In this paper, we study the connected components of an obscuration-free planar polygonal light beam space modeling light propagation in optical systems composed of reflective surfaces and a focal plane. Through homotopy construction, we demonstrate that the connected components of this space are in bijection with the connected components of the reflective polygonal chains space, whose elements are the polygonal chains with their respective mirrors' orientations taken into account. In order to prove this, we introduce a topological invariant that provides an intelligible way for opticians to name homotopy-equivalent obscuration-free optical configurations thanks to previous work with polygonal chains.

Modern physics proposals present deep tensions between seemingly contradictory descriptions of reality. Views of wave-particle duality, black hole complementarity, and the Unruh effect demand explanations that shift depending on how a system is observed. However, traditional models of scientific explanation impose a fixed structure that fails to account for varying observational contexts. This paper introduces context-dependent mapping, a framework that reorganizes physical laws into self-consistent subsets structured around what can actually be observed in a given context. By doing so, it provides a principled way to integrate complementarity into the philosophy of explanation.

Over the course of their studies, students of physics accumulate experience in solving problems for a variety forces, not only developing a strong physical intuition for each force but also having an understanding of how each force is realized in the physical world. With this in mind we believe not enough emphasis is placed on the reciprocal velocity force $F(v)=\frac{C}{v}$. The theoretical importance of this force is illustrated by the units of the constant $C$ which are power. Such a force has constant power as $P(t)=F(v(t))v(t)=C$. The goal of this paper is to use graphical methods to explain how such a peculiar force arises from a device called a transmission, to explore some of the consequences of this force, and to briefly discuss its role in automotive physics.

Vibrations from experimental setups and the environment are a persistent source of noise for low-temperature calorimeters searching for rare events, including neutrinoless double beta ($0\nu\beta\beta$) decay or dark matter interactions. Such noise can significantly limit experimental sensitivity to the physics case under investigation. Here we report the first detection of marine microseismic vibrations using mK-scale calorimeters. This study employs a multi-device analysis correlating data from CUORE, the leading experiment in the search for $0\nu\beta\beta$ decay with mK-scale calorimeters and the Copernicus Earth Observation program, revealing the seasonal impact of Mediterranean Sea activity on CUORE's energy thresholds, resolution, and sensitivity over four years. The detection of marine microseisms underscores the need to address faint environmental noise in ultra-sensitive experiments. Understanding how such noise couples to the detector and developing mitigation strategies is essential for next-generation experiments. We demonstrate one such strategy: a noise decorrelation algorithm implemented in CUORE using auxiliary sensors, which reduces vibrational noise and improves detector performance. Enhancing sensitivity to $0\nu\beta\beta$ decay and to rare events with low-energy signatures requires identifying unresolved noise sources, advancing noise reduction methods, and improving vibration suppression systems, all of which inform the design of next-generation rare event experiments.

The study of microorganisms, or microbiology, has demonstrated significant development since its inception and is currently a key field of biological sciences that has a huge impact on modern society and scientific research. Over the centuries, this discipline has undergone significant changes, shaping our understanding of infectious diseases and food safety. Starting from the simplest observations of microscopic organisms such as bacteria, viruses, fungi and protozoa, and ending with modern molecular and genomic research methods. This article describes a brief historical path of microbiology development. The heuristic, morphological, physiological, immunological, and molecular genetic stages are the main periods into which the development of this science is traditionally divided, despite the lack of full-fledged and precise boundaries between them.

Nanodosimetry relates Ionization Detail (ID) and ionization parameters (Ip) to biological endpoints relevant for charged-particle radiotherapy. This supports a more physics-based modeling of biological effectiveness than traditional dose-response relationships and RBE models. Faddegon et al. (2023) introduced cluster dose g(Ip) as a physical quantity, bridging ID to the treatment planning level, which can be directly optimized. We developed a framework enabling cluster dose optimization via a pencil-beam (PB) algorithm, and validated against Monte Carlo (MC) simulations. The framework, integrated into the treatment planning toolkit matRad, uses precomputed Ip values obtained from MC track-structure simulations. We applied our tool for plan optimization with protons, helium, and carbon ions in a water phantom and a prostate case. Recalculation with TOPAS showed 3D gamma passing rates >97% (phantom) and >98% (patient) using a 3%/3mm criterion and a threshold of 10% of the maximum dose. Cluster dose F5 optimization produced homogeneous target coverage, with heavier ions requiring lower absorbed doses for the same prescribed cluster dose level. This demonstrates the feasibility of fast, accurate cluster dose optimization using PB algorithms.

Simulations of relativistic plasmas traditionally focus on the dynamics of two-species mixtures of charged particles under the influence of external magnetic fields and those generated by particle currents. However, the extreme conditions of astrophysical plasmas near compact objects such as black holes and neutron stars are often characterized by mixtures of electrons, protons, and positrons, whose dynamics can differ significantly because of the considerable mass contrast. We present the first two-dimensional particle-in-cell (PIC) simulations of relativistic turbulence and magnetic reconnection in a three-species plasma, varying the relative abundance of electrons, protons, and positrons while employing realistic mass ratios to achieve unprecedented accuracy. We find that turbulence leads to the formation of magnetic islands, current sheets, and plasmoids. Reconnection occurs between these structures, with plasma composition playing a key role in determining the number of reconnection sites and their energy-conversion efficiency. In particular, as the proton fraction increases, very small-scale features of the turbulence are washed out, while global dissipative effects are amplified. Finally, using a novel generalization of Ohm's law for a relativistic multi-species plasma, we find that the reconnection rate is primarily governed by the electric fields associated to the divergence of the positron and electron pressure tensors. These results provide new insights into dissipation and particle acceleration in turbulent relativistic plasmas, such as those near black holes and neutron stars, and can be used to interpret their high-energy emission and phenomenology.

Dryland vegetation ecosystems are known to be susceptible to critical transitions between alternative stable states when subjected to external forcing. Such transitions are often discussed through the framework of bifurcation theory, but the spatial patterning of vegetation, which is characteristic of drylands, leads to dynamics that are much more complex and diverse than local bifurcations. Recent methodological developments in Early Warning Signal (EWS) detection have shown promise in identifying dynamical signatures of oncoming critical transitions, with particularly strong predictive capabilities being demonstrated by deep neural networks. However, a machine learning model trained on synthetic examples is only useful if it can effectively transfer to a test case of practical interest. These models' capacity to generalize in this manner has been demonstrated for bifurcation transitions, but it is not as well characterized for high-dimensional phase transitions. This paper explores the successes and shortcomings of neural EWS detection for spatially patterned phase transitions, and shows how these models can be used to gain insight into where and how EWS-relevant information is encoded in spatiotemporal dynamics. A few paradigmatic test systems are used to illustrate how the capabilities of such models can be probed in a number of ways, with particular attention to the performances of a number of proposed statistical indicators for EWS and to the supplementary task of distinguishing between abrupt and continuous transitions. Results reveal that model performance often changes dramatically when training and test data sources are interchanged, which offers new insight into the criteria for model generalization.

Pattern formation in active biological matter typically arises from the feedback between chemical concentration fields and mechanical stresses. The actomyosin cortex of cells is an archetypal example of an active thin film that displays such patterns. Here, we show how pulsatory patterns emerge in a minimal model of the actomyosin cortex with a single stress-regulating chemical species that exchanges material with the cytoplasm via a linear turnover reaction. Deriving a low-dimensional amplitude-phase model, valid for a one-dimensional periodic domain and a spherical surface, we show that nonlinear waves arise from a secondary parity-breaking bifurcation that originates from the nonreciprocal interaction between spatial modes of the concentration field. Numerical analysis confirms these analytical predictions, and also reveals analogous pulsatory patterns on impermeable domains. Our study provides a generic route to the emergence of nonreciprocity-driven pulsatory patterns that can be controlled by both the strength of activity and the turnover rate.

This report summarizes our recent data derivation efforts on STPSat-6 ZPS plasma measurements, presenting final outcomes. We begin by outlining the methodology developed to determine local magnetic field directions from measured directional intensities of ~20 keV ions,leveraging symmetry in particles' pitch-angle distributions, and the determined field directions over a storm period are shown with errors analyzed through comparison with NOAA GOES measurements. Next, we further assess the validity of the method and quantify its overall high performance while acknowledging certain caveats. Finally, given local magnetic field directions and ZPS data, we explain how omni-directional fluxes of particles can be derived from ZPS measurements with limited directional coverage, and estimate the related errors under several theoretical scenarios. The methods developed in this study can be applied for processing and augmenting ZPS data for the next, and the insights gained here can also inform instrument design in the future.

Femtosecond laser-writing offers distinct capabilities for fabrication, including three-dimensional, multi-material, and sub-diffraction-limited patterning. In particular, demonstrations of laser-written quantum emitters and photonic devices with superior optical properties have attracted attention. Recently, gallium nitride (GaN) has been reported to host quantum emitters with narrow and bright zero-phonon photoluminescence from ultraviolet to telecom ranges. However, emitters formed during epitaxy are randomly positioned, and until now, it has not been possible to fabricate quantum emitters in ordered arrays. In this paper, we employ femtosecond laser writing to create nano-ablations with sub-diffraction-limited diameter, and use rapid thermal annealing to activate co-located stable emitters. The emitters show MHz antibunched emission with a sharp spectral peak at room temperature. Our study not only presents an efficient approach to laser-written nanofabrication on GaN but also offers a promising pathway for the deterministic creation of quantum emitters in GaN, shedding light on the underlying mechanisms involved.

Effective education in radiotherapy plan quality review requires a robust, regularly updated set of examples and the flexibility to demonstrate multiple possible planning approaches and their consequences. However, the current clinic-based paradigm does not support these needs. To address this, we have developed 'Virtual Dosimetrist' models that can both generate training examples of suboptimal treatment plans and then allow trainees to improve the plan quality through simple natural language prompts, as if communicating with a dosimetrist. The dose generation and modification process is accurate, rapid, and requires only modest resources. This work is the first to combine dose distribution prediction with natural language processing; providing a robust pipeline for both generating suboptimal training plans and allowing trainees to practice their critical plan review and improvement skills that addresses the challenges of the current clinic-based paradigm.

We develop a new procedure that combines the Kinetic Orbit Runaway electrons Code (KORC) and the NIMROD extended-MHD code to simulate runaway electrons (REs) in the post-disruption plateau. KORC integrates guiding-center orbits using a barycentric-based binary search strategy to generate initial guesses for the Newton-Raphson logical-to-physical coordinate inversion, guaranteeing reliable particle-to-mesh mapping in NIMROD, whose fields remain static for the present study. Samples are drawn in accord with experimental parallel current profiles of RE beams during the plateau phase. Deposition in NIMROD is verified through comparison with a Python-based finite element code that ensures periodicity in the poloidal direction and continuity at the magnetic axis. Accurate representation of near-axis fields requires finer mesh resolution to prevent under- and overshoots in current density from orbit inaccuracies. Yet, at a fixed particle count, increasing mesh resolution amplifies statistical noise in the deposited fields. An orbit-averaging method accumulates partial current deposits over multiple kinetic steps and reduces the statistical noise with little added computational cost. By coupling kinetic routines from KORC directly into the NIMROD codebase, these developments lay essential groundwork for future self-consistent KORC-NIMROD coupling.

Modern wireless systems demand compact, power-efficient RF front-end components that support wideband tunability and nonreciprocity. We present a new class of miniature bandpass filter that achieves both continuously tunable frequency operation (4-17.7 GHz) and high nonreciprocity (>25 dB), all within a compact size of 1.07 cm3. The filter employs a microfabricated 18 micrometer thick Yttrium Iron Garnet waveguide with meander-line aluminum transducers, enabling low-loss unidirectional propagation via magnetostatic surface waves. Leveraging a benzocyclobutene planarization fabrication process, this study enables a dispersion profile unique to thick YIG films, resulting in enhanced filter skirt performance with minimal spurious modes. Frequency tuning is enabled by a zero-static-power magnetic bias circuit using transient current pulses, eliminating continuous power consumption. The filter demonstrates low insertion loss (3-5 dB), high out-of-band rejection (>30 dB), narrow bandwidth (100-200 MHz), and robust power handling (>10.4 dBm).

The importance of nonlocality is assessed in modeling mean scalar transport for three-dimensional variable density Rayleigh-Taylor (RT) mixing. Building on the two-dimensional incompressible work of Lavacot et al. (2024, JFM), the present work extends the Macroscopic Forcing Method (MFM) to variable density problems in three-dimensional space to measure moments of the generalized eddy diffusivity kernel in RT mixing for increasing Atwood numbers (A = 0.05, 0.3, 0.5, 0.8). It is found that as A increases: 1) the eddy diffusivity moments become asymmetric, and 2) the higher-order eddy diffusivity moments become larger relative to the leading-order diffusivity, indicating that nonlocality becomes more important at higher A. There is a particularly strong temporal nonlocality at higher A, suggesting stronger history effects. The implications of these findings for closure modeling for finite-Atwood RT are discussed.

The sea surface height (SSH) field measured by Surface Water and Ocean Topography (SWOT) mission's wide-swath altimeter is analyzed with a focus on submesoscale features. Along-track wavenumber spectra of SSH variance are estimated for the global ocean using the 1-day repeat period from March 26 to July 10, 2023. In regions with an energetic mesoscale eddy field, the spectra have a mesoscale plateau, a steep drop-off due to balanced submesoscale turbulence, and a much flatter power-law tail at small scales. These spectra are characterized by fitting a spectral model. For the balanced signal, this fit yields a spectral slope between -4 and -6 for most regions, consistent with expectations and previous observations. The amplitude of the distinct small-scale signal, which typically dominates at wavelengths less than 30 to 50km, is strongly correlated in time and space with the height of surface gravity waves, suggesting aliased wave signals as the most likely source. A simple method is proposed to isolate the balanced signal in regions with negligible internal tides. Maps of the balanced signal in the Antarctic Circumpolar Current show compact cyclones with geostrophic relative vorticities frequently in excess of the local planetary vorticity, challenging the quasi-geostrophic framework commonly used to interpret altimetric data.

We investigate the systematic errors in measured plasma velocity distribution functions and their corresponding velocity moments, arising from the limited energy and angular resolution of top-hat electrostatic analyzers. For this purpose, we develop a forward model of a concept analyzer that simulates observations of typical solar wind proton plasma particles with their velocities following a Maxwell distribution function. We then review the standard conversion of the observations to physical parameters and evaluate the errors arising from the limited resolution of the modeled instrument. We show that the limited resolution of the instrument results in velocity distributions that underestimate the core and overestimate the tails of the actual Maxwellian plasma velocity distribution functions. As a consequence, the velocity moments of the observed plasma underestimate the proton density and overestimate the proton temperature. Moreover, we show that the examined errors become significant for cold and fast plasma protons. We finally determine a mathematical formula that predicts these systematic inaccuracies based on specific plasma inputs and instrument features. Our results inform and contextualize future evaluations of observations by analyzers in various plasma regimes.

This work reports on results from the first national study of the impact of student facilitation of informal physics outreach programs on their physics identity, sense of belonging, and essential career skill development. Drawing on results from studies at a single institution with a well-developed physics outreach program, a national survey was developed and distributed through the Society of Physics Students' network to more than five thousand individuals. Responses to closed-form questions on the survey were analyzed descriptively and using multiple regression analysis to evaluate the relationship between student participation in informal physics outreach programs and the constructs of interest. Results show a strong association between students engaging in outreach with their confidence in communicating their physics knowledge, the development of key career skills, and direct connections with growth mindset and sense of belonging. These results may be useful to physics and other STEM departments around the United States who are seeking to elevate and broaden the learning experience of students.

Topological photonics is developed based on the analogy of Schr\"{o}dinger equation which is mathematically reduced to a standard eigenvalue equation. Notably, several photonic systems are beyond the standard topological band theory as they are described by generalized or nonlinear eigenvalue equations. In this article, we review the topological band theory of this category. In the first part, we discuss topological photonics of generalized eigenvalue equations where the band structure may take complex values even when the involved matrices are Hermitian. These complex bands explain the characteristic dispersion relation of hyperbolic metamaterials. In addition, our numerical analysis predicts the emergence of symmetry-protected exceptional points in a photonic crystal composed of negative index media. In the second part, by introducing auxiliary bands, we establish the nonlinear bulk-edge correspondence under ``weak" nonlinearity of eigenvalues. The nonlinear bulk-edge correspondence elucidates the robustness of chiral edge modes in photonic systems where the permittivity and permeability are frequency dependent.

We present an analysis of the transmission spectra of the twisted bilayer photonic slabs using a modified rigorous coupled wave (RCWA) analysis, where the evanescent bases are replaced by bases with non-zero flux density. By utilizing the modified RCWA we demonstrate the calculation of eigenmodes, which has not been realized before. To counter for the transmission property, we propose a five-layer uniform slab approximation, with an accuracy around 0.04a/c, which is more straightforward and accessible for optical engineers compared to work by Lou et al. [Phys. Rev. Lett. 126, 136101]. The moir\'e pattern perturbation induces a split of resonance, which show great potential for engineering the band structure. Moreover, We observe two distinct transmission phases: the angle-dependent phase and Fabry-P\'erot phase, which is explained by a coupled-mode theory (CMT) with expanded channels brought by the modified eigenmodes. Our work provides a theoretical framework for the design and optimization of twisted bilayer photonic devices.

Microring resonators enable the enhancement of nonlinear frequency mixing processes, generating output fields at frequencies that widely differ from the inputs, in some cases by more than an octave. The efficiency of such devices depends on effective in- and out-coupling between access waveguides and the microrings at these widely separated frequencies. One successful approach is to separate the coupling task across multiple waveguides, with a cutoff waveguide (a waveguide that does not support guided modes above a certain wavelength) being judiciously used to prevent unwanted excessive overcoupling at low frequencies. Here, we examine how such a cutoff waveguide can still induce parasitic loss in the coupling region of a microring resonator, thereby impacting nonlinear device performance. We verified this parasitic loss channel through both experiment and simulation, showing that a waveguide optimized for 532 nm (visible) and 780 nm (near-infrared), while nominally cut off at 1550 nm, can still introduce significant parasitic loss at telecom wavelengths. This is studied in the context of visible-telecom optical parametric oscillation, where the excess parasitic loss can be strong enough to prevent threshold from being reached. Our finding elucidates a major challenge for wideband integrated nonlinear photonics processes when efficient coupling of widely-separated frequencies is needed.

The next generation of technology is rooted in quantum-based advancements. The entangled photon pair sources play a pivotal role in a wide range of advanced quantum applications, including quantum high precision sensors, communication, computing, cryptography and so on. Scalable on-chip quantum photonic devices have the potential to drive game changing developments in this field. This review article highlights recent breakthroughs in the generation of entangled photon pairs in two dimensional (2D) van der Waals (vdW) materials, with a focus on their applicability to quantum technologies and plausible on-chip integration technology. The article begins by discussing the fundamental principles of entangled photon pairs generation. It provides a comprehensive review of the origin and generation of entangled photons in emerging vdW materials, alongside various optical quantum characterization techniques. The review then explores key physical parameters of the quantum states associated with entangled photon pairs. Additionally, it examines concepts related to the realization of paired photon generation at the quantum limit. The final section focuses on the potential for on-chip integrated quantum device applications. Beyond highlighting recent advancements in quantum-based research, the review also outlines current limitations and future prospects aimed at advancing the field

Microcombs, optical frequency combs generated by nonlinear integrated micro-cavity resonators, have the potential to offer the full capability of their benchtop comb based counterparts, but in an integrated footprint. They have enabled breakthroughs in spectroscopy, microwave photonics, frequency synthesis, optical ranging, quantum state generation and manipulation, metrology, optical neuromorphic processing and more. One of their most promising applications has been optical fibre communications where they have formed the basis for massively parallel ultrahigh capacity multiplexed data transmission. Innovative approaches have been used in recent years to phaselock, or modelock different types of microcombs, from dissipative Kerr solitons to dark solitons, soliton crystals and others. This has enabled their use as sources for optical communications including advanced coherent modulation format systems that have achieved ultrahigh data capacity bit rates breaking the petabit/s barrier. Here, we review this new and exciting field, chronicling the progress while highlighting the challenges and opportunities.

The Liquid Electrode eXperiment is a surrogate environment to study the dynamic behavior of liquid metal plasma facing components relevant to a z-pinch device. Current pulses with amplitudes between 50 and 200 kA produce magnetic fields up to 30 T at the surface of a wire 1.5 to 2.5 mm in radius, mounted with one end submerged in a pool of liquid metal. The resulting forces generate a fast-moving annular jet surrounding the wire, preceded in some cases by small ejected droplets of varying sizes. High-speed videography records the motion of the liquid metal free surface upon exposure to magnetic pressures between 0.5 and 10 MPa. The vertical velocity of the resulting jets ranged from 0.6 to 5.3 m/s with consistent radial expansion. The velocities of the ejecta ranged from -3.1 to +18.9 m/s in the vertical direction and from -14.3 to +6.3 m/s in the radial direction. We investigate the likelihood and severity of z-pinch core contamination for repetitively-pulsed plasmas in the context of the observed droplets for liquid metal plasma facing component candidate materials: lithium, gallium, silver, tin, lead, FLiBe, and FLiNaK.

Ovarian cancer remains a challenging malignancy to diagnose and manage, with prognosis heavily dependent on the stage at detection. Accurate grading and staging, primarily based on histopathological examination of biopsy tissue samples, are crucial for treatment planning and predicting outcomes. However, this manual process is time-consuming and subject to inter-observer variability among pathologists. The increasing volume of digital histopathology slides necessitates the development of robust, automated methods to assist in this critical diagnostic step for ovarian cancer. (Methods) This study presents a deep learning framework for the automated prediction of ovarian cancer stage (classified into five categories: 0, I, II, III, IV) using routine histopathological images. We employed a transfer learning approach, fine-tuning a ResNet-101 convolutional neural network pre-trained on ImageNet. The training process incorporated comprehensive data augmentation, weighted random sampling, and class weighting to address dataset characteristics. Hyperparameter optimization for learning rate, dropout rate, and weight decay was performed using a genetic algorithm to enhance model performance and generalization. (Results) Evaluated on an independent test set of ovarian thin tissue brightfield images, the developed model achieved a high overall classification accuracy of 97.62%.

Integrated microwave photonic filters (IMPFs) have emerged as promising candidates for advanced microwave systems owing to their distinctive combination of wide operational bandwidth, reconfigurable functionality, and compact size. Nevertheless, the complex and time-consuming manual manipulation of IMPFs remains a significant impediment to their widespread applications. Here, to the best of our knowledge, we experimentally demonstrate the first intelligent configuration of IMPF featuring wideband center frequency tunability, flexible bandwidth reconfigurability, self-stabilization, and excellent channel equalization simultaneously. The configuration strategy is enabled by our proposed universal hybrid collaboration algorithm, which can fully unleash the hardware potential of the optical device, thus enabling comprehensive synergy of multiple properties. Results show that the center frequency of IMPF is tuned from 2 to 48 GHz, and the bandwidth is reconfigured from 0.66 to 4.15 GHz, with a rejection ratio of up to 37.67 dB. The roll-off rate and shape factor reach as high as 17.50 dB/GHz and 0.78, respectively. Meanwhile, the maximum center frequency drift of IMPF in 3 hours is reduced from 11.950 to 0.051 GHz even without a thermo-electric cooler, indicating that the center frequency stability is enhanced by 234 times. The passband shape of the IMPF can also be dynamically adjusted to equalize frequency-dependent fading, achieving up to 2.42 dB compensation of intra-channel fading. Our work highlights the potential of IMPFs based on intelligent configuration, unlocking new avenues for practical applications of microwave photonic signal processing.

In supersonic and hypersonic flows, the near-wall density variation due to wall cooling poses a challenge for accurately predicting the near-wall velocity and temperature profiles using classical eddy viscosity turbulence models. Compressible turbulent boundary layers are known to follow the universal wall law via semi-local transformation. However, semi-local transformations are not easily incorporated into turbulence models, due to their non-local nature. The current paper proposes a method to integrate near-wall density variation effects using an elliptic equation, which can be used to correct a turbulence model, e.g., the k-$\omega$ two-equation model. The formulation of the elliptic equation only involves dimensional analysis and the proper selection of the local length scale. The variable from the elliptic equation is then incorporated into the $\omega$ equation, which results in a three-equation elliptic k-$\omega$ model. The newly introduced variation is used to modify the slope of the velocity profile starting from the viscous layer to above. Then it achieves a very decent correction of the velocity profile in compressible turbulent channel flows. The proposed elliptic k-$\omega$ model can also improve the velocity predictions in strongly wall-cooled hypersonic turbulent boundary layers.

This study aims to isolate the three effects of the pressure force on the inner and outer layers: the local direct impact (characterized by the pressure gradient (PG) parameter, $\beta$), the local disequilibrating effect (represented here by the normalized streamwise derivative $d\beta/dX$), and the upstream cumulative effect, while accounting for the inevitable Reynolds number influence. To achieve this objective, we draw on several non-equilibrium and near-equilibrium databases from the literature, and employ a methodology based on the selection of PG parameters that capture the local direct impact and the local disequilibration effect of the PG. The pressure force impact on the inner and outer regions is represented by two parameters: the friction-viscous PG parameter, $\beta_i$, and the PG parameter based on Zagarola-Smits velocity, $\beta_{ZS}$. In the non-equilibrium flow cases, both $\beta_i$ and $\beta_{ZS}$ exhibit similar distributions, initially increasing and then decreasing. However, the rate of change of these parameters along the streamwise direction varies among the flows, indicating differing levels of pressure force disequilibration. In the outer layer, it is found that both the local and cumulative disequilibrating effects modify the mean velocity and Reynolds stress profiles at identical $\beta_{ZS}$ values. In the inner layer, which responds much faster to changes in pressure force, the local disequilibrating effect still modifies the mean velocity profile in the viscous sublayer. Notably, when the mean velocity defect is significant, the behavior of u-structures in the inner layer appears to be governed by how outer turbulence responds to pressure force effects. In contrast, the size of uv-structures in the inner layer scales with the mixed pressure-friction length. Unlike inner u-structures, they are independent of large-scale outer structures and flow history.

In the Lighthill-Whitham-Richards (LWR) model for single-lane traffic, vehicle trajectories follow the first-in-first-out (FIFO) principle and can be represented by a monotone three-dimensional surface of cumulative vehicle count. In contrast, the generalized bathtub model, which describes congestion dynamics in transportation networks using relative space, typically violates the FIFO principle, making its representation more challenging. Building on the characteristic distance ordering concept, we observe that trips in the generalized bathtub model can be ordered by their characteristic distances (remaining trip distance plus network travel distance). We define a new cumulative number of trips ahead of a trip with a given remaining distance at a time instant, showing it forms a monotone three-dimensional surface despite FIFO violations. Using the inverse function theorem, we derive equivalent formulations with different coordinates and dependent variables, including special cases for Vickrey's bathtub model and the basic bathtub model. We demonstrate numerical methods based on these formulations and discuss trip-based approaches for discrete demand patterns. This study enhances understanding of the generalized bathtub model's properties, facilitating its application in network traffic flow modeling, congestion pricing, and transportation planning.

A generalized hydrodynamic theory that systematically incorporates elasticity and viscoelasticity had been derived about a quarter of a century ago. It is based on a strictly Euler point of view, as is natural for hydrodynamics. We used and adapted this theory particularly in a linear framework. There, it is straightforward to focus on what are the flow and displacement fields in linearized hydrodynamics and elasticity, which provides some advantages. Since this theoretical approach appears not to be as commonly widespread as it deserves to be, we here overview and review the formalism. Specific further focus is on pointing out relations to the commonly known Kelvin-Voigt model and Maxwell model. They are naturally contained within this description. The two limits of perfect long-term elasticity on one hand and long-term flow on the other hand can be represented by adjusting only one parameter.

Muon Spin Rotation/Relaxation/Resonance ({\mu}SR) is a versatile and powerful non-destructive technology for investigating the magnetic properties of materials at the microscopic level. The {\mu}SR technique typically utilizes fully spin polarized beams of positive muons generated at particle accelerator facilities and measures the evolution of the muon spin polarization inside a sample to extract information about the local magnetic environment in materials. With the development of accelerator technologies, intensities of muon beams are being continuously improved, which will cause a pile-up problem to the {\mu}SR spectrometer. The first muon source in China, named MELODY, is currently under construction and will be a pulsed source of muons operated at a repetition frequency of only 1 Hz due to limitations of the accelerator system at CSNS. Consequently, there is a strong motivation to operate MELODY at significantly higher muon intensities. This necessitates an upgrade of the detector system inside the spectrometer, which should be smaller and faster to accommodate the increased intensity per pulse of muons. The Low Gain Avalanche Diode (LGAD), characterized by a typical pulse width of 2 ns and a segmentation size in the centimeters range, has the potential to significantly improve the counting rates of {\mu}SR spectrometers that utilize a high intensity pulsed muon source. Thus, it is expected that the LGAD detector is a promising candidate to enhance the performance of {\mu}SR spectrometers at the new MELODY muon source.To validate this, tests on the LGAD were conducted at the ISIS pulsed muon source at the Rutherford Appleton Laboratory, UK. This paper will describe the setup of the candidate LGAD devices and the subsequent analysis of the experiment data.

Atomic diffraction through a nanograting is a powerful tool to probe the Casimir-Polder potential. Achieving precise measurements require simulations to bridge theory and experiment. In this context, we present various approximations and methods of Casimir-Polder potentials, and we analyze their impact on matter-wave diffraction patterns. Our analysis includes the pairwise summation approach, the proximity force approximation, and multiple scattering expansion method. Furthermore, we demonstrate that the influence of Casimir-Polder interactions extends up to 25 nm before and after the nanograting slit, highlighting the importance of accounting for this effect in any accurate analysis.

In this work we investigate the fundamental physical mechanism of the transition from Darcy to inertial (Darcy-Forchheimer) regime in the flow through porous media from the perspective of the vortex formation and growth. We focus on investigating the influence of porosity on the tortuosity--Reynolds number relation for periodic stochastic and simple cubic systems by numerically solving the Navier-Stokes equations in the pore-scale. We observe that the tortuosity defined by integrals over the fluid volume exhibits similar behavior in both types of systems. We also show that the tortuosity based on averaging of the streamlines length diverges from the volume-integrated one when the inertia onset takes place. We show that the discrepancy between those two tortuosities at increasing Reynolds number carries information about the geometrical confinement within the porous sample and is largely governed by the growth of the volumes occupied by vortices and their kinetic energy. Our results shed new light on the fundamental mechanisms of the transition between Darcy and the inertial regime and bring a better understanding of the role of the porous media geometry in this transition.

Optical tweezers are a powerful tool for the precise positioning of a variety of small objects, including single neutral atoms. Once trapped, atoms can be cooled to the motional ground state of the tweezers. For a more advanced control of their spatial wavefunction, we report here a simple method to squeeze their motion, and the protocol to measure the squeezing factor based on momentum spreading estimation. We explore the limitations set by the technical imperfections of the tweezers, as well as the more fundamental limit set by their anharmonicity, and finally demonstrate a squeezing of 5.8 dB. The implementation of motional squeezing allows to push back the limit set by the position quantum noise and thus to explore more extreme situations requiring atoms positioned with nanometric precision.

During magma vesiculation, permeability is established when growing bubbles begin to form connected networks, which allow fluids to percolate. This percolation threshold controls the relative rates between magma ascent and volatile exsolution, which in turn dictate eruptive style. Percolation is controlled primarily by total vesicularity and shear conditions. We performed vesiculation experiments on rhyolitic glass in a spatially confined, cylindrical, conduit-like geometry. The amount of shear experienced by the sample is controlled by varying the sample and confining diameters to allow for various degrees of free (isotropic) followed by confined (anisotropic) expansion. Pore anisotropy develops sub-parallel to the flow direction. We measure the total and connected porosity and permeability of the vesiculated samples. We observe two regimes of behavior for samples dominated by 1) isotropic expansion, in which the onset of percolation corresponds with the beginning of shear deformation and 2) anisotropic expansion which shows low percolation thresholds (<20%) with a near-constant permeability even with increasing porosity. We find a good correlation between connected porosity and permeability, with distinct trends for sheared and unsheared samples. The development of anisotropy in vesiculating magmas is ubiquitous in magmatic networks and our data highlight the importance of considering the in situ shear conditions when investigating the percolation threshold.

The injection of transverse jets into supersonic compressible crossflows represents a fundamental configuration relevant to a spectrum of high-speed applications. The intricate interactions arising between the crossflow and the injected jet induce complex flow phenomena, including shock waves and vortical structures, the characteristics of which are significantly contingent upon the thermophysical properties of the injected fuel. While prior investigations have addressed the influence of various fuels, a knowledge gap persists concerning the behaviour of alternative and synthetic multicomponent fuels within this flow regime. The present work employs high-fidelity large-eddy simulations (LES) to examine the impact of ten distinct fuels-hydrogen, methane, ethylene, ammonia, syngas mixture, and a synthetic blend, alongside several NH3/H2/N2 mixtures-on the macroscopic flow structures and mixing attributes within a transverse sonic jet immersed in a Mach 2 crossflow. By maintaining a uniform momentum flux ratio across the investigated cases, the study aims to isolate the influence of the unique thermophysical properties of each fuel on the windward mixing layer, a region critically important for initial entrainment processes. The investigation quantifies the effects of molecular weight, heat capacity ratio, and density on the development and evolution of coherent structures through a detailed examination of instantaneous flow fields, vortex dynamics, scalar distributions, and turbulence statistics. The results are expected to provide pertinent insights into fuel-dependent mixing mechanisms in supersonic flows, thereby contributing to the advancement of more efficient and versatile propulsion systems.

Sensors for particle tracking detectors are required to provide a maximum active area in addition to fulfilling performance criteria concerning radiation hardness, charge collection and operating conditions (e.g. leakage current, depletion voltage and breakdown voltage). While the requirement for optimised coverage within the tracking detector necessitates a slim sensor edge between active region and physical sensor edge, wider edge regions were found to be beneficial for the sensor performance during an early prototyping phase. In order to study the impact of differently sized edge regions, test structures were used to compare their individual active regions. Measurements of each diode were performed using a micro-focused X-ray beam to map its respective active area. This paper presents measurements of these test structures using AREA-X showing that the active area of a silicon particle tracking sensor does not only depend on the size of its bias ring, but also the size and configuration of its edge structure.

Conventionally, 3-dimensional cellular tomography can be done with light sheet or multi-angle observations. Recently, a new technique was introduced where the cell was rotated using convection currents to visualize the outer periphery (Liu et al., Nano Lett., 2023, 23, 5148). However, the work falls short of actually observing intracellular objects like organelles etc. In this manuscript, we modify the technique by relying on computer vision algorithm called Contrast Limited Adaptive Histogram Equalisation (CLAHE) to improve the contrast for better detection of intra-cellular points, and then use optical flow detection technique to extract the in-plane speed of the point. This then is used to extract the vertical location, knowing that at the bottom part of the sphere, the point would be moving in one direction, close to the center there would be much less motion, while in the top portion of the sphere, the point would be moving in the reverse direction than the bottom. The velocity allows the exact localisation of the point in the vertical direction. This process allows for sub-diffractive intracellular tomography. This technique can further allow high-resolution detection of fluorescent molecules inside the cell also, when combined with convective flows.

Thermally activated delayed fluorescence (TADF) offers the promise of highly efficient organic light emitting diodes (OLEDs), without the heavy metals requirement of the previous generation of OLEDs. However, the design of new TADF emitters is complicated by competing requirements, which require opposing design strategies. High throughput virtual screening (HTVS) approaches, however, offer the possibility of identifying new TADF emitters without necessarily relying on existing design rules. In this work the STONED algorithm [A. Nigam et al., Chem. Sci., 2021, 12, 7079] is used to impose random structural mutations starting from a set of twenty parent molecules, composed of both traditional donor-acceptor and multiresonant TADF emitters. Following this, successive filters are applied based on features of the atomic structure through to time-dependent density functional theory calculations. Although the randomised approach proves to be ill-suited to rediscovering existing TADF emitters, the resulting workflow leads to the identification of a number of molecules with promising properties for TADF, across a range of emission colours.

The linear theory of the kinetic-ballooning-mode (KBM) instability is extended to capture a weakly-driven regime in general toroidal geometry where the destabilization is caused by the magnetic-drift resonance of the ions. Such resonantly-destabilized KBMs are characterized by broad eigenfunctions along the magnetic field line and near-marginal positive growth rates, even well below the beta threshold of their non-resonant counterparts. This unconventional (or sub-threshold) KBM, when destabilized, has been shown to catalyze an enhancement of turbulent transport in the Wendelstein 7-X (W7-X) stellarator [1, 2]. Simplifying the energy dependence of key resonant quantities allows for an analytical treatment of this KBM using the physics-based ordering from the more general equations of Tang, Connor, and Hastie [3]. Results are then compared with high-fidelity gyrokinetic simulations for the (st)KBM in W7-X and the conventional KBM in a circular tokamak at both high and low magnetic shear, where good agreement is obtained in all cases. This reduced KBM model provides deeper insight into (sub-threshold) KBMs and their relationship with geometry, and shows promise for aiding in transport model development and geometry-based turbulence optimization efforts going forward.

We introduce a numerical method for investigating interfacial flows coupled with frictional solid particles. Our method combines the lattice Boltzmann method (LBM) to model the dynamics of a two-component fluid and the discrete element method (DEM) to model contact forces (normal reaction, sliding friction, rolling friction) between solid particles and between solid particles and flat solid surfaces. To couple the fluid and particle dynamics, we (1) use the momentum exchange method to transfer hydrodynamic forces between the fluids and particles, (2) account for different particle wettability using a geometric boundary condition, and (3) explicitly account for capillary forces between particles and liquid-fluid interfaces using a 3D capillary force model. We benchmark the contact forces by investigating the dynamics of a particle bouncing off a solid surface and rolling down an inclined plane. To benchmark the hydrodynamic and capillary forces, we investigate the Segr\`e-Silberberg effect and measure the force required to detach a particle from a liquid-fluid interface, respectively. Motivated by the self-cleaning properties of the lotus leaf, we apply our method to investigate how drops remove contaminant particles from surfaces and quantify the forces acting on particles during removal. Our method makes it possible to investigate the influence of various parameters that are often difficult to tune independently in experiments, including contact angles, surface tension, viscosity, and coefficient of friction between the surface and particles. Our results highlight that friction plays a crucial role when drops remove particles from surfaces.

The quantum boomerang effect is a counterintuitive phenomenon where a wave packet, despite having an initial momentum, returns to its starting position in a disordered medium. However, up to now, the experimental exploration of this effect remains largely unexplored. Here, we report the experimental observation of the quantum boomerang effect of light. Our experiment is based on a one-dimensional disordered photonic lattice, which is composed of on-chip optical waveguides with engineered on-site random potential. We first characterize this optical disordered system by demonstrating the static Anderson localization of light beams. Next, through launching a kinetic light beam into the system, we observe that the light beam first moves away from its starting point, arrives at a maximum value, reverses its direction, and returns to its original position over time, confirming the observation of the quantum boomerang effect of light. Surprisingly, we find that optical loss, usually considered to be detrimental to optical experiments, can enhance the quantum boomerang effect by accelerating the light back to its original position. Our work provides new insights into the light-matter interactions in disordered medium and opens an avenue for future study of this phenomenon in nonlinear and many-photon contexts.

Mesoscale eddies dominate the spatiotemporal multiscale variability of the ocean, and their impact on the energy cascade of the global ocean cannot be ignored. Eddy-resolving ocean forecasting is providing more reliable protection for fisheries and navigational safety, but also presents significant scientific challenges and high computational costs for traditional numerical models. Artificial intelligence (AI)-based weather and ocean forecasting systems are becoming powerful tools that balance forecast performance with computational efficiency. However, the complex multiscale features in the ocean dynamical system make AI models still face many challenges in mesoscale eddy forecasting (especially regional modelling). Here, we develop LanTu, a regional eddy-resolving ocean forecasting system based on dynamics-enhanced deep learning. We incorporate cross-scale interactions into LanTu and construct multiscale physical constraint for optimising LanTu guided by knowledge of eddy dynamics in order to improve the forecasting skill of LanTu for mesoscale evolution. The results show that LanTu outperforms the existing advanced operational numerical ocean forecasting system (NOFS) and AI-based ocean forecasting system (AI-OFS) in temperature, salinity, sea level anomaly and current prediction, with a lead time of more than 10 days. Our study highlights that dynamics-enhanced deep learning (LanTu) can be a powerful paradigm for eddy-resolving ocean forecasting.

The super simple Vlasov (ssV) code was developed to study instabilities, turbulence, and reconnection in weakly magnetized plasmas, such as the solar wind in the dissipation range and the edge of fusion plasmas. The ssV code overcomes the limitations of standard gyrokinetic theory by using a hybrid model that incorporates fully kinetic ions and gyrokinetic electrons. This hybrid gyrokinetic model enables accurate modeling in regimes characterized by steep gradients and high-frequency dynamics. To achieve this, ssV implements a set of semi-Lagrangian numerical schemes, including Positive Flux Conservative (PFC), Flux Conservative fifth-order (FCV), FCV with Umeda limiters, and a Semi-Lagrangian Monotonicity-Preserving fifth-order scheme (SLMP5). Benchmark problems such as Landau damping, ion-acoustic waves, ion Bernstein waves, and kinetic Alfven waves were employed to evaluate the schemes. The SLMP5 scheme consistently delivered the best overall accuracy and numerical stability performance. The code also addresses well-known electromagnetic gyrokinetic simulation issues, such as the Ampere cancellation problem, using carefully chosen velocity-space resolutions and accurate integral evaluation.

Dielectric barrier discharges (DBDs) are widely used in applications such as ozone generation and volatile organic compound treatment, where performance can be enhanced through catalyst integration. A fundamental understanding of reactive species generation is essential for advancing these technologies. However, temporally resolving reactive species production, especially during the initial discharges, remains a challenge, despite its importance for controlling production rates and energy efficiency. This study examines atomic oxygen production as a model system for reactive species production in a micro-cavity plasma array, a custom surface DBD confined to micrometer-sized cavities. Optical emission spectroscopy was employed to investigate plasma-chemical processes in helium with 0.1-0.25$\%$ molecular oxygen admixture at atmospheric pressure. The discharge, powered by a 15$\,$kHz, 600$\,$V amplitude triangular voltage, achieved near-complete oxygen dissociation (up to 100$\%$), as determined via helium state-enhanced actinometry (SEA). A novel multi-photomultiplier system enabled precise temporal tracking of atomic oxygen density and dissociation dynamics. To ensure measurement accuracy, a basic 0D chemical model was developed, reinforcing the reliability of the experimental results.

This study is focused on measuring the densities of the excited molecular oxygen species, O$_{2}(\text{a}^{1}\Delta_{\text{g}})$ and O$_{2}(\text{b}^{1}\Sigma_{\text{g}}^{+})$, produced in a COST atmospheric pressure plasma jet using a helium-oxygen mixture. Knowledge of the ozone density is critical for measurements because of its high quenching rate of these species. Additionally O$_{2}(\text{a}^{1}\Delta_{\text{g}})$ is difficult to measure, due to its low emission intensity and sensitivity to background interference in the plasma region. Therefore a flow cell was used to enhance signal detection in the effluent region. To validate the measurements and improve understanding of reaction mechanisms, results were compared with two simulation models: a pseudo-1D plug flow simulation and a 2D fluid simulation. The plug flow simulation provided an effective means for estimating species densities, with a fast computation time. The 2D simulation offered a more realistic description of the flow dynamics, which proved critical to correctly describe the experimental trends. However, it requires long computation times to reach an equilibrium state in the flow cell. Otherwise, it leads to discrepancies to the experimental data. Further discrepancies arose, from an overestimation of the ozone density from the models, as validated from the O$_{2}(\text{b}^{1}\Sigma_{\text{g}}^{+})$ density measurements. Optimizing the reaction rate coefficients for the effluent region might improve the agreement with the experimental results. Despite these limitations both simulations aligned reasonably well with experimental data, showcasing the well validated plasma chemistry of the models, even for complicated effluent geometries.

Path integral Monte Carlo (PIMC) simulations are a cornerstone for studying quantum many-body systems. The analytic continuation (AC) needed to estimate dynamic quantities from these simulations is an inverse Laplace transform, which is ill-conditioned. If this inversion were surmounted, then dynamical observables (e.g. dynamic structure factor (DSF) $S(q,\omega)$) could be extracted from the imaginary-time correlation functions estimates. Although of important, the AC problem remains challenging due to its ill-posedness. To address this challenge, we express the DSF as a linear combination of kernel functions with known Laplace transforms that have been tailored to satisfy its physical constraints. We use least-squares optimization regularized with a Bayesian prior to determine the coefficients of this linear combination. We explore various regularization term, such as the commonly used entropic regularizer, as well as the Wasserstein distance and $L^2$-distance as well as techniques for setting the regularization weight. A key outcome is the open-source package PyLIT (\textbf{Py}thon \textbf{L}aplace \textbf{I}nverse \textbf{T}ransform), which leverages Numba and unifies the presented formulations. PyLIT's core functionality is kernel construction and optimization. In our applications, we find PyLIT's DSF estimates share qualitative features with other more established methods. We identify three key findings. Firstly, independent of the regularization choice, utilizing non-uniform grid point distributions reduced the number of unknowns and thus reduced our space of possible solutions. Secondly, the Wasserstein distance, a previously unexplored regularizer, performs as good as the entropic regularizer while benefiting from its linear gradient. Thirdly, future work can meaningfully combine regularized and stochastic optimization. (text cut for char. limit)

In hydrodynamic problems involving wave impact on structures, air compressibility is crucial for accurate pressure prediction when an air bubble is entrapped. In this work, the consistent $\delta^{+}$-SPH model, originally developed for single-phase scenarios, is extended to multiphase contexts. Although the consistent $\delta^{+}$-SPH model shows good performance for single phase and viscous flow simulations, extending it to multiphase scenarios presents challenges, such as proper implementation of particle shifting for multiphase interfaces. Therefore, within the framework of the consistent $\delta^{+}$-SPH, we introduce the following enhancements: firstly, new strategy for handling $\delta \boldmath{u}$-terms given by the particle shifting technique at multiphase interfaces are proposed to maintain stability and conservation. Secondly, for modeling of incompressible phases, like water, an acoustic damper term is introduced to alleviate acoustic waves resulting from the weakly-compressible assumption, which is expected to achieve smooth pressure field comparable to truly-incompressible hypothesis, thereby reducing the nonphysical pressure wave during the violent impact state; for modeling compressible phases like air, a physical sound speed is adopted in the equation of state to accurately model real gas phase compressibility. To test and validate the present multiphase SPH model, simulations were conducted for six scenarios. In particular, except for sloshing with two-layer liquids, the other scenarios fully consider air pressure oscillations when air is entrapped, compressed, or expanded by surrounding flows. The results demonstrate significant advantages of the present SPH model in simulating multiphase problems involving strong liquid impact and different phase compressibility.

In the search for novel nanostructured materials for UV plasmonics a limited number of choices can be done. Materials such as aluminum, rhodium, gallium and few others can be used. One of the most interesting application for UV plasmonics is Surface Enhanced Raman Spectroscopy. It can be extended to this spectral range to explore spectral properties of biomolecules that have only a small cross section in the visible spectral range. We have recently reported on a functional substrates based on nanoporous aluminum decorated with rhodium nanoparticles. This system showed an interesting behavior for UV excitation at 266 nm, with an unexpected decreasing Raman intensity for increasing rhodium nanoparticles concentrations. We proposed that this effect can be due to the difficult access to the hot spots for the molecules deposited via thermal evaporation. Here we extend this study exploring the performance of the system at another UV excitation wavelengths (325 nm) reporting on experimental results obtained using a deposition process that can bring the molecules at the hot-spots in a more efficient way. Extensive spectroscopic acquisitions, combined with 3D maps, allow to shade a more clear view on the performance of this plasmonic platform. In particular, the photodegration and the potential oxidation of biomolecules driven by the hot-electron/hot-holes produced by the rhodium nanoparticles will be reported.

In this paper, we propose a scheme for frequency conversion between optical photons and microwave photons in non-Markovian environments using both magnetic and mechanical excitations as intermediate media. When the frequencies of optical photons, magnons, phonons, and microwave photons resonance, the conversion efficiency can be made close to reach 98.76$\%$ by adjusting the defined complex cooperativities, while in the case of Markovian, the conversion efficiency is 90.44$\%$. By controlling the environmental spectral widths, the efficiency of frequency conversion exhibits a transition from Markovian regimes to non-Markovian regimes. This transformation simultaneously improves frequency conversion efficiency and conversion bandwidth, which is due to the excitation backflow generated by the interaction between the system and the non-Markovian environments. In the case, when the optical pump power in the non-Markovian regimes are of a large order of magnitude, the conversion bandwidth can be increased, but at the cost of reduced conversion efficiency. Our scheme improves the frequency conversion efficiency and bandwidth between optical photons and microwave photons, breaking the limitations of frequency conversion in Markovian environments and providing a new approach for long-distance quantum communication research of other non-Markovian quantum systems in quantum optics.

Artificial intelligence (AI) models have transformed weather forecasting, but their skill for gray swan extreme events is unclear. Here, we analyze GraphCast and FuXi forecasts of the unprecedented 2024 Dubai storm, which had twice the training set's highest rainfall in that region. Remarkably, GraphCast accurately forecasts this event 8 days ahead. FuXi forecasts the event, but underestimates the rainfall, especially at long lead times. GraphCast's success stems from "translocation": learning from comparable/stronger dynamically similar events in other regions during training via global effective receptive fields. Evidence of "extrapolation" (learning from training set's weaker events) is not found. Even events within the global distribution's tail are poorly forecasted, which is not just due to data imbalance (generalization error) but also spectral bias (optimization error). These findings demonstrate the potential of AI models to forecast regional gray swans and opportunity to improve them through understanding the mechanisms behind their successes and limitations.

The power of photothermal spectroscopic imaging to visualize antimicrobial interaction on the surface of individual bacteria cells has been demonstrated on the model system Bacillus subtilis (B. subtilis) and vancomycin using mid-infrared photo-induced force microscopy (PiF-IR, also mid-IR PiFM). High-resolution PiF contrasts obtained by merging subsequent PiF-IR scans at two different illumination frequencies revealed chemical details of cell wall destruction after 30 and 60 min incubation with vancomycin with a spatial resolution of $\approx 5$ nm. This approach compensates local intensity variations induced by near-field coupling of the illuminating electric field with nanostructured surfaces, which appear in single-frequency contrasts in photothermal imaging methods, as shown by [Anindo et al., J. Phys. Chem C, 2025, 129, 4517]. Spectral shifts associated with hydrogen bond formation between vancomycin and the N-acyl-D-Ala4-D-Ala5 termini in the peptidoglycan cell wall have been observed in chemometrics of PiF-IR spectra from treated and untreated B. subtilis harvested after 30 min from the same experiment. The vancomyin interaction in the piecrust of a progressing septum is located with $\approx 10$ nm resolution exemplarily using PiF contrasts of three selected bands of a PiF-IR hyperspectral scan of an individual B. subtilis cell harvested after 30 min incubation. Our results provide a new perspective for visualizing the chemical interaction of antibiotics on the surface of microbes with few nanometer resolution.

We propose a new technique to fabricate flexible-near-field Argument-Reality (AR) display using modular-molds. A near-eye flexible-AR-display is fabricated based on parameters extracted from simulations. Our AR-display successfully reconstructed images and videos from a light-engine. It opens a new approach to fabricate flexible-near-field AR display with good physical stress and collision-resilience.

We propose a scheme to achieving simultaneous nonreciprocal unconventional photon blockade in a system of two coupled spining resonators marked by modes a and b, each incorporating an degenerate optical parametric amplifier (DOPA). By rotating the resonators, input light from opposite directions induces opposite Sagnac-Fizeau shifts. These shifts result in the emergence or absence of quantum destructive interference in two-photon excitation processes. Specifically, when destructive quantum interference occurs, photons from one input direction are simultaneously blocked in both resonators, whereas the absence of complete destructive quantum interference causes the blockade effect to vanish for inputs from the opposite direction. We analytically give the optimal parameter conditions to achieve simultaneous strong photon blockade with the parametric amplification. By adjusting the Sagnac-Fizeau shifts, we can make mode a nonreciprocal photon blockade, while mode b exhibits photon blockade in both directions. This work lays a theoretical foundation for the development of multimode simultaneous nonreciprocal unconventional single-photon devices, which hold promising potential in multichannel topological optics and chiral quantum technologies.

Metal oxide semiconductors have been thoroughly studied for gas sensing applications due to the electrical transduction phenomenon in the presence of gaseous analytes. The chemiresistive sensors prevalent in the applications have several challenges associated with them inclusive of instability, longevity, temperature/humidity sensitivity, and power consumption due to the need of heaters. Herein, we present a silicon field effect transistor-based gas sensor functionalized with CuO. The oxidized Cu thin film acts as a selective room temperature H2S sensor with impressive response and recovery. Using this methodology, we propose a standalone compact enose based on our results for a wide spectrum of gas detection.

Two maximum likelihood-based algorithms for unfolding or deconvolution are considered: the Richardson-Lucy method and the Data Unfolding method with Mean Integrated Square Error (MISE) optimization [10]. Unfolding is viewed as a procedure for estimating an unknown probability density function. Both external and internal quality assessment methods can be applied for this purpose. In some cases, external criteria exist to evaluate deconvolution quality. A typical example is the deconvolution of a blurred image, where the sharpness of the restored image serves as an indicator of quality. However, defining such external criteria can be challenging, particularly when a measurement has not been performed previously. In such instances, internal criteria are necessary to assess the quality of the result independently of external information. The article discusses two internal criteria: MISE for the unfolded distribution and the condition number of the correlation matrix of the unfolded distribution. These internal quality criteria are applied to a comparative analysis of the two methods using identical numerical data. The results of the analysis demonstrate the superiority of the Data Unfolding method with MISE optimization over the Richardson-Lucy method.

We investigate the Reynolds-shear-stress carrying structures in the outer layer of non-equilibrium pressure-gradient turbulent boundary layers using four direct numerical simulation databases, two cases of non-equilibrium pressure-gradient boundary layers and two of homogeneous shear turbulence. We examine and compare the spatial organization and shapes of the Reynolds-shear-stress structures, specifically sweeps and ejections, across all cases. The analysis includes five streamwise locations in the boundary layers, varying in pressure-gradient sign, intensity, and upstream history. For the boundary layers, two types of three-dimensional velocity fields are considered: fully spatial fields and spatio-temporal fields using Taylor's frozen turbulence hypothesis. Comparisons of the results indicate that the statistics of sweep and ejection shapes are sensitive to the choice of convection velocity in Taylor's hypothesis. The sweep and ejection shapes are consistent across all flows when their sizes range from 1 to 10 Corrsin length scales, suggesting that mean shear plays a similar role in all cases, driving the formation of Reynolds-shear-stress carrying structures and contributing to turbulence production. Sweeps and ejections of different types form side-by-side pairs, while structures of the same type align in an upstream-downstream configuration. This behavior persists regardless of pressure gradient variations or upstream history, emphasizing the dominant influence of local mean shear.

The paper describes the state of the installation based on the linear accelerator LUE-40 after restoration in 2023. Additional devices have been developed and created to conduct nuclear physics research. The main beam parameters are given. The electron energy range has been expanded and is currently 16-95 MeV with an average beam current of up to 6 $\mu$A. The main areas of scientific research are described and the results are presented.

The non-Markovian effects of open quantum systems subjected to external environments are deemed to be valuable resources in quantum optics and quantum information processing. In this work, we investigate the non-Markovian dynamics of a three-level giant atom coupling with a semi-infinite photonic waveguide through multiple coupling points and driven by a classical driving field. We derive the analytical expressions for the probability amplitudes of the driven three-level giant atom and obtain two independent conditions. We find two different types of bound states (including the static bound states and the periodic equal-amplitude oscillating bound states) and discuss the physical origins of the bound states formation. Moreover, we discuss the case of the driven three-level giant atom interacting with the infinite photonic waveguide, where there is only one purely imaginary solution (i.e., only one bound state condition exists) for its complex frequency (coming from the absence of mirror at one end of the waveguide) compared to that of a driven three-level giant atom coupling with a semi-infinite photonic waveguide. With this, we also find two different types of bound states, including the static bound state and the periodic equal-amplitude oscillating bound states. Finally, the above results are generalized to a more general model involving a semi-infinite photonic waveguide coupling with an arbitrary number of noninteracting three-level giant atoms driven by the driving fields. The proposed protocol could provide a pathway to precisely elucidate the non-Markovian dynamics of driven, multi-level giant atoms coupled to semi-infinite or infinite photonic waveguides.

We present a study of the oil-proof base Hamamatsu R5912 photomultiplier tubes that will be used in the SABRE South linear-alkylbenzene liquid scintillator veto. SABRE South is a dark matter direct detection experiment at the Stawell Underground Physics Laboratory, aiming to test the DAMA/LIBRA dark matter annual modulation signal. We discuss the requirements of the liquid scintillator system and its photomultipliers, outline the methods and analysis used for the characterisation measurements, and results from initial tests. We discuss the impact of these measurements on the performance of the active veto system and explore analysis methods to allow for low threshold operation. Finally, we include results from a small scale liquid scintillator detector prototype used to assess the future performance of pulse shape discrimination in the liquid scintillator veto, and how well accommodated it is by the R5912 PMTs.

The study of probabilistic models for the analysis of complex networks represents a flourishing research field. Among the former, Exponential Random Graphs (ERGs) have gained increasing attention over the years. So far, only linear ERGs have been extensively employed to gain insight into the structural organisation of real-world complex networks. None, however, is capable of accounting for the variance of the empirical degree distribution. To this aim, non-linear ERGs must be considered. After showing that the usual mean-field approximation forces the degree-corrected version of the two-star model to degenerate, we define a fitness-induced variant of it. Such a `softened' model is capable of reproducing the sample variance, while retaining the explanatory power of its linear counterpart, within a purely canonical framework.

The phenomenon of Poynting backflow in a single vector Gaussian beam is examined. The paraxial Maxwell equations and their exact solutions containing terms proportional to the small parameter $\varepsilon=\frac{\lambda}{2\pi w_0}$, or to its square, where $w_0$ is the beam waist, are made use of. Explicit analytical calculations show that just these additional expressions are responsible for the occurrence of the reversed Poynting-vector longitudinal component for selected polarizations. Concrete results for the time-averaged vector for several Gaussian beams with $n=1$ ($n$ being the topological index of the beam) are presented. Depending on the choice of polarization, the backflow area is located around the beam's axis, has an annular character or is absent. In the case of $n=2$, backflow areas were found as well. The magnitude of the backflow proved to be proportional to $\varepsilon^4$.

The elementary theory of aftershocks, being relatively simple mathematically, belongs to the basics of earthquake physics. The paper briefly outlines the concepts and ideas of the theory, provides equations for the relaxation of the source after the main shock, and emphasizes the effectiveness of the theory in the search for new approaches to the processing and analysis of aftershock observation data. The main attention is paid to the discussion of two new results obtained on the basis of elementary theory. Firstly, a two-stage relaxation mode of the earthquake source was discovered. In the first stage, called the Omori epoch, the Omori law is strictly observed. The first stage ends with a bifurcation of the source, and the second stage of relaxation begins, during which the evolution of aftershocks proceeds chaotically. Secondly, within the framework of the elementary theory of aftershocks, a fundamentally new concept of the proper time of the source was introduced. Keywords: earthquake source, deactivation coefficient, Omori law, dynamic system, inverse problem, relaxation, proper time. The source proper time has proven useful in experimental studies of seismic activity.

We investigated the nonlinear effects of gravity-driven fluid flow through a two-dimensional, low-porosity, packed bed of stubby stone grains. We focused on preferential channel formation, tortuosity, spatial distribution of kinetic energy, and vortex formation. We show that nonlinear effects dominate at relatively high Reynolds numbers, even though the deviation from Darcy's law is not visible in friction factor measurements. We further notice an increased flow asymmetry of the flow field revealed by vorticity analysis and surprising correlation between tortuosity and apparent permeability in the inertial flow regime.

Space exploration technology continues to expand humanity's reach beyond Earth, and even more ambitious efforts are striving to establish long-duration human settlements on Mars. The dependence of martian settlers on life-support infrastructure and on resupply missions from the host nation could create conditions for tyranny or lead to other extreme and uncontrollable situations, but such risks could be reduced by thinking about the possibilities for effective decision making on Mars before any settlement efforts actually occur. This paper examines the extent to which referendums could be used on Mars as a means of political decision-making and sovereignty adjudication. Our approach draws on three terrestrial case studies -- the Great Idaho Movement in the United States, the Catalan Independence Movement in Spain, and the Quebec Independence Movement in Canada -- as potential analogs for Mars governance. We recommend advance determination of the conditions under which a martian referendum would be recognized as a best practice for any agency seeking to establish a long-duration settlement on Mars. We suggest that referendums can reduce the likelihood of multiple authoritative political entities existing on Mars, which could provide a more procedural approach toward resolving governance issues between Earth and Mars. However, if Mars settlement is successful in establishing settlements on the scale of cities or larger, then other uniquely martian tools may evolve as a supplement or replacement to referendums.

Low-temperature plasmas often present non-equilibrium ion distribution functions due to the collisions with the background gas and the presence of strong electric fields. This non-equilibrium is beyond classical fluid models, often requiring computationally-intensive kinetic simulations. In our work, we study high-order moment models in order to capture the non-equilibrium state with a macroscopic set of equations, which is more computationally efficient than kinetic simulations. We compare numerical simulations of different moment closures: Grad's closure, the hyperbolic quadrature method of moments (HyQMOM), the extended quadrature method of moments, and a method based on entropy maximization. We assess the different closures for plasma applications and propose efficient numerical discretizations. The numerical solution of the high-order moment models is compared to kinetic simulations of an argon plasma between two floating walls at different pressure regimes, from nearly collisionless to collisionally-dominated. In general, all the high-order moment closures capture the ion transport with high fidelity as compared to the kinetic simulations, providing an improvement as compared to classical fluid models. Classical fluid closures such as the Fourier law for the heat flux is shown to be not suitable to capture the sheath or the low pressure regime. In addition, the ability of each moment method to reconstruct the velocity distribution function from the moments is assessed. The high-order moment models are able to capture the non-equilibrium distributions in the bulk and sheath with remarkable fidelity, dramatically improving classical fluid models while having comparable computational cost. In particular, the HyQMOM shows to be a robust method that provides an excellent comparison with the kinetic simulations of both the moments and the distribution function in the bulk and the sheath.

Topological photonic crystals (PhCs) offer robust, disorder-resistant modes engendered by nontrivial band symmetries, but designing PhCs with prescribed topological band properties remains a challenge due to the complexity involved in mapping the continuous real-space design landscape of photonic crystals to the discrete output space of band topology. Here, we introduce a new approach to address this problem, employing Kolmogorov--Arnold networks (KANs) to predict and inversely design the band symmetries of two-dimensional PhCs with two-fold rotational (C2) symmetry. We show that a single-hidden-layer KAN, trained on a dataset of C2-symmetric unit cells, achieves 99% accuracy in classifying the symmetry eigenvalues of the lowest transverse-magnetic band. For interpretability, we use symbolic regression to extract eight algebraic formulas that characterize the band symmetry classes in terms of the Fourier components of the dielectric function. These formulas not only retain the full predictive power of the network but also enable deterministic inverse design. Applying them, we generate 2,000 photonic crystals with target band symmetries, achieving over 92% accuracy even for high-contrast, experimentally realizable structures beyond the training domain.

Energy, momentum, and angular momentum are fundamental properties tied to the symmetries of space and time, with photons and other elementary particles acting as carriers of these quantities. In most optical and optoelectronic devices, energy transfer is crucial, but it often results in undesirable energy absorption. Moreover, non-reciprocal elements such as optical diodes and circulators are difficult to implement in photonics, as they typically require time-dependent perturbations, nonlinear effects, or external magnetic fields. This presents a significant barrier to the development of efficient, compact photonic technologies. We introduce the concept of optical spin current, wherein spin angular momentum is transferred by an electromagnetic field without accompanying energy transfer. This phenomenon is analogous to electron spin currents, where spin is decoupled from charge flow. Building on this principle, we propose optical spin diode and circulator -- devices that enable unidirectional propagation of spin currents while maintaining bidirectional energy flow, thus preserving reciprocity. Furthermore, we demonstrate asymmetric spin transfer between quantum dots mediated by the optical spin diode, highlighting the potential for novel optical spintronic functionalities. These findings lay the foundation for devices that leverage optical spin transfer, opening new avenues for advancements in optical spintronics.

Multireference methods such as multiconfiguration pair-density functional theory (MC-PDFT) offer an effective means of capturing electronic correlation in systems with significant multiconfigurational character. However, their application to train machine learning-based interatomic potentials (MLPs) for catalytic dynamics has been challenging due to the sensitivity of multireference calculations to the underlying active space, which complicates achieving consistent energies and gradients across diverse nuclear configurations. To overcome this limitation, we introduce the Weighted Active-Space Protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrate the method on the TiC+-catalyzed C-H activation of methane, a reaction that poses challenges for Kohn-Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a new paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods.

In pick-up experiments using nanodroplet and nanocluster beams, the size distribution of hosts carrying a specified number of dopants changes when the vapor density in the pick-up region is altered. This change, analyzed here, has quantitative consequences for the interpretation of data that are sensitive to host size, such as mass spectrometric, spectroscopic, and deflection measurements.

Spectroscopic data on alkali-atom dimers residing on the surface of liquid helium nanodroplets have revealed that they are detected primarily in the weakly bound, metastable, spin-triplet state. Here, by measuring the magnetic Stern-Gerlach deflection of a sodium-doped nanodroplet beam, we transparently demonstrate the abundance of high-magnetic-moment dimers. Their electron spins thermalize with the cryogenic superfluid droplets and become fully oriented by the external magnetic field.

Pixel modules are currently being built for the ATLAS ITk Pixel detector upgrade. During the preproduction phase, recurring chip malfunctioning was observed during electrical testing. It was possible to bypass this issue by disabling some pixel core columns in the ITkPix readout chip. Therefore the issue is called "core column issue" which is a direct disqualifier for a pixel module. A concerning number of cases has been observed in pixel modules with ITkPix v1.1 as well as v2 chips which significantly impacts the module yield. However, the behaviour is erratic and there is not any evidence hinting at the origin of this issue. These proceedings outline the investigations of the issue, highlighting the electrical behaviour during testing, present findings from the data collected via our production database and through visual inspection, and point towards possible causes of the issue.

We have shown previously that the dynamics of isolated oil slicks on the water's surface after spills is significantly influenced by surface-wave motion practically at the onset of the spreading process. In this work, we draw our attention to another practical scenario of the oil slick's behaviour in semi-confined geometries under the action of surface waves and in the presence of oil leakage source. We hypothesize that the geometric constraints should qualitatively change the oil layer response to the wave motion leading to localization of oil spill domains even at modest levels of the surface-wave perturbations. Several realistic cases were rigorously explored, with special attention paid to the interplay, the combined effect of the two factors, oil influx and wave motion. It was demonstrated quantitatively how the spreading process can be either facilitated or suppressed leading to potential confinement.

We demonstrate background-free imaging and sideband cooling of a single 133Cs atom via the narrow-line 6S1/2 to 5D5/2 electric quadrupole transition in a 1064 nm optical tweezer. The 5D5/2 state decays through the 6P3/2 state to the ground state, emitting an 852 nm wavelength photon that allows for background-free imaging. By encoding both spin and orbital angular momentum onto the 685 nm excitation light, we achieve background-free fluorescence histograms with 99.58(3)% fidelity by positioning the atom at the dark center of a vortex beam. Tuning the tweezer polarization ellipticity realizes a magic trap for the stretched F = 4, mF = 4 to F' = 6, mF' = 6 cycling transition. We cool to 5 uK in a 1.1 mK trap and outline a strategy for ground-state cooling. We compare cooling performance across different sideband regimes, while also exploring how the orbital angular momentum of structured light controls the selection rules for quadrupole transitions. These results expand the toolbox for high-fidelity quantum control and cooling in alkali-atom tweezer arrays.

Switchable elements are key components of dynamic technological and biological systems, enabling reversible transitions between well-defined states. Here, we present a DNA origami-based, mechanically bistable snap-through mechanism that can be electrically controlled. This nanoscale switch exhibits long-term stability in both states in the absence of external stimuli, while achieving millisecond-scale switching times upon application of an electric field. Individual devices sustain hundreds of thousands of switching cycles over several hours, offering a powerful platform for systematically studying the endurance and failure mechanisms of biomolecular nanoswitches. Functionalization with a gold nanorod further allows polarization-dependent optical modulation, opening avenues for applications in plasmonics. This versatile electromechanical interface has potential uses in molecular information processing, optical nanodevices, and the dynamic control of chemical reactions.

Critical peer review of scientific manuscripts presents a significant challenge for Large Language Models (LLMs), partly due to data limitations and the complexity of expert reasoning. This report introduces Persistent Workflow Prompting (PWP), a potentially broadly applicable prompt engineering methodology designed to bridge this gap using standard LLM chat interfaces (zero-code, no APIs). We present a proof-of-concept PWP prompt for the critical analysis of experimental chemistry manuscripts, featuring a hierarchical, modular architecture (structured via Markdown) that defines detailed analysis workflows. We develop this PWP prompt through iterative application of meta-prompting techniques and meta-reasoning aimed at systematically codifying expert review workflows, including tacit knowledge. Submitted once at the start of a session, this PWP prompt equips the LLM with persistent workflows triggered by subsequent queries, guiding modern reasoning LLMs through systematic, multimodal evaluations. Demonstrations show the PWP-guided LLM identifying major methodological flaws in a test case while mitigating LLM input bias and performing complex tasks, including distinguishing claims from evidence, integrating text/photo/figure analysis to infer parameters, executing quantitative feasibility checks, comparing estimates against claims, and assessing a priori plausibility. To ensure transparency and facilitate replication, we provide full prompts, detailed demonstration analyses, and logs of interactive chats as supplementary resources. Beyond the specific application, this work offers insights into the meta-development process itself, highlighting the potential of PWP, informed by detailed workflow formalization, to enable sophisticated analysis using readily available LLMs for complex scientific tasks.

This paper proposes a novel theoretical model to explain how the human mind and artificial intelligence can approach real-time awareness by reducing perceptual delays. By investigating cosmic signal delay, neurological reaction times, and the ancient cognitive state of stillness, we explore how one may shift from reactive perception to a conscious interface with the near future. This paper introduces both a physical and cognitive model for perceiving the present not as a linear timestamp, but as an interference zone where early-arriving cosmic signals and reactive human delays intersect. We propose experimental approaches to test these ideas using human neural observation and neuro-receptive extensions. Finally, we propose a mathematical framework to guide the evolution of AI systems toward temporally efficient, ethically sound, and internally conscious decision-making processes

The emergence of irreversibility in isolated, deterministic systems remains a central challenge in statistical mechanics. Traditional approaches, such as Boltzmann's H-theorem and Lanford's derivation of the Boltzmann equation, rely on probabilistic assumptions and are limited to dilute gases and short timescales. We introduce Folded State Dynamics (FSD), a framework in which irreversibility arises from the geometric structure of phase space in systems with coupled rotational and translational degrees of freedom. In contrast to conventional models with unfolded, weakly coupled modes, FSD exhibits phase space folding, where deterministic coupling induces instability and chaotic mixing. We show that equilibrium states exponentially dominate the accessible phase space volume, while constrained configurations (e.g., pure rotation) occupy measure-zero subsets. This yields a geometric derivation of entropy growth, with reversal probabilities suppressed as Prev and recurrence times Trec, resolving Loschmidt's and Zermelo's objections without coarse-graining or fine-tuning. FSD is further extended to include charged systems (cFSD), where electromagnetic interactions continuously drive phase space folding. FSD thus offers a deterministic and testable mechanism for irreversibility, with implications ranging from confined colloids to the cosmological arrow of time

Cosmic censorship posits spacetime singularities remain concealed behind event horizons, preserving the determinism of General Relativity. While quantum gravity is expected to resolve singularities, we argue that cosmic censorship remains necessary whenever spacetime has a reliable semi-classical description. Using holography to construct exact solutions to semi-classical gravity, we show backreaction of quantum matter generates horizons -- quantum censors -- to thwart potential violations of censorship. Along with a quantum Penrose inequality, this provides compelling evidence cosmic censorship is robust, even nonperturbatively, in semi-classical gravity.

The amazing quantum effect of `entanglement' was discovered in the 1935 thought experiment by Albert Einstein, Boris Podolsky and Nathan Rosen (`EPR'). The ensuing research opened up fundamental questions and led to experiments that proved that quantum theory cannot be completed by local hidden variables. Remarkably, EPR did not discuss how to create the entanglement in their thought experiment. Here I add this part. What is required in the original EPR thought experiment is a simple elastic particle collision, an unbalanced mass ratio of e.g. 1:3 and initial states that are position and momentum squeezed, respectively. In the limiting case of infinite squeeze factors, the measurement of the position or momentum of one particle allows an absolutely precise conclusion to be drawn about the value of the same quantity of the other particle. The EPR idea has never been tested in this way. I outline a way to do this.

Fast Ewald summation is the most widely used approach for computing long-range Coulomb interactions in molecular dynamics (MD) simulations. While the asymptotic scaling is nearly optimal, its performance on parallel architectures is dominated by the global communication required for the underlying fast Fourier transform (FFT). Here, we develop a novel method, ESP - Ewald summation with prolate spheroidal wave functions (PSWFs) - that, for a fixed precision, sharply reduces the size of this transform by performing the Ewald split via a PSWF. In addition, PSWFs minimize the cost of spreading and interpolation steps that move information between the particles and the underlying uniform grid. We have integrated the ESP method into two widely-used open-source MD packages: LAMMPS and GROMACS. Detailed benchmarks show that this reduces the cost of computing far-field electrostatic interactions by an order of magnitude, leading to better strong scaling with respect to number of cores. The total execution time is reduced by a factor of 2 to 3 when using more than one thousand cores, even after optimally tuning the existing internal parameters in the native codes. We validate the accelerated codes in realistic long-time biological simulations.

We carry out two-dimensional Brownian dynamics simulations of the behavior of rigid inclusion particles immersed in an active fluid bath. The active fluid is modeled as a collection of self-propelled circular disks interacting via a soft repulsive potential and a nematic alignment interaction. The fluid is characterized by its nematic order, polar order and orientational correlation length. The active fluid bath transitions from the isotropic to the nematic phase with increasing number density, increasing nematic interaction strength or increasing P\'eclet number. The inclusion particles are modeled as rigid assemblies of passive circular disks. Four types of inclusions are considered: a rod-like $I$ shape, a boomerang-like $L$ shape, and stair-like shapes $Z$ and $Z^*$, with opposite handedness. When inclusions are introduced into the active fluid bath, their diffusion is significantly enhanced by the force and torque exerted by the active fluid particles and the chiral inclusion particles exhibit constant rotational drift. These diffusion and rotation enhancements increase as the swimming speed of the active fluid particles increases. The translational motion of the inclusion particles also couples with their orientational motion, and the correlation is modulated by the active fluid particles' swimming speed. This work paves the way for future simulations of inclusions in active fluid baths and suggests potential avenues for controlling transport properties in active materials.

In March of 2024 the AAS formed our Graduate Admissions Task Force (GATF), asking us to produce recommendations aimed at improving the state of graduate admissions in astronomy. The task was a timely one, as the past decade has seen dramatic shifts that are currently being exacerbated by rapidly-shifting funding and policy challenges. The GATF surveyed recent applicants to astronomy graduate school and admissions leaders in degree-granting graduate programs, held in-depth conversations with select programs, and explored how other fields approach admissions. Based on this work, we have quantified recent changes in the astronomy graduate admissions landscape, addressed mismatches between perception and reality, and identified several key ways to address some of our current challenges. In this report we summarize the results of this work and the four main recommendations we believe the AAS should take to make lasting positive changes. We emphasize that the goal of our report, and the GATF's work, is not to help programs select students, or to help applicants get into graduate school. Instead, we have focused on ways to make the admissions process itself more streamlined, fair, and efficient for everyone involved.

Acoustic Power Transfer is a relatively new technology. It is a modern type of a wireless interface, where data signals and supply voltages are transmitted, with the use of mechanical waves, through a medium. The simplest application of such systems is the measurement of frequency response for audio speakers. It consists of a variable signal generator, a measuring amplifier which drives an acoustic source and the loudspeaker driver. The receiver contains a microphone circuit with a level recorder. Acoustic Power Transfer could have many applications, such as: Cochlear Implants, Sonar Systems and Wireless Charging. However, it is a new technology, thus it needs further investigation.

Strontium (Sr) emissions have been observed across a wide range of astrophysical phenomena, from kilonovae (KNe) events to white dwarf (WD) stars. Precise and extensive atomic data for low ionisation stages of Sr is required for accurate theoretical modelling and to improve our understanding of evolutionary pathways. We calculated energy levels, Einstein A coefficients and electron-impact excitation collision strengths for Sr I, Sr III, Sr IV and Sr V at the temperature and density ranges of interest in KNe and WD research. We developed new target structures using the GRASP0 and AUTOSTRUCTURE packages. The energies and A-values arising from the new structures were found to be in good agreement with experimental and theoretical equivalents reported in the literature. Maxwellian averaged electron impact collision strengths were calculated using the R-matrix approach, as applied through the DARC and RMBP coding packages. These are presented in adf04 file format. The new data sets allowed us to construct synthetic spectra for the first five ionisation stages of Sr and probe possible density and temperature diagnostic lines. The synthetic spectra within the KNe regime revealed possible Sr IV and Sr V candidate lines at 1027.69nm and 1203.35nm respectively. These may provide useful benchmarks for determining the extent of Sr ionisation that can be reached in an evolving KNe event. Additional diagnostic lines were found to be poor across the Sr ion stages for both KNe and WD regimes due to most levels being in either coronal or Local Thermodynamic Equilibrium (LTE) conditions.

Growing evidence suggests that synaptic weights in the brain follow heavy-tailed distributions, yet most theoretical analyses of recurrent neural networks (RNNs) assume Gaussian connectivity. We systematically study the activity of RNNs with random weights drawn from biologically plausible L\'evy alpha-stable distributions. While mean-field theory for the infinite system predicts that the quiescent state is always unstable -- implying ubiquitous chaos -- our finite-size analysis reveals a sharp transition between quiescent and chaotic dynamics. We theoretically predict the gain at which the system transitions from quiescent to chaotic dynamics, and validate it through simulations. Compared to Gaussian networks, heavy-tailed RNNs exhibit a broader parameter regime near the edge of chaos, namely a slow transition to chaos. However, this robustness comes with a tradeoff: heavier tails reduce the Lyapunov dimension of the attractor, indicating lower effective dimensionality. Our results reveal a biologically aligned tradeoff between the robustness of dynamics near the edge of chaos and the richness of high-dimensional neural activity. By analytically characterizing the transition point in finite-size networks -- where mean-field theory breaks down -- we provide a tractable framework for understanding dynamics in realistically sized, heavy-tailed neural circuits.

This work introduces a lean CNN (convolutional neural network) framework, with a drastically reduced number of fittable parameters (<81K) compared to the benchmarks in current literature, to capture the underlying low-computational cost (i.e., surrogate) relationships between the electron charge density (ECD) fields and their associated effective properties. These lean CNNs are made possible by adding a pre-processing step (i.e., a feature engineering step) that involves the computation of the ECD fields' spatial correlations (specifically, 2-point spatial correlations). The viability and benefits of the proposed lean CNN framework are demonstrated by establishing robust structure-property relationships involving the prediction of effective material properties using the feature-engineered ECD fields as the only input. The framework is evaluated on a dataset of crystalline cubic systems consisting of 1410 molecular structures spanning 62 different elemental species and 3 space groups.

Levitation in vacuum of macroscopic objects is key towards the development of novel types of inertial sensors and pressure sensors, as well as towards the fundamental studies of quantum mechanics and its relation to gravity. Diamagnetic levitation offers a passive method at room temperature to isolate macroscopic objects in vacuum environments, yet eddy current damping remains a critical limitation for electrically conductive materials. We show that there are situations where the motion of conductors in magnetic fields do not, in principal, produce eddy damping, and demonstrate an electrically conducting rotor diamagnetically levitated in an axially symmetric magnetic field in high vacuum. Experimental measurements and finite-element simulations reveal gas collision damping as the dominant loss mechanism at high pressures, while residual eddy damping, which arises from symmetry-breaking factors such as platform tilt or material imperfections, dominates at low pressures. This demonstrates a macroscopic levitated rotor with extremely low rotational damping, and paves the way to fully suppress rotor damping, enabling ultra-low-loss rotors for gyroscopes, pressure sensing, and fundamental physics tests.

Unraveling collective modes arising from coupled degrees of freedom is crucial for understanding complex interactions in solids and developing new functionalities. Unique collective behaviors emerge when two degrees of freedom, ordered on distinct length scales, interact. Polar skyrmions, three-dimensional electric polarization textures in ferroelectric superlattices, disrupt the lattice continuity at the nanometer scale with nontrivial topology, leading to previously unexplored collective modes. Here, using terahertz-field excitation and femtosecond x-ray diffraction, we discovered subterahertz collective modes, dubbed 'skyrons', which appear as swirling patterns of atomic displacements functioning as atomic-scale gearsets. Momentum-resolved time-domain measurements of diffuse scattering revealed an avoided crossing in the dispersion relation of skyrons. We further demonstrated that the amplitude and dispersion of skyrons can be controlled by sample temperature and electric-field bias. Atomistic simulations and dynamical phase-field modeling provided microscopic insights into the three-dimensional crystallographic and polarization dynamics. The discovery of skyrons and their coupling with terahertz fields opens avenues for ultrafast control of topological polar structures.

Trajectory planning is essential for ensuring safe driving in the face of uncertainties related to communication, sensing, and dynamic factors such as weather, road conditions, policies, and other road users. Existing car-following models often lack rigorous safety proofs and the ability to replicate human-like driving behaviors consistently. This article applies multi-phase dynamical systems analysis to well-known car-following models to highlight the characteristics and limitations of existing approaches. We begin by formulating fundamental principles for safe and human-like car-following behaviors, which include zeroth-order principles for comfort and minimum jam spacings, first-order principles for speeds and time gaps, and second-order principles for comfort acceleration/deceleration bounds as well as braking profiles. From a set of these zeroth- and first-order principles, we derive Newell's simplified car-following model. Subsequently, we analyze phases within the speed-spacing plane for the stationary lead-vehicle problem in Newell's model and its extensions, which incorporate both bounded acceleration and deceleration. We then analyze the performance of the Intelligent Driver Model and the Gipps model. Through this analysis, we highlight the limitations of these models with respect to some of the aforementioned principles. Numerical simulations and empirical observations validate the theoretical insights. Finally, we discuss future research directions to further integrate safety, human-like behaviors, and vehicular automation in car-following models, which are addressed in Part 2 of this study \citep{jin2025WA20-02_Part2}, where we develop a novel multi-phase projection-based car-following model that addresses the limitations identified here.

Ensuring safe and human-like trajectory planning for automated vehicles amidst real-world uncertainties remains a critical challenge. While existing car-following models often struggle to consistently provide rigorous safety proofs alongside human-like acceleration and deceleration patterns, we introduce a novel multi-phase projection-based car-following model. This model is designed to balance safety and performance by incorporating bounded acceleration and deceleration rates while emulating key human driving principles. Building upon a foundation of fundamental driving principles and a multi-phase dynamical systems analysis (detailed in Part 1 of this study \citep{jin2025WA20-02_Part1}), we first highlight the limitations of extending standard models like Newell's with simple bounded deceleration. Inspired by human drivers' anticipatory behavior, we mathematically define and analyze projected braking profiles for both leader and follower vehicles, establishing safety criteria and new phase definitions based on the projected braking lead-vehicle problem. The proposed parsimonious model combines an extended Newell's model for nominal driving with a new control law for scenarios requiring projected braking. Using speed-spacing phase plane analysis, we provide rigorous mathematical proofs of the model's adherence to defined safe and human-like driving principles, including collision-free operation, bounded deceleration, and acceptable safe stopping distance, under reasonable initial conditions. Numerical simulations validate the model's superior performance in achieving both safety and human-like braking profiles for the stationary lead-vehicle problem. Finally, we discuss the model's implications and future research directions.

We present an open-source program, QR$^2$-code, that computes double-resonance Raman (DRR) spectra using first-principles calculations. QR$^2$-code can calculate not only two-phonon DRR spectra but also single-resonance Raman spectra and defect-induced DRR spectra. For defect-induced DDR spectra, we simply assume that the electron-defect matrix element of elastic scattering is a constant. Hands-on tutorials for graphene are given to show how to run QR$^2$-code for single-resonance, double-resonance, and defect-induced Raman spectra. We also compare the single-resonance Raman spectra by QR$^2$-code with that by QERaman code. In QR$^2$-code, the energy dispersions of electron and phonon are taken from Quantum ESPRESSO (QE) code, and the electron-phonon matrix element is obtained from the electron-phonon Wannier (EPW) code. All codes, examples, and scripts are available on the GitHub repository.

Modern production rates and the increasing complexity of mechanical systems require efficient and effective manufacturing and assembly processes. The transition to Industry 4.0, supported by the deployment of innovative tools such as Augmented Reality (AR), equips the industry to tackle future challenges. Among critical processes, the assembly and tightening of bolted joints stand out due to their significant safety and economic implications across various industrial sectors. This study proposes an innovative tightening method designed to enhance the reliability of bolted assembly tightening through the use of Augmented Reality and connected tools. A 6-Degrees-of-Freedom (6-DoF) tracked connected torque wrench assists the operator during tightening, ensuring each screw is tightened to the correct torque. The effectiveness of this method is compared with the conventional tightening method using paper instructions. Participants in the study carried out tightening sequences on two simple parts with multiple screws. The study evaluates the impact of the proposed method on task performance and its acceptability to operators. The tracked connected torque wrench provides considerable assistance to the operators, including wrench control and automatic generation of tightening reports. The results suggest that the AR-based method has the potential to ensure reliable torque tightening of bolted joints.

We compare the efficiency and ease-of-use of the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm and Sparse Physics-Informed Discovery of Empirical Relations (SPIDER) framework in recovering the relevant governing equations and boundary conditions from data generated by direct numerical simulations (DNS) of turbulent convective flows. In the former case, a weak-form implementation pySINDy is used. Time-dependent data for two- (2D) and three-dimensional (3D) DNS simulation of Rayleigh-Benard convection and convective plane Couette flow is generated using the Dedalus PDE framework for spectrally solving differential equations. Using pySINDy we are able to recover the governing equations of 2D models of Rayleigh-Benard convection at Rayleigh numbers, R, from laminar, through transitional to moderately turbulent flow conditions, albeit with increasing difficulty with larger Rayleigh number, especially in recovery of the diffusive terms (with coefficient magnitude proportional to 1/R^0.5). SPIDER requires a much smaller library of terms and we are able to recover more easily the governing equations for a wider range of R in 2D and 3D convection and plane flow models and go on to recover constraints (the incompressibility condition) and boundary conditions, demonstrating the benefits and capabilities of SPIDER to go beyond pySINDy for these fluid problems governed by second-order PDEs. [We] demonstrat[e] the potential of machine-learning methods to validate numerical solvers and solutions for such flow problems. We also find that properties of the flow, specifically the correlation time and spatial scales, should inform the initial selection of spatiotemporal subdomain sizes [abbreviated]

Optical cavities can be used to enhance the interaction of light with atoms or molecules. In a regime where cavity resonances are very far from the atomic or molecular transition frequencies, photon absorption can be neglected and the dispersive part of the interaction can induce spectacular phenomena such as squeezing, cooling, and self-organization. So far, this dispersive interaction regime has only been explored using a single or few longitudinal cavity modes at a time. We experimentally study the dispersive interaction between a cold atom cloud and many longitudinal modes of a Fabry-Perot cavity, by detecting collective light shifts in the transmission spectrum of an optical frequency comb probe. Notably, using a high quality resonator coupled to more than $10^5$ intracavity atoms, we detect appreciable shifts of $\sim 100$ cavity modes simultaneously. These results constitute a direct demonstration of dispersive coupling of matter with many longitudinal modes of a linear resonator, and pave the way towards deeper exploration of atom-light dynamics in the multifrequency domain.

The mixing of scalar substances in fluid flows by stirring and diffusion is ubiquitous in natural flows, chemical engineering, and microfluidic drug delivery. Here, we present a spectral quantum algorithm for scalar mixing by solving the advection-diffusion equation in a quantum computational fluid dynamics framework. We derive exact gate decompositions of the advection and diffusion operators in spectral space. For all but the simplest one-dimensional flows, these operators do not commute. Therefore, we use operator splitting and construct quantum circuits capable of simulating arbitrary polynomial velocity profiles, such as the Blasius profile of a laminar boundary layer. Periodic, Neumann, and Dirichlet boundary conditions can be imposed with the appropriate quantum spectral transform plus additional constraints on the Fourier expansion. We evaluate our approach in statevector simulations of a Couette flow, plane Poiseuille flow, and a polynomial Blasius profile approximation to demonstrate its potential and versatility for scalar mixing in shear flows. The number of gates grows with, at most, the cubed logarithm of the number of grid points. This evaluation shows that spectral accuracy allows comparably large time steps even though the operator splitting limits the temporal order.

Embedding materials in optical cavities has emerged as a strategy for tuning material properties. Accurate simulations of electrons in materials interacting with quantum photon fluctuations of a cavity are crucial for understanding and predicting cavity-induced phenomena. In this article, we develop a non-perturbative quantum electrodynamical approach based on a photon-free self-consistent Hartree-Fock framework to model the coupling between electrons and cavity photons in crystalline materials. We apply this theoretical approach to investigate graphene coupled to the vacuum field fluctuations of cavity photon modes with different types of polarizations. The cavity photons introduce nonlocal electron-electron interactions, originating from the quantum nature of light, that lead to significant renormalization of the Dirac bands. In contrast to the case of graphene coupled to a classical circularly polarized light field, where a topological Dirac gap emerges, the nonlocal interactions induced by a quantum linearly polarized photon mode give rise to the formation of flat bands and the opening of a topologically trivial Dirac gap. When two symmetric cavity photon modes are introduced, Dirac cones remain gapless, but a Fermi velocity renormalization yet indicates the relevant role of nonlocal interactions. These effects disappear in the classical limit for coherent photon modes. This new self-consistent theoretical framework paves the way for the simulation of non-perturbative quantum effects in strongly coupled light-matter systems, and allows for a more comprehensive discovery of novel cavity-induced quantum phenomena.

Type II solar radio bursts are generated by electrons accelerated by coronal shock waves. They appear in dynamic spectra as lanes drifting from higher to lower frequencies at the plasma frequency and its harmonic. These lanes can often be split into two or more sub-bands that have similar drift rates. This phenomenon is called band-splitting, and its physical origins are still under debate. Our aim is to investigate the origin of band-splitting using novel imaging and spectropolarimetric observations of a type II solar radio burst from the Low Frequency Array (LOFAR). We used LOFAR imaging at multiple frequencies and time steps to track the locations of the radio sources corresponding to the two components of the band-split emission lane. In addition, we estimated the degree of circular polarisation (dcp) for both components using LOFAR's full Stokes dynamic spectra. From the imaging of the type II burst, we found two close but clearly separated emission regions clustered over several frequencies spanning each split band. One emission region corresponds to the lower frequency band and the other to the higher frequency band of the split lane. Using the full Stokes dynamic spectra, we also found the dcp to be very similar for both bands. The two distinct emission regions suggest that the split bands originate from two separate regions at the shock. The similar values of dcp for both sub-bands correspond to similar values of magnetic field strength in the two regions and indicate little to no change in the emission region plasma. Thus, our findings are in contradiction with previous theories, which have suggested that split bands originate in the same region but upstream and downstream of the shock. Instead, our results suggest that both bands originate in two separate upstream regions since we find a clear separation in locations and no magnetic compression.

Active colloidal particles create flow around them due to non-equilibrium process on their surfaces. In this paper, we infer the activity of such colloidal particles from the flow field created by them via deep learning. We first explain our method for one active particle, inferring the $2s$ mode (or the stresslet) and the $3t$ mode (or the source dipole) from the flow field data, along with the position and orientation of the particle. We then apply the method to a system of many active particles. We find excellent agreements between the predictions and the true values of activity. Our method presents a principled way to predict arbitrary activity from the flow field created by active particles.

We theoretically explore strategies to actively control photon emission from quantum light sources by leveraging the large magneto-optical response of graphene. The quantum electrodynamic response of graphene -- characterized by the Purcell factor and the Lamb shift of a proximal emitter -- is analyzed for extended two-dimensional sheets, one-dimensional nanoribbons, and zero-dimensional nanodisks, all of which are endowed with an intrinsic chiral near-field response under a static perpendicular magnetic field. Using rigorous semianalytical models of these systems, we reveal that the emission properties can be readily tuned by variations in doping charge carrier density and applied magnetic field strength, both with respect to magnetoplasmon resonances (at infrared frequencies) and Shubnikov-de-Haas oscillations (entering telecommunication bands) associated with optical transitions between discrete Landau levels. Localized magnetoplasmons in graphene nanoribbons are predicted to induce large dissymmetry in the spontaneous emission from left-hand and right-hand circularly polarized transitions in a proximal quantum emitter, presenting applications for chiral quantum optical waveguiding. This chiral dissymmetry is further enhanced in gyrotropic graphene nanodisks, signaling that the spatial shaping of near-fields in nanostructured graphene can significantly boost the intrinsic chiral response induced by the magnetic field. These results indicate that magneto-optical graphene constitutes a versatile and highly tunable platform for quantum light generation and manipulation at the nanoscale.

Precise and accurate estimation of cosmological parameters is crucial for understanding the Universe's dynamics and addressing cosmological tensions. In this methods paper, we explore bio-inspired metaheuristic algorithms, including the Improved Multi-Operator Differential Evolution scheme and the Philippine Eagle Optimization Algorithm (PEOA), alongside the relatively known genetic algorithm, for cosmological parameter estimation. Using mock data that underlay a true fiducial cosmology, we test the viability of each optimization method to recover the input cosmological parameters with confidence regions generated by bootstrapping on top of optimization. We compare the results with Markov chain Monte Carlo (MCMC) in terms of accuracy and precision, and show that PEOA performs comparably well under the specific circumstances provided. Understandably, Bayesian inference and optimization serve distinct purposes, but comparing them highlights the potential of nature-inspired algorithms in cosmological analysis, offering alternative pathways to explore parameter spaces and validate standard results.

Quantum frequency conversion serves a key role in the realization of hybrid quantum networks by interfacing between wavelength-incompatible platforms. Here we present the first quantum frequency converter connecting visible and telecom domains on a silicon nitride (SiN) chip, using Bragg-scattering four-wave mixing to upconvert heralded single photons from 1260 to 698 nm, which covers a 192 THz span. We examine the noise sources in SiN and devise approaches to suppress noise photons at the source and target frequencies to enable measurements at the single-photon level. We demonstrate an on-chip conversion efficiency of 5% in photon flux and describe design modifications that can be implemented to significantly improve it. Our results pave the way for the implementation of CMOS-compatible devices in quantum networks.

Lithium-oxygen batteries are among the most promising energy storage systems due to their high theoretical energy density, but their practical implementation is hindered by poor reversibility and parasitic reactions. Redox mediators such as LiBr have emerged as a strategy to enhance reaction kinetics and reduce overpotentials. In this study, we explore the impact of three different solvents, dimethoxyethane (DME), tetraethylene glycol dimethyl ether (TEGDME), and dimethyl sulfoxide (DMSO), on the electrochemical performance and reaction pathways of LiBr-mediated Li-O2 cells. Our results reveal that a 1O2 evolution channel that leads to singlet oxygen-induced cell degradation is active only in the TEGDME-based electrolyte. Both DME and DMSO allow singlet oxygen-free Oxygen Evolution Reaction, but only DME is found chemically stable in the LiBr-mediated Li-O2 cell working conditions. These findings highlight the critical role of solvent-mediator interactions in determining the performance of Li-O2 cells.

Biomolecular condensates are liquid- or gel-like droplets of proteins and nucleic acids formed at least in part through liquid-liquid phase separation. Condensates enable diverse functions of cells and the pathogens that infect them, including self-assembly reactions. For example, it has been shown that many viruses form condensates within their host cells to compartmentalize capsid assembly and packaging of the viral genome. Yet, the physical principles controlling condensate-mediated self-assembly remain incompletely understood. In this article we use coarse-grained molecular dynamics simulations to study the effect of a condensate on the assembly of icosahedral capsids. The capsid subunits are represented by simple shape-based models to enable simulating a wide range of length and time scales, while the condensate is modeled implicitly to study the effects of phase separation independent of the molecular details of biomolecular condensates. Our results show that condensates can significantly enhance assembly rates, yields, and robustness to parameter variations, consistent with previous theoretical predictions. However, extending beyond those predictions, the computational models also show that excluded volume enables control over the number of capsids that assemble within condensates. Moreover, long-lived aberrant off-pathway assembly intermediates can suppress yields within condensates. In addition to elucidating condensate-mediated assembly of viruses and other biological structures, these results may guide the use of condensates as a generic route to enhance and control self-assembly in human-engineered systems.

Clouds and precipitation are important for understanding weather and climate. Simulating clouds and precipitation with traditional numerical weather prediction is challenging because of the sub-grid parameterizations required. Machine learning has been explored for forecasting clouds and precipitation, but early machine learning methods often created blurry forecasts. In this paper we explore a newer method, named score-based diffusion, to nowcast (zero to three hour forecast) clouds and precipitation. We discuss the background and intuition of score-based diffusion models - thus providing a starting point for the community - while exploring the methodology's use for nowcasting geostationary infrared imagery. We experiment with three main types of diffusion models: a standard score-based diffusion model (Diff); a residual correction diffusion model (CorrDiff); and a latent diffusion model (LDM). Our results show that the diffusion models are able to not only advect existing clouds, but also generate and decay clouds, including convective initiation. These results are surprising because the forecasts are initiated with only the past 20 mins of infrared satellite imagery. A case study qualitatively shows the preservation of high resolution features longer into the forecast than a conventional mean-squared error trained U-Net. The best of the three diffusion models tested was the CorrDiff approach, outperforming all other diffusion models, the traditional U-Net, and a persistence forecast by one to two kelvin on root mean squared error. The diffusion models also enable out-of-the-box ensemble generation, which shows skillful calibration, with the spread of the ensemble correlating well to the error.

Genetic algorithm (GA) belongs to a class of nature-inspired evolutionary algorithms that leverage concepts from natural selection to perform optimization tasks. In cosmology, the standard method for estimating parameters is the Markov chain Monte Carlo (MCMC) approach, renowned for its reliability in determining cosmological parameters. This paper presents a pedagogical examination of GA as a potential corroborative tool to MCMC for cosmological parameter estimation. Utilizing data sets from cosmic chronometers and supernovae with a curved $\Lambda$CDM model, we explore the impact of GA's key hyperparameters -- such as the fitness function, crossover rate, and mutation rate -- on the population of cosmological parameters determined by the evolutionary process. We compare the results obtained with GA to those by MCMC, analyzing their effectiveness and viability for cosmological application.

We investigate the generation of magnetic fields above black hole accretion discs due to the non-zero curl of the disc radiation field. By self consistently computing the components of the radiation flux and their curl, we show that the rotational nature of the radiation field induces charge separation, leading to magnetic field generation in the plasma above the disc. Solving the magnetohydrodynamic equations, we derive the time evolution of these fields and demonstrate that they grow over astrophysically relevant timescales. For a standard Keplerian accretion disc, the produced magnetic fields remain weak, on the order of a few Gauss, consistent with previous predictions. However, when a luminous corona is present in the inner disc region ($r_d < 3-10 r_g$), the generated fields reach dynamically significant strengths of up to $10^5$ Gauss where the magnetic energy density approaching few percentage of equipartition with the gas pressure. These fields develop within realistic growth timescales (such as viscous timescale) and can be dynamically significant in governing disc and jet evolution. Our findings suggest that radiation-driven magnetic fields play a crucial role in accretion flow magnetization, influencing both disc dynamics and observational signatures. The predicted field strengths could affect the thermal emission, synchrotron radiation, and polarization properties of black hole accretion systems, with implications for X-ray binaries, AGN, and jet formation. Future numerical simulations and high-resolution polarimetric observations, such as those from IXPE, eXTP, and EHT, may provide observational confirmation of our findings.

The non-Hermitian skin effect, nonreciprocity-induced anomalous localization of an extensive number of eigenstates, represents a hallmark of non-Hermitian topological systems with no analogs in Hermitian systems. Despite its significance across various open classical and quantum systems, the influence of nonlinearity has remained largely unclear. Here, we reveal the Hopf bifurcation of the nonlinear skin effect as a critical phenomenon unique to nonlinear non-Hermitian systems. We demonstrate that nonlinearity destabilizes skin states and instead gives rise to the emergence of delocalized states associated with limit cycles in phase space. We also uncover the algebraically localized critical skin effect precisely at the Hopf bifurcation point. We illustrate these behavior in a nonlinear extension of the Hatano-Nelson model in both continuum and lattice. Our work shows a significant role of nonlinearity in the skin effect and uncovers rich phenomena arising from the interplay between non-Hermiticity and nonlinearity.

White light endoscopy is the clinical gold standard for detecting diseases in the gastrointestinal tract. Most applications involve identifying visual abnormalities in tissue color, texture, and shape. Unfortunately, the contrast of these features is often subtle, causing many clinically relevant cases to go undetected. To overcome this challenge, we introduce Multi-contrast Laser Endoscopy (MLE): a platform for widefield clinical imaging with rapidly tunable spectral, coherent, and directional illumination. We demonstrate three capabilities of MLE: enhancing tissue chromophore contrast with multispectral diffuse reflectance, quantifying blood flow using laser speckle contrast imaging, and characterizing mucosal topography using photometric stereo. We validate MLE with benchtop models, then demonstrate MLE in vivo during clinical colonoscopies. MLE images from 31 polyps demonstrate an approximate three-fold improvement in contrast and a five-fold improvement in color difference compared to white light and narrow band imaging. With the ability to reveal multiple complementary types of tissue contrast while seamlessly integrating into the clinical environment, MLE shows promise as an investigative tool to improve gastrointestinal imaging.

Modal synthesis methods are a long-standing approach for modelling distributed musical systems. In some cases extensions are possible in order to handle geometric nonlinearities. One such case is the high-amplitude vibration of a string, where geometric nonlinear effects lead to perceptually important effects including pitch glides and a dependence of brightness on striking amplitude. A modal decomposition leads to a coupled nonlinear system of ordinary differential equations. Recent work in applied machine learning approaches (in particular neural ordinary differential equations) has been used to model lumped dynamic systems such as electronic circuits automatically from data. In this work, we examine how modal decomposition can be combined with neural ordinary differential equations for modelling distributed musical systems. The proposed model leverages the analytical solution for linear vibration of system's modes and employs a neural network to account for nonlinear dynamic behaviour. Physical parameters of a system remain easily accessible after the training without the need for a parameter encoder in the network architecture. As an initial proof of concept, we generate synthetic data for a nonlinear transverse string and show that the model can be trained to reproduce the nonlinear dynamics of the system. Sound examples are presented.

Air pollution poses a significant threat to public health, causing or exacerbating many respiratory and cardiovascular diseases. In addition, climate change is bringing about more extreme weather events such as wildfires and heatwaves, which can increase levels of pollution and worsen the effects of pollution exposure. Recent advances in personal sensing have transformed the collection of behavioural and physiological data, leading to the potential for new improvements in healthcare. We wish to capitalise on this data, alongside new capabilities in AI for making time series predictions, in order to monitor and predict health outcomes for an individual. Thus, we present a novel workflow for predicting personalised health responses to pollution by integrating physiological data from wearable fitness devices with real-time environmental exposures. The data is collected from various sources in a secure and ethical manner, and is used to train an AI model to predict individual health responses to pollution exposure within a cloud-based, modular framework. We demonstrate that the AI model -- an Adversarial Autoencoder neural network in this case -- accurately reconstructs time-dependent health signals and captures nonlinear responses to pollution. Transfer learning is applied using data from a personal smartwatch, which increases the generalisation abilities of the AI model and illustrates the adaptability of the approach to real-world, user-generated data.

Beyond neural scaling laws, little is known about the laws underlying large language models (LLMs). We introduce Neural Thermodynamic Laws (NTL) -- a new framework that offers fresh insights into LLM training dynamics. On the theoretical side, we demonstrate that key thermodynamic quantities (e.g., temperature, entropy, heat capacity, thermal conduction) and classical thermodynamic principles (e.g., the three laws of thermodynamics and the equipartition theorem) naturally emerge under river-valley loss landscape assumptions. On the practical side, this scientific perspective yields intuitive guidelines for designing learning rate schedules.