Papers

This manuscript presents an historical perspective prepared by Barry Ninham and Colin Pask in 1969 on the connection between quantum electrodynamics theory and nucleon interactions. A new theory of strong interactions based on electromagnetic considerations is proposed. Energy and force range magnitudes are correctly given. A new theory of the pion emerges and the pion mass and lifetime are calculated. No strong interaction coupling constant is required.

The use of bioceramics as hard tissue substitutes is extensive due to their excellent biocompatible and osteogenic behaviors. Among various bioceramics, Ca-Mg silicates are unique from the viewpoints of osteoinductive and mechanical properties, as well as their outstanding osteoconductive and angiogenic behaviors owing to the release of Si, Ca and Mg. Despite these distinct advantages, different compositions of these bioceramics still require mechanical and biological enhancements for further applications. For this purpose, doping with some ions like F-, Sr2+, Cu2+, Eu2+, Ba+, Ce3+ and some alkali cations has been proved to be a valued approach. This review attempts to bring together areas for the performance improvement of the further researched Ca-Mg silicates (i.e., diopside, akermanite, bredigite and monticellite) and the alteration of these compositions via ion-doping. It is concluded that a correct choice of dopants incorporated at the optimal concentration makes these silicates ideal bone substitutes competing or even superior to calcium phosphates (apatites) and bioglasses which are known as the most prominent bioceramics.

We aim to enhance ophthalmologists' decision-making when diagnosing the Neovascular Age-Related Macular Degeneration (nAMD). We developed three tools to analyze Optical Coherence Tomography Angiography images: (1) extracting biomarkers such as mCNV area and vessel density using image processing; (2) generating a 3D visualization of the neovascularization for a better view of the affected regions; and (3) applying an ensemble of three white box machine learning algorithms (decision tree, support vector machines and DL-Learner) for nAMD diagnosis. The learned expressions reached 100% accuracy for the training data and 68% accuracy in testing. The main advantage is that all the learned models white-box, which ensures explainability and transparency, allowing clinicians to better understand the decision-making process.

We demonstrate a three-dimensional fin lithium niobate (FinLN) acoustic resonator fabricated from thick lithium niobate on insulator using deep ion etching and sidewall electrode patterning. The FinLN geometry enables strong three-dimensional acoustic confinement and enhanced electromechanical coupling while maintaining a compact device footprint. The fabricated devices operate near 300 MHz and exhibit an effective electromechanical coupling coefficient of 6.2% with a mechanical quality factor of 430, in good agreement with finite-element simulations. Compared to planar surface acoustic wave resonators fabricated on the same wafer, the FinLN configuration achieves an 1.8x enhancement in effective coupling. This work establishes FinLN as a promising platform for compact, high-performance RF MEMS and piezo-optomechanical systems.

Non-degenerate two-photon absorption (NTA) offers an attractive route for wide-field mid-infrared (MIR) imaging by mapping long wavelength information into the spectral detection windows of mature near-infrared detector technologies. However, existing NTA implementations rely almost exclusively on complex, large-footprint femtosecond laser systems, severely limiting practicality and scalability. Here, we demonstrate an NTA imaging platform that replaces the ultrafast laser with a compact nanosecond mid-IR source coupled to a high-definition indium gallium arsenide (InGaAs) camera. Operating in the nanosecond regime removes stringent temporal-overlap requirements, dramatically simplifying system architecture while preserving high nonlinear sensitivity. Using this approach, we achieve chemically selective, wide-field imaging deep into the mid-IR molecular fingerprint region and demonstrate, for the first time, video-rate NTA imaging in this spectrally rich regime. By combining relaxed alignment constraints, compact excitation, and high-speed fingerprint-region imaging, this work establishes nanosecond NTA as a practical and scalable foundation for next-generation mid-IR chemical imaging.

Simple analytical criteria are derived to determine whether axisymmetric base flows in annuli and pipes are stable or unstable. Both axisymmetric and non-axisymmetric inviscid disturbances are considered. Our sufficient condition for stability improves upon the classical result of \cite{Batchelor_Gill_1962}, following the idea of the second Kelvin-Arnol'd stability theorem. A novel sufficient condition for instability is also derived by extending the recently proposed hurdle theorem for parallel flows \citep{Deguchi_Hirota_Dowling_2024}. These analytical criteria are applied to annular and pipe model flows and are shown to effectively predict the neutral parameters obtained from eigenvalue computations of the stability problem.

Attempts to disentangle shear-flow turbulence often focus on identifying relatively simple solutions, such as travelling waves or periodic orbits. We show, however, that capturing multiscale features requires considering states at least as complex as quasi-time-periodic solutions. Approximations of these states can be computed efficiently using a quasi-linear model, consistent with the large-Reynolds-number asymptotic analysis. The quasi-linear structure is key to producing multiscale critical layers that generate vortices obeying Taylor frozen-flow hypothesis.

We develop and evaluate MlPET, a fast localized machine learning approach for probabilistic PET image analysis addressing the noise-resolution trade-off in conventional reconstructions. MlPET replaces computationally demanding Markov chain Monte Carlo sampling with a localized neural network trained to estimate posterior mean voxel activity from small image neighborhoods. The method incorporates scanner-specific point spread functions, spatially correlated noise modeling, and flexible priors. Performance was evaluated on NEMA IEC phantom data from three PET systems (GE Discovery MI, Siemens Biograph Vision 600, and Quadra) under varying reconstruction settings and acquisition times. On phantom data, MlPET achieved contrast recovery coefficients consistently higher than standard PET and close to 1.0 (including 10 mm spheres), while reducing background noise and improving spatial definition. Effective pointspread function full width at half maximum decreased from approximately 2 mm in standard PET to below 1 mm with MlPET, a 2.5 fold reduction in blur. Comparable image quality was obtained at 40-80 s acquisition time with MlPET versus 900 s with conventional PET. MlPET provides an efficient approach for quantitative probabilistic post-reconstruction PET analysis. By combining informed priors with neural network speed, it achieves noise suppression and resolution enhancement without altering reconstruction algorithms. The method shows promise for improved small-lesion detectability and quantitative reliability in clinical PET imaging. Future studies will evaluate performance on patient data.

Molecular Communication (MC) is a pivotal enabler for the Internet of Bio-Nano Things (IoBNT). However, current research often relies on super-capable individual agents with complex transceiver architectures that defy the energy and processing constraints of realistic nanomachines. This paper proposes a paradigm shift towards collective intelligence, inspired by the cortical networks of the biological brain. We introduce a decentralized network architecture where simple nanomachines interact via a diffusive medium using a threshold-based firing mechanism modeled by Greenberg-Hastings (GH) cellular automata. We derive fixed-point equations for steady-state populations via mean-field analysis and validate them against stochastic simulations. We demonstrate that the network undergoes a second-order phase transition at a specific activation threshold. Crucially, we show that both pairwise and collective mutual information peak exactly at this critical transition point, confirming that the system maximizes information propagation and processing capacity at the edge of chaos.

Görtler vortices developing over a concave wall support rapidly oscillating wavelike disturbances through secondary instabilities. Although experiments indicate that the finite-amplitude evolution of these waves acts as a precursor to turbulence transition, accurate and efficient prediction has remained out of reach. We overcome this limitation by using the Parabolised Coherent Structures (PCS) method of Song & Deguchi (2025), which incorporates the nonlinear vortex-wave interaction into a standard spatial-marching approach. Our computations successfully reproduce the wave amplitude and displacement thickness observed in the widely known experiments of Swearingen & Blackwelder (1987).

Mechanical metamaterials utilize intricate architectural designs to achieve advanced properties beyond those of their bulk counterparts. Existing metamaterial designs often rely on design inspirations and extensive experimental and numerical studies operated by design professionals, which can be time- and resource-consuming and limited in exploring the vast design space. Here, we transform metamaterial design by developing ChatMetamaterials based on large language models, a prompt-based generative metamaterial design engine capable of inventing architecture codes, and conducting reasoning-based diagnostics and evolution for complex metamaterial systems based on simple text prompts or hand-drawn sketches. This approach changes the way metamaterials are designed, and provides new opportunities for high-throughput metamaterial discovery.

Growing synthetic networks that follow power law distributions of a node's degree often involves adding one node at a time. Each node is added to the network with a fixed amount of edges and those edges are frozen for all future time steps. Yet real world networks often continuously evolve with edges being added and removed while new nodes are added to the network. Many existing growth models based on preferential attachment do not account for this evolutionary capability and when you extend their growth methods to add and remove edges to existing nodes the node degree distribution quickly loses its scale-free structure. This paper will go over a method to extend well known preferential attachment growth models to allow for the evolution of edges within a network while still maintaining a power law node degree distribution.

The mutual shadow area of pairs of surface regions is used for guiding the study of the spectral components and rank of their wave interaction, as captured by the corresponding moment matrix blocks. It is demonstrated that the mutual shadow area provides an asymptotically accurate predictor of the location of the singular value curve knee. This predicted knee index is shown to partition the interacting parts of the range and domain of blocks into two subspaces that can be associated with different wave phenomena: an "aperture" subspace of dimension that scales with the subdomains area (or length in 2-D) and a remainder "diffraction" subspace of dimension that scales much slower with the electrical length, depending on the geometric configuration. For interactions between open surface domains typical for the common hierarchical partitioning in most fast solvers, the latter can be attributed to the domain edges visible by its interacting counterpart. For interactions in 3-D with a small aspect angles between the source and observers, the diffraction subspace dimension is dominant in determining the rank until fairly large electrical lengths are reached. This explains the delayed asymptotic scaling of ranks and impressive fast solver performance observed in recent literature for seemingly arbitrary scatterers with no special geometric characteristics. In the extreme cases of "endfire" reduced dimensionality interactions, where the shadow area vanishes, the diffraction governs also the asymptotic rank, which translates to superior asymptotic solver performance.

The decay of magnetically dominated turbulence exhibits robust inverse transfer of magnetic energy even in the absence of net magnetic helicity, challenging traditional cascade-based phenomenology. While recent studies suggest that magnetic reconnection governs the evolution of such systems, a comprehensive understanding has been lacking. Here we test a reconnection-mediated model for decaying magnetic turbulence in two-dimensional (strict-2D), 2.5D, and three-dimensional (3D) systems with both helical and nonhelical initial conditions. We show that the magnetic-energy decay timescale scales with the Lundquist number in a manner consistent with Sweet-Parker-type reconnection rather than Alfvenic or purely resistive timescales. We develop a broken power-law model for the magnetic energy spectra and provide analytic predictions for the temporal evolution of energy across both sub-inertial and inertial ranges, which are confirmed by high-resolution simulations. In nonhelical turbulence, these results favor anastrophy as the dominant constraint over helicity fluctuations. Using Minkowski functionals to analyze reconnecting current sheets in real space, we find that the structures controlling the decay are substantially smaller than the global magnetic correlation scale, implying local Lundquist numbers well below the system-scale value. This explains the weak sensitivity of global decay laws to current-sheet resolution and that the current-sheet aspect ratios converge toward Sweet-Parker predictions only at sufficiently high resolution. Together, these results establish magnetic reconnection as the organizing principle underlying inverse transfer, spectral evolution, and decay in magnetically dominated turbulence, providing a unified picture applicable across dimensionality and helicity regimes with direct implications for astrophysical plasmas.

We provide several diagrams for the graphical determination of certain elements of a Gaussian beam based on prior knowledge of other elements. For example, these diagrams allow us to determine the plane of the beam waist and the Rayleigh range from knowledge of two wavefronts constituting the beam, or to determine the size of the light spot on a given wavefront. We also present a simple method for determining the waist position and the Rayleigh range of the image of a Gaussian beam formed by a lens.

Terahertz (THz) spectroscopy holds transformative potential for non-invasive sensing, yet characterizing individual nanoparticles in complex biological environments remains challenging due to the far-field diffraction limit. While near-field dipolar theory is well established, its application to characterizing/identifying nanoparticles immersed in complex media at THz frequencies is largely unexplored. This work utilizes numerical simulations of magneto-optical (MO) heterodimers – comprising n-doped Indium Antimonide (n-InSb) and isotropic or birefringent particles (e.g., SiO2, GaSe) – under counter-propagating, circularly polarized THz illumination. We demonstrate that while far-field observables like absorption cross-sections are often dominated by the MO-active particle, mechanical variables-specifically induced binding forces and spin/orbital torques-exhibit superior sensitivity for detecting "hidden" neighboring components. Because these mechanical signatures depend directly on near-field interactions, they provide higher information density regarding interparticle coupling. Key findings reveal material-specific spectral "hotspots" and "zeros" that serve as robust calibration markers even within dispersive biological surrogates. We show that the spin torque on non-MO particles is significantly modified by MO-neighbor proximity, a phenomenon controllable via static magnetic fields. Furthermore, these variables exhibit high angular sensitivity in perpendicular configurations. Our results provide a roadmap for using optomechanical signatures as high-resolution detectors for in-vivo diagnostics, signal transduction, and low-energy nanocircuit control.

We report on the first experimental realization of multi-material dielectric mirror coatings designed through a multi-objective optimization algorithm to simultaneously minimize thermal noise and optical losses. We validate this design strategy by fabricating and characterizing two distinct ternary systems: a SiNx -based proof-of-concept and a Ti:GeO2 -based system targeting lower optical losses. The performance of the SiN x coating shows remarkable agreement with predictions, demonstrating a noise amplitude spectral density reduction of 0.82 with respect to current reference coatings, and validating our design-to-fabrication pipeline. The Ti:GeO2 -based system achieves the crucial goal of sub-ppm absorption; its measured thermal noise, however, is higher than the theoretically predicted level of 0.71, extrapolated from single-layer material characterization. A dedicated tolerance analysis confirms that this discrepancy is not attributable to random thickness errors, emphasizing that further studies of the manufacturing process are needed to fully exploit this combination of materials. This work establishes a robust methodology for producing complex, high-performance optical coatings tailored for precision experiments.

Quantum interferometers offer phase measurement capabilities that surpass the standard quantum limit (SQL), with phase sensitivity and quantum enhancement factor serving as key performance metrics. However, practical implementations face severe degradation of both metrics due to unavoidable losses, representing the foremost challenge in advancing quantum interferometry toward real-world applications. To address this challenge, we propose a coherent-amplifier-empowered quantum interferometer. The coherent amplifier dramatically suppresses the decay of both sensitivity and quantum enhancement under high-loss conditions, maintaining phase sensitivity beyond the original SQL even for losses exceeding 90%. Using an injected 4.2 dB squeezed-vacuum state in experimental demonstration, our scheme reduces the quantum enhancement degradation under 90% loss from 3.7 dB in a conventional quantum interferometer (CQI) to only 1.5 dB. More importantly, the phase sensitivity degradation under the same loss is limited to 4.0 dB, markedly outperforming the 11.2 dB degradation observed in a CQI. This improvement is enabled by the coherent amplifier's phase-sensitive photon amplification and its protection of the quantum state. This breakthrough in amplifier-empowered quantum interferometry overcomes the critical barrier to practical deployment, enabling robust quantum-enhanced measurements in lossy environments.

This study presents a comprehensive experimental investigation of scalar dispersion from the free end of wall-mounted cylindrical obstacles immersed in a large-Reynolds-number turbulent boundary layer. A key focus is the characterization of transition behavior between distinct dispersion regimes: elevated plumes (EP), ground-level plumes (GLP), and ground-level sources (GLS). Experiments systematically vary the primary and secondary aspect ratios ($AR_1, AR_2$) and the velocity ratio ($ r$) to explore their effects on the evolution of scalar plumes. Plume classification is governed by the non-dimensional parameter $\tilde{h}_s / \delta_{cz}$, which quantifies the progressive interaction between the plume and the ground. Here, $\tilde{h}_s$ denotes the effective source height and $\delta_{cz}$, the vertical plume half-width. Detailed concentration measurements demonstrate that the EP–GLP–GLS transitions substantially modify both vertical and lateral dispersion characteristics. The measurements reveal systematic departures from classical dispersion-coefficient scaling. To assess the capability of existing models under these conditions, the experimentally determined dispersion coefficients are used to evaluate the Gaussian Dispersion Model (GDM) and a Wall Similarity Model (WSM). The GDM captures general trends but deviates in specific regimes, whereas the WSM offers improved representation under GLS conditions. The resulting dataset, grounded in systematic laboratory measurements, establishes a critical benchmark for validating numerical simulations and informing the development of next-generation predictive models. Finally, leveraging these results, a concise data-informed predictive framework is introduced that captures the EP–GLP–GLS transitions and provides first-order estimates of ground-level concentration across geometric and momentum-ratio parameter space.

We introduce a smooth B-spline discretization in polar coordinates on the unit disc that corrects the loss of regularity present at the origin caused by the coordinate singularity in standard tensor-product B-spline formulations. The method constructs "smooth polar splines" via a Galerkin projection of harmonic polar functions $S_l^{-m}(r,\theta) := r^l \sin(m\theta)$ and $S_l^{m}(r,\theta) := r^l \cos(m\theta)$, derived from the polar representation of Cartesian monomials, onto the central tensor-product B-spline basis in the innermost radial region. The radial component reproduces $r^l$ exactly for $0 \le l \leq p$, where $p$ is the B-spline degree, satisfying the near-origin regularity condition. However, exact compatibility with $C^\infty$-regularity at the origin is recovered only in the limit $\Delta\theta \to 0$, when the angular component resolves all angular harmonics accurately. The smooth polar splines are linear combinations of standard tensor-product B-splines and lie in the same function space, enabling mapping between the $C^\infty$-regular subspace and the original discretization space via an exact prolongation operator and a corresponding restriction operator acting on the discrete variables. They match standard tensor-product B-splines away from the origin, preserve orthogonality among the newly constructed origin-centered basis functions, and maintain local support and sparse matrices. This smoothness and locality improve the conditioning of mass and stiffness matrices, conserve charge, and reduce statistical errors in particle-in-cell simulations near the origin, while eliminating spurious eigenvalues in eigenvalue problems. The approach provides a robust, high-order, and efficient adaptation of tensor-product B-splines for polar coordinates in physics simulations.

How cooperation originates and persists among self-interested individuals is a central question in the social and behavioural sciences. In the canonical two-dimensional spatial Prisoner's Dilemma with unconditional imitation introduced by Nowak and May (1992), simulations on a Moore lattice show an abrupt drop in cooperation near the temptation $T\approx5/3$, yet even under these harsh conditions cooperative structures can still arise. However, the nucleation rates of these motifs, and their contribution along the full cooperation curve had not been quantified. Here we show, using large-scale Monte Carlo simulations combined with automatic cluster classification, that on the Moore lattice for $T\ge5/3$ residual cooperation is sustained exclusively by $3\times3$ (or larger) rectangular cooperator bricks, whereas on degree-8 random-regular graphs for $T\gtrsim1.5$ it is dominated by star-like motifs (1 hub + 8 leaves). Once the dynamics becomes nucleation limited, the macroscopic cooperation level is therefore governed by the statistics of a few exceptionally resilient shapes, rather than by many different cooperator motifs. Furthermore, we show that the lattice cooperation collapse near $T=5/3$ is kinetic rather than critical: the reduction in cooperation is not due to a loss of growth capacity of rectangular bricks, but to the progressive destabilisation of the subcritical motifs that dominate just below this threshold. Our results show that residual cooperation at high temptation is a rare-event nucleation phenomenon governed by a small set of topological traps, and highlight the value of motif-level analysis for explaining and engineering cooperation in spatial, social, and technological networks.

High-resolution lithographic techniques are often limited by low volumetric throughput, since there is no universal and scalable manufacturing process that can produce 3D metasurfaces. In this work, we demonstrate a high-speed holographic 3D printing platform based on spatiotemporal beam shaping, exceeding the repetition rate while keeping the resolution high. The system integrates a femtosecond laser source with a spectral pulse compressor and a beam shaper to project uniform, axially confined light fields to project patterns directly on the advanced photoresists using a Digital Micromirror Device DMD. We investigate the process window for rapid polymerization, optimizing the photoinitiator choice to eliminate thermal crosstalk at high repetition rates. Using this setup, we achieve a production throughput of more than a million voxels per second with sub-micron resolution below 400 nm. The system's reliability is validated through the fabrication of large-area woodpile-like lattices and uniform micropillar arrays, establishing a workflow for scalable manufacturing of micro-optical components.

This study introduces and validates a cloud condensation model on insoluble nuclei using a novel comparative analysis of simultaneous radiosonde and ceilometer data. A transformation of the radiosonde's temperature and relative humidity profiles into a simulated optical backscatter signal is implemented via the model, which includes a critical rate-limiting activation step. By comparing this simulated signal with the ceilometer's measured data, the model can determine the minimum effective size of dust particles required to act as cloud condensation nuclei. This approach has direct implications for improving the parameterization of cloud formation in local weather models, particularly for rainfall forecasting in dust-prone arid and semi-arid regions.

We investigate the estimation of dephasing-induced decoherence in continuous-variable quantum systems using non-Gaussian probe states. By purifying the open system, we identify optimal probes, specifically squeezed cat and symmetric squeezed compass states, via quantum Fisher information. These results are in agreement with numerical simulation. In settings where the intra-cavity field is inaccessible and standard measurements are impractical, utilizing an ancilla approach where a qubit traverses or interacts with the cavity field, leading to measurement of the qubit, hence allowing estimation of the dephasing rate via Wigner function reconstruction or less costly marginal distribution.

Active flow control remains a significant challenge due to the high-dimensional, nonlinear nature of fluid dynamics. Quantum machine learning may prove effective in addressing these issues, given that quantum computing possesses superiority over traditional computing in some extend. Thus, this study developed a quantum reinforcement learning (QRL) based active flow control framework, integrating variational quantum circuits (VQCs) with the proximal policy optimization (PPO) algorithm to learn a real time controller. Firstly, we tested the QRL in a CartPole problem. The QRL shows parameter efficiency and enhanced learning capability, indicating VQC acts as promising candidates for advancing RL, particularly in scenarios requiring both computational efficiency and robust performance. The active control of flow past a square circular cylinder at a Reynolds number of 100 was tested via QRL. Our hybrid architecture encodes high-dimensional flow states into a quantum policy network, which generates continuous blowing/suction actions on the cylinder surface, and thus suppress the vortex shedding to achieve drag reduction. Numerical simulations demonstrate the QRL successfully reduces the mean drag and attenuates lift oscillations. Flow field analysis confirms that QRL control effectively suppresses large-scale vortex shedding, leading to a narrower wake compared to the uncontrolled baseline. These results validate the potential of quantum-enhanced learning for tackling complex fluid dynamics problems. The proposed QRL framework establishes a promising blueprint for quantum-AI accelerated solutions in aerospace design, energy-efficient turbomachinery, and other applications involving sophisticated fluid-structure interactions.

We measured the Q-value and the scattering angle distributions for non-dissociative state selective single electron capture in collisions of 7.5 keV H$^+$ and 15 keV H$_2^+$ with He. The experimental data are compared with semiclassical close-coupling calculations and predictions from the classical trajectory Monte Carlo simulations. By analogy with Fraunhofer diffraction, we also developed a toy model to reconstruct an imaginary screen that reflects the reaction impact-parameter dependence, in channels where the magnetic quantum number remains unchanged. It is well established that H$_2^+$ acts as a molecular double-slit in scattering processes. By demodulating the Young's double-slit-type interference pattern, we extracted the individual slit diffraction pattern of H$_2^+$ and compared it with that of the H$^+$ atomic single-slit. For ground state electron capture, we found that the single and the double slit diffraction patterns have equal fringe width, whereas for excited state electron capture, diffraction patterns are quite different.

This study aims to develop a universal, parameter-free model for sediment transport and riverbed evolution using a rigorous statistical physics framework. It seeks to overcome the limitations of traditional deterministic and empirical approaches by establishing formulas with general applicability. The river channel is conceptualized as an isothermal-isobaric ensemble containing numerous non-identical sediment particles. The macroscopic state of the system, defined by the scour-and-silt volume, is derived from the statistical mechanics of particle distributions. The Gibbs free energy and partition function for the ensemble are formulated, considering the two primary states of particles (suspended load and bed load) and the transitions between them. This theoretical framework yields a universal formula for the number of particles in transport and the consequent volumetric change. The model was applied to six reaches of the Lower Yellow River from 2000-2001. Calculations revealed a seasonal pattern in the number of transported particles, higher in winter and lower in summer. The results showed an alternation between scour (January-July) and siltation (July-January), with a net scour volume over the 24-month period. The magnitude of scour-and-silt volume decreased from upstream to downstream, findings that are consistent with independent observational records following the operation of the Xiaolangdi Reservoir. The model successfully simulates riverbed evolution without empirical parameters, demonstrating that statistical physics provides a robust framework for predicting complex fluvial processes. Its general formulation suggests potential applicability to other similar multi-particle systems.

Human social systems bear striking similarities, yet similar conditions can give rise to markedly different historical trajectories. A central challenge for quantitative social science is therefore to use time-series data to identify features common to the evolution of different social systems without obscuring the variation distinguishing individual histories. Here, we address this challenge by fitting stochastic differential equations (SDEs) to ensembles of observed trajectories through spaces of socially relevant variables. Importantly, the SDE allows the average tendency and variability of change to depend on the current state of the system. This flexibility allows us to use the SDE to analyze myriad aspects of social dynamics. Specifically, the fitted SDEs identify regions of the state space where stochastic variability is greater, allowing a broader range of plausible subsequent changes. The fitted SDE also allows us to quantify the dynamical irreversibility of observed trajectories and how that irreversibility depends on the values of the social variables. In addition, we show how the SDEs can be used to identify statistically atypical transitions in individual trajectories, indicative of exogenous perturbations impacting the associated society. We apply these tools to three domains: modern political economy, quantitative history, and cultural analytics. This reveals state-dependent change in the organization of modern political-economic systems; a broad but stochastic tendency toward increasing social complexity in historical societies; and time-dependent reorganization of lexical dynamics in English. Looking forward, we argue that our framework provides a set of powerful quantitative tools that can be used to investigate a broader range of longitudinal social phenomena.

Extended Self-Similarity (ESS) is a widely used tool for uncovering universal power-law scaling in systems dominated by nonlinear interactions. This work demonstrates that ESS scaling can also emerge in a system governed by purely linear physics: the propagation of coherent light in a multimode fiber. The system produces complex speckle patterns arising solely from deterministic linear mode interference. We analyze the intensity structure functions of these speckles and observe a robust extended scaling range. The measured scaling exponents align with the classical Kolmogorov scaling exponents. This finding establishes that the statistical signatures captured by ESS are not exclusive to nonlinear systems, revealing a broader applicability of this scaling framework to complex linear systems.

Radiotherapy planning naturally leads to a multi-criteria optimization problem which is subject to different sources of uncertainty. In order to find the desired treatment plan, a decision maker must balance these objectives as well as the level of robustness towards uncertainty against each other. This paper showcases a quantitative approach to do so, which combines the theoretical model with the ability to deal with practical challenges. To this end, the uncertainty, which can be expressed via the so-called dose-influence matrix, is modelled using interval matrices. We use inverse robustness to introduce an additional objective, which aims to maximize the volume of the uncertainty set. A multi-criteria approach allows to handle the uncertainty while keeping appropriate values of the other objective functions. We solve the resulting quadratically constrained quadratic optimization problem (QCQP) by first relaxing it to a convex semidefinite problem (SDP) and then reconstructing optimal solutions of the QCQP from solutions of the SDP.