Papers

Despite extensive experimental evidence of turbulence in Hagen Poiseuille flow, linear stability analysis has not yet confirmed its instability. One challenge is the singularity introduced by the term 1/r in the center of the pipe, which complicates traditional stability approaches. In this study, we explore a global stability analysis using a meshless framework. Although this approach did not recover the expected unstable modes, it revealed a new set of modes with distinct characteristics from those observed in local stability analysis. We analyze these modes and their impact on transient energy growth, demonstrating the effectiveness of the global approach in capturing localized instabilities without requiring multiple simulations.

The growing complexity of integrated photonics necessitates compact, low-power devices that transcend traditional, material-centric design approaches. In this study, we harness non-Hermitian physics to uncover novel properties of coupled plasmonic waveguide modes exhibiting exceptional point (EP) degeneracy. Our hybrid plasmonic waveguide architecture, capable of supporting both strong and weak coupling regimes between plasmonic and dielectric waveguide modes, is precisely engineered to reach an EP where eigenmodes coalesce. This strategic tuning not only enhances the modal contrast between minimized-loss and highly dissipative states but also enables unprecedented control over device characteristics. Our findings introduce a new paradigm in integrated photonics, paving the way for ultracompact modulators and highly tunable on-chip communication systems with reduced power consumption.

As photonic systems progress toward enhanced miniaturization, dynamic reconfigurability, and improved energy efficiency, a central challenge endures: the accurate and independent control of optical losses and resonant properties on scalable, CMOS-compatible platforms. To address this challenge, we present a hybrid plasmonic dimer that functions in a non-Hermitian regime, capitalizing on the synergistic interplay between Epsilon Near Zero (ENZ) materials and phase-change materials (PCMs) to achieve superior reconfigurability through electrical modulation. Our approach harnesses non-Hermitian physics by precisely modulating the loss differential among coupled modes alongside their resonant frequencies, thereby steering the system to an Exceptional Point (EP) characterized by emergent phenomena and enhanced perturbation sensitivity. By integrating ENZ materials to control dissipation with PCMs to fine-tune resonant frequencies, our structure achieves robust programmability, delivering at least 16 distinct operational states for coupled resonators. This capability supports deep subwavelength confinement and transitions between EP and non-EP regimes, while the inherently low power consumption of ENZ materials and PCMs under deep-subwavelength confinement offers significant advantages even in high-dimensional configurations. We believe that this work outlines a significant route for next-generation programmable photonics, delivering subwavelength confinement, energy-efficient operation, and high-dimensional optical reconfigurability within an integrated, scalable, and manufacturable platform.

We introduce a hybrid classical-quantum algorithm to compute dynamical correlation functions and excitation spectra in many-body quantum systems, with a focus on molecular systems. The method combines classical preparation of a perturbed ground state with short-time quantum evolution of product states sampled from it. The resulting quantum samples define an effective subspace of the Hilbert space, onto which the Hamiltonian is projected to enable efficient classical simulation of long-time dynamics. This subspace-based approach achieves high-resolution spectral reconstruction using shallow circuits and few samples. Benchmarks on molecular systems show excellent agreement with exact diagonalization and demonstrate access to dynamical timescales beyond the reach of purely classical methods, highlighting its suitability for near-term and early fault-tolerant quantum hardware.

Current progress in electro-optical modulation within silicon integrated photonics, driven by the unique capabilities of advanced functional materials, has led to significant improvements in device performance. However, inherent constraints in dimensionality and tunability still pose challenges for further innovation. In this work, we propose a strategy that exploits the principles of non-Hermitian physics–specifically, the concept of exceptional points (EPs)–to transcend these limitations and pave the way for the next generation of versatile, high-performance photonic devices. Our multilayer structure supports hybrid plasmonic waveguide modes that can manifest as various orders of quasiparticles. By judiciously setting spatial parameters, the system can be tuned to exhibit both weak and strong coupling regimes between the plasmonic and dielectric modes, leading to the controlled formation of EP degeneracies. Furthermore, the integration of low-loss phase-change materials (Sb2S3 and Sb2Se3) enables dynamic electrical tuning, resulting in pronounced modulation of propagation loss and transmission coefficients over sub-micron distances. This superior performance not only sets a new benchmark for device responsivity and compactness but also opens promising avenues for future research, including the incorporation of gain media for loss compensation at EPs and the exploration of alternative tunable materials for next-generation ultracompact photonic devices.

The Inspection Game is the canonical model for the strategic conflict between law enforcement (inspectors) and citizens (potential criminals). Its classical Mixed-Strategy Nash Equilibrium (MSNE) is afflicted by a paradox: the equilibrium crime rate is independent of both the penalty size ($p$) and the crime gain ($g$), undermining the efficacy of deterrence policy. We re-examine this challenge using evolutionary game theory, focusing on the long-term fixation probabilities of strategies in finite, asymmetric population sizes subject to demographic noise. The deterministic limit of our model exhibits stable limit cycles around the MSNE, which coincides with the neutral fixed point of the equilibrium analysis. Crucially, in finite populations, demographic noise drives the system away from this cycle and toward absorbing states. Our results demonstrate that high absolute penalties $p$ are highly effective at suppressing crime by influencing the geometry of the deterministic dynamics, which in turn biases the fixation probability toward the criminal extinction absorbing state, thereby restoring the intuitive role of $p$. Furthermore, we reveal a U-shaped policy landscape where both high penalties and light penalties (where $p \approx g$) are successful suppressors, maximizing criminal risk at intermediate penalty levels. Most critically, we analyze the realistic asymptotic limit of extreme population sizes asymmetry, where inspectors are exceedingly rare. In this limit, the system's dynamic outcome is entirely decoupled from the citizen payoff parameters $p$ and $g$, and is instead determined by the initial frequency of crime relative to the deterrence threshold (the ratio of inspection cost to reward for catching a criminal). This highlights that effective crime suppression requires managing the interaction between deterministic dynamics, demographic noise, and initial conditions.

Better wall plug efficiency of deep-ultraviolet light emitting diodes (DUV-LEDs) requires simultaneous low resistivity p-type and n-type contacts, which is a challenging problem. In this study, the co-optimization of p-InGaN and n- AlGaN contacts for DUV LEDs are investigated. We find that using a thin 7%InGaN cap is effective in achieving ohmic p-contacts with specific contact resistivity of 3.10x10^{-5} Ohm.cm^2. Upon monolithic integration of p- and n- contacts for DUV LEDs, we find that the high temperature annealing of 800C required for the formation of low resistance contacts to n-AlGaN severely degrades the p-InGaN layer, thereby reducing the hole concentration and increasing the specific contact resistivity to 9.72x10^{-4} Ohm.cm^2. Depositing a SiO2 cap by plasma-enhanced atomic layer deposition (PE-ALD) prior to high temperature n-contact annealing restores the low p-contact resistivity, enabling simultaneous low-resistance p- and n-contacts. DUV-LEDs emitting at 268 nm fabricated with the SiO2 capping technique exhibit a 3.5 V reduction in operating voltage at a current level of 400 A/cm^2 and a decrease in differential ON-resistance from 6.4 mOhm.cm^2 to 4.5 mOhm.cm^2. This study highlights a scalable route to high-performance, high-Al-content bipolar AlGaN devices.

The 60 mV$/$decade subthreshold limit at room temperature, coined as the Boltzmann tyranny, remains a fundamental obstacle to the continued down-scaling of conventional transistors. While several strategies have sought to overcome this constraint through non-thermal carrier injection, most rely on ferroelectric-based or otherwise material-specific mechanisms that require complex fabrication and stability control. Here, we develop a universal theoretical framework showing that intrinsic memory effects in nanometric field-effect transistors can naturally bypass this limit. Within the Landauer-Büttiker quantum transport formalism, we incorporate charge-trapping mechanisms that dynamically renormalize the conduction band edge. The resulting analytical expression for the subthreshold swing explicitly links memory dynamics to gate efficiency, revealing that a reduced carrier generation rate or enhanced trapping activity leads to sub-thermal switching, thus breaking the Boltzmann barrier. The model captures key experimental features and provides clear, generalizable design principles, establishing memory-assisted transistors as a robust pathway toward ultra-low-power and multifunctional electronic architectures.

Diffusion magnetic resonance imaging (MRI) has revealed important insights into white matter microstructure, but its application to gray matter remains comparatively less explored. Here, we investigate whether global patterns of gray-matter microstructure can be captured through neurite orientation dispersion and density imaging (NODDI) and whether such patterns are predictive of cognitive performance. Using diffusion MRI and behavioral data from the Human Connectome Project Young Adult study, we derive region averaged NODDI parameters and apply principal component analysis (PCA) to construct general gray-matter microstructure factors. We find that the factor derived from isotropic volume fraction explained substantial inter-individual variability and was significantly correlated with specific cognitive scores collected from the NIH Toolbox. In particular, the isotropic volume fraction factor is linked to reading and vocabulary performance and to cognitive fluidity. Our findings demonstrate that PCA-based global indicators of gray-matter microstructure provide complementary markers of structure-function relationships, extending beyond region-specific analyses. Our results suggest that general microstructure factors may serve as population-level exploratory biomarkers for studying cognition and cortical organization.

Cesium based halide perovskites, such as CsPbI3 and CsSnI3, have emerged as exceptional candidates for next generation photovoltaic and optoelectronic technologies, but their practical application is limited by temperature dependent phase transitions and structural instabilities. Here, we develop machine learning interatomic potentials within the LATTE framework to simulate these materials with near experimental accuracy at a fraction of the computational cost compared to previous computational studies. Our molecular dynamics simulations based on the trained MLIPs reproduce energies and forces across multiple phases, enabling large scale simulations that capture cubic tetragonal orthorhombic transitions, lattice parameters, and octahedral tilting with unprecedented resolution. We find that Pb based perovskites exhibit larger octahedral tilts and higher phase transition temperatures than Sn based analogues, reflecting stronger bonding and enhanced structural stability, whereas Sn based perovskites display reduced tilts and lower barriers, suggesting tunability through compositional or interface engineering. Beyond these systems, our work demonstrates that MLIPs can bridge first principles accuracy with simulation efficiency, providing a robust framework for exploring phase stability, anharmonicity, and rational design in next generation halide perovskites.

A robust and streamlined method is presented for efficiently extracting spectral diffusion from two-dimensional coherent spectra by employing the projection-slice theorem. The method is based on the optical Bloch equations for a single resonance that include a Frequency-Frequency Correlation Function (FFCF) in the time domain. Through the projection slice theorem (PST), analytical formulation of the diagonal and cross-diagonal projections of time-domain two-dimensional spectra are calculated that include the FFCF for arbitrary inhomogeneity. The time-domain projections are Fourier transformed to provide frequency domain slices that can be fit to slices of experimental spectra. Experimental data is used to validate our lineshape analysis and confirm the need for the inclusion of the FFCF for quantum wells that experience spectral diffusion.

We extend our previous work on a semi-Lagrangian adaptive rank (SLAR) integrator, in the finite difference framework for nonlinear Vlasov-Poisson systems, to the general high-order tensor setting. The proposed scheme retains the high-order accuracy of semi-Lagrangian methods, ensuring stability for large time steps and avoiding dimensional splitting errors. The primary contribution of this paper is the novel extension of the algorithm from the matrix to the high-dimensional tensor setting, which enables the simulation of Vlasov models in up to six dimensions. The key technical components include (1) a third-order high-dimensional polynomial reconstruction that scales as $O(d^2)$, providing a point-wise approximation of the solution at the foot of characteristics in a semi-Lagrangian scheme; (2) a recursive hierarchical adaptive cross approximation of high-order tensors in a hierarchical Tucker format, characterized by a tensor tree; (3) a low-complexity Poisson solver in the hierarchical Tucker format that leverages the FFT for efficiency. The computed adaptive rank kinetic solutions exhibit low-rank structures within branches of the tensor tree resulting in substantial computational savings in both storage and time. The resulting algorithm achieves a computational complexity of $O(d^4 N r^{3+\lceil\log_2d\rceil})$, where $N$ is the number of grid points per dimension, $d$ is the problem dimension, and $r$ is the maximum rank in the tensor tree, overcoming the curse of dimensionality. Through extensive numerical tests, we demonstrate the efficiency of the proposed algorithm and highlight its ability to capture complex solution structures while maintaining a computational complexity that scales linearly with $N$.

Hydrogen-deuterium exchange (HDX) of protein backbone amides provides a powerful probe of conformational dynamics. However, when experiments are performed in H2O/D2O mixtures, quantitative interpretation is hindered by back exchange and isotope effects not captured by the classical Linderstrom-Lang (LL) model. We introduce a generalized Linderstrom-Lang (GLL) framework that explicitly accounts for forward and reverse exchange and for changes in protection upon isotopic substitution. Analytical solutions describe equilibrium enrichment (fractionation) and protection factors in mixtures, reducing to the LL model in pure D2O. Application to HDX/NMR of the molecular chaperone DNAJB1 in 50% D2O demonstrates that the GLL model recovers protection factors at 100% D2O. Ignoring back exchange (i.e., using the LL model) causes protection factors to be systematically underestimated. A particularly powerful feature of our approach is that a single HDX experiment in a mixture (e.g., 50% D2O) simultaneously provides protection factors that report on conformational dynamics and local stability, and fractionation factors that are sensitive to the local hydrogen-bonding environment.

A liquid drop impacting a non-wetting rigid substrate laterally spreads, then retracts, and finally jumps off again. An elastic solid, by contrast, undergoes a slight deformation, contacts briefly, and bounces. The impact force on the substrate - crucial for engineering and natural processes - is classically described by Wagner's (liquids) and Hertz's (solids) theories. This work bridges these limits by considering a generic viscoelastic medium. Using direct numerical simulations, we study a viscoelastic sphere impacting a rigid, non-contacting surface and quantify how the elasticity number ($El$, dimensionless elastic modulus) and the Weissenberg number ($Wi$, dimensionless relaxation time) dictate the impact force. We recover the Newtonian liquid response as either $El \to 0$ or $Wi \to 0$, and obtain elastic-solid behavior in the limit $Wi \to \infty$ and $El \ne 0$. In this elastic-memory limit, three regimes emerge - capillary-dominated, Wagner scaling, and Hertz scaling - with a smooth transition from the Wagner to the Hertz regime. Sweeping $Wi$ from 0 to $\infty$ reveals a continuous shift from materials with no memory to materials with permanent memory of deformation, providing an alternate, controlled route from liquid drops to elastic beads. The study unifies liquid and solid impact processes and offers a general framework for the liquid-to-elastic transition relevant across systems and applications.

The cluster dose concept offers an alternative to the radiobiological effectiveness (RBE)-based model for describing radiation-induced biological effects. This study examines the application of a neural network to predict cluster dose distributions, with the goal of replacing the computationally intensive simulations currently required. Cluster dose distributions are predicted using a U-Net that was initially pretrained on conventional dose distributions. Using transfer learning techniques, the decoder path is adapted for cluster dose estimation. Both the training and pretraining datasets include head and neck regions from multiple patients and carbon ion beams of varying energies and positions. Monte Carlo (MC) simulations were used to generate the ground truth cluster dose distributions. The U-Net enables cluster dose estimation for a single pencil beam within milliseconds using a graphics processing unit (GPU). The predicted cluster dose distributions deviate from the ground truth by less than 0.35%. This proof-of-principle study demonstrates the feasibility of accurately estimating cluster doses within clinically acceptable computation times using machine learning (ML). By leveraging a pretrained neural network and applying transfer learning techniques, the approach significantly reduces the need for large-scale, computationally expensive training data.

Constitutive laws relate fluid stress to deformation and underpin predictions of non-Newtonian behavior in industrial and biological fluids. Standard characterization relies on measurements in idealized flows that often miss physics relevant to complex geometries. Existing data-driven methods overfit sparse data, lack geometry portability, or presuppose constitutive forms. To unify measurement and constitutive discovery, we developed an end-to-end framework that leverages automatic differentiation through a full physics simulation. By embedding a frame-invariant tensor basis neural network (TBNN) within a differentiable non-Newtonian solver, we learn constitutive laws from any flow observable without presupposing a specific model, spanning generalized Newtonian, viscoelastic, and yield-stress behavior. Unlike coordinate-dependent methods, learning local material response enables accurate flow predictions in unseen geometries and conditions without retraining. We then distill the TBNN closure into symbolic form via automated model selection using the Bayesian Information Criterion, extracting interpretable physical parameters. This work establishes a foundation for comprehensive characterization of complex fluids directly within their operating environment ("digital rheometry") with broad applicability to constitutive discovery across engineering and the physical sciences.

Using an ultracold gas of $^{87}$Rb$^{133}$Cs molecules, we perform hyperfine-resolved spectroscopy of transitions from the vibronic ground state to the lowest rovibrational states of the electronic state $\mathrm{b}\,^3\Pi_0$, as a function of magnetic field. These transitions are spin forbidden, resulting in narrow linewidths, and feature near-diagonal Franck-Condon factors. We develop a model of the hyperfine and Zeeman structure that includes coupling between the $0^+$ and $0^-$ components of $\mathrm{b}\,^3\Pi_0$. We fit the spectra to obtain rotational and hyperfine coupling constants. We measure transition dipole moments associated with specific transitions by directly observing Rabi oscillations as a function of a resonant laser pulse duration. Using resonant $\pi$ pulses, we prepare molecules in the electronically excited state and directly measure the spontaneous emission rate.

We investigate a process of growth of a signed network that strictly adheres to Heider structural balance rules, resulting in two opposing, growing factions. New agents make contact with a random existing agent and join one of the factions with the bias $p$ towards the group they made contact with. The evolution of the group sizes can be mapped to a randomized Pólya urn model. Aside from $p=1$, the relative sizes of the two factions always tend towards $1/2$, but the behavior differs in the anti-bias regime ($p<1/2$) and the biased one ($p>1/2$). In the anti-bias regime, the expected faction sizes converge toward equality, regardless of initial differences, while in the biased regime, initial size difference persists over time. This difference is obscured by fluctuations, with the faction size distribution remaining unimodal even above $p>1/2$, up until a characteristic point $p^{ch}$, where it becomes bimodal, with initially larger and smaller factions featuring their own distinguishable peaks. We discuss several approaches to estimate this characteristic value. At $p=1$, differences between the relative sizes of factions can persist indefinitely, although still subject to fluctuations.

New techniques for imaging electromagnetic near-fields in nanostructures drive advancements in nanotechnology, optoelectronics, materials science, and biochemistry. Most existing techniques probe near-fields along surfaces, lacking the ability to extract near-fields confined within the structure. Notable exceptions use free electrons to traverse through nanostructures, integrating the field along their trajectories, extracting 2D near-field projections rather than the complete field. Here, drawing inspiration from computed tomography (CT), we present a tomography concept providing full 3D reconstruction of vectorial time-harmonic near-fields. We develop a Radon-like algorithm incorporating the electron wave-nature and the time dependency of its interaction with vector fields. To show the prospects of electron near-field tomography, we propose and analyze its ability to resolve the sub-wavelength zigzag profile of highly confined hyperbolic polaritons and to reconstruct 3D phase singularities in a chiral near-field, raising exciting goals for next-generation experiments in ultrafast transmission electron microscopes.

Electron transfer (ET) at electrochemical interfaces is central to energy conversion and storage, yet its theoretical and computational modeling remain active research areas. This review elucidates key concepts and theories of ET kinetics, focusing on coupling between classical solvent fluctuations and quantum electronic states of metallic electrodes and redox species. We begin with fundamental rate theories, reaction coordinates, and electrochemical timescales, then explore weak, strong, and intermediate electronic coupling regimes. Special attention is given to solvent dynamics and the structure of the electrical double layer (EDL), which critically impact ET kinetics. Atomistic simulations, particularly density functional theory (DFT) and molecular dynamics (MD), are highlighted for testing linear response and determining solvent reorganization energy, electronic coupling strengths, and solvent relaxation dynamics. A central theme is linear response enabling tractable treatments across Marcus theory, empirical valence bond (EVB) models, the Anderson-Newns-Schmickler framework, and generalized Langevin dynamics. While linear response offers useful simplifications, we assess its limitations, particularly for strong solvation changes or inner-sphere ET at catalytic interfaces. We discuss advances, including mapping Hamiltonian-based EVB-MD, constrained DFT, and non-Gaussian free energy formulations, enabling rigorous tests and access to diabatic and adiabatic free energy surfaces. We outline opportunities to advance multiscale, quantum-classical models that integrate EDL effects, multiple reaction coordinates, solvent-controlled dynamics, and transitions between adiabatic and nonadiabatic regimes. This review serves as a conceptual guide and practical resource for researchers integrating theory and simulation in studying electrochemical ET across diverse systems.

In this paper, we present the Distributed Inter-Strand Coupling Current (DISCC) model. It is a finite element (FE) model based on a homogenization approach enabling efficient and accurate simulation of the transient magnetic response of superconducting Rutherford cables without explicitly representing individual strands. The DISCC model reproduces the inter-strand coupling current dynamics via a novel mixed FE formulation, and can be combined with the Reduced Order Hysteretic Magnetization (ROHM) and Flux (ROHF) models in order to reproduce the effects of internal strand dynamics: hysteresis, eddy, and inter-filament coupling currents, as well as ohmic effects. The DISCC model offers a massive reduction of the computational time compared to fully detailed FE models and still accounts for all types of loss and magnetization contributions. As a result, Rutherford cables homogenized with the DISCC model can be directly included in FE models of magnet cross-sections for efficient electro-magneto-thermal simulations of their transient response. We present two possible FE formulations for the implementation of the DISCC model, a first one based on the h-phi-formulation, and a second one based on the h-phi-a-formulation, which is well suited for an efficient treatment of the ferromagnetic regions in magnet cross-sections.

A short review on two-step laser processing of material is presented. The main focus is on the processes which can be called recording-development ones. Here, the first step of laser processing provides an initial pattern on the material surface, which is enhanced or developed at the second step. A new type of the recordingd-development process is considered. The first process is the UV third harmonic of an Nd:YAG laser initiated gold nanoparticle growth in a polystyrene film. A grating of 20-um period is recorded through the corresponding mask. The lines of the grating are of red color corresponding to the plasmon resonance absorption of gold nanoparticles. The second, development step is carbonization of the matrix just near the gold nanoparticles performed by homogeneous irradiation of the recorded pattern by the powerful radiation of the second harmonic of the same laser. As a result, a black carbonized grating is obtained. The patterns possessing both red and black parts are also presented, demonstrating the opportunity to combine within the same matrix the microstructures of nanocomposites of different kinds.

Simulating turbulent fluid flows is a computationally prohibitive task, as it requires the resolution of fine-scale structures and the capture of complex nonlinear interactions across multiple scales. This is particularly the case in direct numerical simulation (DNS) applied to real-world turbulent applications. Consequently, extensive research has focused on analysing turbulent flows from a data-driven perspective. However, due to the complex and chaotic nature of these systems, traditional models often become unstable as they accumulate errors through autoregression, severely degrading even short-term predictions. To overcome these limitations, we propose a purely stochastic approach that separately addresses the evolution of large-scale coherent structures and the closure of high-fidelity statistical data. To this end, the dynamics of the filtered data (i.e. coherent motion) are learnt using an autoregressive model. This combines a VAE and Transformer architecture. The VAE projection is probabilistic, ensuring consistency between the model's stochasticity and the flow's statistical properties. To recover high-fidelity velocity fields from the filtered latent space, Gaussian Process (GP) regression is employed. This strategy has been tested in the context of a Kolmogorov flow exhibiting chaotic behaviour analogous to real-world turbulence. We compare the performance of our model with state-of-the-art probabilistic baselines, including a VAE and a diffusion model. We demonstrate that our Gaussian process-based closure outperforms these baselines in capturing first and second moment statistics in this particular test bed, providing robust and adaptive confidence intervals.

Plasma-based accelerators are beginning to employ relativistic beams with unprecedented charge and ultrashort durations. These dense driver beams can drive wakes even in high-density plasmas ($\gtrsim10^{19}$ cm$^{-3}$), where betatron radiation becomes increasingly important and begins to affect the dynamics of the accelerated beam. We show that betatron cooling leads to a strong structuring of the phase space of the beam. This gives rise to bunched, ring-like structures with positive radial position and momentum gradients, i.e., population inversion of the amplitude of oscillation. We derive the characteristic timescales for this process analytically and confirm our predictions with multi-dimensional Particle-in-Cell simulations. The radiation-dominated regime of beam dynamics fundamentally alters the acceleration process and produces self-structured beams\reviewtwo{; Particle-in-Cell simulations of the resulting beams show that it becomes unstable to the ion-channel laser and lead to exponential amplification of radiation.

For a gas confined in a container, particle-wall interactions produce modifications to the partition function involving the average surface density of gas particles. While such correlations have a vanishing effect in the thermodynamic limit, examining them is beneficial for a sharper understanding of how the limit is attained. We contrast a classical and a quantum model of particle-wall correlations within the canonical ensemble.

In this paper, we formulate the distributional uncharged and charged stress-energy tensors. These are integrals, along a worldline, of derivatives of the delta-function. These distributions are also multipoles and they are prescribed to any order. They represent an extended region of non-self-interacting uncharged or charged dust, shrunken to a single point in space. We show that the uncharged dust stress-energy multipole is divergence-free, while the divergence of the charged dust stress-energy multipole is given by the current and the external electromagnetic field. We show that they can be obtained by squeezing a regular dust stress-energy tensor onto the worldine. We discuss the aforementioned calculations in a coordinate-free manner.

We develop a preconditioned nonlinear conjugate-gradient solver for ground states of binary dipolar Bose-Einstein condensates within the extended Gross-Pitaevskii equation including Lee-Huang-Yang corrections. The optimization is carried out on the product-of-spheres normalization manifold and combines a manifold-preserving analytic line search, derived from a second-order energy expansion and validated along the exact normalized path, with complementary Fourier-space kinetic and real-space diagonal (Hessian-inspired) preconditioners. The method enforces monotonic energy descent and exhibits robust convergence across droplet, stripe, and supersolid regimes while retaining spectrally accurate discretizations and FFT-based evaluation of the dipolar term. In head-to-head benchmarks against imaginary-time evolution on matched grids and tolerances, the solver reduces iteration counts by one to two orders of magnitude and overall time-to-solution, and it typically attains slightly lower energies, indicating improved resilience to metastability. We reproduce representative textures and droplet-stability windows reported for dipolar mixtures. These results establish a reliable and efficient tool for large-scale parameter scans and phase-boundary mapping, and for quantitatively linking numerically obtained metastable branches to experimentally accessible states.

Multi-beam ptychography (MBP) offers a scalable solution to improve the throughput of state-of-the-art ptychography by increasing the number of coherent beams that illuminate the sample simultaneously. However, increasing the number of beams in ptychography makes ptychographical reconstructions more challenging and less robust. It has been demonstrated that MBP reconstructions can be made more robust by using well-structured and mutually separable probes. Here, we present a quantitative framework to assess probe sets based on separability, uniformity, and fabrication feasibility. We show that Hadamard-based binary phase masks consistently outperform Zernike polynomials, experimentally feasible phase plates, and spiral phase masks across varying scan densities. While spiral masks yield comparable resolution, they scale less efficiently due to increased structural complexity. Our results establish practical criteria for evaluating and designing structured probes to enable more robust and scalable implementation of MBP in high-throughput coherent X-ray and EUV imaging.

Developmental patterning comprises processes that range from purely instructed, where external signals specify cell fates, to fully self-organized, where spatial patterns emerge autonomously through cellular interactions. We propose that both extremes – as well as the continuum of intermediate cases – can be conceptualized as information processing systems, whose operation can be described using “Marr's three levels of analysis”: the computational problem being solved, the algorithms employed, and their molecular implementation. At the first level, we argue that normative theories, such as information-theoretic optimization principles, provide a formalization of the computational problem. At the second level, we show how simplified information processing architectures provide a framework for developmental algorithms, which are formalized mathematically using dynamical systems theory. At the third level, the implementation of developmental algorithms is described by mechanistic biophysical and gene regulatory network models.

Hypothesis: Bacterial contamination of surfaces poses a major threat to public health. Designing effective antibacterial or self-cleaning surfaces requires understanding how bacteria-laden droplets interact with solid substrates and how readily they can be removed. We hypothesize that bacterial motility critically influences the early-stage surface interaction (i.e., surface adhesion) of bacteria-laden droplets, which cannot be captured by conventional contact angle goniometry. Experiments: Sessile droplets containing live and dead Escherichia coli (E. coli) were studied to probe their wetting and interfacial behavior. Contact angle goniometry was used to probe dynamic wetting, while a cantilever-deflection-based method was used to quantify adhesion. Internal flow dynamics were visualized using micro-particle image velocimetry (PIV) and analyzed statistically. Complementary sliding experiments on moderately wettable substrates were performed to assess contact line mobility under tilt. Findings: Despite lower surface tension, droplets containing live bacteria exhibited lower surface adhesion forces than their dead counterparts, with adhesion further decreasing at higher bacterial concentrations. Micro-PIV revealed that flagellated live E. coli actively resist evaporation-driven capillary flow via upstream migration, while at higher concentrations, collective dynamics emerge, producing spatially coherent bacterial motion despite temporal variability. These coordinated flows disrupt passive transport and promote depinning of the contact line, thereby reducing adhesion. Sliding experiments confirmed enhanced contact line mobility and frequent stick-slip motion in live droplets, even with lower receding contact angles and higher hysteresis. These findings provide mechanistic insight into droplet retention, informing the design of self-cleaning/antifouling surfaces.