This article presents a mathematical model for identifying the safest travel routes during a pandemic by minimizing disease contraction risks, such as COVID-19. We formulate this as the LEAST INFECTION PROBABILITY PATH (LIPP) problem, which optimizes routes between two nodes in a transportation network based on minimal disease transmission probability. Our model evaluates risk factors including environmental density, likelihood of encountering carriers, and exposure duration across multiple transportation modes (walking, subway, BRT, buses, and cars). The probabilistic framework incorporates additional variables such as ventilation quality, activity levels, and interpersonal distances to estimate transmission risks. Applied to Tehran's transportation network using routing applications (Neshan and Balad), our model demonstrates that combined pedestrian-subway-BRT routes exhibit significantly lower infection risks compared to car or bus routes, as illustrated in our case study of peak-hour travel between Sadeghiyeh Square and Amirkabir University. We develop a practical routing algorithm suitable for integration with existing navigation software to provide pandemic-aware path recommendations. Potential future extensions include incorporating additional variables like waiting times and line changes, as well as adapting the model for other infectious diseases. This research offers a valuable tool for urban travelers seeking to minimize infection risks during pandemic conditions.

Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed. However, the ability of these ML models to generalize beyond the training distribution remains an open question. In this study, we evaluate the climate response of several state-of-the-art ML models (ACE2-ERA5, NeuralGCM, and cBottle) to a uniform sea surface temperature warming, a widely used benchmark for evaluating climate change. We assess each ML model's performance relative to a physics-based general circulation model (GFDL's AM4) across key diagnostics, including surface air temperature, precipitation, temperature and wind profiles, and top-of-the-atmosphere radiation. While the ML models reproduce key aspects of the physical model response, particularly the response of precipitation, some exhibit notable departures from robust physical responses, including radiative responses and land region warming. Our results highlight the promise and current limitations of ML models for climate change applications and suggest that further improvements are needed for robust out-of-sample generalization.

Africa is the world region that is most affected by malaria. Among the therapies used, injectable quinine is considered to be one of the effective antimalarial drug, however non-quality assured antimalarials clearly have a strong market penetration across Africa. To overcome this problem, it becomes more and more necessary to set up quantitative and qualitative analysis system for antimalarial quality control. The objective of the present investigation is an attempt to use customized multispectral microscope equipped with UV-Visible lasers for injectable quinine quality assurance control routinely. For that, we have established the calibration curve of quinine solution concentration as a function of area under light intensity histogram crossing the solution. From this calibration curve, we can check the conformity of any injectable quinine according to the pharmacopoeia involved. The proposed technique is a promising alternative for drug quality control routinely.

Previous papers have outlined nowcasting methods to track the current state of earthquake hazard using only observed seismic catalogs. The basis for one of these methods, the "counting method", is the Gutenberg-Richter (GR) magnitude-frequency relation. The GR relation states that for every large earthquake of magnitude greater than MT , there are on average NGR small earthquakes of magnitude MS. In this paper we use this basic relation, combined with the Receiver Operating Characteristic (ROC) formalism from machine learning, to compute the probability of a large earthquake. The probability is conditioned on the number of small earthquakes n(t) that have occurred since the last large earthquake. We work in natural time, which is defined as the count of small earthquakes between large earthquakes. We do not need to assume a probability model, which is a major advantage. Instead, the probability is computed as the Positive Predictive Value (PPV) associated with the ROC curve. We find that the PPV following the last large earthquake initially decreases as more small earthquakes occur, indicating the property of temporal clustering of large earthquakes as is observed. As the number of small earthquakes continues to accumulate, the PPV subsequently begins to increase. Eventually a point is reached beyond which the rate of increase becomes much larger and more dramatic. Here we describe and illustrate the method by applying it to a local region around Los Angeles, California, following the January 17, 1994 magnitude M6.7 Northridge earthquake.

Genetic engineering of cells has a range of applications in treating incurable diseases. Plasmid DNA is a popular choice of nucleic acid for cell engineering due to its low cost and stability. However, plasmid DNA must survive the protective mechanisms present in the cell's cytoplasm to enter the nucleus for translation. Many of the existing methods for nucleic acid delivery, such as chemical-based and virus-based delivery, suffer from drawbacks induced by the nucleic acid carrier itself. Mechanical methods present an alternative to nucleic acid carriers by physically producing openings in the cell to deliver cargos. However, in most systems, the cell membrane openings are too small to deliver large cargos, or the poration process leads to low cell viability. In this study, we present a microfluidic device with integrated high aspect ratio nanostructures that repeatably rupture the cell membrane and nuclear envelope. These sharp-tipped nanolancets penetrate the cell deep enough to allow direct delivery of cargos into the nucleus, but still allow for cell recovery after treatment. We show the device's ability to deliver cargo to a variety of cell types while maintaining high viability. Then, we demonstrate the rapid onset of plasmid DNA expression that results from direct nuclear delivery of naked DNA, showing expression speeds comparable to microinjection, but with significantly greater throughput. We envision the use of this device as a tool to quickly produce high quantities of genetically engineered cells to treat a myriad of diseases.

Sickle cell disease (SCD) remains a critical global health issue, with high child mortality in low-resource regions. Early screening and diagnosis is essential for improving health outcomes, but conventional screening methods are unsuitable for widespread use due to the high costs of laboratory equipment. There is an urgent need for portable, cost-effective, and user-friendly point-of-care tools that can quickly assess blood health. Here, we explore two new biomarkers enabled by acoustic probing for rapid SCD screening: cell membrane stability from measuring red blood cell (RBC) lysis temperature in whole blood, and plasma protein concentration from measuring relative protein precipitation in blood plasma. Both biomarkers effectively differentiate healthy HbAA samples from pre-/no transfusion HbSS samples with high accuracy. Additionally, the RBC lysis biomarker can distinguish post-transfusion exchange HbSS samples with a lower percentage of sickled cells, indicating the potential to initially screen for milder forms of SCD as well as sickle cell trait.

Blood transfusion remains a cornerstone of modern medicine, saving countless lives daily. Yet the quality of transfused blood varies dramatically among donors-a critical factor often overlooked in clinical practice. Rapid, benchtop, and cost-effective methods for evaluating stored red blood cells (RBCs) at the site of transfusion are lacking, with concerns persisting about the association between metabolic signatures of stored RBC quality and transfusion outcomes. Recent studies utilizing metabolomics approaches to evaluate stored erythrocytes find that donor biology (e.g., genetics, age, lifestyle factors) underlies the heterogeneity associated with blood storage and transfusion. The appreciation of donor-intrinsic factors provides opportunities for precision transfusion medicine approaches for the evaluation of storage quality and prediction of transfusion efficacy. Here we propose a new platform, the Surface Acoustic Wave Hemolysis Assay (SAW-HA), for on-site evaluation of stored RBCs utilizing SAW Hemolysis Temperature (SAWHT) as a marker for RBC quality. We report SAWHT as a mechanism-dependent reproducible methodology for evaluating stored human RBCs up to 42 days. Our results define unique signatures for SAW hemolysis and metabolic profiles in RBCs from two of the six donors in which high body mass index (BMI) and RBC triglycerides associated with increased susceptibility to hemolysis. Metabolic age of the stored RBCs - a recently appreciated predictor of post-transfusion efficacy-reveal that RBCs from the two low SAWHT units were characterized by disrupted redox control, deficient tryptophan metabolism, and high BMI. Together, these findings indicate the potential of the SAW-HA as a point-of-care analysis for transfusion medicine.

Large sea spray drops - of up to 2mm in diameter - constitute one of the most uncertain factors controlling the intensification of hurricanes and severe storms because their generation mechanisms are not understood. Wave splashing produces among the largest spray drops, but observational data regarding these drops is difficult to obtain and hence cannot inform current modelling efforts. In this study, we instead propose a sea spray generation function (SSGF) for ocean wave splashing by assembling a model from first principles. First, we introduce the transverse collision of two cylindrical liquid rims as the basic mechanism for drop production. We characterize the resulting drop production in terms of three competing processes: ligament production and merging, drop generation by end-pinching, and gravity which arrests the mechanism. Second, we formulate a theoretical model which explains the drop size distributions produced by the colliding rims and test it against existing experimental and numerical data. Finally, the model can be developed into a full SSGF by incorporating sea state information with relatively few tuning parameters. The model is flexible and can be extended by including related effects such as finite droplet lifetime and secondary breakup. Altogether, our model suggests that wave splashing can efficiently produce numerous secondary droplets, challenging prior assumptions that it is an inefficient generation mechanism for sea spray.

Analysing biological spiking neural network models with synaptic plasticity has proven to be challenging both theoretically and numerically. In a network with N all-to-all connected neurons, the number of synaptic connections is on the order of $N^2$, making these models computationally demanding. Furthermore, the intricate coupling between neuron and synapse dynamics, along with the heterogeneity generated by plasticity, hinder the use of classic theoretical tools such as mean-field or slow-fast analyses. To address these challenges, we introduce a new variable which we term a typical neuron X. Viewed as a post-synaptic neuron, X is composed of the activity state V , the time since its last spike S, and the empirical distribution $\xi$ of the triplet V , S and W (incoming weight) associated to the pre-synaptic neurons. In particular, we study a stochastic spike-timing-dependent plasticity (STDP) model of connection in a probabilistic Wilson-Cowan spiking neural network model, which features binary neural activity. Taking the large N limit, we obtain from the empirical distribution of the typical neuron a simplified yet accurate representation of the original spiking network. This mean-field limit is a piecewise deterministic Markov process (PDMP) of McKean-Vlasov type, where the typical neuron dynamics depends on its own distribution. We term this analysis McKean-Vlasov mean-field (MKV-MF). Our approach not only reduces computational complexity but also provides insights into the dynamics of this spiking neural network with plasticity. The model obtained is mathematically exact and capable of tracking transient changes. This analysis marks the first exploration of MKV-MF dynamics in a network of spiking neurons interacting with STDP.

Flash droughts are increasingly occurring worldwide due to climate change, causing widespread socioeconomic and agricultural losses. However, timely and accurate flash drought forecasting remains challenging for operational forecast systems due to uncertain representations of interactive hydroclimate processes. Here, we introduce the Interpretable Transformer for Drought (IT-Drought), a deep learning model based on publicly available hydroclimate data. IT-Drought skillfully forecasts soil moisture up to 42 days and flash droughts up to 11 days ahead on average across the contiguous U.S., substantially outperforming existing forecast systems limited to 10-day skillful soil moisture forecasting and unable to forecast flash droughts. IT-Drought offers novel data-driven interpretability on flash drought onset, benchmarking the memory effects of soil moisture, and revealing heterogeneous impacts and effective time ranges of climatic conditions, predominantly radiation and temperature. The findings highlight the potential of leveraging IT-Drought for early warning and mechanistic investigation of flash droughts, ultimately supporting drought risk mitigation.

The structure of road networks impacts various urban dynamics, from traffic congestion to environmental sustainability and access to essential services. Recent studies reveal that most roads are underutilized, faster alternative routes are often overlooked, and traffic is typically concentrated on a few corridors. In this article, we examine how road network structure, and in particular the presence of mobility attractors (e.g., highways and ring roads), shapes the counterpart to traffic concentration: route diversification. To this end, we introduce DiverCity, a measure that quantifies the extent to which traffic can potentially be distributed across multiple, loosely overlapping near-shortest routes. Analyzing 56 diverse global cities, we find that DiverCity is influenced by network characteristics and is associated with traffic efficiency. Within cities, DiverCity increases with distance from the city center before stabilizing in the periphery, but declines in the proximity of mobility attractors. We demonstrate that strategic speed limit adjustments on mobility attractors can increase DiverCity while preserving travel efficiency. We isolate the complex interplay between mobility attractors and DiverCity through simulations in a controlled setting, confirming the patterns observed in real-world cities. DiverCity provides a practical tool for urban planners and policymakers to optimize road network design and balance route diversification, efficiency, and sustainability. We provide an interactive platform (this https URL) to visualize the spatial distribution of DiverCity across all considered cities.

Lubrication theory leverages the assumption of a long and thin fluid domain to formulate a linearized approximation to the Navier-Stokes equations. Models in extended lubrication theory consider relaxations of the thin film assumption, leading to the inclusion of higher order terms. However, such models are sensitive to large surface gradients which lead the assumptions of the model to break down. In this paper, we present a formulation of extended lubrication theory, and compare our models with several existing models, along with the numerical solution to the Stokes equations. The error in pressure and velocity is characterized for a variety of fluid domain geometries. Our results indicate that the new solution is suitable for a wide range of geometries. Nonetheless, the magnitude of surface variation and the length scale ratio are both important factors influencing the accuracy of the extended lubrication theory models.

Si-PIN detectors can be microstructured to achieve angular-selective particle detection capabilities, which we call active Transverse Energy Filter (aTEF). The microstructuring consists of a honeycomb structure of deep hexagonally-shaped holes with active silicon side walls, while the bottom of the holes is made insensitive to ionizing radiation. The motivation for this kind of detector arises from the need to distinguish background electrons from signal electrons in a spectrometer of MAC-E filter type. We have demonstrated the angular-dependent detection efficiency of self-fabricated aTEF prototypes in a test setup using an angular-selective photoelectron source to illuminate the detector from various incidence angles.

Simulations of optical quantum systems are essential for the development of quantum technologies. However, these simulations are often computationally intensive, especially when repeated evaluations are required for data fitting, parameter estimation, or real-time feedback. To address this challenge, we develop a Long Short-Term Memory neural network capable of replicating the output of these simulations with high accuracy and significantly reduced computational cost. Once trained, the surrogate model produces spectra in milliseconds, providing a speed-up of 5000x relative to traditional numerical solvers. We focus on applying this technique to Doppler-broadened Electromagnetically Induced Transparency in a ladder-type scheme for Rydberg-based sensing, achieving near-unity agreement with the physics solver for resonant and off-resonant regimes. We demonstrate the effectiveness of the LSTM model on this representative optical quantum system, establishing it as a surrogate tool capable of supporting real-time signal processing and feedback-based optimisation.

In this paper, we study two years of access card validation records from Bogotá's multimodal public transport system, comprising over 2.3 billion trips across bus rapid transit, feeder buses, dual-service buses, and an aerial cable network. By reconstructing user trajectories as motifs, we identify recurrent mobility patterns that extend beyond simple round trips, enabling the construction of an integrated origin-destination (OD) matrix covering 2,828 urban zones. Similarity analysis using the Jensen-Shannon divergence confirms the temporal stability of mobility structures across semesters, despite infrastructure changes and fare policy adjustments. From the obtained OD matrices, we derive transition probabilities between zones and uncover a robust power-law relationship with geographical distance, consistent with Lévy flight dynamics. We validate our model using Monte Carlo simulations showing that reproduces both local and long-range displacements, with similar scaling exponents across time. These findings demonstrate that Bogotá's public transport mobility can be effectively modeled through Lévy processes, providing a novel framework for analyzing complex transportation systems based solely on user access records.

This study examines how interactions among artificially intelligent (AI) agents, guided by large language models (LLMs), drive the evolution of collective network structures. We ask LLM-driven agents to grow a network by informing them about current link constellations. Our observations confirm that agents consistently apply a preferential attachment mechanism, favoring connections to nodes with higher degrees. We systematically solicited more than a million decisions from four different LLMs, including Gemini, ChatGPT, Llama, and Claude. When social attributes such as age, gender, religion, and political orientation are incorporated, the resulting networks exhibit heightened assortativity, leading to the formation of distinct homophilic communities. This significantly alters the network topology from what would be expected under a pure preferential attachment model alone. Political and religious attributes most significantly fragment the collective, fostering polarized subgroups, while age and gender yield more gradual structural shifts. Strikingly, LLMs also reveal asymmetric patterns in heterophilous ties, suggesting embedded directional biases reflective of societal norms. As autonomous AI agents increasingly shape the architecture of online systems, these findings contribute to how algorithmic choices of generative AI collectives not only reshape network topology, but offer critical insights into how AI-driven systems co-evolve and self-organize.

The capabilities of the world's highest energy and peak-power pulsed lasers are limited by optical damage, and further advances in high-intensity laser science will require optics that are substantially more robust than existing components. We describe here the experimental demonstration of off-axis diffractive gaseous lenses capable of withstanding extreme laser fluence and immune to cumulative damage. We used less than 8 mJ of energy from interfering ultraviolet laser pulses to holographically write millimeter-scale diffractive gas lenses into an ozone, oxygen, and carbon-dioxide gas mixture. These lenses allowed us to focus, defocus, and collimate 532-nm nanosecond laser pulses with up to 210 mJ of energy at efficiencies above 50% and fluences up to 35 J/cm$^2$. We also show that the gas lenses have sufficient bandwidth to efficiently diffract 35-fs 800-nm pulses and that beam pointing, divergence, and diffraction efficiency are stable while operating at 10 Hz. These diffractive lenses are simple holograms, and the principles demonstrated here could be extended to other types of optics, suggesting that gaseous optics may enable arbitrary, damage-resistant manipulation of intense light for next-generation ultra-high-power lasers.

The directed self-assembly (DSA) of block copolymers (BCPs) offers a highly promising approach for the fabrication of contact holes or vertical interconnect access at sub-7nm technology nodes. To fabricate circular holes with precisely controlled size and positions, the self-assembly of block copolymers requires guidance from a properly designed template. Effectively parameterizing the template shape to enable efficient optimization remains a critical yet challenging problem. Moreover, the optimized template must possess excellent manufacturability for practical applications. In this work, we propose a Gaussian descriptor for characterizing the template shape with only two parameters. We further propose to use AB/AB binary blends instead of pure diblock copolymer to improve the adaptability of the block copolymer system to the template shape. The Bayesian optimization (BO) is applied to co-optimize the binary blend and the template shape. Our results demonstrate that BO based on the Gaussian descriptor can efficiently yield the optimal templates for diverse multi-hole patterns, all leading to highly matched self-assembled morphologies. Moreover, by imposing constraints on the variation of curvature of the template during optimization, superior manufacturability is ensured for each optimized template. It is noteworthy that each key parameter of the blend exhibits a relatively wide tunable window under the requirement of rather high precision. Our work provides valuable insights for advancing DSA technology, and thus potentially propels its practical applications forward.

We present a comprehensive review of the emerging crystal structure determination method Parameter Space Concept (PSC), which solves and refines either partial or complete crystal structures by mapping each experimental or theoretical observation as a geometric interpretation, bypassing the conventional Fourier inversion. The PSC utilizes only a few \xray (equivalently neutron) diffraction amplitudes or intensities and turns them into piecewise analytic hyper-surfaces, called isosurfaces, embedded in a higher-dimensional orthonormal Cartesian space (Parameter Space (PS)). It reformulates the crystal determination task into a geometric interpretation, searching for a common intersection point of different isosurfaces. The art of defining various kinds of isosurfaces, based on signs, amplitude or intensity values, normalized ratios, and ranking of reflections, offers multiple choices of adapting methods in PSC based on available experimental or theoretical observations. The elegance of PSC stems from one-dimensional projections of atomic coordinates, enabling the construction of full three-dimensional crystal structures by combining multiple projections. By these means, the user may explore homometric and non-homometric solutions within both centric and acentric structures with spatial resolution remarkably down to pm, even with a limited number of diffraction reflections. Having demonstrated the potential of PSC for various synthetic structures and exemplarily verified direct application to realistic crystals, the origin and development of PSC methods are coherently discussed in this review. Notably, this review emphasizes the theoretical foundations, computational strategies, and potential extensions of PSC, outlining the roadmap for future applications of PSC in the broader context of structure determination from experimental diffraction observations.

The Belle II experiment at the SuperKEKB accelerator is designed to explore physics beyond the Standard Model with unprecedented luminosity. As the beam intensity increased, the experiment faced significant challenges due to higher beam-induced background, leading to a high trigger rate and placing limitations on further luminosity increases. To address this problem, we developed trigger logic for tracking using deep neural network (DNN) technology on an FPGA for the Belle II hardware trigger system, employing high-level synthesis techniques. By leveraging drift time and hit pattern information from the Central Drift Chamber and incorporating a simplified self-attention architecture, the DNN track trigger significantly improves track reconstruction performance at the hardware level. Compared to the existing neural track trigger, our implementation reduces the total track trigger rate by 37% while improving average efficiency for the signal tracks from 96% to 98% for charged tracks with transverse momentum > 0.3 GeV. This upgrade ensures the long-term viability of the Belle II data acquisition system as luminosity continues to increase.

Existing research has yet to reach a consensus on whether and how small flying animals utilize elastic energy storage mechanisms to reduce flight energy expenditure, and there is a lack of systematic and universal methods for assessment. To address these gaps, this study proposes a method to evaluate elastic energy storage capacity based on wing kinematic parameters (flapping amplitude and flapping frequency), grounded in the hypothesis that animals tend to minimize flight energy expenditure. By establishing a simplified power model, the study calculates the optimal kinematic parameters corresponding to the minimum mechanical power requirements under two extreme conditions: no elastic energy storage and complete elastic energy storage. These optimal parameters are then compared with measured data from various small flying animals. The results show that the measured parameters of hummingbirds, ladybugs, and rhinoceros beetles are close to the no-storage optimum, indicating relatively weak elastic energy storage capacity; whereas hoverflies, bumblebees, and honeybees align closely with the complete-storage optimum, suggesting strong elastic energy storage ability. Furthermore, the wing kinematic adjustment strategies these animals employ in response to changes in load or air density are consistent with the predicted elastic storage capacities. This study provides a systematic new approach for assessing biological elastic energy storage capacity and offers a theoretical basis for the low-power design of flapping wing micro air vehicles.

Periodically driven optical materials and metamaterials have recently emerged as a promising platform for realizing photonic time crystals (PTCs) -- systems whose optical properties are strongly and periodically modulated on time scales comparable to the optical cycle of light. These time-varying structures are the temporal counterparts of spatial photonic crystals (SPCs), for which a large and periodic dielectric contrast is achieved spatially on wavelength scales. Just as SPCs have revolutionized control over light-matter interactions by engineering the photonic density of states in space, PTCs promise comparable breakthroughs from a fundamentally new perspective: a temporal one. However, harnessing such phenomena at optical frequencies poses severe experimental challenges, as it requires order-unity modulation depths of the optical properties at optical cycle rates, a regime that has remained elusive to date. Here, we report the first optical realization of a photonic time crystal, achieved with a surface plasmon cavity metamaterial operating at Terahertz frequencies. We demonstrate strong (near-unity) and coherent (sub-optical cycle) periodic driving of the plasmonic metamaterial enabled by field-induced dynamical modulation of the carriers' kinetic energy and effective mass -- reaching up to 80% of their rest mass, an exceptionally high value that forms the basis for time-crystalline phenomena with plasmons. Our experimentally informed theory reveals rich physics within the experimentally accessible parameter regime of this system, including parametric amplification and entangled plasmon generation, and establishes a robust new platform for time-domain photonics.

Purpose: Inhomogeneous magnetization transfer (ihMT) effect reflects dipolar order with a dipolar relaxation time ($T_{1D}$), specific to motion-restricted macromolecules. We aim to quantify $T_{1D}$ using spin-lock MRI implemented with a novel rotary-echo sequence. Methods: In proposed method, we defined a relaxation rate $R_{dosl}$ that is specific to dipolar order and obtained as the difference of dual-frequency $R_{1rho}^{dual}$ relaxation and single-frequency $R_{1rho}^{single}$ relaxation. A novel rotary-echo spin-lock sequence was developed to enable dual-frequency acquisition. We derive the framework to estimate $T_{1D}$ from $R_{dosl}$ under macromolecular pool fraction (MPF) map constraints. The proposed approach was validated via Bloch-McConnell-Provotorov simulation, phantom studies, and in-vivo white matter studies on a 3T scanner. Results: Simulations demonstrated that $R_{dosl}$ exhibits an approximately linear relationship with $T_{1D}$. Phantom experiments showed robust ihMT contrast in $R_{dosl}$ and confirmed the feasibility and reliability of $T_{1D}$ quantification via $R_{dosl}$. In vivo white-matter studies further supported the clinical potential of this $T_{1D}$ mapping approach. Conclusion: We propose a novel, clinical feasible method for $T_{1D}$ quantification based on spin-lock MRI. This method requires substantially fewer contrast-prepared images compared to the conventional $T_{1D}$ quantification approach. This technique provides a promising pathway for robust MPF and $T_{1D}$ quantification in a single rapid scan with reduced confounds.

Electro-optic modulation is an attractive approach for generating flat, stable, and low-noise optical frequency combs with relatively high power per comb line. However, a key limitation of electro-optic combs is the restricted number of comb lines imposed by the available RF source power. To overcome this limitation, a nonlinear spectral broadening stage is typically employed. The phase noise characteristics of an electro-optic comb are well described by the standard phase noise model, which depends on two parameters: the seed laser and the RF source phase noise. A fundamental question that arises is how nonlinear broadening processes affect the phase noise properties of the expanded comb. To address this, we employ coherent detection, digital signal processing, and subspace tracking. Our experimental results show that the nonlinearly broadened comb preserves the standard phase noise model of the input electro-optic comb. In other words, the nonlinear processes neither introduce additional phase noise terms nor amplify the existing contributions from the seed laser and RF source. Hence, nonlinear broadening can be viewed as equivalent to driving the electro-optic comb with a much higher RF modulation power.

The evolution of urban landscapes is rapidly altering the surface of our planet. Yet, our understanding of the urbanisation phenomenon remains far from complete. A fundamental challenge is to describe spatiotemporal changes in the built environment. A dynamic theory of urban evolution should account for both vertical and horizontal city expansion, analogous to the dynamical behaviour of surface growth in physical and biological systems. Here we show that building-height dynamics in cities around the world are well described by a zero-dimensional geometric Brownian motion (GBM), where multiplicative noise drives stochastic fluctuations around a deterministic drift associated with economic growth. To account for intra-city correlations, we extend the GBM with spatial coupling, revealing how local interactions effectively mitigate noise-driven fluctuations and shape urban morphology. The continuum limit of this spatial model can be recasted into the Kardar-Parisi-Zhang (KPZ) equation and we find that empirical estimates of the roughness exponent are in the range of the KPZ prediction for most cities. Together, these results show that multiplicative noise, moderated by local interactions, governs the evolution of urban roughness, anchoring spatiotemporal city dynamics in a well-established statistical physics framework.

The development of computing paradigms alternative to von Neumann architectures has recently fueled significant progress in novel all-optical processing solutions. In this work, we investigate how the coherence properties can be exploited for computing by expanding information onto a higher-dimensional space in the photonic extreme learning machine framework. A theoretical framework is provided based on the transmission matrix formalism, mapping the input plane onto the output camera plane, resulting in the establishment of the connection with complex extreme learning machines and derivation of upper bounds for the hidden space dimensionality as well as the form of the activation functions. Experiments using free-space propagation through a diffusive medium, performed in low-dimensional input space regimes, validate the model and the proposed estimator for the dimensionality. Overall, the framework presented and the findings enclosed have the potential to foster further research in a multitude of directions, from the development of robust general-purpose all-optical hardware to a full-stack integration with optical sensing devices toward edge computing solutions.

We present the Thickness Stabilization Scheme (TSS), a numerical stabilization scheme suitable for the Shallow Shelf Approximation (SSA), one of the most widely-used models for large-scale Antarctic and Greenland ice sheet simulations. The TSS is constructed by inserting an adapted, explicit Euler thickness evolution equation into the driving stress term, thereby treating the term implicitly. We investigate the applicability of TSS across low- and high-shear idealized scenarios, by altering the inflow velocity and initial ice thickness. TSS demonstrates an increase in the numerical stability of SSA, allowing large time-step sizes of dt = 50-100 years to remain numerically stable and accurate, while time-step sizes of dt > 5 in high-shear simulations show significant error without TSS. Remarkably, a time step size as large as dt = 10 000 years is numerically stable with TSS, albeit with a reduction in accuracy. TSS offers greater flexibility for ice-sheet modeling by allowing the re-allocation of computational resources. This method is applicable not only to ice-sheet modeling, including in coupled frameworks, but also to other vertically-integrated computational fluid dynamics problems that couple momentum and geometry evolution equations.

In this work, we extend selected configuration interaction (SCI) methods beyond energies and expectation values by introducing a linear response (LR) framework for molecular response properties. Existing SCI approaches are capable of approximating the energy of the full configuration interaction (FCI) wave function with high accuracy but at a much lower cost. However, conventional determinant selection will, by design, mainly select determinants that are expected to improve energies, and this can lead to the omission of many determinants that are important for wave function response. We address this by introducing two new selection criteria motivated by linear response theory. Using these extended determinant selection criteria, we demonstrate that LR-SCI can systematically converge toward the FCI limit for static polarizabilities. Using a damped LR formulation, we compute the water K-edge X-ray absorption spectrum in active spaces up to (10e, 58o). Finally, we use LR-SCI to compute NMR spin-spin coupling constants for water, where we find that accuracy beyond that offered by CCSDT can be achieved. Overall, LR-SCI offers a promising route to compute response properties with near-FCI accuracy to systems beyond the reach of exact FCI.

A calculation of the QED vacuum polarization potential in the Coulomb field of a pointlike nucleus was presented in an earlier publication by the author and his collaborators. Corrections up to order $\alpha^2 (Z\alpha)^7$ were evaluated, where $Z$ is the nuclear charge number and $Z\alpha$ is treated as an independent variable. These corrections correspond to two-loop Feynman graphs with proper propagators of fermions in the external field. The calculation employed a reduction to free QED, leading to free QED Feynman graphs with up to eight independent loops. The method of calculation is described here in detail.

Deep learning (DL) has demonstrated promise for accelerating and enhancing the accuracy of flow physics simulations, but progress is constrained by the scarcity of high-fidelity training data, which is costly to generate and inherently limited to a small set of flow conditions. Consequently, closures trained in the conventional offline paradigm tend to overfit and fail to generalise to new regimes. We introduce an online optimisation framework for DL-based Reynolds-averaged Navier--Stokes (RANS) closures which seeks to address the challenge of limited high-fidelity datasets. Training data is dynamically generated by embedding a direct numerical simulation (DNS) within a subdomain of the RANS domain. The RANS solution supplies boundary conditions to the DNS, while the DNS provides mean velocity and turbulence statistics that are used to update a DL closure model during the simulation. This feedback loop enables the closure to adapt to the embedded DNS target flow, avoiding reliance on precomputed datasets and improving out-of-distribution performance. The approach is demonstrated for the stochastically forced Burgers equation and for turbulent channel flow at $Re_\tau=180$, $270$, $395$ and $590$ with varying embedded domain lengths $1\leq L_0/L\leq 8$. Online-optimised RANS models significantly outperform both offline-trained and literature-calibrated closures, with accurate training achieved using modest DNS subdomains. Performance degrades primarily when boundary-condition contamination dominates or when domains are too short to capture low-wavenumber modes. This framework provides a scalable route to physics-informed machine learning closures, enabling data-adaptive reduced-order models that generalise across flow regimes without requiring large precomputed training datasets.

This report describes major features of the most recent version of the MARS code as well as ongoing developments. The list of features includes various options for geometry models, a beam line builder based on MADX code, import of geometry models in GDML format, use of structured and unstructured meshes for scoring purposes, an update to the recent TENDL library for a number of projectiles at low energies (up to 250 MeV), and a recently implemented method to calculate spatial distribution of residual dose in a single computer run without an intermediate source. Examples of the code application to various projects are presented as well.

Organic aerosols (OA) comprise a major fraction of atmospheric particulate matter and frequently contain acidic species, yet their contribution to overall aerosol acidity has not been explicitly considered in global climate models. We implement concentration-dependent OA acid dissociation, including recently demonstrated surface-specific effects, into the ECHAM-HAMMOZ global climate model and assess the impacts on aqueous aerosol sulfate chemistry and aerosol--cloud--climate interactions. We show that enhanced aerosol acidity from OA acid dissociation drives increased sulfate formation from aqueous-phase oxidation of $\mathrm{SO_2}$. The microphysics of additional secondary sulfate aerosol changes global cloud droplet number concentrations (CDNC), with enhancements up to $13.9\%$. Increased cloud formation leads to a significant global mean cooling effect with a shortwave cloud radiative forcing (SWCRF) up to $-0.97~\mathrm{W\,m^{-2}}$. We also find that surface-specific acid dissociation effects can further modify both aerosol chemistry and resulting aerosol--cloud--climate responses, in some cases with even stronger impact than bulk acidity conditions. Our results demonstrate significant effects of considering OA acidity, as well as surface-specific phenomena, in global climate models.

Coherent localization microscopy enables three-dimensional localization with nanoscale precision. Previous studies have characterized the fundamental precision limits for on-axis illumination geometries. In this paper, we theoretically analyze 3D particle localization precision under oblique illumination in interferometric scattering microscopy (iSCAT). We show that (quantum) Cramer-Rao bounds reveal an information gain in the off-axis case, potentially enabling enhanced localization precision. We find that rotating coherent scattering microscopy (ROCS), which employs off-axis illumination, provides worse localization precision than \iscat, despite offering higher spatial resolution. This shows that key metrics, like spatial resolution, are often insufficient to fully characterize a microscopy technique.

Existing accounts of the cosmological arrow of time face a dilemma: generalist approaches that posit time-asymmetric laws lack independent motivation, while particularist approaches that invoke a Past Hypothesis face serious conceptual and explanatory problems. We propose a novel account that dispenses with the need for both time-asymmetric laws and a Past Hypothesis. Instead, it is centred around a symmetry argument that reveals attractors and so-called 'Janus points'. The main idea is that the global spatial scale of the Universe is not empirically accessible, and should therefore be treated as surplus structure. By contrast, the Hubble parameter, which encodes the relative rate of change of scale, is empirically accessible. Once the scale redundancy is eliminated, the empirically meaningful description of cosmic dynamics involves a drag-like variable that transforms in a way that preserves time-reversal invariance. The resulting space of dynamical possibilities possesses universal attractors and Janus points, giving rise to particular states in which observers experience a cosmological arrow of time akin to our own. We illustrate the proposal by applying it to cosmological and gravitational N-body models, showing how it accounts, respectively, for the rapid cooling of the early universe and its relative smoothness.

The growth of cities has traditionally been studied from a population perspective, while urban sprawl - its spatial growth - has often been approached qualitatively. However, characterizing and modeling this spatial expansion is crucial, particularly given its parallels with surface growth extensively studied in physics. Despite these similarities, approaches to urban sprawl modeling are fragmented and scattered across various disciplines and contexts. In this review, we provide a comprehensive overview of the mathematical modeling of this complex phenomenon. We discuss the key challenges hindering progress and examine models inspired by statistical physics, economics and geography, and theoretical ecology. Finally, we highlight critical directions for future research in this interdisciplinary field.

Enabling coherent light-matter interactions is a critical step toward next-generation quantum technologies. However, achieving this under ambient temperature conditions remains challenging due to rapid dephasing in optically excited systems. Optical metasurfaces based on quasi-bound states in the continuum have recently emerged as a powerful platform for reaching the strong light-matter coupling regime in flat, subwavelength thickness devices. Here, we investigate ultrafast exciton-polariton dynamics in self-hybridized WS$_2$ thin-film metasurfaces. Using hyperspectral momentum-resolved imaging, we reconstruct the highly anisotropic exciton-polariton dispersion, with a transition from positive to negative effective mass along orthogonal symmetry axes. Femtosecond pump-probe and multidimensional spectroscopy reveal detuning-dependent polariton dynamics with a coherence time up to ~110 fs, and allow direct observation of the coherent dynamics through ultrafast Rabi oscillations with ~45 fs period. We describe this behaviour with a three-eigenstate model that couples the photonic resonance with both bright and dark excitons, extending the conventional two-state picture of strong coupling. Our results establish van der Waals metasurfaces as a promising platform for next-generation polaritonic devices, enabling coherent quantum transfer of matter excitations at room temperature.

Herein, a self-assembled hierarchical structure of hematite ($\alpha$-$Fe_2O_3$) was synthesized via a one-pot hydrothermal method. Subsequently, the nanomaterial was doped to get $M_{x}Fe_{2-x}O_3$ (M = Mn-Co; x = 0.01, 0.05, 0.1) at precise concentrations. The electrode was fabricated by coating the resulting nanocomposite onto a Nickel Foam (NF) substrate. The electrochemical characterization demonstrated the excellent performance of cobalt-doped $\alpha$-$Fe_2O_3$, among which, $Co_0.05Fe_0.95O_3$ (CF5) exhibited superior performance, showing a two-fold increase in sensitivity of 1364.2 $\mu$A.m$M^{-1}$.$cm^{-2}$ $(\pm 0.03, n = 3)$ in 0.5 M KOH, a Limit of Detection (LOD) of $\sim 0.17 mM$, and a Limit of Quantification (LOQ) of {\sim}0.58 mM. Density Functional Theory (DFT) was performed to understand the doping prompting in the reduced bandgap. The fabricated electrode displayed a rapid response time of 2 s and demonstrated 95% stability, excellent reproducibility, and selectivity, as confirmed by tests with several interfering species. A comprehensive evaluation of the electrode's performance using human blood serum highlighted its robustness and reliability for cholesterol detection in clinical settings, making it a promising tool for clinical and pharmaceutical applications.

Periodically time-varying media, known as photonic time crystals (PTCs), provide a promising platform for observing unconventional wave phenomena. We analyze the scattering of electromagnetic waves from spatially finite PTCs using the multispectral Floquet scattering matrix, which naturally incorporates the frequency-mixing processes intrinsic to such systems. For dispersionless, real, and time-periodic permittivities, this matrix is pseudounitary. Here we demonstrate that this property leads to multiple symmetry-breaking transitions: for increasing driving strength, scattering matrix eigenvalues lying on the unit circle (unbroken symmetry regime) meet at exceptional points (EPs), where they break up into inverse complex conjugate pairs (broken symmetry regime). We identify the symmetry operator associated with these transitions and show that, in time-symmetric systems, it corresponds to the time-reversal operator. Remarkably, at the parametric resonance condition, one eigenvalue vanishes while its partner diverges, signifying simultaneous coherent perfect absorption (CPA) and lasing. Since our approach relies solely on the Floquet scattering matrix, it is not restricted to a specific geometry but instead applies to any periodically time-varying scattering system. To illustrate this universality, we apply our method to a variety of periodically time-modulated structures, including slabs, spheres, and metasurfaces. In particular, we show that using quasi-bound states in the continuum resonances sustained by a metasurface, the CPA and lasing conditions can be attained for a minimal modulation strength of the permittivity. Our results pave the way for engineering time-modulated photonic systems with tailored scattering properties, opening new avenues for dynamic control of light in next-generation optical devices.

In astrophysical shear flows, the Kelvin-Helmholtz (KH) instability is generally suppressed by magnetic tension provided a sufficiently strong streamwise magnetic field. This is often used to infer upper (or lower) bounds on field strengths in systems where shear-driven fluctuations are (or are not) observed, on the basis that fluctuations cannot grow in the absence of linear instability. On the contrary, by calculating the maximum growth that small-amplitude perturbations can achieve in finite time for such a system, we show that perturbations can grow in energy by orders of magnitude even when the flow is sub-Alfvénic, suggesting that shear-driven turbulence is possible even in the presence of strong magnetic fields, and challenging inferences from the observed presence or absence of shear-driven fluctuations. We further show that magnetic fields introduce additional nonmodal growth mechanisms relative to the hydrodynamic case, and that 2D simulations miss key aspects of these growth mechanisms.

Mid-spatial frequency errors of optical components cause degradation of images which are rather difficult to quantify. In this work, we present a model for calculating the point-spread function in the presence of mid-spatial frequency errors which is based on diffraction this http URL results of the model are compared with experiments.

Stokes polarimetry has been considered as an alluring platform that enables a plethora of applications ranging from single-molecule orientation to deep-space sensing. Existing polarimetry avenues, however, rely primarily on the transversely polarized field reconstruction, thus suffering from several challenges such as multiple time sequenced detections, complex demodulation algorithms, and intricate engineering procedures. To circumvent these challenges, here we first demonstrate a longitudinally polarized field mediated recipe for the realization of efficacious and refined Stokes polarimetry in situ. This is achieved by unraveling the spin-to-orbit momentum conversion under non-paraxial focusing conditions enabling the direct mapping of the polarization ellipse. Leveraging this mechanism, we reveal an analytical solution of polarization ellipse via the local sampling of longitudinal field, in which the robustness can be fairly reinforced by relevant global retrieval based on convolutional neural network. The resultant Stokes polarimetry is shown to simultaneously exhibit in-situ signal acquisition (direct discernment), unparalleled demodulation time (up tomicrosecond level), superior detection efficiency (no need of troublesome design), and ultra-high retrieved accuracy (less than 1%), which is fundamentally inaccessible with traditional polarimetry methods. Our work holds great promise for empowering an allin-one versatile vector polarimeter, which opens up a host of applications relevant to polarization control.

Polarization maintaining (PM), all-fiber amplifiers offer the benefits of alignment free and environmentally stable operation. To achieve high output powers, particularly in pulsed operation, it is necessary to balance the need to reduce deleterious nonlinear effects, often through the use of large mode area (LMA) fibers, with the onset of transverse mode instability whereby higher order modes (HOMs) mix with the desired fundamental mode output. Over the last few years, advances in high HOM loss, ytterbium-doped LMA fibers have enabled continuous wave (CW) output powers up to 5 kW and pulse energies up to 2 mJ in non-PM fibers. In CW operation, LMA PM fibers have shown up to 2 kW of average power. In this contribution, we present all-fiber nanosecond pulsed amplification in a high HOM loss, Yb-doped LMA fiber with a 26 micrometer mode field diameter and 2.4 dB/m of pump absorption at 976 nm, achieving 1 mJ of pulse energy at 1 kW of average power, and 1.5 mJ of pulse energy at 750 W of average power. The polarization extinction ratios were 20 dB or higher and the M2 was near the diffraction limit. We measured the in-pulse to out-of-pulse energy and found 99.9% or more of the measured power remained in-pulse.

Optical non-reciprocity, a phenomenon that allows unidirectional flow of optical field is pivoted on the time reversal symmetry breaking. The symmetry breaking happens in the cavity optomechanical system (COS) due to non uniform radiation pressure as a result of light-matter interaction, and is crucial in building non-reciprocal optical devices. In our proposed COS, we study the non-reciprocal transport of optical signals across two ports via three optical modes optomechanically coupled to the mechanical excitations of two nano-mechanical resonators (NMRs) under the influence of strong classical drive fields and weak probe fields. By tuning different system parameters, we discover the conversion of reciprocal to non-reciprocal signal transmission. We reveal perfect nonreciprocal transmission of output fields when the effective cavity detuning parameters are near resonant to the NMRs' frequencies. The unidirectional non-reciprocal signal transport is robust to the optomechanical coupling parameters at resonance conditions. Moreover, the cavities' photon loss rates play an inevitable role in the unidirectional flow of signal across the two ports. Bidirectional transmission can be fully controlled by the phase changes associated with the incoming probe and drive fields via two ports. Our scheme may provide a foundation for the compact non-reciprocal communication and quantum information processing, thus enabling new devices that route photons in unconventional ways such as all-optical diodes, optical transistors and optical switches.

In this thesis I demonstrate that isospectral domains, that is domains of differing geometric shapes that possess identical spectra, do not remain isospectral when subject to uniform rotation. One thus *can* hear the shape of a rotating drum. It is shown that the spectra diverge as $\propto\left(\frac{\omega}{c}\right)^2$ similarly to a square but different from a circle, whose degenerate eigenfrequencies split $\propto \left(\frac{\omega}{c}\right)$. The latter two cases are studied analytically and used as test cases for the selection of numerical solution methods. I demonstrate that the presence of a simple medium attenuates the effects of rotation on the spectrum differences. Further I show that the common but linearised augmented wave equation yields eigenmodes with sensible structure for non-physical parameters, whereas a full equation destroys the structure as intended. The full equation is obtained from first principles using Maxwell equations formulated as differential forms and a careful application of the 3+1 foliation of space-time. The differences of the result from literature arXiv:physics/0607016 are highlighted and their effects studied. The equation is treated analytically on simple domains to obtain reference data for numerical analysis. Nodal and modal discretisations of the PDE are tested and a self-written FEM implementation is chosen due to higher precision of the results. As part of the validation process I demonstrate that results of arXiv:physics/0607016 are reproducible. The resulting gyroscopic quadratic eigenvalue problem is linearised into a Hamiltonian/skew-Hamiltonian pencil and a suitable shift operator for the Arnoldi iteration is determined. The resulting formulation is solved using ARPACK.

In a shallow-water radial outflow the horizon of a hydrodynamic white hole coincides with a standing circular hydraulic jump. The jump, caused by viscosity, makes the horizon visible as a circular front, standing as a barrier against the entry of waves within its circumference. The blocking of waves causes a pile-up at the horizon of the white hole, for which surface tension is mainly responsible. Conversely, it is also because of surface tension that the waves can penetrate the barrier. The penetrating waves (analogue Hawking quanta) tunnel through the barrier with a decaying amplitude, but a large-amplitude instability about the horizon is possible.

Czachor's model of hierarchical arithmetics begins with a valid formal premise but fixes the key probability mapping g by importing the Born rule and Fubini-Study metric from standard quantum mechanics, where Born probabilities are Kolmogorov within a fixed measurement context. This g is then applied in a non-Newtonian hidden-variable setting, producing a hybrid framework whose agreement with quantum correlations is built in by design, not derived from new physics, and thus does not constitute a genuine counterexample to Bell's theorem

While in electromagnetism we have space-time symmetry, magnetoelectric (ME) processes are characterized by space-time symmetry breaking. Our goal is to show that quantum vacuum fields with both time reversal and space inversion symmetry breaking (so-called ME fields) can be observed in subwavelength regions of electromagnetic (EM) radiation. The sources of ME fields are ME meta-atoms, subwavelength elements made of magnetic insulators, where the dynamical ME behavior is due to modulations and topological coupling of magnetization and electric polarization. The interaction between EM and ME systems occurs through virtual structures, ME virtual photons. Experimentally, ME quantum vacuum states can be detected in a microwave cavity. The topic of these studies concerns fundamental aspects of ME quantum electrodynamics (MEQED).

Non-Hermitian quantum systems, governed by nonunitary evolution, offer powerful tools for manipulating quantum states through engineered loss. A prime example is coherent absorption, where quantum states undergo phase-dependent partial or complete absorption in a lossy medium. Here, we demonstrate a fully programmable implementation of nonunitary transformations that emulate coherent absorption of quantum light using a programmable integrated linear photonic circuit, with loss introduced via coupling to an ancilla mode [Phys. Rev. X 8, 021017; 2018]. Probing the circuit with a single-photon dual-rail state reveals phase-controlled coherent tunability between perfect transmission and perfect absorption. A two-photon NOON state input, by contrast, exhibits switching between deterministic single-photon and probabilistic two-photon absorption. Across a range of input phases and circuit configurations, we observe nonclassical effects such as anti-coalescence and bunching, along with continuous and coherent tuning of output Fock state probability amplitudes. Classical Fisher information analysis reveals phase sensitivity peaks of 0.79 for single-photon states and 3.7 for NOON states, exceeding the shot-noise limit of 2 and approaching the Heisenberg limit of 4. The experiment integrates quantum state generation, programmable photonic circuitry, and photon-number-resolving detection, establishing ancilla-assisted circuits as powerful tools for programmable quantum state engineering, filtering, multiplexed sensing, and nonunitary quantum simulation.

In this paper, we re-explore a simple textbook Landau model describing improper ferroelectricity and show that in the limit where both proper and improper instabilities exist and compete, improper ferroelectrics can display switching between multiple polarisation states. Using first principles calculations we highlight how the hexagonal tungsten bronze materials may be an archetypal case, with the possibility to switch between improper and proper phases. The resulting functional characteristics are akin to "ferrielectrics", with switching behaviour in the form of a triple hysteresis loop. Such functionality could be ideal for creating non-volatile multistate systems for use in memory devices or as a backbone for neuromorphic computing.

We present a new mechanochemical modeling framework to explore the shape deformation and pattern formation in multicomponent vesicle membranes. In this framework, the shape of the membrane is described by an elastic bending model, while phase separation of membrane-bound activator proteins is determined by an Ohta-Kawasaki (OK) model. The coupled dynamics consist of an overdamped force-balanced equation for the membrane geometry and an OK-type advection-reaction-diffusion equation on the deformable membrane. We implement efficient spectral methods to simulate these dynamics in both two- and three-dimensions. Numerical experiments show that the model successfully reproduces a wide range of experimentally observed membrane morphologies \cite{baumgart2003imaging}. Taken together, the framework unifies curvature mechanics, microphase separation, and active forcing, providing new insight into membrane-bounded multicomponent vesicle dynamics and a practical platform for studying multicomponent biomembrane morphology.

Spectroscopy is the most important method for probing the structure of molecules. However, predicting molecular spectra on classical computers is computationally expensive, with the most accurate methods having a cost that grows exponentially with molecule size. Quantum computers have been shown to simulate simple types of optical spectroscopy efficiently -- with a cost polynomial in molecule size -- using methods such as time-dependent simulations of photoinduced wavepackets. Here, we show that any type of spectroscopy can be efficiently simulated on a quantum computer using a time-domain approach, including spectroscopies of any order, any frequency range, and involving both electric and magnetic transitions. Our method works by computing any spectroscopic correlation function based on the corresponding double-sided Feynman diagram, the canonical description of spectroscopic interactions. The approach can be used to simulate spectroscopy of both closed and open molecular systems using both digital and analog quantum computers.

Inorganic scintillators continue to be widely used within astrophysical X-ray and gamma-ray detectors. This is in part thanks to the development of new scintillators, such as GAGG:Ce, as well as the availability of new scintillator readout sensors such as Silicon Photomultipliers and Silicon Drift Detectors. In order to use such scintillator materials for spectrometry or polarimetry, a detailed understanding of their response is important. One parameter that can affect the scintillator performance, particularly at lower photon energies, is their Birks' coefficienct, which correlates the relative light yield to the ionization energy. While for many high-Z inorganic scintillators this effect can be ignored, for GAGG:Ce this appears to not be the case. Here we provide a measurement of the Birks' coefficient for GAGG:Ce using data from two different detectors irradiated in the 20-80 keV energy range at the LARIX-A X-ray beam in Ferrara, Italy. While the effects due to Birks' law are visible below 30 keV, they also significantly influence the performance of GAGG:Ce performance near one of the K-edges, affecting both the measured gain and the energy resolution. Here, we use beam test data to derive the Birks' coefficient from GAGG:Ce. The results indicate that for usage in hard X-ray and soft gamma-ray missions, this coefficient has a significant effect on the measurements.

This work presents Ti/Al bi-layer Microwave Kinetic Inductance Detectors (MKIDs) based on lens-coupled spiral absorbers as the quasi-optical coupling mechanism for millimeter-wavelength radiation detection. From simulations, the lens-coupled absorbers provide a 70% lens aperture efficiency in both polarizations over an octave band with a spiral array absorber and over 10% relative bandwidth with a single spiral. We have fabricated and measured two devices with bare Ti/Al MKIDs: a 3x3 cm chip with 9 pixels to characterize the optical response at 85 GHz of the two variations of the absorber; and a large format demonstrator with 253 spiral-array pixels showing potential towards a large format millimeter-wavelength camera. We find a sensitivity of 1 mK/sqrt{Hz} and a detector yield of 95%.

Artificial Light At Night (ALAN) has been increasing steadily over the past century, particularly during the last decade. This leads to rising light pollution, which is known to have adverse effects on living organisms, including humans. We present a new software package to model light pollution from ground radiance measurements. The software is called Otus 3 and incorporates innovative ALAN diffusion models with different atmospheric profiles, cloud covers and urban emission functions. To date, light pollution modelling typically focused on calculating the zenith luminance of the skyglow produced by city lights. In Otus 3 we extend this and additionally model the horizontal illuminance on the ground, including the contributions from skyglow and the direct illumination. We applied Otus 3 to France using ground radiance data from the Visible Infrared Imaging Radiometer Suite (VIIRS). We calibrated our models using precise sky brightness measurements we obtained over 6 years at 139 different locations and make this dataset publicly available. We produced the first artificial illuminance map for France for the periods of 2013-2018 and 2019-2024. We found that the artificial ground illuminance in the middle of the night decreased by 23 % between these two periods, in stark contrast to the global trend.

Optical non-reciprocity is a fundamental phenomenon in photonics. It is crucial for developing devices that rely on directional signal control, such as optical isolators and circulators. However, most research in this field has focused on systems in equilibrium or steady states. In this work, we demonstrate a room-temperature Rydberg atomic platform where the unidirectional propagation of light acts as a switch to mediate time-crystalline-like collective oscillations through atomic synchronization. We find that thermal-motion-induced coupling asymmetry, enabled by counterpropagating probe and control fields, generates persistent oscillations; conversely, co-propagation quenches this effect. We identify, through both numerical and analytical approaches, the criteria for realizing optical non-reciprocity within a synchronization regime. These results provide key insights for chiral quantum optics and promote the on-chip integration of non-reciprocal devices in nonequilibrium many-body systems.

We present a critical analysis of the classical approaches to energy subjects, based on the work-energy theorem and the conservation of mechanical energy proposed in the courses of the first years of tertiary education. We show how these approaches present a series of inconsistencies and errors that are a source of conceptual difficulties among students. We then analyze a modern treatment of mechanical courses based on the results of research in physics education over the last 40 years. We place special emphasis on the principle of conservation of energy as one of the fundamental principles of nature, prioritizing the concepts of system, surrounding, and energy transfer and transformation.

Scintillation detectors are essential tools for radiation measurement, but calibrating them accurately can be challenging, especially when full-energy peaks are not prominent. This is common in detectors like plastic scintillators. Current methods for calibrating these detectors often require manual adjustments. To address this, we propose a new method called the convolution model. This model accurately calibrates the energy-channel relationship of the Compton edge in various detectors. We tested it with plastic scintillator BC408, NaI crystal, and LaBr$_3$ crystal. Using ${}^{137}$Cs radioactive sources, we calibrated NaI and LaBr$_3$ detectors using full-energy peaks, then applied the convolution model to fit the Compton edge. Our results show errors within 1\% when compared to full-energy peak calibration.

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex congestion patterns, making existing approaches unsuitable for repeatable and scalable analysis of traffic waves in these data. This paper makes a first step towards addressing this challenge by introducing an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. It enables the measurement of wave properties at scale. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. Our results show insights on the complexity of traffic waves. Traffic waves can bifurcate and merge at a scale that has never been observed or described before. The clustering analysis of all the identified wave components reveals the different topological structures of traffic waves. We explored that the wave merge or bifurcation points can be explained by spatial features. The gallery of all the identified wave topologies is demonstrated at this https URL.

Transcranial ultrasound imaging is usually limited by skull-induced attenuation and high-order aberrations. By using contrast agents such as microbubbles in combination with ultrafast imaging, not only can the signal-to-noise ratio be improved, but super-resolution images down to the micrometer scale of the brain vessels can also be obtained. However, ultrasound localization microscopy (ULM) remains affected by wavefront distortions that limit the microbubble detection rate and hamper their localization. In this work, we show how ultrasound matrix imaging, which relies on the prior recording of the reflection matrix, can provide a solution to these fundamental issues. As an experimental proof of concept, an in vivo reconstruction of deep brain microvessels is performed on three anesthetized sheep. The compensation of wave distortions is shown to markedly enhance the contrast and resolution of ULM. This experimental study thus opens up promising perspectives for a transcranial and nonionizing observation of human cerebral microvascular pathologies, such as stroke.

Free-space wavefront manipulation devices have emerged as powerful platforms for advanced optical information systems. In response to the challenges posed by the exponential growth of optical information, optical multiplexing and dynamic reconfigurable devices are being actively explored to the enhance system capacity. Among them, coarse-grained mechanically reconfigurable mechanism offers a cost-effective and low-complexity approach for capacity enhancement. However, the channel numbers achieved in current studies are insufficient for practical applications because of inadequate mechanical transformations and suboptimal optimization models. In this article, a diffractive magic cube network (DMCN) is proposed to advance the multiplexing capacity of mechanically reconfigurable system. We utilized the diffractive deep neural network (D2NN) model to jointly optimize the subset of channels generated by the combination of three mechanical operations, permutation, translation, and rotation. The 144-channel holograms, 108-channel single/double focus, 60-channel single/multi-mode OAM beam generation were experimentally demonstrated using diffractive optical elements (DOEs). An equivalent connectivity law was formulated to improve model scalability. Our strategy not only provides a novel paradigm to improve system capacity to super-high level with low crosstalk, but also paves the way for new advancements in optical storage, computing, communication, and photolithography.

We examine the interaction between floating cylindrical objects and surface waves in the gravity regime. Since the impact of resonance phenomena associated with floating bodies, particularly at laboratory scales, remains underexplored, we focus on the influence of the floats' resonance frequency on wave emission. First, we study the response of floating rigid cylinders to external mechanical perturbations. Using an optical reconstruction technique to measure surface wave fields in both space and time, we study the natural resonance frequency of floats with different sizes. The results indicate that the resonance frequency is influenced by the interplay between the cylinder geometry and the solid-to-fluid density ratio. Second, these floating objects are placed in an incoming wave field. These experiments demonstrate that floats diffract incoming waves, while radiating secondary waves that interfere with the incident wavefield. Minimal wave generation is observed at resonance frequencies. These findings can provide insights for elucidating the behavior of larger structures, such as sea ice floes, in natural wave fields.

When waves propagate through a complex medium, they undergo several scattering events. This phenomenon is detrimental to imaging, as it causes full blurring of the image. Here we describe a method for detecting, localizing and characterizing any scattering target embedded in a complex medium. We introduce a fingerprint operator that contains the specific signature of the target with respect to its environment. When applied to the recorded reflection matrix, it provides a likelihood index of the target state. This state can be the position of the target for localization purposes, its shape for characterization or any other parameter that influences its response. We demonstrate the versatility of our method by performing proof-of-concept ultrasound experiments on elastic spheres buried inside a strongly scattering granular suspension and on lesion markers, which are commonly used to monitor breast tumours, embedded in a foam mimicking soft tissue. Furthermore, we show how the fingerprint operator can be leveraged to characterize the complex medium itself by mapping the fibre architecture within muscle tissue. Our method is broadly applicable to different types of waves beyond ultrasound for which multi-element technology allows a reflection matrix to be measured.

The calibration of the CR39 and Makrofol Nuclear Track Detectors of the MoEDAL experiment at the CERN-LHC was performed by exposing stacks of detector foils to heavy ion beams with energies ranging from 340 MeV/nucleon to 150 GeV/nucleon. After chemical etching, the base areas and lengths of etch-pit cones were measured using automatic and manual optical microscopes. The response of the detectors, as measured by the ratio of the track-etching rate over the bulk-etching rate, was determined over a range extending from their threshold at Z/$\beta\sim7$ and $\sim50$ for CR39 and Makrofol, respectively, up to Z/$\beta\sim92$

Rays and skates tend to have different fin kinematics depending on their proximity to a ground plane such as the seafloor. Near the ground, rays tend to be more undulatory (high wavenumber), while far from the ground, rays tend to be more oscillatory (low wavenumber). It is unknown whether these differences are driven by hydrodynamics or other biological pressures. Here we show that near the ground, the time-averaged lift on a ray-like fin is highly dependent on wavenumber. We support our claims using a ray-inspired robotic rig that can produce oscillatory and undulatory motions on the same fin. Potential flow simulations reveal that lift is always negative because quasisteady forces overcome wake-induced forces. Three-dimensional flow measurements demonstrate that oscillatory wakes are more disrupted by the ground than undulatory wakes. All these effects lead to a suction force toward the ground that is stronger and more destabilizing for oscillatory fins than undulatory fins. Our results suggest that wavenumber plays a role in the near-ground dynamics of ray-like fins, particularly in terms of dorsoventral accelerations. The fact that lower wavenumber is linked with stronger suction forces offers a new way to interpret the depth-dependent kinematics of rays and ray-inspired robots.

The error function of real argument can be uniformly approximated to a given accuracy by a single closed-form expression for the whole variable range either in terms of addition, multiplication, division, and square root operations only, or also using the exponential function. The coefficients have been tabulated for up to 128-bit precision. Tests of a computer code implementation using the standard single- and double-precision floating-point arithmetic show good performance and vectorizability.

We present the first three-dimensional theoretical model of microparticle-assisted super-resolution imaging, enabling accurate simulation of virtual image formation. The model reveals that accounting for partial spatial coherence of illumination is a fundamental prerequisite for achieving super-resolution. We also propose a novel illumination strategy based on suppressing the normal component of incident light, which enhances image contrast and resolution. The results establish a consistent wave-optical framework that reproduces experimentally observed subwavelength imaging and clarifies the underlying physical mechanisms.

Optical path length and dispersion matching in both measurement and reference arms of an OCT system is critical for achieving bandwidth-limited axial resolution. To minimize or eliminate dispersion mismatch, most, if not all, fiber-based OCT realisations employ a reference arm configuration that is as closely identical to the measurement arm as possible. This typically includes a collimator, dispersion compensating material (or sometimes a set of lenses), as well as a mirror (or retro-reflector) mounted on a translation stage. However, this solution makes the total instrument cost higher and the setup bulkier than necessary and it also renders the reference arm mechanically unstable. Here, a simple yet robust, low-cost reference arm setup is presented and its ability to compensate for measurement arm dispersion is demonstrated. We use a single-mode fiber cleaved and polished perpendicular to the fiber axis to construct the reference arm. The length and material of the fibre is determined by considering the optical path length and dispersion of the measurement arm. Experimental images demonstrate the operation of the novel reference arm in our Swept-source Optical Coherence Tomography.

Low-noise gigahertz (GHz) frequencies sources are essential for applications in signal processing, sensing, and telecommunications. Surface acoustic wave (SAW) resonator-based oscillators offer compact form factors and low phase noise due to their short mechanical wavelengths and high quality (Q) factors. However, their small footprint makes them vulnerable to environmental variation, resulting in their poor long-term frequency stability. Injection locking is widely used to suppress frequency drift of lasers and oscillators by synchronizing to an ultra-stable reference. Here, we demonstrate injection locking of a 1-GHz SAW phononic crystal oscillator, achieving 40-dB phase noise reduction at low offset frequencies and unperturbed low noise at large offset frequencies. Compared to a free-running SAW oscillator, which typically exhibits frequency drifts of several hundred hertz over minutes, the injection-locked oscillator reduces the frequency deviation to below 0.35 Hz. We also investigate the locking range and oscillator dynamics in the injection pulling region. The demonstrated injection-locked SAW oscillator could find applications in high-performance portable telecommunications and sensing systems.

Solid state phonon detectors used in the search for dark matter and coherent neutrino nucleus interactions (CE$\nu$NS) require excellent energy resolution (eV-scale or below) and low backgrounds. An unknown source of phonon bursts, the low energy excess (LEE), dominates other above-threshold backgrounds and generates excess shot noise from sub-threshold bursts. In this paper, we measure these phonon bursts for 12 days after cooldown in two nearly identical 1 cm$^2$ silicon detectors that differ only in the thickness of their substrate (1 mm vs. 4 mm thick). We find that both the channel-correlated shot noise and near-threshold shared LEE relax with time since cooldown. Additionally, both the correlated shot noise and LEE rates scale linearly with substrate thickness. When combined with previous measurements of other silicon phonon detectors with different substrate geometries and mechanical support strategies, these measurements strongly suggest that the dominant source of both above and below threshold LEE is the bulk substrate. By monitoring the relation between bias power and excess phonon shot noise, we estimate that the energy scale for sub-threshold noise events is $0.68 \pm 0.38$ meV. In our final dataset, we report a world-leading energy resolution of 258.5$\pm$0.4 meV in the 1 mm thick detector. Simple calculations suggest that these silicon substrate phonon bursts are likely a significant source of quasiparticle poisoning in superconducting qubits operated in well shielded and vibration free environments.

Field-deployable integrated photonic devices co-packaged with electronics will enable important applications such as optical interconnects, quantum information processing, precision measurements, spectroscopy, and microwave generation. Significant progress has been made over the past two decades on increasing the functional complexity of photonic chips. However, a critical challenge that remains is the lack of scalable techniques to overcome thermal perturbations arising from the environment and co-packaged electronics. Here, we demonstrate a fully integrated scheme to monitor and stabilize the temperature of a high-Q microresonator on a Si-based chip, which can serve as a photonic frequency reference. Our approach relies on a thin-film metallic resistor placed directly above the microcavity, acting as an integrated resistance thermometer, enabling unique mapping of the cavity's absolute resonance wavelength to the thermometer's electrical resistance. Following a one-time calibration, the microresonator can be accurately and repeatably tuned to any desired absolute resonance wavelength using thermometry alone with a root-mean squared wavelength error of <0.8 pm over a timespan of days. We frequency-lock a distributed feedback (DFB) laser to the microresonator and demonstrate a 48x reduction in its frequency drift, resulting in its center wavelength staying within +-0.5 pm of the mean over the duration of 50 hours in the presence of significant ambient fluctuations, outperforming many commercial DFB and wavelength-locker-based laser systems. Finally, we stabilize a soliton mode-locked Kerr comb without the need for photodetection, paving the way for Kerr-comb-based photonic devices that can potentially operate in the desired mode-locked state indefinitely.

Inspired by the Turing test, we present a novel methodological framework to assess the extent to which a population of machines mirrors the philosophical views of a population of humans. The framework consists of three steps: (i) instructing machines to impersonate each human in the population, reflecting their backgrounds and beliefs, (ii) administering a questionnaire covering various philosophical positions to both humans and machines, and (iii) statistically analyzing the resulting responses. We apply this methodology to the debate on scientific realism, a long-standing philosophical inquiry exploring the relationship between science and reality. By considering the outcome of a survey of over 500 human participants, including both physicists and philosophers of science, we generate their machine personas using an artificial intelligence engine based on a large language model. We reveal that the philosophical views of a population of machines are, on average, similar to those endorsed by a population of humans, irrespective of whether they are physicists or philosophers of science. As compared to humans, however, machines exhibit a weaker inclination toward scientific realism and a stronger coherence in their philosophical positions. Given the observed similarities between the populations of humans and machines, this methodological framework may offer unprecedented opportunities for advancing research in the empirical social sciences by complementing human participants with their machine-impersonated counterparts.

A free-boundary, axisymmetric magnetohydrodynamic (MHD) equilibrium code, pyIPREQ, has been developed for Tokamak plasmas using finite difference and Green's function approach. The code builds upon the foundational frameworks of the PEST and IPREQ codes, introducing several enhancements and new capabilities. Notably, pyIPREQ supports the specification of limiter boundaries and enables the computation of key physical quantities. The code has also been extended to compute equilibria constrained by a prescribed magnetic axis position, which is particularly useful when such information can be inferred from the diagnostics data. In addition, pyIPREQ includes functionality to address vertical instabilities, a requirement for accurately modeling elongated plasma configurations. Benchmarking has been carried out against published results and the original IPREQ code. Applications are demonstrated for ADITYA-U Tokamak experiments, where magnetic axis measurements are available, and predictions are also made for SST-1 and ADITYA-U Tokamaks under various operational scenarios.

Motion remains a key challenge in MRI, as both involuntary (e.g., head motion) and voluntary (e.g., joint motion) movement can degrade image quality or provide opportunities for dynamic assessment. Existing motion sensing methods, such as external tracking or navigator sequences, often require additional hardware, increase SAR, or demand sequence modification, which limits clinical flexibility. We propose a computationally efficient, self-navigated motion sensing technique based on spoke energy derived from 3D radial k-space data. Using the Fourier Slice and Parseval's theorems, spoke energy captures object-coil alignment and can be computed without altering the sequence. A sliding window summation improves robustness, and a second principal component analysis (2ndPCA) strategy yields a unified motion-sensitive signal. Beyond conventional head motion correction, we demonstrate the novel application of this method in enhancing dynamic 4D MRI of the ankle and knee under a continuous movement protocol. By sorting spokes based on position rather than time, we achieve motion-resolved reconstructions with improved anatomical clarity. This approach enables real-time motion detection and supports broader adoption of motion-aware dynamic MRI.

We present FastNet version 1.0, a data-driven medium range numerical weather prediction (NWP) model based on a Graph Neural Network architecture, developed jointly between the Alan Turing Institute and the Met Office. FastNet uses an encode-process-decode structure to produce deterministic global weather predictions out to 10 days. The architecture is independent of spatial resolution and we have trained models at 1$^{\circ}$ and 0.25$^{\circ}$ resolution, with a six hour time step. FastNet uses a multi-level mesh in the processor, which is able to capture both short-range and long-range patterns in the spatial structure of the atmosphere. The model is pre-trained on ECMWF's ERA5 reanalysis data and then fine-tuned on additional autoregressive rollout steps, which improves accuracy over longer time horizons. We evaluate the model performance at 1.5$^{\circ}$ resolution using 2022 as a hold-out year and compare with the Met Office Global Model, finding that FastNet surpasses the skill of the current Met Office Global Model NWP system using a variety of evaluation metrics on a number of atmospheric variables. Our results show that both our 1$^{\circ}$ and 0.25$^{\circ}$ FastNet models outperform the current Global Model and produce results with predictive skill approaching those of other data-driven models trained on 0.25$^{\circ}$ ERA5.

We report on a Nb$_3$Sn-coated low-beta superconducting radio frequency (SRF) cavity intended for accelerating ions. We aim to apply the cavity in ATLAS, our Argonne National Laboratory user facility for nuclear physics studies with ion beams in the energy range of 5-20 MeV/u. The Nb$_3$Sn-coated cavity, a 145 MHz quarter-wave optimized for ions moving with velocity $\beta$=v/c=0.08 exhibits an order-of-magnitude reduction in radiofrequency (RF) losses into helium at $4.4\,\mathrm{K}$ compared to a superconducting niobium (Nb) cavity at the same frequency and temperature. Experimentally measured fields are among the highest to date for any Nb$_3$Sn-coated cavity, reaching a peak surface magnetic field of 105 mT. We also present a practical solution to the problem of cavity frequency tuning. Tuning by mechanical deformation has been a challenge with Nb3Sn due to its brittle nature, however, using a set of techniques tailored to the properties of thin-film Nb$_3$Sn on Nb, we can repeatably tune the cavity to the ATLAS master clock frequency after it is cooled, while maintaining the excellent performance characteristics. The same Nb$_3$Sn cavity technology offers broad benefits for future ion accelerators.

Plasmas in various astrophysical systems are in non-equilibrium states as evidenced by direct in-situ measurements in the solar wind, solar corona and planetary environments as well as by indirect observations of various sources of waves and emissions in astrophysical systems. Specific are non-Maxwellian velocity distributions with suprathermal tails, for whose description the most-used are the Kappa (power-law) distributions. With this paper we introduce a modeling alternative for linear waves in plasmas described by another non-equilibrium model, namely the generalized Druyvesteyn distribution. This can reproduce not only high-energy tails, but also low-energy flat-tops of velocity distributions, like those of electrons associated with the Earth's bow shock and interplanetary shocks or of electrons in the solar transition region. We derive the corresponding dispersion relation for longitudinal waves in terms of the newly introduced Druyvsteyn dispersion function, numerically compute, for the isotropic case, the dispersion curves as well as damping rates, and provide analytical approximation in the limit of weak damping. Thereby, we provide a new modeling tool that facilitates the quantitative treatment of a variety of non-Maxwellian plasmas.

The FRIB heavy-ion accelerator, commissioned in 2022, is a leading facility for producing rare isotope beams (RIBs) and exploring nuclei beyond the limits of stability. These RIBs are produced via reactions between stable primary beams and a graphite target. Approximately 20-40 \% of the primary beam power is deposited in the target, requiring efficient thermal dissipation. Currently, FRIB operates with a primary beam power of up to 20 kW. To enhance thermal dissipation efficiency, a single-slice rotating graphite target with a diameter of approximately 30 cm is employed. The effective target region is a 1 cm-wide outer rim of the graphite disc. To achieve high RIB production rates, the areal thickness variation must be constrained within 2 \%. This paper presents physical thickness characterizations of FRIB production targets with various nominal thicknesses, measured using a custom-built non-contact thickness measurement apparatus.

Non-Hermitian physics has emerged as a rich field of study, with applications ranging from $PT$-symmetry breaking and skin effects to non-Hermitian topological phase transitions. Yet most studies remain restricted to small-scale or classically tractable systems. While quantum computing has shown strong performance in Hermitian eigenproblems, its extension to the non-Hermitian regime remains largely unexplored. Here, we develop a quantum algorithm to address general non-Hermitian eigenvalue problems, specifically targeting eigenvalues near a given line in the complex plane -- thereby generalizing previous results on ground state energy and spectral gap estimation for Hermitian matrices. Our method combines a fuzzy quantum eigenvalue detector with a divide-and-conquer strategy to efficiently isolate relevant eigenvalues. This yields a provable exponential quantum speedup for non-Hermitian eigenproblems. Furthermore, we discuss the broad applications in detecting spontaneous $PT$-symmetry breaking, estimating Liouvillian gaps, and analyzing classical Markov processes. These results highlight the potential of quantum algorithms in tackling challenging problems across quantum physics and beyond.

We describe the results of a haloscope axion search performed with an 11.7 T dipole magnet at CERN. The search used a custom-made radio-frequency cavity coated with high-temperature superconducting tape. A set of 27 h of data at a resonant frequency of around 8.84 GHz was analysed. In the range of axion mass 36.5676 $\mu$eV to 36.5699 $\mu$eV, corresponding to a width of 554 kHz, no signal excess hinting at an axion-like particle was found. Correspondingly, in this mass range, a limit on the axion to photon coupling-strength was set in the range between g$_{a\gamma}\gtrsim$ 6.2e-13 GeV$^{-1}$ and g$_{a\gamma}\gtrsim$ 1.59e-13 GeV$^{-1}$ with a 95% confidence level.

Affective polarization and increasing social divisions affect social mixing and the spread of information across online and physical spaces, reinforcing social and electoral cleavages and influencing political outcomes. Here, using individual survey data and aggregated and de-identified co-location and online network data, we investigate the relationship between partisan exposure and vote choice in the US by comparing offline and online dimensions of partisan exposure. By leveraging various statistical modeling approaches, we consistently find that partisan exposure in the physical space, as captured by co-location patterns, more accurately predicts electoral outcomes in US counties, outperforming online and residential exposures. Similarly, offline ties at the individual level better predict vote choice compared to online connections. We also estimate county-level experienced partisan segregation and examine its relationship with individuals' demographic and socioeconomic characteristics. Focusing on metropolitan areas, our results confirm the presence of extensive partisan segregation in the US and show that offline partisan isolation, both considering physical encounters or residential sorting, is higher than online segregation and is primarily associated with educational attainment. Our findings emphasize the importance of physical space in understanding the relationship between social networks and political behavior, in contrast to the intense scrutiny focused on online social networks and elections.

The lubricated motion of an object near a deformable boundary presents striking subtleties arising from the coupling between the elasticity of the boundary and lubricated flow, including but not limited to the emergence of a lift force acting on the object despite the zero Reynolds number. In this study, we characterize the hydrodynamic forces and torques felt by a sphere translating in close proximity to a fluid interface, separating the viscous medium of the sphere's motion from an infinitely-more-viscous medium. We employ lubrication theory and perform a perturbation analysis in capillary compliance. The dominant response of the interface owing to surface tension results in a long-ranged interface deformation, which leads to a modification of the forces and torques with respect to the rigid reference case, that we characterise in details with scaling arguments and numerical integrations.

In the present paper we consider linear and isotropic Maxwell equations with inhomogeneous interface conditions. We discretize the problem with the discontinuous Galerkin method in space and with the leapfrog scheme in time. An analytical setting is provided in which we show wellposedness of the problem, derive stability estimates, and exploit this in the error analysis to prove rigorous error bounds for both the spatial and full discretization. The theoretical findings are confirmed with numerical experiments.

Nonlinear dynamical systems are ubiquitous in nature and they are hard to forecast. Not only they may be sensitive to small perturbations in their initial conditions, but they are often composed of processes acting at multiple scales. Classical approaches based on the Lyapunov spectrum rely on the knowledge of the dynamic forward operator, or of a data-derived approximation of it. This operator is typically unknown, or the data are too noisy to derive its faithful representation. Here we propose a new data-driven approach to analyze the local predictability of dynamical systems. This method, based on the concept of recurrence, is closely linked to the well-established framework of local dynamical indices. When applied to both idealized systems and real-world datasets arising from large-scale atmospheric fields, our new approach proves its effectiveness in estimating local predictability. Additionally, we discuss its relationship with other local dynamical indices, and how it reveals the scale-dependent nature of predictability. Furthermore, we explore its link to information theory, its extension that includes a weighting strategy, and its real-time application. We believe these aspects collectively demonstrate its potential as a powerful diagnostic tool for complex systems.

High-energy nuclear collisions have recently emerged as a promising ``imaging-by-smashing'' approach that may reveal the intrinsic shapes of atomic nuclei. Here, I outline a conceptual framework for this technique, explaining how nuclear shapes are encoded during quark-gluon plasma formation and evolution, and how they can be decoded from final-state particle distributions. I highlight the method's potential to advance our understanding of both nuclear structure and quark-gluon plasma physics.

The ripening dynamics in passive systems is governed by the theory of Lifshitz-Slyozov-Wagner (LSW). Here, we present an analog theory for reversed ripening in active systems. To derive the dynamic theory for the droplet size distribution, we consider a minimal ternary emulsion with one active reaction, leading to one conserved quantity. Even for cases where single droplets constantly grow, coupling many droplets via the conserved density in the far field leads to a self-organized reversal of ripening and, thus, a monodisperse emulsion. For late times, we find a scaling ansatz leading to the collapse of the rescaled size distributions, different from the LSW theory. This scaling behavior arises from a stable fixed point in the single droplet dynamics and may capture the late-time behavior of many active matter systems exhibiting reversed ripening.

Using spectrally correlated photon pairs instead of classical laser light and coincidence detection instead of light intensity detection, Quantum Optical Coherence Tomography (Q-OCT) outperforms classical OCT in several experimental terms. It provides twice better axial resolution with the same spectral bandwidth and it is immune to even-order chromatic dispersion, including Group Velocity Dispersion responsible for the bulk of axial resolution degradation in the OCT images. Q-OCT has been performed in the time domain configuration, where one line of the two-dimensional image is acquired by axially translating the mirror in the interferometer's reference arm and measuring the coincidence rate of photons arriving at two single-photon-sensitive detectors. Although successful at producing resolution-doubled and dispersion-cancelled images, it is still relatively slow and cannot compete with its classical counterpart. Here, we experimentally demonstrate Q-OCT in a much faster Fourier-domain configuration, theoretically proposed in 2020, where the reference mirror is fixed and the joint spectrum is acquired by inserting long fibre spools in front of the detectors. We propose two joint spectrum pre-processing algorithms, aimed at compensating resolution-degrading effects within the setup. While the first one targets fibre spool dispersion, an effect specific to this configuration, the other one removes the effects leading to the weakening of even-order dispersion cancellation, the latter impossible to be mitigated in the time-domain alternative. Being additionally contrasted with both the time-domain approach and the conventional OCT in terms of axial resolution, imaging range and multilayer-object imaging, Fourier-domain Q-OCT is shown to be a significant step forward towards a practical and competitive solution in the OCT arena.

An impurity immersed in a Bose condensate can form a quasiparticle known as a Bose polaron. When the impurity-boson interaction is short-ranged, the quasiparticle properties can be characterized in terms of the impurity-boson scattering length $a_{\mathrm{IB}}$ and the condensate coherence length $\xi$, a universal description that remains valid irrespective of the bath density $n_0$. Long-ranged interactions -- such as provided by Rydberg or ionic impurities -- introduce an effective interaction range $r_{\mathrm{eff}}$ as the third length scale. These competing length scales raise the question of whether a universal description remains valid across different bath densities. In this study, we discuss the quasiparticle nature of long-range impurities and its dependence on the length scales $n_0^{-1/3}$, $r_\mathrm{eff}$, and $\xi$. We employ two complementary theories -- the coherent state Ansatz and the perturbative Gross-Pitaevskii theory -- which incorporate beyond-Fröhlich interactions. We derive an analytical expression for the beyond-Fröhlich effective mass for a contact interaction and numerically compute the effective mass for long-range impurities. We argue that the coupling parameter $|a_{\mathrm{IB}}|n_0^{1/3}$ remains the principal parameter governing the properties of the polaron. For weak ($|a_\mathrm{IB}|n_0^{1/3}\ll 1$) and intermediate ($|a_\mathrm{IB}|n_0^{1/3}\simeq 1$) values of the coupling parameter, long-range impurities in a Bose condensate are well-described as quasiparticles with a finite quasiparticle weight and a well-defined effective mass. However, the quasiparticle weight becomes significantly suppressed as the effective impurity volume is occupied by an increasing number of bath particles ($r_{\mathrm{eff}}n_0^{1/3} \gg 1$).

The shallow water equations often assume a constant velocity profile along the vertical axis. However, this assumption does not hold in many practical applications. To better approximate the vertical velocity distribution, models such as the shallow water moment expansion models have been proposed. Nevertheless, under non-slip bottom boundary conditions, both the standard shallow water equation and its moment-enhanced models struggle to accurately capture the vertical velocity profile due to the stiff source terms. In this work, we propose modified shallow water equations and corresponding moment-enhanced models that perform well under both non-slip and slip boundary conditions. The primary difference between the modified and original models lies in the treatment of the source term, which allows our modified moment expansion models to be readily generalized, while maintaining compatibility with our previous analysis on the hyperbolicity of the model. To assess the performance of both the standard and modified moment expansion models, we conduct a comprehensive numerical comparison with the incompressible Navier--Stokes equations -- a comparison that is absent from existing literature.

Electron channeling contrast imaging (ECCI) is a scanning electron microscopy (SEM) based technique that enables bulk-sample characterization of crystallographic defects (e.g. dislocations, stacking faults, low angle boundaries). Despite its potential, ECCI remains underused for quantitative defect analysis as compared to transmission electron microscope (TEM) based methods. Here, we overcome barriers that limit the use of ECCI including optimizing signal-to-noise contrast, precise determination of the incident beam vector with calibrated and easy to use simulations and experimental selected area electron channeling patterns (SA-ECP). We introduce a systematic ECCI workflow, alongside a new open-source software tool (AstroECP), that includes calibration of stage tilting, SA-ECP field of view, and the energy that forms the ECP/ECCI contrast using dynamical simulations. The functionality of this workflow is demonstrated with case studies that include threading dislocations in GaAs and the cross validation of precession based ECCI-contrast, which is otherwise known as Electron Channeling Orientation Determination (eCHORD). To assist the reader, we also provide best practice guidelines for ECCI implementation to promote high-resolution defect imaging in the SEM.

One-dimensional Bose gases present an interesting setting to study the physics of Bose polarons, as density fluctuations play an enhanced role due to reduced dimensionality. Theoretical descriptions of this system have predominantly relied on contact pseudopotentials to model the impurity-bath interaction, leading to unphysical results in the strongly coupled limit. In this work, we analytically solve the Gross-Pitaevskii equation, using a square well potential instead of a zero-range potential, for the ground-state wave function of a static impurity. We compute perturbative corrections arising from infinitesimally slow impurity motion. The polaron energy and effective mass remain finite in the strongly coupled regime, in contrast to the divergent behavior obtained using a contact potential. In this limit, we characterize the polaron properties in terms of the dimensionless ratio $\bar{w}\equiv w/\xi$ between the interaction range $w$ of the impurity-bath potential and the coherence length $\xi$ of the Bose gas. The effective mass exhibits a $1/\bar{w}$ scaling. The energy of the attractive polaron scales as $-1/\bar{w}^3$, whereas the repulsive polaron features subleading corrections to the dark soliton energy at the order $\bar{w}^3$.

We propose a gravitational wave detector based on ultrastable optical cavities enabling the detection of gravitational wave signals in the mostly unexplored $10^{-5}-1$ Hz frequency band. We illustrate the working principle of the detector and discuss that several classes of gravitational wave sources, both of astrophysical and cosmological origin, may be within the detection range of this instrument. Our work suggests that terrestrial gravitational wave detection in the milli-Hz frequency range is potentially within reach with current technology.

A shift of paradigm to obtain (sub-)Kelvin environment is currently on-going with the democratization of cryogen-free cryocoolers, boosted by their easy-to-use and continuous operation without the need of liquid helium whose cost and scarcity globally increase. Thanks to their large sample space and cooling power, they can host a superconducting magnet and are an adapted platform for quantum technologies, material science, low temperature detectors and even medical fields. The drawback is that this type of system is inherently based on gas compression that induces a certain level of vibrations and electromagnetic perturbations, which can potentially prevent the determination of low amplitude signals or spoil their stability. In this paper we demonstrate that pulse-tube based cryocoolers can be used for electrical precision measurements, using a commercial cryomagnetic system combined with our home-made a coaxial cryoprobe. In particular, parts-per-billion level of measurement uncertainties in resistance determination, based on quantum Hall resistance standards, is achievable at the level of state-of-the-art measurements involving conventional cryostats based on liquid helium. We performed an extensive characterization of the cryomagnetic system to determine the level of vibrations and electromagnetic perturbations, and revealed that although the magnetic field has a drastic effect on the noise level, only marginal interplays on the measurement are observed as long as the working frequencies of the instrumentation are not in the vicinity of the ones of the perturbations. The set of characterization measurements presented here are easily implementable in laboratories, which can help to determine the vibrations and electromagnetic pollution generated by any cryocooler.