two-dimensional (2D) materials. However, the coupling strength is often limited by the electromagnetic field being confined inside the dielectric, reducing spatial overlap with the active excitonic material. Here, we demonstrate a scheme for enhanced light-matter coupling by embedding excitonic tungsten disulfide (WS$_2$) within dielectric hexagonal boron nitride (hBN), forming a van der Waals (vdW) heterostructure that optimizes the field overlap and alignment between excitons and optical waveguide modes. To tailor diffractive coupling between free-space light and the waveguide modes in the vdW heterostructure, we fabricate Fourier surfaces in the top hBN layer using thermal scanning-probe lithography and etching, producing sinusoidal topographic landscapes with nanometer precision. We observe the formation of exciton-polaritons with a Rabi splitting indicating that the system is at the onset of strong coupling. These results demonstrate the potential of Fourier-tailored vdW heterostructures for exploring advanced optoelectronic and quantum devices.

Through Random Telegraph Noise (RTN) analysis, valuable information can be provided about the role of defect traps in fine tuning and reading of the state of a nanoelectronic device. However, time domain analysis techniques exhibit their limitations in case where unstable RTN signals occur. These instabilities are a common issue in Multi-Level Cells (MLC) of resistive memories (ReRAM), when the tunning protocol fails to find a perfectly stable resistance state, which in turn brings fluctuations to the RTN signal especially in long time measurements and cause severe errors in the estimation of the distribution of time constants of the observed telegraphic events, i.e., capture/emission of carriers from traps. In this work, we analyze the case of the unstable filaments in silicon nitride-based ReRAM devices and propose an adaptive filter implementing a moving-average detrending method in order to flatten unstable RTN signals and increase sufficiently the accuracy of the conducted measurements. The te and tc emission/capture time constants of the traps, respectively, are then calculated and a cross-validation through frequency domain analysis (Lorentzian fitting) was performed proving that the proposed method is accurate.

Several resistive memory technologies (RRAMs) are prominent, but few are fulfilling the requirements for CMOS integration and meet the commercialization standards. In this work, the fabrication and electrical characterization of a fully compatible CMOS process on SOI substrate of 1R silicon SiN-based resistance switching (RS) MIS devices is presented. The RS characteristics are compared with the same devices previously fabricated on bulk silicon.

Lugiato-Lefever equation (LLE) is a nonlinear Schr\"odinger equation with damping, detuning and driving terms, introduced as a model for Kerr combs generation in ring-shape resonators and more recently, in the form of a variant, in Fabry-Perot (FP) resonators. The aim of this paper is to present some numerical methods that complement each other to solve the LLE in its general form both in the dynamic and in the steady state regimes. We also provide some mathematical properties of the LLE likely to help the understanding and interpretation of the numerical simulation results.

Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce the $\textit{end-to-end}$ optimization framework AIDO that leverages a diffusion model as a surrogate for the full simulation and reconstruction chain, enabling gradient-based design exploration in both continuous and discrete parameter spaces. Although this framework is applicable to a broad range of detectors, we illustrate its power using the specific example of a sampling calorimeter, focusing on charged pions and photons as representative incident particles. Our results demonstrate that the diffusion model effectively captures critical performance metrics for calorimeter design, guiding the automatic search for layer arrangement and material composition that aligns with known calorimeter principles. The success of this proof-of-concept study provides a foundation for future applications of end-to-end optimization to more complex detector systems, offering a promising path toward systematically exploring the vast design space in next-generation experiments.

Optical clock networks connected by phase-coherent links offer significant potential for advancing fundamental research and diverse scientific applications. Free-space optical frequency transfer extends fiber-based connectivity to remote areas and holds the potential for global coverage via satellite links. Here we present a compact and robust portable, rack-integrated two-way free-space link characterization system. Equipped with plug-and-play capabilities, the system enables straightforward interfacing with various optical systems and facilitates quick deployment for field experiments. In this work, we achieve a fractional frequency instability of $2.0 \times 10^{-19}$ for an averaging time of 10 s over a 3.4 km horizontal fully folded intra-city free-space link. Moreover, the system maintains an uptime of $94\%$ over 15 hours, illustrating its reliability and effectiveness for high-precision optical frequency comparisons over free-space.

Metal phthalocyanines, a highly versatile class of aromatic, planar, macrocyclic molecules with a chelated central metal ion, are topical objects of ongoing research and particularly interesting due to their magnetic properties. However, while current focus lies almost exclusively on spin-Zeeman-related effects, the high symmetry of the molecule and its circular shape suggests the exploitation of light-induced excitation of twofold degenerate vibrational states in order to generate, switch and manipulate magnetic fields at the nanoscale. The underlying mechanism is a molecular pseudorotation that can be triggered by infrared pulses and gives rise to a quantized, small but controllable magnetic dipole moment. We investigate the optical stimulation of vibrationally-induced molecular magnetism and estimate changes in the magnetic shielding constants for confirmation by future experiments.

Magnetotelluric (MT) forward modeling is fundamental for improving the accuracy and efficiency of MT inversion. Neural operators (NOs) have been effectively used for rapid MT forward modeling, demonstrating their promising performance in solving the MT forward modeling-related partial differential equations (PDEs). Particularly, they can obtain the electromagnetic field at arbitrary locations and frequencies. In these NOs, the projection layers have been dominated by multi-layer perceptrons (MLPs), which may potentially reduce the accuracy of solution due to they usually suffer from the disadvantages of MLPs, such as lack of interpretability, overfitting, and so on. Therefore, to improve the accuracy of MT forward modeling with NOs and explore the potential alternatives to MLPs, we propose a novel neural operator by extending the Fourier neural operator (FNO) with Kolmogorov-Arnold network (EFKAN). Within the EFKAN framework, the FNO serves as the branch network to calculate the apparent resistivity and phase from the resistivity model in the frequency domain. Meanwhile, the KAN acts as the trunk network to project the resistivity and phase, determined by the FNO, to the desired locations and frequencies. Experimental results demonstrate that the proposed method not only achieves higher accuracy in obtaining apparent resistivity and phase compared to the NO equipped with MLPs at the desired frequencies and locations but also outperforms traditional numerical methods in terms of computational speed.

The Tadah! code provides a versatile platform for developing and optimizing Machine Learning Interatomic Potentials (MLIPs). By integrating composite descriptors, it allows for a nuanced representation of system interactions, customized with unique cutoff functions and interaction distances. Tadah! supports Bayesian Linear Regression (BLR) and Kernel Ridge Regression (KRR) to enhance model accuracy and uncertainty management. A key feature is its hyperparameter optimization cycle, iteratively refining model architecture to improve transferability. This approach incorporates performance constraints, aligning predictions with experimental and theoretical data. Tadah! provides an interface for LAMMPS, enabling the deployment of MLIPs in molecular dynamics simulations. It is designed for broad accessibility, supporting parallel computations on desktop and HPC systems. Tadah! leverages a modular C++ codebase, utilizing both compile-time and runtime polymorphism for flexibility and efficiency. Neural network support and predefined bonding schemes are potential future developments, and Tadah! remains open to community-driven feature expansion. Comprehensive documentation and command-line tools further streamline the development and application of MLIPs.

Large-scale circulation (LSC) quasi-stably emerges in the turbulent Rayleigh-B\'{e}nard convection, and intermittently reverses its rotational direction in two-dimensional turbulent convection. In this paper, direct numerical simulations of the intermittent reversals of the LSC in a two-dimensional square domain are performed, and the time series of the total angular momentum indicating the rotational direction of the LSC is predicted by reservoir computing whose input consists of the shear rates and temperatures at six locations on the sidewalls. The total angular momentum in the simulation after times shorter than half the typical duration of the quasi-stable states is successfully reproduced by the locally-measurable quantities on the sidewalls because the secondary rolls accompanied by the boundary flow characterize the reversal of the LSC. The successful prediction by such sparse input derived from local measurements on the sidewalls demonstrates that the reservoir computing prediction of the reversal is feasible in laboratory experiments and industrial applications. On the other hand, long-term prediction often provides the total angular momentum opposite in sign to the one in the simulations in the late parts of long quasi-stable states. The statistical independence of each reversal implies that the prediction after the reversal is difficult or even impossible, and the training data in the late part in the long quasi-stable state, which rarely appears, is contaminated by the statistically-independent angular momentum in the subsequent quasi-stable state.

In this paper, a Burnett-level discrete Boltzmann model (DBM) is proposed for the compressible flow in a force field, and a discrete velocity set with 25 velocities is constructed for the DBM, featuring good spatial symmetry. In the discrete Boltzmann equation, both the discrete equilibrium distribution function and the force term satisfy 25 independent moment relations and are computed with the matrix inversion method. This approach ensures high physical accuracy, computational efficiency, and ease of implementation. Through the Chapman-Enskog expansion analysis, it is demonstrated that the current DBM can recover the Burnett equations for the compressible system under force in the continuum limit. Moreover, the DBM has the capability of capturing essential thermodynamic nonequilibrium behaviors. Finally, the model is validated through five typical benchmarks, including the free falling, Sod shock tube, sound wave, thermal Couette flow, and Rayleigh-Taylor instability.

In recent years, the gain suppression mechanism has been studied for large localized charge deposits in Low-Gain Avalanche Detectors (LGADs). LGADs are a thin silicon detector with a highly doped gain layer that provides moderate internal signal amplification. Using the CENPA Tandem accelerator at the University of Washington, the response of LGADs with different thicknesses to MeV-range energy deposits from a proton beam were studied. Three LGAD prototypes of 50~$\mu$m, 100~$\mu$m, 150~$\mu$m were characterized. The devices' gain was determined as a function of bias voltage, incidence beam angle, and proton energy. This study was conducted in the scope of the PIONEER experiment, an experiment proposed at the Paul Scherrer Institute to perform high-precision measurements of rare pion decays. LGADs are considered for the active target (ATAR) and energy linearity is an important property for particle ID capabilities.

In a recent paper, a continuum theory of immiscible and incompressible two-phase flow in porous media based on generalized thermodynamic principles was formulated (Transport in Porous Media, 125, 565 (2018)). In this theory, two immiscible and incompressible fluids flowing in a porous medium are treated as an effective fluid, substituting the two interacting subsystems for a single system with an effective viscosity and pressure gradient. In assuming Euler homogeneity of the total volumetric flow rate and comparing the resulting first order partial differential equation to the total volumetric flow rate in the porous medium, one can introduce of a novel velocity that relates the two pairs of velocities. This velocity, the co-moving velocity, describes the mutual co-carrying of fluids due to immiscibility effects and interactions between the fluid clusters and the porous medium itself. The theory is based upon general principles of thermodynamics, and allows for many relations and analogies to draw upon for a two-phase flow systems. The goal of this work is to provide relations between geometric concepts and the variables appearing in the thermodynamics-like theory of two-phase flow. We encounter two interpretations of the velocities of the fluids: as tangent vectors (derivations) acting on functions, or as coordinates on an affine line. The two views are closely related, with the former viewpoint being more useful in relation to the underlying geometrical structure of equilibrium thermodynamics, and the latter being more useful in concrete computations and finding examples of constitutive relations. We apply these straightforward geometric contexts to interpret the relations between the velocities, and from this obtain a general form for the co-moving velocity.

The pressure swing adsorption (PSA) process is one of the widely utilized techniques for air separation. Operating on the Skarstrom cycle, the porous adsorbent columns of a PSA system alternate between adsorption and desorption phases to selectively enrich the desired component in a gas mixture. The current work presents a robust and generalizable digital twin CFD model of a PSA system that can significantly help in design and device characterization. Using an axisymmetric representation, the digital twin accurately mimics all the key components of an air separation plant, including the air reservoir, adsorbent columns, product buffer tank, pressure regulator, solenoidal valves, and mesh filters. The model simulates the flow and adsorption processes in the system by solving the conservation equations for mass, momentum, energy, and species, along with the equation for adsorption kinetics. The cyclic operation of the PSA plants, typically controlled by solenoid valves, is emulated by dynamically modifying the boundary conditions of different subdomains. Such an integrated approach is shown here to closely replicate the performance of an in-house PSA pilot setup producing oxygen in terms of purity and pressure transience. Also, both the numerical and the experimental results yield an optimum performance for the same process parameters, such as pressurization time (26 s), purge time (2 s), and equalization time (4 s). The proposed numerical model is versatile and can be adapted to various industrial applications of PSA technology, such as hydrogen purification and carbon capture. Thus, it offers a cost-effective tool for designing and optimizing PSA systems.

We construct a cellular automaton (CA) model that describes the movement of a particle in a disordered system. The mathematical properties of the CA model were examined by varying the configuration of grid and determining the number of percolating paths. Through this model, we were able to develop a computer simulation that shows particle transport. Under particle hopping mechanism, with or without tunneling(or backscattering), it was found out that there is an exponential behavior of percolation probability. However, the onset of the percolation probability is shifted to a smaller value when tunneling and backscattering are present.

The escalating plastic waste crisis demands global action, yet mechanical recycling - currently the most prevalent strategy - remains severely underutilized. Only a small fraction of the total plastic waste is recycled in this manner, largely due to the significant variability in recycled plastics' mechanical properties. This variability stems from compositional fluctuations and impurities introduced throughout the materials' lifecycle and the recycling process, deterring industries with stringent product specifications from adopting recycled plastics on a wider scale. To overcome this challenge, we propose a composite structure inspired by nacre's microstructure - a natural material known for its exceptional mechanical performance despite its inherent randomness across multiple length scales. This bio-inspired design features stiff recycled plastic platelets ("bricks") within a soft polymeric matrix ("mortar"). We use a tension-shear-chain model to capture the deformation mechanism of the structure, and demonstrate, through a case study of commercial stretch wrap, that the proposed design reduces variability in effective elastic modulus by 89.5% and in elongation at break by 42%, while achieving the same modulus as the virgin stretch wrap material. These findings highlight the potential of the proposed bio-inspired design to enhance the mechanical performance of recycled plastics, but also demonstrate that a universally applicable, chemistry-agnostic approach can substantially broaden their applications, paving the way for sustainable plastic waste management.

The RPCs employed at the LHC experiments are currently operated in avalanche mode, with a mixture containing a large fraction of C$_{2}$H$_{2}$F$_{4}$ ($\approx$90\% or more) with the addition of i-C$_{4}$H$_{10}$ and SF$_{6}$ in different concentrations. However, C$_{2}$H$_{2}$F$_{4}$ and SF$_{6}$ are fluorinated greenhouse gases (F-gases) with Global Warming Potential (GWP) of $\approx$1400 and $\approx$22800, respectively. EU regulations imposed a progressive phase-down of C$_{2}$H$_{2}$F$_{4}$ production and consumption, aiming at strongly reducing its emission. This is already resulting in an increase of its price and reduction in availability. The most desirable long-term solution to this problem is to find an alternative, F-gases-free gas mixture, able to maintain similar detector performance. To address this challenge, the RPC ECOGasas@GIF++ collaboration (including RPC experts of ALICE, ATLAS, CMS, SHiP/LHCb, and the CERN EP-DT group) was created in 2019. The collaboration is currently studying a gas from the olefine family, the C$_{3}$H$_{2}$F$_{4}$ (or simply HFO, with GWP $\approx$6), to be used, in combination with CO$_{2}$, as a substitute for C$_{2}$H$_{2}$F$_{4}$. This contribution will focus on the signal shape studies that have been carried out by the collaboration during dedicated beam test periods. The methodology used in the data analysis will be presented, together with the results obtained with several HFO-based gas mixtures, and with the currently employed one. Furthermore, results on the counting-rate dependence of the RPC performance, obtained by combining the muon beam with the GIF++ $^{137}$Cs source with different attenuation factors, will also be presented.

The uniform electron gas (UEG) is a cornerstone of density-functional theory (DFT) and the foundation of the local-density approximation (LDA), one of the most successful approximations in DFT. In this work, we extend the concept of UEG by introducing excited-state UEGs, systems characterized by a gap at the Fermi surface created by the excitation of electrons near the Fermi level. We report closed-form expressions of the reduced kinetic and exchange energies of these excited-state UEGs as functions of the density and the gap. Additionally, we derive the leading term of the correlation energy in the high-density limit. By incorporating an additional variable representing the degree of excitation into the UEG paradigm, the present work introduces a new framework for constructing local and semi-local state-specific functionals for excited states.

Particle suspensions in confined geometries can become clogged, which can have a catastrophic effect on function in biological and industrial systems. Here, we investigate the macroscopic dynamics of suspensions in constricted geometries. We develop a minimal continuum two-phase model that allows for variation in particle volume fraction. The model comprises a ``wet solid'' phase with material properties dependent on the particle volume fraction, and a seepage Darcy flow of fluid through the particles. We find that spatially varying geometry (or material properties) can induce emergent heterogeneity in the particle fraction and trigger the abrupt transition to a high-particle-fraction ``clogged'' state.

Rotational symmetry is a defining feature of many tomography systems, including computed tomography (CT) and emission computed tomography (ECT), where detectors are arranged in a circular or periodically rotating configuration. This study revisits the image reconstruction process from the perspective of hardware-induced rotational symmetry and introduces a cyclic group equivariance framework for deep learning-based reconstruction. Specifically, we derive a mathematical correspondence that couples cyclic rotations in the projection domain to discrete rotations in the image domain, both arising from the same cyclic group inherent in the hardware design. This insight also reveals the uniformly distributed circular structure of the projection space. Building on this principle, we provide a cyclic rotation equivariant convolution design method to preserve projection domain symmetry and a cyclic group equivariance regularization approach that enforces consistent rotational transformations across the entire network. We further integrate these modules into a domain transform reconstruction framework and validate them using digital brain phantoms, training on discrete models and testing on more complex and realistic fuzzy variants. Results indicate markedly improved generalization and stability, with fewer artifacts and better detail preservation, especially under data distribution deviation. These findings highlight the potential of cyclic group equivariance as a unifying principle for tomographic reconstruction in rotationally symmetric systems, offering a flexible and interpretable solution for scenarios with limited data.

In this work, we demonstrate how extended Lagrangian Born-Oppenheimer quantum-based molecular dynamics (XL-BOMD) can be used to simulate heterogeneous electrocatalytic reactions. In particular, we apply our framework to study the oxygen reduction reaction (ORR) mechanism on nitrogen-doped graphene in an aqueous solution. The electronic ground state and total energy of XL-BOMD are stabilized through nuclear and electronic equations of motion assisted by an integral kernel updated with low-rank approximations. A species charge analysis reveals that the XL-BOMD simulations can be used to detect electron transfer between the catalyst surface and the solvated molecular oxygen mediated by water molecules, resulting in the molecular dissociation of O$_2$. Simulations of the ORR under high electrochemical biases contribute to a larger body of work elucidating an outer-sphere ORR mechanism in which reduction occurs through water networks without direct adsorption of molecular oxygen.

Recent advancements in particle physics demand pixel detectors that can withstand increased luminosity in the future collider experiments. In response, MALTA, a novel monolithic active pixel detector, has been developed with a cutting-edge readout architecture. This new class of monolithic pixel detectors is found to have exceptional radiation tolerance, superior hit rates, higher resolution and precise timing resolution, making them ideally suited for experiments at the LHC. To optimize the performance of these sensors before their deployment in actual detectors, comprehensive electrical characterization has been conducted. This study also includes comparative DAC analyses among sensors of varying thicknesses, providing crucial insights for performance enhancement. For the further understanding of the effect of radiation, the sensors are being exposed to different fluence using high intensity X-ray source.

Extreme precipitation is projected to become more frequent and more intense due to climate change and associated thermodynamical effects, but the local response of atmospheric circulation under future climate scenarios remains uncertain due mainly to dynamical differences. In this study, we outline a methodology for a regional assessment of future extreme precipitation based on the Lamb Weather Type classification and to evaluate future changes in weather patterns. While anticyclonic days occur most frequently over Belgium, extreme rainfall is mostly associated with days of cyclonic, westerly and south-westerly weather patterns. GCMs from CMIP6 are first selected based on their reliability in representing local atmospheric circulation patterns during days with extreme rainfall days. It was found that for our case study over Belgium, the future (end-of-the-century SSP3-7.0) changes in intensity and likelihood of rainfall extremes can be primarily attributed to thermodynamic factors, with minimal contribution from changes in atmospheric dynamics. Both intensity and probability of extreme rainfall increase for all seasons. While extreme-rainfall probabilities mostly increase in fall and winter, the associated intensity changes are dominated by positive changes in spring and summer. Additionally, the weather patterns that are historically associated with extreme rainfall, disproportionally contribute to these changes, especially to thermodynamic changes. More specifically, robust changes arise from an increased extreme-rainfall occurrence probability in case of cyclonic, south-westerly and westerly circulation types.

This paper advances the use of physics-informed neural networks (PINNs) architectures to address moving interface problems via the level set method. Originally developed for other PDE-based problems, we particularly leverage PirateNet's features, including causal training, sequence-to-sequence learning, random weight factorization, and Fourier feature embeddings, and tailor them to achieve superior performance in modeling interface dynamics. Numerical experiments validate this framework on benchmark problems such as Zalesak's disk rotation and time-reversed vortex flow. We demonstrate that PINNs can efficiently solve level set problems exhibiting significant interface deformation without the need for upwind numerical stabilization, as generally required by classic discretization methods. Additionally, geometric reinitialization or mass conservation schemes have been revealed as unnecessary for accurate and efficient solutions. However, incorporating an Eikonal regularization term in the loss function with an appropriate weight can further enhance results in specific scenarios. Our results indicate that PINNs with the PirateNet architecture surpass conventional PINNs in accuracy, achieving state-of-the-art error rates of $L^2=0.14\%$ for Zalesak's disk and $L^2=0.85 \%$ for the time-reversed vortex flow problem, as compared to reference solutions.

In addition to the omnipresent Galactic Cosmic Rays (GCRs), sudden solar energetic particle (SEP) events present considerable health hazards for manned space missions. These events not only contribute to an increased long-term cancer risk, but can, in extreme cases, cause acute radiation syndromes. Forecasting their imminent occurrence could significantly reduce radiation exposure by warning astronauts to move to shelter. However, all currently available tools are primarily designed for the Earth or Earth-Moon system, which limits their applicability to future Mars missions. To address this, we developed a nowcasting system for SEP events applicable in deep space and on the Martian surface, which serves as a reliable last-resort backup when forecasts fail. The methodology of this system is based on dose rates measured by the Radiation Assessment Detector (RAD) onboard the Mars Science Laboratory (MSL), which recorded 5 SEP events during the seven-month flight to Mars and 16 since its landing on Mars on August 6, 2012. An SEP event is triggered, and an astronaut is warned as soon as dose rates exceed the omnipresent background level by at least 25%. This approach suggests that our system can provide astronauts with at least 30 minutes to avoid both peak radiation exposure and the majority of the cumulative dose from SEP events. Our nowcasting system is robust, easily implementable in real-life scenarios, and achieves a near-zero false alarm rate both in deep space and on the Martian surface.

This study investigates the complex dynamics of thermodynamic nonequilibrium effects (TNEs) and their relative importance during the development of the Kelvin-Helmholtz instability (KHI) using high-order discrete Boltzmann models (DBMs). First, the capabilities and differences among various discrete velocity sets in capturing TNEs and distribution functions are assessed. Based on this analysis, practical guidelines for constructing discrete velocity stencils are proposed to enhance phase-space discretization and improve the robustness of high-order DBMs. At different stages of KHI and under varying initial conditions, multiscale TNEs, such as viscous stresses of different orders, emerge with distinct dominant roles. Specifically, three scenarios are identified: (i) regimes dominated by first-order TNEs, (ii) alternation between first- and second-order TNEs, and (iii) states where second-order TNEs govern the system's behavior. To quantitatively capture these transitions, criteria for TNE dominance at different orders in KHI evolution are established based on the relative thermodynamic nonequilibrium intensity. In scenarios dominated by second-order TNEs, differences between first-order and second-order models are compared in terms of macroscopic quantities, nonequilibrium effects, and moment relations, revealing the physical limitations of low-order models in capturing TNEs. Furthermore, the effectiveness, extensibility, and limitations of a representative high-order model are examined under second-order TNE-dominated conditions. To encapsulate these findings, a nonequilibrium phase diagram that visually maps the multiscale characteristics of KHI is constructed. This diagram not only provides intuitive insights into the dynamic interplay of different nonequilibrium effects but also serves as a kinetic roadmap for selecting suitable models under diverse nonequilibrium conditions.

A spectral approach based on the Fourier diffraction theorem is combined with a pair of U-NETs to perform quantitative microwave imaging of an anthropomorphic breast phantom. The U-NET pair is trained on a spectral database constructed from combinations of different realistic parts of the breast. Some preliminary numerical results are presented to show the major improvement brought by the U-NET.

The advent of 6G communication demands seamlessly integrated terminals operating above 100 GHz with low power consumption for human-centric applications. In this work, we report high-performance, flexible radio-frequency (RF) transistors based on aligned carbon nanotube (CNT) arrays, achieving, for the first time, as-measured current gain cutoff frequency ($f_{\mathrm{T}}$) and power gain cutoff frequency ($f_{\mathrm{max}}$) both exceeding 100 GHz. Electro-thermal co-design improves both heat dissipation and RF performance, despite the low thermal conductivity of the flexible substrate. The transistors deliver 0.947 mA/ $\mathrm{\mu}$m on-state current and 0.728 mS/ $\mathrm{\mu}$m transconductance. Peak extrinsic $f_{\mathrm{T}}$ and $f_{\mathrm{max}}$ reach 152 GHz and 102 GHz, with low power consumption of 199 mW/mm and 147 mW/mm, respectively, setting new performance records for flexible CNT-based RF transistors by nearly $100\times$, outperforming all other flexible RF devices. Additionally, flexible RF amplifiers achieve output power of 64 mW/mm and power gain of 11 dB in the K-band (18 GHz), marking a significant milestone in the development of flexible RF technologies for next-generation wireless communication systems.

Single-wavelength interferometry achieves high resolution for smooth surfaces but struggles with rough, industrially relevant ones due to limited unambiguous measuring range and speckle effects. Multi-wavelength interferometry addresses these challenges by using synthetic waveleths, enabling a balance between extended measurement range and resolution by combining several synthetic wavelengths. This approach holds immense potential for diverse industrial applications, yet it remains largely untapped due to the lack of suitable light sources. Existing solutions are constrained by limited flexibility in synthetic-wavelength generation and slow switching speeds. We demonstrate a light source for multi-wavelength interferometry based on electro-optic single-sideband modulation. It reliably generates synthetic wavelengths with arbitrary values from centimeters to meters and switching times below 30 ms. This breakthrough paves the way for dynamic, reconfigurable multi-wavelength interferometry capable of adapting to complex surfaces and operating efficiently even outside laboratory settings. These capabilities unlock the full potential of multi-wavelength interferometry, offering unprecedented flexibility and speed for industrial and technological applications.

Directional couplers are a fundamental building block in integrated photonics, particularly in quantum applications and optimization-based design where precision is critical. Accurate functionality is crucial to ensure reliable operation within classical and quantum circuits. However, discrepancies between simulations and measurements are frequently observed. These inaccuracies can compromise the performance and scalability of integrated photonic systems, underscoring the critical need for advanced, precise simulation methods that bridge the gap between design and implementation. In this work, we show that this discrepancy can be mainly attributed to density changes in the oxide cladding. We conduct a systematic study involving experimental optical measurements, numerical simulations, and direct electron microscopy imaging to investigate this discrepancy in directional couplers. We find that the impact of cladding density variations on performance increases as feature gaps shrink. By incorporating these effects into our simulations using a novel and physically motivated Effective Trench Medium Model (ETMM), we achieve highly accurate reproduction of experimental measurements. We quantify the effects of cladding density variations on the SU(2) symmetry parameters that govern light propagation in directional couplers. This insight is crucial for advancing the precision of compact device fabrication, enabling reliable simulation of photonic integrated devices.

We demonstrate a novel shot-to-shot acquisition method for optical pump - keV electron energy probe in ultrafast scattering experiments. We integrate a phase-locked acquisition scheme at a repetition rate of 20kHz in a conventional ultrafast electron diffraction (UED) setup. We proceed to a full characterization of the noise level in different configurations and of the temporal resolution as a function of electron flux. The shot-to-shot acquisition improves the signal-to-noise ratio (SNR) by one order of magnitude in a low perturbation regime.

Polarisation doping of Al$_x$Ga$_{1-x}$N, through grading of $x$, has realised major improvements in $p$-type conductivity in ultraviolet (UV) light-emitting diodes (LEDs) compared to conventional impurity doping. However, the exact balance between the two doping regimes to achieve the best device performance is not clear, especially as a function of operating wavelength. In this work, 330 nm LEDs with varied $p$-doping approaches were characterised as a function of temperature: Mg doped only (reference); polarisation doped and Mg doped (co-doped); and polarisation doped only. At room temperature, the co-doped LED showed the highest electroluminescence (EL) intensity, with a similar operating voltage to the reference LED. The highest hole concentration, confirmed by Hall effect measurements, as well as improved injection efficiency revealed by simulations, are credited as the main reasons for EL improvement. A parasitic near-UV luminescence tail, analogous to the "blue luminescence" in $p$-GaN, was observed in both the reference and co-doped LEDs, but was absent in the polarisation doped LED. The reference LED demonstrated the highest increase in operating voltage with decreasing temperature, while the LED with only polarisation doping showed a temperature-independent behaviour, demonstrating the benefits of a polarisation field-induced carrier concentration. Further optimisation of the compositional grading with concurrent Mg doping can potentially produce higher performance LEDs with cleaner spectra.

Numerical simulation of multi-component flow systems characterized by the simultaneous presence of pressure-velocity coupling and pressure-density coupling dominated regions remains a significant challenge in computational fluid dynamics. Thus, this work presents a novel approach that combines the Godunov-type scheme for high-speed flows with the projection solution procedure for incompressible flows to address this challenge. The proposed hybrid approach begins by splitting the inviscid flux into the advection part and the pressure part. The solution variables are first updated to their intermediate states by solving the advection part with the all-speed AUSM (Advection Upwind Splitting Method) Riemann solver. The advection flux in AUSM is modified to eliminate the pressure flux term that deteriorates the accuracy at the low Mach region. To prevent the advection flux from causing spurious velocities when surface tension is present, the pressure-velocity coupling term is modified to ensure it vanishes at material interfaces. Then, we derive the pressure Helmholtz equation to solve the final pressure and update the intermediate states to the solution variables at the next time step. The proposed hybrid approach retains the upwind property of the AUSM scheme for high Mach numbers while recovering central schemes and the standard projection solution for low Mach limits. To accurately resolve the complex flow structures including shock waves and material interfaces without numerical oscillations, a newly proposed homogenous ROUND (Reconstruction Operator on Unified Normalised-variable Diagram) reconstruction strategy is employed in this work. By simulating high-speed compressible multiphase flows and incompressible multiphase flows, this study demonstrates the ability of the proposed method to accurately handle flow regimes across all Mach numbers.

Modern science emerged from reasoning over repeatedly-observed planetary motions. We present Gravity-Bench-v1, an environment-based benchmark that challenges AI agents on tasks that parallel this historical development. Gravity-Bench-v1 evaluates agents on the discovery of physics concealed within a dynamic environment, using rigorous gravitational dynamics simulations. Gravity-Bench includes out-of-distribution cases, i.e. with physics that deviates from the real world, to evaluate true scientific generalization capabilities. Agents must plan to collect data within an experimental budget and must perform a dynamic form of data analysis and reasoning to solve tasks efficiently. Our benchmark admits an open-ended space of solutions. PhD-level solutions for each task are provided, to calibrate AI performance against human expertise. Technically at an upper-undergraduate level, our benchmark proves challenging to baseline AI agents. Gravity-Bench-v1 and planned extensions should help map out AI progress towards scientific discovery capabilities.

A reexamination of period finding algorithms is prompted by new large area astronomical sky surveys that can identify billions of individual sources having a thousand or more observations per source. This large increase in data necessitates fast and efficient period detection algorithms. In this paper, we provide an initial description of an algorithm that is being used for detection of periodic behavior in a sample of 1.5 billion objects using light curves generated from Zwicky Transient Facility (ZTF) data (Bellm et al. 2019; Masci et al. 2018). We call this algorithm "Fast Periodicity Weighting" (FPW), derived using a Gaussian Process (GP) formalism. A major advantage of the FPW algorithm for ZTF analysis is that it is agnostic to the details of the phase-folded waveform. Periodic sources in ZTF show a wide variety of waveforms, some quite complex, including eclipsing objects, sinusoidally varying objects also exhibiting eclipses, objects with cyclotron emission at various phases, and accreting objects with complex waveforms. We describe the FPW algorithm and its application to ZTF, and provide efficient code for both CPU and GPU.

Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorter training time than randomly initialized recurrent neural networks.

We investigate the effects of Einsteinian cubic gravity in the strong gravitational regime. In the first part, we explore analytical solutions for a static, spherically symmetric metric, establishing the existence of maximally symmetric de Sitter solutions, as well as asymptotically de Sitter solutions, with an effective cosmological constant. We also study, analytically and numerically, how the horizon properties are affected by cubic gravity. Our results reveal that a positive coupling constant reduces the horizon size, while a negative one increases it. In the second part, we analyze potential observational signatures of cubic terms, focusing on their effects on the bending of light. Specifically, we investigate the angular difference, related to the deflection angle but valid near the source, along with the behavior of the photon sphere. Our findings show that the strongest effects of the cubic terms occur in the strong gravity regime, and there exists a direct relationship between the value of the coupling constant and the photon sphere position, opening up the possibility to constrain cubic gravity with black hole shadows.

Understanding the behavior of materials under irradiation is crucial for the design and safety of nuclear reactors, spacecraft, and other radiation environments. The threshold displacement energy (Ed) is a critical parameter for understanding radiation damage in materials, yet its determination often relies on costly experiments or simulations. This work leverages the machine learning-based Sure Independence Screening and Sparsifying Operator (SISSO) method to derive accurate, analytical models for predicting Ed using fundamental material properties. The models outperform traditional approaches for monoatomic materials, capturing key trends with high accuracy. While predictions for polyatomic materials highlight challenges due to dataset complexity, they reveal opportunities for improvement with expanded data. This study identifies cohesive energy and melting temperature as key factors influencing Ed, offering a robust framework for efficient, data-driven predictions of radiation damage in diverse materials.

Engineers learn from every design they create, building intuition that helps them quickly identify promising solutions for new problems. Topology optimization (TO) - a well-established computational method for designing structures with optimized performance - lacks this ability to learn from experience. Existing approaches treat design tasks in isolation, starting from a "blank canvas" design for each new problem, often requiring many computationally expensive steps to converge. We propose a meta-learning strategy, termed meta-neural TO, that finds effective initial designs through a systematic transfer of knowledge between related tasks, building on the mesh-agnostic representation provided by neural reparameterization. We compare our approach against established TO methods, demonstrating efficient optimization across diverse test cases without compromising design quality. Further, we demonstrate powerful cross-resolution transfer capabilities, where initializations learned on lower-resolution discretizations lead to superior convergence in 74.1% of tasks on a higher-resolution test set, reducing the average number of iterations by 33.6% compared to standard neural TO. Remarkably, we discover that meta-learning naturally gravitates toward the strain energy patterns found in uniform density designs as effective starting points, aligning with engineering intuition.

A major challenge for medical X-ray CT imaging is reducing the number of X-ray projections to lower radiation dosage and reduce scan times without compromising image quality. However these under-determined inverse imaging problems rely on the formulation of an expressive prior model to constrain the solution space while remaining computationally tractable. Traditional analytical reconstruction methods like Filtered Back Projection (FBP) often fail with sparse measurements, producing artifacts due to their reliance on the Shannon-Nyquist Sampling Theorem. Consensus Equilibrium, which is a generalization of Plug and Play, is a recent advancement in Model-Based Iterative Reconstruction (MBIR), has facilitated the use of multiple denoisers are prior models in an optimization free framework to capture complex, non-linear prior information. However, 3D prior modelling in a Plug and Play approach for volumetric image reconstruction requires long processing time due to high computing requirement. Instead of directly using a 3D prior, this work proposes a BM3D Multi Slice Fusion (BM3D-MSF) prior that uses multiple 2D image denoisers fused to act as a fully 3D prior model in Plug and Play reconstruction approach. Our approach does not require training and are thus able to circumvent ethical issues related with patient training data and are readily deployable in varying noise and measurement sparsity levels. In addition, reconstruction with the BM3D-MSF prior achieves similar reconstruction image quality as fully 3D image priors, but with significantly reduced computational complexity. We test our method on clinical CT data and demonstrate that our approach improves reconstructed image quality.

The Keck Planet Imager and Characterizer (KPIC) is a series of upgrades for the Keck II Adaptive Optics (AO) system and the NIRSPEC spectrograph to enable diffraction limited, high resolution (R>30000) spectroscopy of exoplanets and low mass companions in the K and L bands. Phase I consisted of single mode fiber injection/extraction units (FIU/FEU) used in conjunction with a H band pyramid wavefront sensor. The use of single mode fibers provides a gain in stellar rejection, a substantial reduction in sky background, and an extremely stable line spread function in the spectrograph. Phase II, deployed and commissioned in 2022, brought a 1000 actuator deformable mirror, beam shaping optics, a vortex mask, and other upgrades to the FIU/FEU. An additional service mission in 2024 extended operations down to y band, delivered an atmospheric dispersion corrector, and provided access to two laser frequency combs. KPIC phase II brings higher planet throughput, lower stellar leakage and many new observing modes which extend its ability to characterize exoplanets at high spectral resolution, building on the success of phase I. In this paper we present a description of the final phase II version of KPIC, along with results of system level laboratory testing and characterization showing the instrument's phase II throughput, stability, repeatability, and other key performance metrics prior to delivery and during installation at Keck. We outlined the capabilities of the various observing modes enabled by the new modules as well as efforts to compensate for static aberrations and non common path errors at Keck, which were issues that plagued phase I. Finally, we show results from commissioning.

The WISPR imager on Parker Solar Probe provides a unique view the young solar wind, flying through solar wind structures at high speed. It is of interest to use WISPR image sequences to measure the velocity of both large features (such as CMEs) and the background, ambient wind. However, WISPR's close-up, rapidly-moving perspective makes the usual methods for measuring velocities from images difficult or impossible to apply, as most apparent motion through the image is due to the motion or rotation of the imager. In this work, we propose a new method of looking for features at the "stationary point" -- a direction from which some plasma parcels appear to approach the spacecraft, remaining at a constant direction in the image sequence. This direction is a function of the plasma's radial velocity, the encounter geometry, and the spacecraft velocity, allowing the former two to be inferred. We demonstrate the technique with forward-modeled images, and we apply it to WISPR observations, inferring the speed and trajectory of a particular density feature. This method promises to enable speed measurements of the young solar wind in an important acceleration region, from a close-up perspective and at latitudes well outside the PSP orbital plane. And while we present this method in a solar wind context, it is broadly applicable to any situation of a moving viewpoint traveling through an expanding cloud of features.

Using open astronomical multifrequency databases, we constructed light curves and developed a comprehensive visualisation and sonification analysis for the blazars Mrk~501, Mrk~1501, Mrk~421, BL~Lacerta, AO~0235+164, 3C~66A, OJ~049, OJ~287, and PKS~J2134-0153. This study employed Musical Instrument Digital Interface (MIDI) and Parameter Mapping Sonification (PMSon) techniques to generate waveforms, spectrograms, and sonifications. These representations demonstrate that data visualisation and sonification are powerful tools for analysing astronomical objects like blazers, providing insights into their multifrequency variability. This work highlights how sonification and visualisation can aid in identifying potential patterns, power variations, regularities, and gaps in the data. This multimodal approach also underscores the importance of inclusivity in scientific communication, offering accessible methods for exploring the complex behaviour of blazers.

During brain function, groups of neurons fire synchronously. When these groups are large enough, the resulting electrical signals can be measured on the scalp using Electroencephalography (EEG). The amplitude of these signals can be significant depending on the size and synchronization of the neural activity. EEG waves exhibit distinct patterns based on the brain's state, such as whether it is asleep, awake, engaged in mental calculations, or performing other cognitive functions. Additionally, these patterns can be modified by external factors, such as transcranial magnetic stimulation (TMS). TMS involves bringing an antenna that generates variable electromagnetic fields close to specific areas of the skull to treat certain pathologies. Given that the human body naturally generates magnetic fields, a question arises: Can these fields influence the EEG by modulating neuronal function, causing a resonance effect, or through some unknown interaction? This study investigated whether approaching the palm of the hand to the top of the head (Intervention) could induce effects in the EEG. Power Spectral Density (PSD) was obtained for the 30 seconds preceding the intervention (PSD_pre) and the final 30 seconds of the intervention (PSD_last). The exact Wilcoxon signed-rank test suggests that the median of PSD_pre is greater than the median of PSD_last at the 95% confidence level (p-value = 0.004353). In contrast, in the control group, the test indicates that at the 95% confidence level (p-value = 0.7667), the median of PSD_pre is not greater than the median of PSD_last.

Superconducting quantum computers require microwave control lines running from room temperature to the mixing chamber of a dilution refrigerator. Adding more lines without preliminary thermal modeling to make predictions risks overwhelming the cooling power at each thermal stage. In this paper, we investigate the thermal load of SC-086/50-SCN-CN semi-rigid coaxial cable, which is commonly used for the control and readout lines of a superconducting quantum computer, as we increase the number of lines to a quantum processor. We investigate the makeup of the coaxial cables, verify the materials and dimensions, and experimentally measure the total thermal conductivity of a single cable as a function of the temperature from cryogenic to room temperature values. We also measure the cryogenic DC electrical resistance of the inner conductor as a function of temperature, allowing for the calculation of active thermal loads due to Ohmic heating. Fitting this data produces a numerical thermal conductivity function used to calculate the static heat loads due to thermal transfer within the wires resulting from a temperature gradient. The resistivity data is used to calculate active heat loads, and we use these fits in a cryogenic model of a superconducting quantum processor in a typical Bluefors XLD1000-SL dilution refrigerator, investigating how the thermal load increases with processor sizes ranging from 100 to 225 qubits. We conclude that the theoretical upper limit of the described architecture is approximately 200 qubits. However, including an engineering margin in the cooling power and the available space for microwave readout circuitry at the mixing chamber, the practical limit will be approximately 140 qubits.

We propose ReMiDi, a novel method for inferring neuronal microstructure as arbitrary 3D meshes using a differentiable diffusion Magnetic Resonance Imaging (dMRI) simulator. We first implemented in PyTorch a differentiable dMRI simulator that simulates the forward diffusion process using a finite-element method on an input 3D microstructure mesh. To achieve significantly faster simulations, we solve the differential equation semi-analytically using a matrix formalism approach. Given a reference dMRI signal $S_{ref}$, we use the differentiable simulator to iteratively update the input mesh such that it matches $S_{ref}$ using gradient-based learning. Since directly optimizing the 3D coordinates of the vertices is challenging, particularly due to ill-posedness of the inverse problem, we instead optimize a lower-dimensional latent space representation of the mesh. The mesh is first encoded into spectral coefficients, which are further encoded into a latent $\textbf{z}$ using an auto-encoder, and are then decoded back into the true mesh. We present an end-to-end differentiable pipeline that simulates signals that can be tuned to match a reference signal by iteratively updating the latent representation $\textbf{z}$. We demonstrate the ability to reconstruct microstructures of arbitrary shapes represented by finite-element meshes, with a focus on axonal geometries found in the brain white matter, including bending, fanning and beading fibers. Our source code will be made available online.

The camera of the Large-Sized Telescopes (LSTs) of the Cherenkov Telescope Array Observatory (CTAO) consists of 1855 pixels that are grouped into 265 high-performance photomultiplier tube (PMT) modules. Each module comprises a seven-light-guide plate, seven PMT units, a slow control board, and a readout board with a trigger board. %In this paper we describe The requirements for the PMT modules include various aspects, such as photon detection efficiency, dynamic range, buffer depth, and test pulse functionality. We have developed a high-performance PMT module that fulfills all these requirements. Mass-production and quality control (QC) of modules for all four LSTs of the northern CTAO have been completed. Here we report on the technical details of each element of the module and its performance, together with the methods and results of QC measurements.

The contact issue for two-dimensional (2D) materials-based field-effect transistors (FETs) has drawn enormous attention in recent years. Although ohmic behavior is achieved at room temperature, the drain current of 2DFETs shifts from ohmic to non-ohmic behavior at cryogenic temperatures. In this work, we demonstrate that the shift is attributed to the asymmetric current reduction at the metal-semiconductor contact at low temperature. Under low drain bias, carriers tunnel from the source to the channel but diffuse to the drain side due to the channel-to-drain barrier, resulting in the current suppression. By studying the property of ohmic metal-semiconductor contact at different temperatures, we analyzed the mechanisms behind this phenomenon and the dependence on metal-to-semiconductor barrier height. The work opens the semiconductor physics of 2D material contact at cryogenic temperature and the importance of contact metal selection in the development of 2DFET at cryogenic temperature.

We propose a new stochastic epidemiological model defined in a continuous space of arbitrary dimension, based on SIS dynamics implemented in a spatial $\Lambda$-Fleming-Viot (SLFV) process. The model can be described by as little as three parameters, and is dual to a spatial branching process with competition linked to genealogies of infected individuals. Therefore, it is a possible modelling framework to develop computationally tractable inference tools for epidemics in a continuous space using demographic and genetic data. We provide mathematical constructions of the process based on well-posed martingale problems as well as driving space-time Poisson point processes. With these devices and the duality relation in hand, we unveil some of the drivers of the transition between extinction and survival of the epidemic. In particular, we show that extinction is in large parts independent of the initial condition, and identify a strong candidate for the reproduction number $R_0$ of the epidemic in such a model.

Stoichiometric SiNx layers (x = [N]/[Si] = 1.33) are doped with Si atoms by ultra-low energy ion implantation (ULE-II) and subsequently annealed at different temperatures in inert ambient conditions. Detailed material and memory cells characterization is performed to investigate the effect of Si dopants on the switching properties and performance of the fabricated resistive memory cells. In this context extensive dc current-voltage and impedance spectroscopy measurements are carried out systematically and the role of doping in dielectric properties of the nitride films is enlightened. The dc and ac conduction mechanisms are investigated in a comprehensive way. Room temperature retention characteristics of resistive states are also presented.

The molecular dynamics of deep eutectic solvents (DESs) are complex, characterized by nanoscale spatial and temporal heterogeneity. Understanding these dynamics is crucial for tailoring transport properties like diffusion, viscosity and ionic conductivity. Molecular diffusion in DESs stems from transient caging and translation jumps, necessitating an understanding of how molecular structure regulates these processes. This study explores the influence of alkyl chain length on the nanoscopic dynamics of alkylamide-lithium perchlorate based DESs using quasielastic neutron scattering (QENS) and molecular dynamics (MD) simulations. QENS results show that, despite its shorter chain length and lighter mass, acetamide (ACM) exhibited the lowest mobility among the alkylamides, including propanamide (PRM) and butyramide (BUT). Detailed analysis of QENS data reveals that long-range jump diffusion is fastest in ACM and slowest in BUT, essentially due to their differences in molecular size, mass and also enhanced complexation in longer alkyl chain molecules. However, the localized dynamics follows an unusual trend, where PRM is the fastest and ACM is the slowest. Despite greater flexibility in BUT, the slower caged dynamics impedes its localized motion. These findings highlight the interplay between alkyl chain length and DES dynamics, emphasizing role of molecular structure in governing transport properties.

Sliding ferroelectricity, which is a unique polarity recently discovered in bilayer van der Waals materials, achieves polarization switching through in-plane interlayer sliding. The unique mechanism, combining intralayer stiffness and interlayer slipperiness, leads to wider domain walls (DWs) and faster DW motion compared to conventional ferroelectrics. Herein, using machine-learning-assisted molecular dynamics simulations and field theory analysis, we find the DW in classical sliding ferroelectric bilayer 3R-MoS2 system exhibits uniformly accelerated motion under an external field, like the Newtonian particle with undamped motion. Remarkably, DW velocity remains constant even after the external field removal, completely deviating from the velocity breakdown observed in conventional ferroelectrics. We further propose an experimental approach to validate this undamped soliton-like DW behavior in sliding ferroelectric systems.

Quantum optimal control methods, such as gradient ascent pulse engineering (GRAPE), are used for precise manipulation of quantum states. Many of those methods were pioneered in magnetic resonance spectroscopy where instrumental distortions are often negligible. However, that is not the case elsewhere: the usual gallimaufry of cables, resonators, modulators, splitters, filters, and amplifiers can and would distort control signals. Those distortions may be non-linear, their inverse functions may be ill-defined and unstable; they may even vary from one day to the next, and across the sample. Here we introduce the response-aware gradient ascent pulse engineering (RAW-GRAPE) framework, which accounts for any cascade of differentiable distortions directly within the GRAPE optimisation loop, does not require response function inversion, and produces control sequences that are resilient to user-specified distortion cascades with user-specified parameter ensembles. The framework is implemented into the optimal control module supplied with versions 2.10 and later of the open-source Spinach library; the user needs to provide function handles returning the actions by the distortions and, optionally, parameter ensembles for those actions.

The LHCb experiment at CERN has been upgraded for the Run 3 operation of the Large Hadron Collider (LHC). A new concept of tracking detector based on Scintillating Fibres (SciFi) read out with multichannel silicon photomultipliers (SiPMs) was installed during its upgrade. One of the main challenges the SciFi tracker will face during the Run 4 operation of the LHC is the higher radiation environment due to fast neutrons, where the SiPMs are located. To cope with the increase in radiation, cryogenic cooling with liquid nitrogen is being investigated as a possible solution to mitigate the performance degradation of the SiPMs induced by radiation damage. Thus, a detailed performance study of different layouts of SiPM arrays produced by Fondazione Bruno Kessler (FBK) and Hamamatsu Photonics K.K. is being carried out. These SiPMs have been designed to operate at cryogenic temperatures. Several SiPMs have been tested in a dedicated cryogenic setup down to 100 K. Key performance parameters such as breakdown voltage, dark count rate, photon detection efficiency, gain and direct cross-talk are characterized as a function of the temperature. The main results of this study are going to be presented here.

Heat-assisted magnetic recording (HAMR) is a recent advancement in magnetic recording, allowing to significantly increase the areal density capability (ADC) of hard disk drives (HDDs) compared to the perpendicular magnetic recording (PMR) technology. This is enabled by high anisotropy FePt media, which needs to be heated through its Curie temperature ($T_C$) to facilitate magnetization reversal by an electromagnetic write pole. HAMR micromagnetic modeling is therefore challenging, as it needs to be performed in proximity to and above $T_C$, where a ferromagnet has no spontaneous magnetization. An atomistic model is an optimal solution here, as it doesn't require any parameter renormalization or non-physical assumptions for modeling at any temperature. However, a full track atomistic recording model is extremely computationally expensive. Here we demonstrate a true multiscale HAMR modeling approach, combining atomistic spin dynamics modeling for high temperature regions and micromagnetic modeling for lower temperature regions, in a moving simulation window embedded within a long magnetic track. The advantages of this approach include natural emergence of $T_C$ and anisotropy distributions of FePt grains. Efficient GPU optimization of the code provides very fast running times, with a 60~nm wide track of twenty-five 20~nm - long bits being recorded in several hours on a single GPU. The effects of realistic FePt L$_{10}$ vs simple cubic crystal structure is discussed, with the latter providing further running time gains while keeping the advantages of the multiscale approach.

Synchronization among a group of active agents is ubiquitous in nature. Although synchronization based on direct interactions between agents described by the Kuramoto model is well understood, the other general mechanism based on indirect interactions among agents sharing limited resources are less known. Here, we propose a minimal thermodynamically consistent model for the altruistic resource-sharing (ARS) mechanism wherein resources are needed for individual agent to advance but a more advanced agent has a lower competence to obtain resources. We show that while differential competence in ARS mechanism provides a negative feedback leading to synchronization it also breaks detailed balance and thus requires additional energy dissipation besides the cost of driving individual agents. By solving the model analytically, our study reveals a general tradeoff relation between the total energy dissipation rate and the two key performance measures of the system: average speed and synchronization accuracy. For a fixed dissipation rate, there is a distinct speed-accuracy Pareto front traversed by the scarcity of resources: scarcer resources lead to slower speed but more accurate synchronization. Increasing energy dissipation eases this tradeoff by pushing the speed-accuracy Pareto front outwards. The connections of our work to realistic biological systems such as the KaiABC system in cyanobacterial circadian clock and other theoretical results based on thermodynamic uncertainty relation are also discussed.

We rigorously analyze the asymptotics of soliton gases to the short-pulse (SP) equation. The soliton gas is formulated in terms of a RH problem, which is derived from the RH problems of the $N$-soliton solutions with $N \to \infty$. Building on prior work in the study of the KdV soliton gas and orthogonal polynomials with Jacobi-type weights, we extend the reflection coefficient to two generalized forms on the interval $\left[\eta_1, \eta_2\right]$: $r_0(\lambda) = \left(\lambda - \eta_1\right)^{\beta_1}\left(\eta_2 - \lambda\right)^{\beta_2}|\lambda - \eta_0|^{\beta_0}\gamma(\lambda)$, $r_c(\lambda) = \left(\lambda - \eta_1\right)^{\beta_1}\left(\eta_2 - \lambda\right)^{\beta_2}\chi_c(\lambda)\gamma(\lambda)$, where $0 < \eta_1 < \eta_0 < \eta_2$ and $\beta_j > -1$ ($j = 0, 1, 2$), $\gamma(\lambda)$ is continuous and positive on $\left[\eta_1, \eta_2\right]$, with an analytic extension to a neighborhood of this interval, $\chi_c(\lambda) = 1$ for $\lambda \in \left[\eta_1, \eta_0\right)$ and $\chi_c(\lambda) = c^2$ for $\lambda \in \left(\eta_0, \eta_2\right]$, where $c>0$ with $c \neq 1$. The asymptotic analysis is performed using the steepest descent method. A key aspect of the analysis is the construction of the $g$-function. To address the singularity at the origin, we introduce an innovative piecewise definition of $g$-function. To establish the order of the error term, we construct local parametrices near $\eta_j$ for $j = 1, 2$, and singularity $\eta_0$. At the endpoints, we employ the Airy parametrix and the first type of modified Bessel parametrix. At the singularity $\eta_0$, we use the second type of modified Bessel parametrix for $r_0$ and confluent hypergeometric parametrix for $r_c(\lambda)$.

The quest for efficient and sustainable green energy solutions has led to a growing interest in half Heusler alloys, particularly for thermoelectric and spintronic applications. This study investigates the multifaceted nature of cobalt based half Heusler alloy, CoVAs, employing DFT with advanced computational techniques, such as the FLAPW method. The elastic, electronic, magnetic, thermodynamic, and optical properties of CoVAs are meticulously analyzed. Structural and mechanical evaluations reveal mechanical stability and brittleness under varying pressures. Electronic and magnetic properties are examined through band structure and DOS analysis, revealing a half metallic nature with a minority spin band gap. The total magnetic moment aligns with the Slater Pauling rule, further confirming ferromagnetism and half metallicity. Thermodynamic investigations, based on the quasi-harmonic Debye approximation, provide insights into temperature- and pressure dependent behavior, including thermal expansion, heat capacity, and Debye temperature, establishing CoVAs as a viable candidate for high temperature applications. Additionally, the optical properties underestimate its potential in optoelectronic applications due to high absorption in the UV region, showing a distinct absorption edge corresponding to the electronic band gap. Phonon dispersion relations reflect the stability of the alloy, and the figure of merit confirms the alloy's suitability for thermodynamics applications. The findings highlight the potential of CoVAs as a promising candidate for spintronic photovoltaic and optoelectronic applications, providing insights into its fundamental properties that could facilitate experimental synthesis and industrial implementation for green energy and advanced technological applications.

Research has demonstrated that zeolite nucleation and growth can be controlled by fine-tuning chemical composition, temperature, and pressure, resulting in structures with diverse porosities and functionalities. Nevertheless, current energy delivery methods lack the finesse required to operate on the femto- and picosecond timescales of silica polymerisation and depolymerisation, limiting their ability to direct synthesis with high precision. To overcome this limitation, we introduce an ultrafast laser synthesis technique capable of delivering energy at these timescales with unprecedented spatiotemporal precision. Unlike conventional or emerging approaches, this method bypasses the need for specific temperature and pressure settings, as nucleation and growth are governed by dynamic phenomena arising from nonlinear light-matter interactions, such as convective flows, cavitation bubbles, plasma formation, and shock waves. These processes can be initiated, paused, and resumed within fractions of a second, effectively freezing structures at any stage of self-assembly. Using this approach, we traced the entire nucleation and growth pathway of laser-synthesized TPA-silicate-1 zeolites, from early oligomer formation to fully developed crystals. The unprecedented spatiotemporal control of this technique unlocks new avenues for manipulating reaction pathways and exploring the vast configurational space of zeolites.

In high-energy physics, the increasing luminosity and detector granularity at the Large Hadron Collider are driving the need for more efficient data processing solutions. Machine Learning has emerged as a promising tool for reconstructing charged particle tracks, due to its potentially linear computational scaling with detector hits. The recent implementation of a graph neural network-based track reconstruction pipeline in the first level trigger of the LHCb experiment on GPUs serves as a platform for comparative studies between computational architectures in the context of high-energy physics. This paper presents a novel comparison of the throughput of ML model inference between FPGAs and GPUs, focusing on the first step of the track reconstruction pipeline$\unicode{x2013}$an implementation of a multilayer perceptron. Using HLS4ML for FPGA deployment, we benchmark its performance against the GPU implementation and demonstrate the potential of FPGAs for high-throughput, low-latency inference without the need for an expertise in FPGA development and while consuming significantly less power.

Ni/Al reactive multilayers are promising materials for applications requiring controlled local energy release and superior mechanical performance. This study systematically investigates the impact of compositional variations, ranging from 30 to 70 at.% Ni, and bilayer thicknesses (30 nm and 50 nm) on the mechanical properties and reaction dynamics of Ni/Al multilayers. Multilayers with varying Ni-to-Al ratios were fabricated and subjected to instrumented nanoindentation testing to evaluate hardness and elastic modulus. Combustion experiments, conducted on dogbone-shaped multilayers deposited onto silicon wafers with thermal barrier coatings, characterized the reaction front's speed, temperature, and the resulting phases. The findings revealed that composition variations within this range enable precise tuning of reaction speed and temperature without significant changes in mechanical properties, while deviations in modulus and hardness at higher nickel concentrations suggest microstructural influences. Notably, phase formation in Al-rich samples deviated from equilibrium predictions, highlighting the role of kinetic factors, such as diffusion and rapid quenching, in driving non-adiabatic processes during phase evolution. Molecular dynamics simulations provided complementary atomistic insights into mechanical responses and reaction kinetics, bridging experimental observations with theoretical predictions. This integrated approach advances the understanding of Ni/Al multilayers, offering a framework for optimizing their composition and structural design to achieve tailored performance for application-specific requirements.

The outstanding opto-electronic properties of lead halide perovskites have been related to the formation of polarons. Nevertheless, the observation of the atomistic deformation brought about by one electron-hole pair in these materials has remained elusive. Here, we measure the diffraction patterns of single CsPbBr$_3$ quantum dots (QDs) with and without resonant excitation in the single exciton limit using serial femtosecond crystallography (SFX). By reconstructing the 3D differential diffraction pattern, we observe small shifts of the Bragg peaks indicative of a crystal-wide deformation field. Building on DFT calculations, we show that these shifts are consistent with the lattice distortion induced by a delocalized electron and a localized hole, forming a mixed large/small exciton polaron. This result creates a clear picture of the polaronic deformation in CsPbBr$_3$ QDs, highlights the exceptional sensitivity of SFX to lattice distortions in few-nanometer crystallites, and establishes an experimental platform for future studies of electron-lattice interactions.

Surface convection is important for the presence of magnetic activity at stars. So far, this convection is thought to be a result of heating from below, where convection cells rise and break up. New models reveal that surface convection is instead strongly driven by cooling from above. We compare two simulations of surface convection, one with a significant heating from below and one without. We obtain surface convection in both cases, and they show similar granulation patterns. The deep convection driven by heating from below is still evolving and asymptotically approaches a steady-state solution. We find that convection from below is not needed at all to form typical photospheric granulation. This indicates the possibility of a surface dynamo acting on stars without a convecting envelope. Even stars without a convecting envelope could therefore exhibit stronger magnetic and coronal activity than expected so far.

In complex networks, the rich-get-richer effect (nodes with high degree at one point in time gain more degree in their future) is commonly observed. In practice this is often studied on a static network snapshot, for example, a preferential attachment model assumed to explain the more highly connected nodes or a rich-club}effect that analyses the most highly connected nodes. In this paper, we consider temporal measures of how success (measured here as node degree) propagates across time. By analogy with social mobility (a measure people moving within a social hierarchy through their life) we define hierarchical mobility to measure how a node's propensity to gain degree changes over time. We introduce an associated taxonomy of temporal correlation statistics including mobility, philanthropy and community. Mobility measures the extent to which a node's degree gain in one time period predicts its degree gain in the next. Philanthropy and community measure similar properties related to node neighbourhood. We apply these statistics both to artificial models and to 26 real temporal networks. We find that most of our networks show a tendency for individual nodes and their neighbourhoods to remain in similar hierarchical positions over time, while most networks show low correlative effects between individuals and their neighbourhoods. Moreover, we show that the mobility taxonomy can discriminate between networks from different fields. We also generate artificial network models to gain intuition about the behaviour and expected range of the statistics. The artificial models show that the opposite of the "rich-get-richer" effect requires the existence of inequality of degree in a network. Overall, we show that measuring the hierarchical mobility of a temporal network is an invaluable resource for discovering its underlying structural dynamics.

Accurate identification of druggable pockets is essential for structure-based drug design. However, most pocket-identification algorithms prioritize their geometric properties over downstream docking performance. To address this limitation, we developed RAPID-Net, a pocket-finding algorithm for seamless integration with docking workflows. When guiding AutoDock Vina, RAPID-Net outperforms DiffBindFR on the PoseBusters benchmark and enables blind docking on large proteins that AlphaFold 3 cannot process as a whole. Furthermore, RAPID-Net surpasses PUResNet and Kalasanty in docking accuracy and pocket-ligand intersection rates across diverse datasets, including PoseBusters, Astex Diverse Set, BU48, and Coach420. When accuracy is evaluated as ``at least one correct pose in the ensemble'', RAPID-Net outperforms AlphaFold 3 on the PoseBusters benchmark, suggesting that our approach can be further improved with a suitable pose reweighting tool offering a cost-effective and competitive alternative to AlphaFold 3 for docking. Finally, using several therapeutically relevant examples, we demonstrate the ability of RAPID-Net to identify remote functional sites, highlighting its potential to facilitate the development of innovative therapeutics.

We propose a generative model of temporally-evolving hypergraphs in which hyperedges form via noisy copying of previous hyperedges. Our proposed model reproduces several stylized facts from many empirical hypergraphs, is learnable from data, and defines a likelihood over a complete hypergraph rather than ego-based or other sub-hypergraphs. Analyzing our model, we derive descriptions of node degree, edge size, and edge intersection size distributions in terms of the model parameters. We also show several features of empirical hypergraphs which are and are not successfully captured by our model. We provide a scalable stochastic expectation maximization algorithm with which we can fit our model to hypergraph data sets with millions of nodes and edges. Finally, we assess our model on a hypergraph link prediction task, finding that an instantiation of our model with just 11 parameters can achieve competitive predictive performance with large neural networks.

In this contribution, we give an overview of the ELPA library and ELSI interface, which are crucial elements for large-scale electronic structure calculations in FHI-aims. ELPA is a key solver library that provides efficient solutions for both standard and generalized eigenproblems, which are central to the Kohn-Sham formalism in density functional theory (DFT). It supports CPU and GPU architectures, with full support for NVIDIA and AMD GPUs, and ongoing development for Intel GPUs. Here we also report the results of recent optimizations, leading to significant improvements in GPU performance for the generalized eigenproblem. ELSI is an open-source software interface layer that creates a well-defined connection between "user" electronic structure codes and "solver" libraries for the Kohn-Sham problem, abstracting the step between Hamilton and overlap matrices (as input to ELSI and the respective solvers) and eigenvalues and eigenvectors or density matrix solutions (as output to be passed back to the "user" electronic structure code). In addition to ELPA, ELSI supports solvers including LAPACK and MAGMA, the PEXSI and NTPoly libraries (which bypass an explicit eigenvalue solution), and several others.

Deformable boundaries are omnipresent in the habitats of swimming microorganisms, leading to intricate hydroelastic couplings. Employing a perturbation theory, valid for small deformations, we study the swimming dynamics of pushers and pullers near instantaneously deforming boundaries, endowed with a bending rigidity and surface tension. Our results reveal that pushers can both reorient away from the boundary, leading to overall hydroelastic scattering, or become trapped by the boundary, akin to the enhanced trapping found for pullers. These findings demonstrate that the complex hydroelastic interactions can generate behaviors that are in striking contrast to swimming near planar walls.

With the stability of integrated photonics at network nodes and the advantages of photons as flying qubits, photonic quantum information processing (PQIP) makes quantum networks increasingly scalable. However, scaling up PQIP requires the preparation of many identical single photons which is limited by the spectral distinguishability of integrated single-photon sources due to variations in fabrication or local environment. To address this, we introduce frequency auto-homogenization via group-velocity-matched downconversion to remove spectral distinguishability in varying quantum emitters. We present our theory using $\chi^{(2)}$ quantum frequency conversion and show proof-of-principle data in a free-space optical setup.

Several studies indicate that deep learning models can learn to detect breast cancer from mammograms (X-ray images of the breasts). However, challenges with overfitting and poor generalisability prevent their routine use in the clinic. Models trained on data from one patient population may not perform well on another due to differences in their data domains, emerging due to variations in scanning technology or patient characteristics. Data augmentation techniques can be used to improve generalisability by expanding the diversity of feature representations in the training data by altering existing examples. Image-to-image translation models are one approach capable of imposing the characteristic feature representations (i.e. style) of images from one dataset onto another. However, evaluating model performance is non-trivial, particularly in the absence of ground truths (a common reality in medical imaging). Here, we describe some key aspects that should be considered when evaluating style transfer algorithms, highlighting the advantages and disadvantages of popular metrics, and important factors to be mindful of when implementing them in practice. We consider two types of generative models: a cycle-consistent generative adversarial network (CycleGAN) and a diffusion-based SynDiff model. We learn unpaired image-to-image translation across three mammography datasets. We highlight that undesirable aspects of model performance may determine the suitability of some metrics, and also provide some analysis indicating the extent to which various metrics assess unique aspects of model performance. We emphasise the need to use several metrics for a comprehensive assessment of model performance.

An ensemble of negatively charged nitrogen-vacancy centers in diamond can act as a precise quantum sensor even under ambient conditions. In particular, to optimize thier sensitivity, it is crucial to increase the number of spins sampled and maximize their coupling to the detection system, without degrading their spin properties. In this paper, we demonstrate enhanced quantum magnetometry via a high-quality buried laser-written waveguide in diamond with a 4.5 ppm density of nitrogen-vacancy centers. We show that the waveguide-coupled nitrogen-vacancy centers exhibit comparable spin coherence properties as that of nitrogen-vacancy centers in pristine diamond using time-domain optically detected magnetic resonance spectroscopy. Waveguide-enhanced magnetic field sensing is demonstrated in a fiber-coupled integrated photonic chip, where probing an increased volume of high-density spins results in 63 pT.Hz$^{-1/2}$ of DC-magnetic field sensitivity and 20 pT.Hz$^{-1/2}$ of AC magnetic field sensitivity. This on-chip sensor realizes at least an order of magnitude improvement in sensitivity compared to the conventional confocal detection setup, paving the way for microscale sensing with nitrogen-vacancy ensembles.

Recent results demonstrate how deviations from equilibrium fluctuation-dissipation theorem can be quantified for active field theories by deriving exact fluctuations dissipation relations that involve the entropy production [M. K. Johnsrud and R. Golestanian, arXiv:2409.14977]. Here we develop and employ diagrammatic tools to perform perturbative calculations for a paradigmatic active field theory, the Non-Reciprocal Cahn-Hilliard (NRCH) model. We obtain analytical results, which serve as an illustration of how to implement the recently developed framework to active field theories, and help to illuminate the specific non-equilibrium characteristics of the NRCH field theory.

Optical properties of the short period {CdO/ZnO} superlattices grown by plasma assisted MBE were analyzed. The superlattice (SLs) structures were successfully obtained at different growth temperatures from 360 to 550 {\deg}C. Interestingly, the growth temperature of the SLs influences quality of multilayers and also optical properties of these structures. After annealing at 900{\deg}C by rapid thermal method various defect luminescence located at different energetic positions , were detected, and intensity of luminescence strongly depends on applied growth temperature.