Papers

Stationary multi-magnetometer arrays are routinely deployed in geomagnetic observatories, laboratory shielded rooms, and ground-based monitoring stations. The standard analysis pipeline reduces each sensor to an independent power spectrum, discarding any inter-sensor structure that is itself diagnostic of measurement health and of localised magnetic activity. This paper develops a purely theoretical framework for a deliberately simple visualisation that maps the short-time Fourier (STFT) power spectra of three concurrent magnetometers into the red, green, and blue channels of a single image: the cross-sensor RGB spectrogram. Inter-sensor coherence appears as neutral grey or white, while spectral energy that is unique to one or two sensors stands out as saturated colour. We formalise the construction of the image, derive its time-frequency resolution properties, give an explicit account of the per-channel normalisation choice, and present a colour-anomaly taxonomy that distinguishes coherent broadband activity, single-sensor faults, asymmetric pairwise sources, and slow temporal drift. A companion long-window variant is described for resolving features in the ultra-low frequency (ULF) band. The construction is presented without reference to any particular dataset or implementation; it is intended as a self-contained methodological building block that can be inserted into any monitoring pipeline whose front end is a synchronously sampled magnetometer triad. Because the construction operates on scalar magnitude time series alone, it applies equally to classical fluxgate sensors and to quantum magnetometers – optically pumped magnetometers (OPMs), nitrogen-vacancy (NV) centre arrays, and superconducting quantum interference devices (SQUIDs) – where distinguishing quantum-limited noise from technical artefacts is a central diagnostic challenge.

Binary amplitude spatial light modulators, such as digital micromirror devices (DMDs), are increasingly relevant for computer generated holography due to their high refresh rates, low cost, and due to the emergence of subwavelength pixel architectures. However, the binary constraint limits the reconstruction quality, as conventional approaches rely on a binarization applied as a final step after hologram computation which leads to reduced efficiency and contrast. We introduce an iterative estimation approach for the generation of off axis binary amplitude holograms, in which the binarization constraint is applied at each iteration. We validate the approach through numerical simulations and experimental reconstruction using a DMD based optical setup. Quantitative and qualitative comparisons with random superposition and Gerchberg Saxton methods demonstrate significant improvements in image contrast, light efficiency, and reconstruction fidelity, with comparable computational cost. The proposed method provides a practical route toward high quality CGH using binary modulators and supports emerging applications requiring high speed and high resolution holographic projection.

In this contribution we present recent developments in the formulation and solution of Initial Boundary Value Problems (IBVPs). Building upon a modern variational action formulation of classical dynamics, we treat Initial Boundary Value Problems directly on the action level, bypassing governing equations. We show that by including coordinate maps as dynamical degrees of freedom together with propagating fields two key results emerge. Space-time symmetries remain protected even after discretization, leading to an exact conservation of Noether charges even for discrete IBVPs. The dynamical nature of the coordinate maps leads to an adjustment of space-time resolution, guided by Noether charge conservation, realizing a form of automatic adaptive mesh refinement. We stress that as long as SBP operators are used for the discretization, our results are independent of whether the dynamics are solved on the action or governing equation level and hold in particular also at high order. As proof-of-principle for our approach we present its application to scalar wave-propagation in 1+1 dimensions.

Low-energy $\mu$SR (LE-$\mu$SR) enables depth-resolved studies of magnetic and electronic properties from the surface into the near-surface region, but the measured transverse-field asymmetry is not an intrinsic constant and depends on implantation energy and beamline conditions. Following an upgrade of the single-muon tagging system at the LEM beamline at PSI in 2023, updated asymmetry calibrations became necessary. Here, we present updated reference measurements that establish the energy-dependent maximum asymmetry using a silver reference and quantify spurious contributions from reflected muons using a nickel reference. In addition, we address systematic reductions of the measured asymmetry arising from incomplete beam–sample overlap by introducing a sample-size-dependent correction factor. This factor is obtained from musrSim/Geant4 simulations incorporating an updated electrostatic field map of the sample environment and is benchmarked using implantation-energy scans on SrTiO$_3$ samples of different lateral dimensions. Together, these updated calibrations and the simulation-based overlap correction provide a practical and up-to-date framework for LE-$\mu$SR data correction under current beam conditions and enable quantitative depth-resolved analysis of volume fractions.

We propose a novel measurement-free scheme for stabilizing a spin-oscillator hybrid qubit via autonomous quantum error correction. The engineered Lindbladian renders the code space into an attractive steady-state subspace, realized by coupling the storage mode to a rapidly cooled bath through a controlled beam-splitter and spin-dependent displacement interactions. The continuous variable-discrete variable hybrid approach to autonomous quantum error correction preserves the hardware efficiency of conventional dissipation engineering while simplifying the required system-bath coupling. The construction is compatible with simple logical gates and leverages primitives already demonstrated in experimental platforms, such as trapped-ion systems, suggesting a practical route to hardware-efficient, noise-biased logical qubits without repeated syndrome measurements and feedforward.

The search for quantum-like wave formulations of the Navier-Stokes (Schrödinger-Navier-Stokes, SNS for short) equations describing classical dissipative fluids has met with increasing attention in the recent years, due to the large portfolio of potential applications in science and engineering. A SNS formulation of classical fluids was first presented in a largely un-noticed paper by Dietrich and Vautherin back in 1985(Journal de Physique). In this paper, we revisit this specific SNS approach and assess its viability for quantum implementations based on Carleman embedding/linearization techniques. Specifically, we i) Clarify in full mathematical detail why the SNS dissipator presents a steep challenge for quantum computers and propose a way out strategy based on the Hamilton-Jacobi (HJ) formulation of fluid dynamics; ii) Develop a corresponding quantum algorithm using a new technique based on a tensor-network representation of Carleman embedding of the HJ equations (CHJ) which permits substantial memory savings; iii) Emulate the CHJ quantum algorithm on a classical computer and analyse its convergence and accuracy for the specific case of Kolmogorov-like flows at moderate Reynolds numbers. To the best of our knowledge, this is the first quantum algorithm based on a quantum-like wave formulation of the genuine Navier-Stokes equations, including pressure, dissipation and vorticity.

In this paper, we propose a method to build molecular cages designed to capture a specific substrate. We model a cage as a graph of atoms with coordinates in space, and several constraints on their edges (degree, length and angle). We use a simple method to place binding patterns which are able to interact with certain parts of the substrate. We then propose an algorithm which considers all possible ways of connecting these binding patterns and try to construct the smallest possible molecular paths realizing these connections. We investigate many variants of our method in order to obtain the most efficient algorithm, able to build cages of more than a hundred atoms.

Turbulent polymeric flows show strong deviations from Kolomogorov-like behaviour resulting from more complex dynamics compared to Newtonian turbulence. We now study the nature of mixing in polymeric turbulence via Eulerian passive scalar fields of varying molecular diffusivities, given by the Schmidt number Sc. We show that polymeric turbulence is a less efficient mixer than the Newtonian one at small to moderate Sc numbers. Newtonian scalar turbulence (NST) forms large islands of fluctuations with extended, contiguous fronts. In contrast, polymeric scalar turbulence (PST) is marked by small, interspersed patches of strong but less intermittent fluctuations. These patches collectively comprise a larger volume fraction of strong fluctuations, indicating a less efficient mixing, alongwith smaller scalar gradients and therefore smaller average flux across their boundaries. Box counting dimensions reveal a smoother and more space filling nature of patch boundaries in PST compared to NST fronts. Finally, spatial changes of the scalar are stronger in PST, but with a slower self-similar growth and less intermittency as revealed by the kurtosis of scalar differences. Overall, these observations hint at reduced mixing in PST where fluctuations are typically stronger while the average scalar flux is smaller in a stationary state.

Multiphase turbulent flow phenomena are observed not only in industrial devices but also in environmental flows, and direct numerical simulation (DNS) plays a key role in their investigation. Many numerical models have been developed; nevertheless, few models are highly optimized for GPU platforms, which represent the current mainstream in high-performance computing (HPC). In this study, we developed LCS.jl (Lagrangian Cloud Simulator in Julia), a single-source and multi-platform multiphase turbulence simulation model implemented in Julia language and KernelAbstractions.jl. Validation results confirmed that the present fluid and particle statistics agree well with those obtained in prior studies. A GPU-native particle communication algorithm based on prefix-scan reduced the particle communication cost from approximately 78% (CPU-delegated) to 10% of total execution time. LCS.jl achieved computational performance equivalent to the Fortran implementation in many-processes computations. For GPUs, strong scaling efficiency was maintained above 85% (up to 256 GPUs) and weak scaling efficiency above 90% (up to 216 GPUs) on TSUBAME4.0 (a GPU supercomputer at the Institute of Science Tokyo). LCS.jl achieved a maximum speedup of 18.0x on GPUs over CPUs. A trial heterogeneous execution achieved a 72% reduction in execution time compared to the CPU-only configuration even in configurations where the GPU was not the primary compute device. These results demonstrate that LCS.jl is a multiphase turbulence simulation platform that achieves both portability and scalability across a variety of computational resource configurations.

Recent experiments on K3C60 revealed a giant enhancement of the light-induced superconducting-like optical response for pump frequencies near 10 THz, with an efficiency roughly two orders of magnitude larger than for off resonant excitation. Here we show that a resonant enhancement of pair correlations arises naturally in a driven purely electronic model of K$_3$C$_{60}$ with ab initio parameters. The underlying mechanism is a symmetry constrained two-photon pathway: the first photon drives the system from the even-parity ground state to an intermediate odd-parity manifold, while the second photon induces a transition to an even-parity excited state with enhanced pair correlations. Larger-cluster calculations show that the corresponding resonance energy is strongly renormalized downward with system size and connectivity, reflecting the kinetic-energy gain of delocalized photo-excited doublon-holon configurations. A simplified single-orbital model reproduces this scaling trend and reaches a 14-site fcc cluster, where the resonant peak is pushed to 30 THz, with a trend compatible with a further reduction toward the experimental 10 THz scale in larger systems. Varying the Hubbard coupling strength, we find that the resonance is lowest and the pairing enhancement strongest near intermediate couplings, where doublon-holon excitations are both well defined and mobile. Our results establish a purely electronic mechanism for resonant light-enhanced pair correlations in K$_3$C$_{60}$ and support the interpretation of the experimental 10 THz resonance as optical access to a paired many-body excited state rather than improved metallicity. More broadly, they suggest that related resonant pathways may arise in other intermediate-coupling Hubbard materials.

Interatomic potentials which accurately describe long-range electrostatics require atom-centred charges. One such method to determine these atom-centred charges from density functional theory (DFT) calculations is the density-derived atomic point (DDAP) charge method. DDAP fits atom-centred Gaussians to the ground-state DFT charge density and preserves the multipole moments that govern long-range electrostatics. While these charges accurately predict long-range behaviour, in practice, they are limited by their reliance on fixed, heuristic parameters and a constrained solver that becomes numerically unstable for complex or covalent systems. In this work, we present opt-DDAP, which solves this limitation by reformulating the algorithm as a differentiable computational graph. This reformulation allows for the optimisation of Gaussian basis parameters and the reciprocal-space cutoff using automatic differentiation. To ensure numerical robustness through this automatic differentiation process, we replace the conventional Lagrange-multiplier approach with a pseudo-inverse solution followed by charge renormalisation, maintaining stability even in the presence of ill-conditioned matrices. We validate the framework on NaCl vacancy supercells and on MoS$_2$, demonstrating faithful reconstruction of both absolute and difference charge densities. The optimised charges are intended to serve as inputs to effective electrostatic models in machine-learning and empirical interatomic potentials that incorporate long-range interactions.

Hydrodynamic coarsening of bicontinuous domains is a central process in liquid-liquid phase separation, yet how soluble surfactants regulate this process remains poorly understood. Using a validated two-order-parameter phase-field model coupled to the incompressible Navier-Stokes equations, we show that hydrodynamic coarsening is suppressed primarily by surfactant-induced Marangoni stresses rather than by the reduction of mean interfacial tension alone. These stresses hinder interfacial coalescence, reorganize the local vortical flow, and thereby redirect the morphological evolution of bicontinuous domains. A central result is that this suppression depends non-monotonically on the surfactant Péclet number, with the strongest inhibition occurring at an intermediate value, $Pe_\psi=10$, rather than at $Pe_\psi=1$ or 100. Analyses of force evolution, interfacial surfactant statistics, and decomposed surfactant flux budgets show that this non-monotonicity arises from a competition between surfactant replenishment and gradient retention. At low $Pe_\psi$, diffusion efficiently replenishes the interface but smooths interfacial concentration gradients; at high $Pe_\psi$, advection preserves interfacial heterogeneity but leaves the interface insufficiently supplied with surfactant. The strongest suppression therefore occurs when sufficient interfacial surfactant loading coexists with persistent concentration gradients. These results establish a transport-controlled mechanism by which soluble surfactants regulate bicontinuous hydrodynamic coarsening.

Moire lattices provide a powerful route for engineering emergent symmetries and length scales through the relative rotation of periodic structures. However, their implementation in polaritonic systems remains relatively unexplored, and a general framework describing how twist modifies the interaction of optical modes in momentum space is still lacking. Here, we investigate how twist-induced moire periodicities can control symmetry and momentum-space coupling in surface phonon polariton (SPhP) metasurfaces. We fabricate twisted overlapping dual-grating metasurfaces on a polar dielectric substrate with dielectric overlayer and characterize their optical response using polarization-resolved Fourier-transform infrared microscopy. Experimental measurements are combined with full-wave simulations and momentum-space analysis to identify the resonant SPhP and SPhP-like waveguide (WG) modes arising from both individual grating periodicities and emergent moire periodicities. The results reveal twist-controlled symmetry breaking manifested as asymmetry between p to s and s to p polarization conversion, along with twist-dependent interactions between SPhP and SPhP-like WG modes. Our analysis reveals that the twist-engineered polarization-conversion asymmetry enables directional biasing of infrared radiative heat transfer. These findings establish twisted phonon-polaritonic metasurfaces as a versatile platform for geometry-controlled symmetry engineering in the mid-infrared. Future work may leverage such twist-programmable polaritonic interactions to enable directional thermal emission, polarization-selective detection, and reconfigurable infrared photonic devices.

Forecasting extreme precipitation is essential yet challenging due to its rarity and complexity. We develop a large deviation framework to estimate the return times of extreme precipitation events. We first find that the Landau distribution, originally introduced in plasma physics, accurately captures extreme precipitation at approximately 93% of global locations, outperforming conventional extreme value distributions with 76% matched locations under the same accuracy criterion. Enriching rare event samples by the fitted Landau distribution, we obtain more accurate estimates of large deviation rate functions and return times, enabling forecasts beyond historically observed precipitation intensities. Mapping historical return times to future projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we show that return time curves under different emission scenarios collapse onto a unified relation, revealing a sharply increased lifetime exposure to extreme precipitation for 21st-century birth cohorts under most future emission scenarios.

Nanoplastics pose serious environmental and health risks due to their widespread presence in aquatic systems. Detecting trace amounts of nanoplastics is a challenging task, which currently requires sophisticated equipment and tedious sample preparation (e.g., ultrafiltration). In this work, we demonstrate an ultra-sensitive Shrinking Surface Bubble Deposition (SSBD) technique for nanoplastics detection. SSBD leverages plasmonic photothermal effects to generate a surface bubble and the resulting Marangoni flow to concentrate sparsely suspended nanoplastics onto the bubble surface. The collected nanoplastic particles are subsequently deposited on the substrate after the bubble shrinks and vanishes. To quantify the detection limit of SSBD for nanoplastics in water, core-shell gold plasmonic nanoparticles are mixed with the aqueous sample to enable photothermal bubble generation, while also supporting surface-enhanced Raman spectroscopy (SERS) for signal enhancement. Results show that the limits of detection are 10 ng/mL, 10-1 ng/mL and 10-3 ng/mL for polystyrene (PS) particles with diameters of 500 nm, 200 nm and 30 nm, respectively. We further used SSBD to detect plastics particles in real drinking water (e.g., bottled and fountain water) and found polyamides (PA) and polypropylene (PP) micro/nanoplastics, demonstrating the potential of the SSBD-SERS technique as a versatile and sensitive platform for detecting trace-level nanoplastic contamination and assessing human exposure risk.

In large-scale AI systems, allocating scarce resources such as GPU compute time and bandwidth among multiple agents is a critical challenge. Conventional policies focus on efficiency metrics, potentially leading to dominance concentration that undermines system diversity and stability. We propose Computable Fair Division (CFD), a framework that reinterprets the Boltzmann-Softmax function not as a selection tool but as a probabilistic resource allocation mechanism, redefining the inverse temperature parameter $\beta$ as a computable control variable governing the efficiency-fairness balance. Static analysis reveals a Pareto frontier with a near-optimal Stability Corridor where total loss remains approximately constant across policy weights. In the dynamic setting, AHC++ (Adaptive Hard-Cap Controller++) updates $\beta$ in real time using the error between observed dominance and a policy-specified target as feedback. Simulations show that AHC++ suppresses extreme dominance concentration under exogenous shocks while tracking fairness targets without substantial throughput degradation. Scalability analysis confirms that a 100x increase in agents yields only approximately 5.5x increase in execution time. Code: https://github.com/entrofy-ai/computable-fairness

The rapid development of artificial intelligence (AI), Internet of Things (IoT), and edge computing applications has posed severe challenges to conventional memory technologies in terms of density, speed, and energy consumption. Herein, a single-ion transport mechanism is proposed to achieve picosecond (ps) switching capability. For monolayer hexagonal boron nitride (h-BN) with single-atom vacancy defects, first-principles calculations reveal that single-ion penetration across the BN plane dominates the resistive switching. The trapping and release of a single ion correspond to different states of the memory device for one bit of information. Experimentally fabricated single-ion memory exhibits nonvolatile resistive switching with ultra-fast switching speed of 20 ps and ultra-low energy consumption of 310 aJ/bit. This high performance is attributed to the extremely short distance for the single ion to travel through. Such devices pave the way for the realization of high-performance nonvolatile memory with ultra-fast speed, ultra-low energy consumption, and high storage density, that is called the "Unified Memory" long desired by the whole industry.

Seismic noise with an amplitude higher than that of the sought signal is a challenge for detection. Several techniques have been developed to suppress the ambient noise and to reduce the detection threshold in order to find signals with the lowest possible amplitudes produced by events with the magnitudes significant for scientific research and technical applications. Seismic arrays were introduced in the late 1950s as a method for improving underground test monitoring, potentially reducing detection thresholds by fivefold or more by exploiting destructive interference effects of a quasi-random noise. The beamforming method is the backbone of data processing at the International Data Centre (IDC) with more than 30 array stations of the International Monitoring System (IMS) installed around the globe. The matched filter method allows for the suppression of noise incoherent to the sought signal. It employs waveform cross-correlation (WCC) with templates based on actual and simulated seismic signals to improve the signal-to-noise ratio estimates for similar signals. The performance of this method is significantly enhanced when it is applied to a seismic array. A novel technique combined with WCC, is the noise stochastization or the addition of scaled random noise to the actual data before calculating the cross-correlation coefficient. The stochastic component can easily be generated by a computer program. Alternatively, a regular signal propagating at an angle of around 90{\deg} to the plane of the sought signal can play a role of stochastic component at array stations. We demonstrate the separate and joint effects of these noise reduction techniques on the WCC performance, when applied to filtered data from selected IMS arrays and various waveform templates of historical events available at the IDC.

Accurate reconstruction of localized extreme structures remains a critical bottleneck in the physics-informed modeling of electro-thermal-convective flows. Although conventional physics-informed neural networks effectively capture smooth global dynamics, they frequently suffer from numerical diffusion and distortion when attempting to resolve sharp charge boundary layers or abrupt multiphase interfaces. To address these limitations, we propose a Residual-Attention Physics-Informed Neural Network (RA-PINN) that embeds gated attention modulation within a residual feature framework to adaptively enhance local sensitivity to steep physical gradients. The proposed architecture is rigorously evaluated against standard and recurrent network baselines using canonical electrohydrodynamic scenarios, encompassing near-electrode exponential boundary layers and sharply concentrated charge fields. Quantitative analyses demonstrate that the RA-PINN significantly reduces localized errors and faithfully preserves critical interface topologies without compromising the global consistency dictated by the coupled governing equations. Ultimately, this methodology establishes a highly robust predictive framework for resolving complex interfacial and boundary layer phenomena in advanced fluid dynamics applications.

We present the application of Parnassus, a generative model for full detector simulation and reconstruction, to the ALEPH detector at the Large Electron-Positron Collider (LEP). Training on simulated $e^+e^-$ to Z to qqbar events processed through the ALEPH detector simulation and reconstruction, we demonstrate that Parnassus faithfully reproduces the detector response at the event, jet, and particle levels, with substantially better agreement than the Delphes fast simulation. The clean $e^+e^-$ environment, free of pileup and characterized by simple event topologies, provides a well-controlled benchmark for evaluating the generative model's fidelity. Our results demonstrate that modern neural-network-based generative simulation approaches, developed primarily for LHC experiments, generalize naturally to historical collider experiments with distinct detector geometries and physics environments. This work shows that Parnassus can be applied beyond the LHC context and serves as an important tool for legacy data analysis where archival software tools are challenging to resurrect.

The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration by exploiting optical bandwidth and parallelism to reshape data movement and computation. This review reframes photonic computing from a circuits-and-systems perspective, moving beyond building-block progress toward cross-layer system analysis and full-stack design automation. We synthesize recent advances through a bottleneck-driven taxonomy that delineates the operating regimes and scaling trends where photonics can deliver end-to-end sustained benefits. A central theme is cross-layer co-design and workload-adaptive programmability to sustain high efficiency and versatility across evolving application domains at scale. We further argue that Electronic-Photonic Design Automation (EPDA) will be pivotal, enabling closed-loop co-optimization across simulation, inverse design, system modeling, and physical implementation. By charting a roadmap from laboratory prototypes to scalable, reproducible electronic-photonic ecosystems, this review aims to guide the CAS community toward an automated, system-centric era of photonic machine intelligence.

Flexible structures in an incoming perpendicular flow typically undergo elastic reconfiguration that reduces drag; however, at higher velocities, they are prone to dynamical instabilities that entail complex wake dynamics and fluctuating loads. In this study, we investigate the wake of a thin, flexible plate clamped at its midpoint and oriented normal to an airflow, modelling reconfigurable natural systems such as trees and sea-grasses. By combining Proper Orthogonal Decomposition, Robust Principal Component Analysis, and Singular Value Decomposition with non-time-resolved Particle Image Velocimetry, we reconstruct the periodic coherent flow structures across both static and vibrating regimes. We demonstrate that structural oscillation symmetry directly dictates the wake topology. The symmetric vibration regime is characterised by two parallel 2S-type vortex shedding patterns on either side of the plate-herein termed the S-2S mode-whereas the antisymmetric regime exhibits a classic 2P-type shedding pattern. Furthermore, an impulse-based force analysis links these wake circulations to drag, revealing an additional mean drag penalty in the antisymmetric regime. Our approach offers a practical framework to extract and interpret coherent wake structures from limited temporal data, enhancing our understanding of fluid-structure interactions and informing aerodynamic load predictions.

A well-known shortcoming of High Harmonic Generation (HHG) is the strong dependence of the broadband HHG spectra (HGS) on the carrier envelope phase (CEP) of the driver. Here we numerically show that compared to the current well-established scalar (linearly polarized) schemes for generating broadband HGS, namely a short driver [Amplitude gating (AG)], Polarization-Gating (PG) or Time-Gating (TG), the vectorial driver of the Vectorial-Time-Polarization-Gating (VTPG) scheme renders the cutoff HGS much less sensitive to the CEP of the driver. The polarization state (helicity) of the emitted radiation is likewise CEP-resilient. Unlike scalar schemes, where the number of recollisions heavily depends on the CEP, in VTPG the CEP keeps this number almost unchanged, and only controls the partitioning of the recollisions between two orthogonal directions. This reduces the CEP-dependence of the HGS and decreases the spectral modulations. The CEP-resilience of the VTPG scheme holds promise for a variety of applications in attosecond science benefiting from quasicontinuous, helical HHG sources liberated from the necessity to stabilize the CEP of the laser.

Epidemic modelling on complex networks has been studied intensively all the time. The majority of relative research assumes that the time scale of the underlying network evolution is much larger compared to the propagation dynamics on it, while the co-evolution of epidemics and networks needs exploring further. In this paper, we investigate how our recently proposed birth-death evolving network impacts the Susceptible-Infected-Recovered-Susceptible (SIRS) epidemic process. Our evolving network considers the increase and the heritable deletion of nodes, which enables to depicting individual behaviors during an epidemic, e.g., population migration and indirect contacts. To model the above processes, we construct a Markovian queueing network and perform analyses for the variation of population size of different epidemic states. In simulations, we reveal how the population migration and indirect contacts caused by our network dynamic properties influence the population sizes of each epidemic state, and find that newly-created indirect contacts facilitate epidemic transmission.

We simulate a third-order nonlinear signal in a pump-probe spectroscopy from the interference between first- and second-order wavepackets (WPs), as well as from a state-to-state transition for Stokes and coherent anti-Stokes pathways in the context of impulsive stimulated Raman scattering (ISRS) excitation. We present a detailed step-by-step description of both methods. The results obtained from these two methods are compared and contrasted through simulations of the excited-state absorption signal. While within the ISRS framework, vibrational dynamics is often attributed primarily to coherences between adjacent vibrational levels in the excited electronic states, our results show that coherences involving non-adjacent vibrational levels needs to be calculated for a better agreement with the WP approach. We also show that for the specific choice of pump/probe spectral bandwidths, the coherent anti-Stokes pathway majorly contributes to the observed signal.

Europa's double ridges often display lower albedo and redder color than their surroundings. Their unique topography may cause sublimation-driven darkening due to illumination and self-heating, a process known as thermal segregation. We apply an advanced 3D thermophysical model, including shadowing and self-heating through mutual exchange of radiation, to digital elevation models of double ridges at a range of latitudes and orientations. Results show that self-heating in ridge troughs can markedly increase temperatures and sublimation rates, with a difference in maximum trough temperatures of up to 20 K, which may have implications for detection of endogenic heat. Incorporating a simple exosphere model and assuming an initial 10% concentration of 1 $\mu$m non-ice particles, we find thermal segregation can produce reddening in the form of dark lag layers from the equator to the middle latitudes, but is generally negligible at 60 degrees or higher. Lag formation timescales in ridge troughs are 10 - 100 yr to produce an optically thick layer. Modeling suggests that low-albedo lag layer formation provides positive feedback, further increasing surface heating. These effects may also darken Europa's surface in areas surrounding the ridges. However, the net mass balance controlling sublimation and lag formation is highly sensitive to the global water exosphere density: values $\sim 10^{16}$ molec/m$^{2}$ produce reddening in the trough and ablation of $\sim1~\mu\mathrm{m~yr^{-1}}$ of material, while values $\sim10^{18}$ molec/m$^{2}$ result in net deposition of $\sim 10~\mu\mathrm{m~yr^{-1}}$. Model predictions of resulting low albedo material in double ridge troughs are provided, which can be tested with eventual data from Europa Clipper.

Colloid models have frequently been used to successfully describe the influence of protein-protein interactions on antibody solution properties, but they suffer from inherent problems due to the anisotropic shape of the particles. The net charge required to describe electrostatic interactions is an effective quantity that cannot directly be obtained from the known molecular structure of an antibody, and the solution structure caused by excluded volume interactions is strongly overestimated at high concentrations due to the assumption of hard sphere interactions. As a result, these models have descriptive rather than predictive power. Here we present an improved, soft penetrable sphere model based on analogies to soft colloids and star polyelectrolytes that take into account the Y-shaped antibody form and the corresponding charge and ion distribution. The model not only correctly describes the concentration and ionic strength dependence of thermodynamic and collective dynamics quantities such as the osmotic compressibility and the apparent hydrodynamic radius, but also reproduces the center-of-mass static structure factor obtained in computer simulations using a weakly coarse-grained model, in which the antibody is described at an amino acid level. We demonstrate that this soft penetrable sphere model quantitatively reproduces experimental data from static and dynamic light scattering at low and high ionic strength for two well-characterized monoclonal antibodies (mAbs) using the net charges and the overall mAb dimensions directly obtained from their molecular structure.

Quasi-biennial oscillations (QBOs) are shorter-term periodic signals that occur alongside the dominant 11-year solar cycle. In this study, we examine the spatial and temporal evolution of QBOs using helioseismic p-mode frequency shifts from the Global Oscillation Network Group (GONG) across solar Cycles 23 and 24 and the ascending phase of Cycle 25. By applying wavelet analysis to frequency shifts, we studied the changes in QBO periodicities to determine whether the QBO period and amplitude vary with latitude. Our results show that QBO periods exhibit a weak latitudinal dependence, with shorter and less persistent signals at low latitudes, while at higher latitudes the periods are nearly constant at $\sim$3 years. Cycle 24 tends to display slightly longer periods than Cycle 23, though within uncertainties. At all latitudes, QBO amplitudes increase with mode frequency, which is consistent with previous studies. Higher amplitude QBOs are found at low latitudes, reflecting the distribution of surface magnetic activity. The ratio of QBO to cycle amplitude is systematically higher in Cycle 24 than in Cycle 23, and above $20^\circ$ latitude the amplitude ratio is nearly uniform in Cycle 23 but shows modest variations in Cycle 24. A linear relation between QBO amplitude and cycle amplitude is found in both cycles, but with significantly different slopes, indicating that QBO amplitudes are not wholly governed by the solar cycle strength and are at least partially decoupled from it. Finally, we find no evidence that QBO period depends on QBO amplitude, consistent with a linear oscillation regime.

To answer the questions of whether global warming is accelerating and when the 1.5{\deg}C Paris Agreement target will be exceeded, the global mean surface temperature from 1880 to 2025 is first examined using a purely graphical approach and later, in a more conventional way, using various time-domain and frequency-domain methods. In an effort to reduce variability, exogenous variables such as El Niño and solar variations are taken into account. Although it ultimately remains unclear to what extent these variables are actually helpful, we feel confident in summarizing the empirical results of this study to suggest that global warming is indeed accelerating and that a breach of the 1.5{\deg}C Paris Agreement target is imminent. But when it comes to statistical significance, caution should still be exercised. While the acceleration hypothesis can be confirmed with a fair degree of certainty under reasonably plausible assumptions (albeit with the help of a bit of data snooping, which is unavoidable when building on the results of earlier studies that used virtually the same data), there is currently not enough evidence to prove that the 1.5{\deg}C target has already been exceeded. However, if 2026 and 2027 turn out to be very warm due to the approaching El Niño, that could change very soon.

We propose a theoretical framework to dynamically control the degree of polarization of light by using the superposition of incoherent orthogonally polarized beams in a Mach-Zehnder interferometer incorporating a twisted nematic liquid crystal cell in one of its arms. The liquid crystal acts as an elecro-optically controlled polarization rotator, where the applied electric field changes the twist of molecules inside the nematic liquid crystal, thereby altering the plane of polarization. This controllable voltage dependent polarization rotation causes manipulation of the output degree of polarization. The resulting system allows real-time, tunable control over the degree of polarization, offering advantages over traditional static or reflection-based approaches, which often suffer from intensity losses or manual errors. We also observe that in the interference of fully coherent orthogonally polarized beams through a similar configuration, the degree of polarization is always equal to 1, whereas the orientation of linear state of polarization is changed with voltage.