Papers

A coupled analytical framework is developed to predict the internal boundary layer (IBL) height, wall shear stress and the mean velocity profile downstream of a surface roughness transition in a neutral atmospheric boundary-layer. The new model combines a three-layer analytical velocity formulation with a modified diffusion analogy for the IBL growth rate. The diffusion analogy links the growth rate of the IBL to turbulent diffusion and to mean vertical advection. Our formulation rectifies physical inconsistencies in a previous model that assumed that the turbulent diffusion is controlled only by the upstream surface conditions and that the characteristic streamwise velocity difference caused by streamline displacement is independent of downstream distance from the roughness transition. Key model parameters, namely the turbulent diffusion coefficient and the eddy viscosity augmentation coefficient, are modelled as functions of the upstream-to-downstream aerodynamic roughness length ratio. The coupled model, along with three other analytical models, is tested against twelve datasets covering a wide range of roughness ratios, including wind-tunnel experiments and large-eddy simulations. The new model accurately predicts wall shear stress, mean velocity, and IBL height for all combinations of upstream and downstream roughness values evaluated, for both smooth-to-rough and rough-to-smooth transitions.

Terahertz time-domain spectroscopy (THz-TDS) provides direct access to both spectral information and time-of-flight features, making it attractive for dynamic sensing. However, conventional high-speed THz-TDS methods typically rely on a fixed delay trajectory, limiting their ability to selectively acquire only the relevant temporal window while tracking target motion. Here, we demonstrate motion-tracking THz-TDS based on time-programmable frequency combs (TPFCs), in which the THz measurement window is actively locked to a moving time-domain waveform. By continuously modulating the phase-lock set point of one TPFC, we perform real-time apodized acquisition of a specific temporal window centered on the main THz peak at rates up to 308 Hz. Simultaneously, the residual peak position within the window is detected in real time and fed back to the other comb, enabling the measurement window to follow target displacements exceeding the original acquisition window. The feedback command together with the residual peak position further enables reconstruction of the relative target displacement. As a proof-of-concept demonstration, we use a moving gold mirror to simulate respiration- and heartbeat-related motion superimposed on a much larger displacement representing body motion, and successfully resolve the two vital-sign-related components from the measured displacement. This work extends arbitrary-delay control with TPFCs to THz-TDS and establishes a motion-compensated spectroscopy platform for simultaneous displacement tracking and THz waveform acquisition.

Machine learning capabilities are expanding into scientific domains at an accelerating pace. When applied to high-energy physics pattern discovery, they will generate candidates faster than traditional evaluation can absorb. ML finds patterns in past data it is inherently post hoc. Whether those patterns reflect structure or coincidence is unknowable at discovery time; this limitation applies equally to human and computational pattern-finding. What differs is scale: ML candidate generation is effectively unbounded, while human evaluation capacity remains fixed. When generation rate exceeds evaluation bandwidth, binary accept/reject degenerates to random sampling. Information-theoretically, the only response that preserves ranking under finite evaluation budget is stratification. By focusing on stratification rather than binary filtering, rule adjustments can be made retroactively, thresholds tuned as results accumulate, and evaluation bandwidth focused on top-ranked candidates. This paper attempts to codify those criteria, proposing seven computationally evaluable standards for stratifying ML-generated patterns. The goal is not to deliver verdicts but to prioritize which candidates merit pre-registration and longitudinal tracking. The framework preserves the essential paradigm: pattern plus theory equals potentially real physics. Patterns alone, however striking, remain candidates until theoretical understanding arrives. Making these criteria explicit enables prefiltering at scale while creating a collaborative resource rather than a competitive one. ML capabilities extend what physicists can search while preserving how physicists evaluate. We offer this provisional framework for community calibration, with the goal of developing validation infrastructure before the capability fully arrives.

Prediction-market probabilities increasingly appear in news coverage, yet little is known about which market movements become news or how much trading money sits behind the numbers journalists quote. Unlike a poll, a market price can be moved by anyone willing to trade, so the cost of manufacturing a number that circulates as news bears directly on the information environment. We link 173.7 million signed Polymarket trades to news coverage from 2024-2025. From 6,990 articles mentioning prediction-market venues, an LLM-based, human-validated matcher extracts 1,582 sentences quoting market odds and attributes 918 to the specific market whose price they cite. We then detect 44,976 price dislocations, movements of at least five percentage points backed by concentrated one-sided trading, and ask whether a market is cited more often afterward. In the days after a dislocation, a market's citation rate is about 33% higher than its matched baseline (log citation-rate ratio $\tau_{\mathrm{cite}}=0.283$, permutation $p=0.001$), robust to binary and Poisson count outcomes. Yet move size is not the strongest predictor of citation: prominence dominates (standardized $\beta=0.610$ vs. $\beta=0.159$ for move size). Finally, we combine the dollar flow behind a given price change with observed citation rates into a metric we call epistemic leverage, the dollars needed to move a market five points and have the move cited. It stays near \$0.7-1.0 million across prominence quintiles, because cheaper-to-move markets are proportionally less likely to be cited. The implied threat model centers not on the long tail of cheaply moved markets but on the few prominent markets newsrooms treat as informational infrastructure, where a seven-figure price of influence sits within the budgets of actors with a large stake in the quoted number. We release aggregate event-study data and validation materials.

Rotating magnetic microparticles are building blocks for field-driven assembly and microrobotic control. As the elementary interaction rule for larger assemblies, pair motion in these systems is governed not only by magnetic forcing, but also by hydrodynamic coupling and other long-range interactions. Here we develop a reduced framework for two synchronized rotating magnetic particles by resolving the interactions into radial components that change the interparticle distance and transverse components that rotate the line of centers. The competition between magnetic dipolar interaction, additional radial repulsion, and rotation-induced transverse coupling selects three pair-motion modes: rigid-body rotation, contact-separation rotation, and irreversible separation. We derive transition criteria for the rigid-body state, reversed orbital motion, and the separation boundary, and obtain an asymptotic solution for the separation dynamics. Lattice Boltzmann simulations of particles rotating near a wall provide a hydrodynamic realization of the model, in which inertial secondary flow generates radial repulsion and rotational flow produces transverse coupling. The resulting phase diagram in the physical $(\mathrm{Re},C_m)$ plane is consistent with the reduced-model predictions. These results provide design rules for programming elementary pair interactions in rotating magnetic-particle systems and may help guide the control of microrobotic assemblies.

The quest for autonomous alignment and maintenance of complex free-space optical systems is increasingly urgent for large-scale neutral-atom quantum processors. Leveraging a high-finesse optical cavity as a sensitive probe, we introduce a data-driven framework for structural diagnostics and closed-loop control that achieves cold-start convergence within tens of seconds. These results establish a rapid, scalable diagnostic-control methodology for autonomous alignment and continuous maintenance of complex free-space optical architectures.

Functional materials realize functionalities through pre-designed structures, whereas physical neural networks are trained by prescribing input-output behavior. Unifying these views would allow materials to acquire functions directly from dynamical tasks. Here we introduce an in situ local learning framework for dynamic network materials. A forward drive and a time-reversed adjoint error drive are applied to the same mechanical network, enabling the material to compute gradients of a time-domain loss through its own dynamics. The resulting update rules are local, compatible with the principle of physical learning. Each bond or node requires only co-located forward and adjoint fields. The method trains spring constants, nodal masses, bond damping coefficients and nodal damping coefficients, and programs dynamical functions including broadband wave cloaking, evanescent-wave imaging, passive transient output enhancement and vowel classification. These examples involve distinct physical mechanisms, such as wave scattering, near-field information transfer, dissipative non-normal dynamics and machine learning, but are obtained by simply changing the loss function, rather than leveraging expert knowledge. Therefore, dynamic network materials can act both as programmable matter and as physical neural networks, providing a route to material systems that learn temporal responses in situ.

The electromagnetic vacuum stress tensor in a system consisting of classical point-like atoms on a cubic lattice is derived directly from the electric field and, alternatively, using the known more phenomenological formula involving the (usually unknown) derivative of the dielectric tensor. The results agree. The stress tensor contains a nontrivial constant and therefore cannot be obtained from macroscopic electrodynamics alone. A system consisting of a mosaic of randomly oriented grains of a cubic crystal is used as a model of an amorphous solid. The electromagnetic stress tensor in this system is deduced by averaging the corresponding quantity in a cubic crystal over all orientations. Two components remain: the component in the direction of the electric field and the component perpendicular to it. The transverse component agrees with the analogous quantity for liquids derived by Peierls in an entirely different way.

This paper develops a phase-reduced description of transonic buffet, treating buffet as a stable autonomous fluid limit cycle that is weakly perturbed by harmonic structural motion. When combined with aerodynamic work, the phase reduction model yields closed-form expressions for the equivalent aerodynamic damping and stiffness, together with a unified description of phase locking, phase slipping and aeroelastic stability of the fluid-structural system. The resulting analysis provides new insight into the mechanisms governing aeroelastic instability in oscillatory flows, including aerodynamic damping maps, singular behaviour at zero detuning, and broad regions of subcritical aeroelastic instability.

This work compared the radiobiological responses of SAS oral squamous cell carcinoma cells and MCF-7 breast cancer cells following accelerator-based boron neutron capture therapy (BNCT). Neutrons were generated by bombarding a lithium target with proton beams, followed by moderation to obtain sufficient thermal neutrons for BNCT irradiation. Boronophenylalanine (BPA) was used as the boron delivery agent. BNCT-induced biological responses were evaluated by gamma-H2AX immunofluorescence staining, cell-cycle analysis, apoptosis analysis, and clonogenic survival assays. BNCT induced marked gamma-H2AX foci formation in both cell lines, indicating DNA damage-associated responses after irradiation. The two cell lines further showed distinct post-irradiation outcomes. SAS cells exhibited stronger clonogenic suppression and prominent G2/M accumulation, whereas MCF-7 cells showed sustained G0/G1 accumulation and delayed apoptosis. These results suggest that BNCT sensitivity is determined by both boron accumulation and cell-line-specific biological characteristics. This work provides experimental evidence highlighting the importance of tumor-dependent cellular responses in understanding and optimizing BNCT efficacy.

The Normalized Energy Density (NED) was introduced in Goto et al.(2011) as a quantity that, unlike the conventional energy, is conserved across material interfaces in a layered elastic medium. Its conservation was proved analytically only for a two-layer structure; for three or more layers it was supported by Monte Carlo simulation. I supply the missing proof for a general multi-layer (n-layer) structure in the 2D SH problem. The key step is to stop treating the angular frequency as a single variable and instead lift the layer phases to independent coordinates on the n-torus. The obstruction identified in the original paper—that the ratio of travel-time combinations is generically irrational, so that the periodicity argument used for two layers fails and disappears, and the proof reduces to Weyl's equidistribution theorem combined with n repetitions of the same elementary integral used in the two-layer case. I prove conservation in two independent settings that together cover all cases of practical interest: travel times that are linearly independent over Q, and commensurable travel times, including the equal-travel-time (Goupillaud) discretization. I also show that an arbitrary layered structure can be subdivided by reflectionless interfaces into one whose travel times take only d distinct, Q-independent values, where d is the dimension of the Q-span of the original travel times; this places the two proved cases at the endpoints d=1 and d=n and isolates the remaining open case 1<d<n in a canonical form.

Estimation of seismic attenuation (inverse quality factor) is important for gas reservoir prediction. Two key issues in seismic attenuation estimation are the development of a physically consistent reflection coefficient equation and stable estimation of seismic attenuation from seismic data. To address these issues, this study, within the framework of isotropic linear viscoelastic media, starts from the Kramers-Kronig relations and expresses the viscoelastic stiffness matrix as a function of seismic attenuation. Under the assumptions of weak attenuation and small elastic and attenuation contrasts across the interface, a frequency-dependent PP-wave reflection coefficient equation explicitly containing seismic attenuation terms is derived using scattering theory. The derived reflection coefficient equation not only satisfies the causality constraint, but also preserves a compact mathematical form. Based on this reflection coefficient equation, seismic attenuation is estimated within the framework of frequency-dependent AVO inversion. Synthetic seismic data tests show that the estimated P-wave attenuation attribute is sensitive to variations in reservoir gas saturation, with reservoirs of higher gas saturation exhibiting stronger P-wave attenuation anomalies. Application to field seismic data further demonstrates that the P-wave attenuation anomalies agree well with the gas saturation log and effectively identify high gas saturation reservoirs. This study provides a new approach for extracting P-wave attenuation information from seismic data and achieving high resolution prediction of gas reservoirs.

Compact delay elements remain a central challenge in photonic neuromorphic processors. Here, we demonstrate an optoelectronic delayed-feedback reservoir computer that incorporates a foundry-fabricated silicon nitride (Si$_3$N$_4$) true-time-delay circuit within its feedback loop. Eight cascaded Archimedean spirals provide a \SI{1.76}{\metre} on-chip optical path and a calculated passive group delay of \SI{11.63}{\nano\second}. At a feedback gain of $G=0.0223$, the system classifies sinusoidal and square waveforms without error (word error rate, WER\,$=$\,0 across all cross-validation folds), predicts the Mackey–Glass chaotic series with a best-fold normalized mean-square error (NMSE) of $0.0082$, and performs nine-class Japanese Vowels speaker classification with WER\,$=$\,0.0898. We also introduce a subcarrier phase-encoding method that maps the calculated $0.232\pi$ spiral-to-reference phase contrast onto the measured reservoir states through deliberate aliasing, achieving NMSE\,$=$\,0.0767 and WER\,$=$\,0 without increasing the insertion-loss-limited feedback gain. These results show that on-chip propagation delay in Si$_3$N$_4$ can operate as a functional component of an optoelectronic reservoir computer and motivate lower-loss, more-integrated implementations.

Simple synthesis of graphitic compounds, their high conductivity, and integrability with other materials motivate the effort toward graphite-based thermoelectric generators. At the same time, semimetallic nature of graphite and graphene results in nearly-zero thermopower due to electron-hole compensation. Here, we observe large transverse thermopower in chemically derived multilayer graphene films with strong fluctuations of thickness and carrier density at low magnetic fields $B$. Using the scanning laser-induced heating of macroscopic film, we find that transverse (Nernst) thermoelectric voltage becomes comparable to the longitudinal thermoelectric voltage at the metal-doped graphene contact at $B^*\approx4$ mT and ambient conditions. Estimates of transverse thermopower $S_{xy}$ based on the known laser-induced temperature show that it is as large as $\sim 10$ $\mu$V/K at $B^*$, and raises in a sub-linear fashion to 250 $\mu$V/K at $B\approx315$ mT, the maximum field we reach with centimeter-sized permanent magnet. Extra increase in thermoelectric signal is achieved upon voltage measurement at Hall probes when the dc field lines are co-directional with local Nernst current. Our results show the promise of large-scale multilayer graphene for thermoelectricity generation.

High-resolution, high-sensitivity microwave (MW) magnetic field imaging is indispensable for non-destructive integrated circuit (IC) testing, radio-frequency device characterization, and spintronic research. Yet, the practical utility of these techniques is severely constrained by the pervasive challenge of isolating weak magnetic signatures from intense optical and electronic noise, which fundamentally limits both acquisition speed and detection sensitivity. Here, we overcome this barrier by introducing a wide-field imaging scheme based on an ensemble of diamond nitrogen-vacancy (NV) centers, synergistically combined with digital lock-in amplification (DLA). By exploiting digital demodulation, the DLA precisely extracts the MW-field response at a specific modulation frequency from background noise (e.g., laser intensity fluctuations), dramatically improving the signal-to-noise ratio (SNR). Consequently, our system attains a magnetic field sensitivity of 126 nT/$\sqrt(Hz)$. Critically, the unprecedented SNR permits a pixel dwell time of under one millisecond, allowing full-field images to be acquired within seconds-more than an order of magnitude faster than state-of-the-art NV-based wide-field techniques. This combination of speed, sensitivity, and micron-scale spatial resolution (1.6 $\mu$m) paves the way for quasi-real-time, non-invasive diagnostics of dynamic MW devices and integrated circuits.

Elastic buckling has explained shape formation in growing tissues, yet the role of tissue fluidity remains elusive. We derive a minimal fluidized growth-elasticity model as a nonlinear analogue of Maxwell rheology. Analysis of a growing strip reveals a different picture of growth-induced morphogenesis: rather than emerging at a critical stress, symmetry breaking develops continuously during growth. Fluidity regulates stress evolution, the rate of shape-symmetry breaking, and flow patterns, establishing it as an active regulator of morphogenesis beyond its intuitive role in stress relaxation.

We present the design and performance of an optical cryostat for testing silicon photomultipliers (SiPMs) with vacuum-ultraviolet (VUV) light. The cryostat operates within a temperature range of 120 to 340 K, achieving a temperature stability of 1.2 mK, measured in a rolling 10-minute window once settled within a liquid nitrogen fill-cycle, and 13.8 mK over a complete fill-cycle including the refill transient. The cryostat includes a VUV-transparent CaF$_2$ viewport with a 142 mm diameter clear aperture, which limits the optically accessible sample area to 158 cm$^2$. The viewport is integrated into a sealed dark box and optical table to facilitate light delivery. VUV illumination is achieved using a custom xenon scintillation lamp or a xenon flash lamp, with a motorized two-axis linear rail system ensuring precise light positioning over the sample. This paper details the cryostat and optical system design, as well as commissioning current-voltage (IV) measurements using Hamamatsu VUV-sensitive SiPMs from 165 to 295 K and a measurement of the photodetection efficiency at 165 K.

An approximate analytical solution for the unsteady lift of a thin airfoil with a general unsteady motion is derived from a viscous-flow perspective, where the wake vortex-sheet strength is given in an explicit convolution-type expression as an approximate solution of the Wagner integral equation. For validation, this analytical solution is applied to the Wagner and Theodorsen problems, giving the explicit integral forms of the Wagner and Theodorsen functions as the reduced cases. Further, this analytical solution is applied to the starting flow with a finite timescale in the generalized Wagner problem, revealing the self-similarity of the re-normalized circulatory lift coefficient and its equivalence to the re-normalized Wagner function in a finite time domain. More importantly, the self-similar Wagner effect is found in numerical simulation of the flow over a starting flat-plate airfoil at low Reynolds numbers even when the flow is moderately separated. This self-similarity represents the Reynolds-number-invariance.

The high-frequency (HF) band, ranging from 3 to 30 MHz, is widely used in a number of applications such as beyond-line-of-sight (BLOS) communications and over-the-horizon radar systems. The long-range propagation characteristics of the HF band rely on the Earth's ionosphere as a refracting medium to establish BLOS wireless links. However, traditional HF communications links often use channels with narrow bandwidths (typically 3 kHz), which limits the amount of data that can be transmitted. In this perspective article, we discuss the challenges of establishing wideband HF communications links with emphasis on the ionosphere, noise characteristics, antenna performance, and available bandwidth. Solar radiation drives ionospheric layering, where fluctuating electron densities determine usable frequencies that change with time of day, seasons, and latitude. Since background noise at HF decreases with increasing frequency, operating at the upper HF limit is advantageous. We present a noise prediction model following ITU Recommendation P.372-17 and highlight the role of high-directivity antennas such as Log-Periodic Dipole Arrays, in optimizing the signal-to-noise ratio of the link. Using ray tracing techniques, we model several representative wideband HF links that employ near-vertical-incidence skywave (NVIS) or long-range skywave modes over distances ranging from approximately 390 to 5,200 km. We examine the performance of these links for bandwidths up to 1 MHz and present their day and night ionograms, with transmitter and receiver locations spanning near-equatorial to high northern latitudes. The modeled maximum usable frequencies are corroborated against crowdsourced FT8 reception data and direct ionosonde measurements over the same period for links for which propagation data was available.

The attribution of the condition div A + (1/c^2) dPhi/dt = 0 has been under discussion since 1938 as a question of priority between Ludvig Lorenz and Hendrik Lorentz. This work argues that the problem is of a different nature, and that the discussion does not close because it has never been specified what is being attributed. Drawing on the three versions of the 1867 paper and on six further primary sources, it is documented that three separable acts have been designated under a single name: the obtaining of the relation between potentials, its imposition as a restriction on the admissible potentials, and its denomination as a gauge. It is further documented that in none of the three versions of the 1867 paper does the word potential appear, that the magnetic field does not figure in it as a quantity of the theory, and that the vocabulary in which that paper is described today enters the literature after it was written. It is concluded that the disjunction between the two denominations, posed without specifying which of the three acts is being attributed, is ill formed.

In situ adjoint training extracts parameter gradients directly from measurement, but has so far been limited to reciprocal or restricted systems. Here, we introduce the physical counterpart of the policy gradient theorem: a stochastic-adjoint gradient estimator that lifts these constraints by trading reciprocity for nondegenerate diffusion. As validation, we train a nonlinear resonator network, whose own dynamics supply the policy, against antagonistic temporal modulations with gradients from measured stochastic trajectories alone, without finite differences or a separate adjoint experiment.

We develop a continuous event weighting framework for resonance searches that uses auxiliary event information without dividing the data into increasingly fine sensitivity categories. A hard selection is a binary weight, finite categories correspond to piecewise constant weights, and in the background dominated limit their fine partition approaches a continuous signal to background density ratio weight. The construction retains auxiliary discrimination in a single physically interpretable resonance spectrum, with calculable statistical variance and direct recovery of the selected signal yield. One million fully explicit event level pseudoexperiments using Gaussian, Gamma, and Beta auxiliary observables verify yield recovery, uncertainty propagation, and frequentist coverage while substantially improving discovery sensitivity. The method is further tested on publicly available CMS Run2016G and Run2016H diphoton Open Data as an independent real data benchmark, using detector level gluon fusion Higgs simulation only as the signal reference. This is a methodological test of the framework, not a CMS Higgs analysis. The results demonstrate a practical route to exploiting continuous auxiliary information without proliferating sensitivity driven event categories.

Kinematic fitting is widely used in particle physics experiments as a powerful tool to improve experimental resolutions, to suppress background and to provide selection criteria for the identification of specific reactions. Kinematic fitting methods, however, typically assume that the fitted quantities follow Gaussian distributions; an assumption which does not always hold. This is particularly true for energy measurements from electromagnetic calorimeters. This issue can largely be overcome by performing a transformation of the non-Gaussian variables into variables which follow a Gaussian distribution. The new adapted kinematic fitting procedure allows to better account for the non-Gaussian nature of the measured calorimeter energies, at the same time improving the accuracy and robustness of the kinematic fit. This leads to improved pull and confidence-level distributions of the kinematic fit as well as improved invariant mass distributions and signal-to-background ratios in the final event sample.

Background and objective. Carotid Doppler ultrasound is the most widely available bedside probe of cerebral haemodynamics, and machine-learning classifiers fed carotid velocity spectra can localise intracranial occlusions—but at the price of black-box models, thousands of training samples, and fragility to anatomical variants. We ask whether the same information can be obtained from an interpretable physical model. Methods. We formulate a linear frequency-domain one-dimensional model of a 26-segment circle-of-Willis network in which each cardiac harmonic propagates on a Womersley transmission line and is scattered independently by a lesion. The model is validated against a nonlinear one-dimensional solver and three-dimensional stenosis computations at two severities, coupled to a physics-based synthetic spectral-Doppler pipeline, and used to generate a 9{,}600-case virtual cohort for occlusion-localisation classification with physics-guided features. Results. Mean flow divisions agree with the nonlinear solver to three significant figures, harmonic magnitudes within a ratio of 0.90–1.04, and carotid waveforms to 6.1\,% in relative $L_2$ norm after a single kinematic area correction. The benchmark reproduces healthy flow splits within 1–9\,%, collateral-channel flows within 11–16\,%, and the collateral-pathway hierarchy. Physics-guided harmonic-ratio features localise twelve lesion classes at 95.1\,% accuracy from five training samples per class—over an order of magnitude fewer than waveform-driven convoluted neural networks; augmentation partially restores robustness to measurement noise and anatomical variants. Conclusions. The framework explains the physical origin of the empirically discriminative 2–12\,Hz Doppler band and makes occlusion localisation interpretable and sample-efficient.

Interstitial flow plays a key role in drug delivery, angiogenesis, cancer, and edema. Recent studies have identified connected interstitial pathways that can transport material over long distances. However, the spatial scale of physiological interstitial flow remains unclear: despite the existence of connected pathways, flow may be dominated by nearby vascular filtration or extend over longer distances. Here, we identify organ-scale interstitial flow patterns in the chick yolk sac and elucidate the mechanisms that determine their spatial scale. The yolk sac contains an organ-scale interstitial region that enables large-scale flow patterns to be identified without truncation. Our approach combines experimental imaging with computational modeling of coupled blood and interstitial flow in the whole yolk sac. Scaling analysis identifies a hydraulic conductivity ratio that controls the transition from small-scale to organ-scale flow. In organ-scale patterns, we find interstitial flow speed to be substantially greater than the transvascular flow speed. Mechanistically, the organ-scale patterns arise from the spatial segregation of the leakage and drainage of interstitial fluid from the vessels. These findings have important implications for biological transport by interstitial flow, suggesting that flow-mediated cues may be sensed locally but generated nonlocally.

In 1911 Einstein suggested that photons which pass close to the Sun are deflected by its effective mass which would apparently shift the positions of stars by up to one arcsecond, something that should be observable by astronomers during solar eclipses. Einstein`s ideas including the later doubling of the value due to relativistic space curvature met with enthusiastic responses from astronomers. The history of these efforts, their motivations and interactions with Einstein is presented here, beginning with the expedition that failed due to WWI of the young Erwin Freundlich in 1914 and ending with the unsuccessful Potsdam campaign to Öland (Sweden) in 1954. A newly found conference photograph from the first post-war meeting of the Astronomische Gesellschaft in 1921 shows Albert Einstein as a new member of the AG on the Telegraphenberg in Potsdam.

Fringe online message boards are often studied in the context of the extreme ideology that they produce. So far, however, not much of this research has focused on direct real-world harm in the all-too-common form of collective harassment. We directly analyze the complex dynamics of KiwiFarms, an online message board dedicated largely to the harassment of individuals from vulnerable communities. We conduct exploratory analyses of the hyperlink structure of the platform and linguistic changes over time, and prospective modeling of thread size. After establishing this broader picture of the complex traits of the system, we observe the temporal evolution of community-specific vocabulary, finding that the community's framing of their harassment targets persistently evokes more danger in the early 2020s than the late 2010s. We lastly find that early thread-growth behavior is predictive of longer-term thread virality. We discuss the implications for broader understanding of toxic online behavior and threat assessment, and make the case for studying fringe platforms as complex systems with significant societal impact.

Inverse design has emerged as a powerful strategy for realizing compact photonic devices; however, its application to logic operations remains constrained by challenges in physical interpretability, architectural generality, and experimental validation. This work introduces an experimentally validated, unified, and physics-driven inverse design framework that implements all fundamental Boolean logic gates within a single silicon photonic architecture. Devices are fabricated within a 2 x 2 um^2 design region on a silicon-on-insulator platform, representing one of the smallest areas reported for photonic logic elements. By integrating amplitude, phase, and energy conservation into a composite figure of merit, the proposed approach enables direct control over constructive and destructive interference. Consequently, all logic functions, including XOR and three-input NAND/NOR operations, are achieved using a standardized configuration with two logical inputs and a bias port. Experimental results exhibit good agreement with numerical simulations across the C-band, confirming both the predictive accuracy and fabrication robustness of the method. Performance benchmarking reveals competitive contrast ratios compared to previous implementations, while providing a unified and scalable design strategy. These findings establish a physically interpretable and experimentally validated paradigm for inverse-designed photonic logic, advancing the development of compact, integrated optical computing systems.

Generative AI can produce perfect solutions to standard physics homework, weakening the connection between submitted work and knowledge a student can use independently. We report a redesign of the introductory nuclear and particle physics course at Ruhr University Bochum, in which AI was permitted and encouraged alongside traditional tools on unusually difficult, research-shaped assignments. We found that most students remained engaged with the assignments throughout the course and produced ambitious work, while the course did not reliably secure its foundational objective. We offer this mixed experience as a reference point for discussion.

In general geometry, plasma fluid equations include nonlinear geometric sources associated with fictitious forces, which pose significant challenges to computer simulations. We reformulate the plasma fluid hierarchy to rigorously preserve geometry and conservation properties critical to numerical simulations, while concealing the geometric sources. In their discrete form, the reformulated models conserve mass, angular momentum, and energy naturally, by simple analogy with the continuum equations. These conservation properties have minimal requirements in discrete space, namely, the anti-symmetry of the first derivative and the orthogonality of the scalar and cross products. By decoupling magnetic geometry, coordinate systems, and numerical discretization, this enables maximum flexibility while preserving physics fidelity. As a testbed, we apply the novel representation to the resistive magnetohydrodynamic system, which involves a complete set of curvilinear operations. We verify the correctness of the approach using steady state liquid metal flows and the classic Orszag-Tang vortex.